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Introduction to Health Informatics Lecture Notes
DIMITRIOS ZIKOS, PhD Health Informatics
Chapter
1. Introduction to the Discipline
The information in healthcare •
In health sciences there are procedures of high complexity with reference to biological organizations & their functions
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The information is produced and communicated during the above mentioned procedures
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Information changing dynamically
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Information deriving from the combination of many individual health data
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Information coming from the analysis of numerical, boolean data but also from images, sound etc.
Why Health Informatics? •
Health Informatics provides information to make decisions
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Better information leads to better decisions
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Health care, management, planning and policy all need good information
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Health care, health management, health policy and health planning all depend on having good information to make decisions.
Types of Information in Healthcare •
Clinical
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Nurses-MDs specific interest
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Administrative
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Financial
The above mentioned information is used in various ways to facilitate decision making.
Some other important considerations “Information is data that have meaning. It can be presented in any medium (text, lists or graphics) in the manner that the end user prefers” Access & delivery methods of information: We are moving from Traditional electronic methods Dynamic information -info changing all time to keep up to date VS Static information - info that remains the same after publication. 2
“Knowledge base and experience plus acquisition of the ability to identify the best resources AND use the knowledge gained” Definition of Informatics and ICT Informatics (from the French ‘informatique’) includes the
science of information
information processing
the engineering of information systems
Informatics studies the structure, behavior, and interactions of natural and artificial systems that store, process and communicate information.
ICT – Computer, communication and multimedia technologies that can be used to receive, process, store, display and disseminate information.
Key Elements of Informatics Acquisition: capture data produced during healthcare Storage (and retrieval): save data so that it can be retrieved Communication: data moves from point of collection to storage, for analysis, and finally to point of use Manipulation: data usually needs to be manipulated, combined with other data, aggregated Display: how the data can be displayed so that it can be easily understood and used
From data to information and new knowledge: the knowledge circle 3
The area has evolved during the last decades
From “Computers in Medicine” to specializations of health informatics
Definitions of Health Informatics World Health Organization Definition ‘an umbrella term referring to the application of the methodologies and techniques of information science, computing, networking and communications to support health and health related disciplines such as medicine, nursing, pharmacy, dentistry etc……’ Edward H. Shortliffe Definition ‘the field that concerns itself with the cognitive, information processing, and communication tools of medical practice, education and research including the information science and the technology to support these tasks’ Health Informatics is therefore an intersection of information science, computer science, and health care. It deals with the resources, devices and methods required to optimize the:
acquisition
storage
retrieval
use of information in health.
It is an Interdisciplinary field combining health, computer science, statistics, engineering etc. Multidisciplinary field where information, ICT and cognitive knowledge come together. The image below presents the scope of health informatics (information processing, cognitive processing and methodologies), the context of health informatics (practice, research, education) and finally the technologies facilitating its scope.
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Health Informatics uses information to improve health care.
As an interdisciplinary field it applies technology & information to enhance healthcare delivery, biomedical research
Closely bonded with fostering education of health professionals and the public
Health Informatics studies the process health data, information, and knowledge are collected, stored, processed, communicated, and used to support health care delivery to clients and providers, administrators, and health organizations
Related terms Consumer Health Informatics: both healthy individuals & patients want to be informed on medical topics. Health knowledge management: e.g. in an overview of latest medical journals, best practice guidelines or epidemiological tracking. Bioinformatics: a branch of biological science which deals with the study of methods for storing, retrieving and analyzing biological data, such as nucleic acid (DNA/RNA) and protein sequence, structure, function and genetic interactions. Biomedical Engineering: the application of engineering principles and design concepts to medicine and biology. 5
Primary Healthcare Informatics
Nursing Informatics
Public Health Informatics etc.
Example of Bioinformatics: Applications in basic research Human Genome Project – Scientists used fundamental research methods and techniques to map the complete human genome
Provide enormous opportunity to understand human body in ways not previously possible
Relied heavily on IT to sort and manage the data to map human genome
Ability to identify and treat human diseases
m-Health
Mobile Health
The practice of medicine and public health, supported by mobile devices.
Mobile technologies such as mobile phones to collect and access health information.
It has emerged as a sub-segment of eHealth
Mobile devices using modern communication technologies help nurses and doctors in their everyday practice
e-Health eHealth is a broad term for healthcare practice which is supported by electronic processes and communication. Relatively recent term The term can encompass a range of services in healthcare and information technology. It is not clearly defined (is some use it instead of healthcare informatics, others use the term describing healthcare practice using the Internet). 6
Health informatics tools and methods Health Informatics are not just “Computers in Healthcare”. They also include
clinical guidelines
medical terminologies
Clinical dictionaries and nomenclatures
Information and communication systems
Health Informatics ≠ IT
Information Technology in hospitals is not Health Informatics
Information technology is hardware & software
Health Informatics helps IT ‘work appropriately.’
But it also “works” vice versa: IT is used to facilitate the use and integration of health informatics methods and technologies.
The IT sector is very important enabling field for advanced health informatics applications and methods. The introduction of ICT technologies have sky rocketed the discipline of health informatics.
Health informatics is not new! Since the beginning of healthcare provision in an organized manner, the need for information management was raised.
International Classification of Diseases: was initiated in 1893 7
clinical guidelines: decades before the appearance of computers
Hospital information management before the 70s (and in some countries even nowadays…) was limited to file maintenance & life cycle management of paper-based files, other media & records.
All these were health informatics applications, although back then the field was not yet defined
Health Informatics addresses... A single hospital department
The Needs of a Healthcare Organization
A large area or a district
The Healthcare System at a National level
But also health informatics is also in the community care with specialized applications for population based healthcare services, promotion, disease prevention and syndromic surveillance (public health).
Who are involved in Health Informatics •
Clinical Staff – they need suitable information in caring for patients
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Nonclinical Staff: educators, administrators, research scientists – they need relevant data and information to perform their duties
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Information science – IT professionals use computing technologies to manage information to fulfill need and requirements of other end users
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External “Players”-policy makers, insurance companies 8
In a broader sense: who are served by Health Informatics
Patients
Government Bodies and Policy makers
The Community
Health care providers (MDs, nurses, pharmacists...)
Facility management/operational management
Healthcare researchers
Primary Care/GP’s
Healthcare educators and their students
Management in Hospitals
As said before health informatics is a multidisciplinary field. Various knowledge areas are directly or indirectly related with health informatics. In addition information and communication technologies are used to facilitate the scope of health informatics. These technologies make it possible to develop advanced applications (ie EMR, HIS) and integrated methods to support healthcare. These applications are ruled by specific standards and protocols. Knowledge Areas of health informatics
Technologies in health informatics
Applications Architecture
Standards used
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Hereby you can see four important aspects of Health Informatics. Quality of Care is the ultimate Goal of Health Informatics and this also requires inteprofessional collaboration of different specializations and education and training of users to be able to get the most out of the new technologies. Actually health informatics should be seen by themselves as an extension of their own skills.
Areas of Health Informatics
Communication Systems and networks
Biosignal and Image Processing
Modeling, Classification and coding
Tele-care and Telemedicine
Healthcare Information Systems
Health reports
Electronic Health Record Systems
Education and Consultation
Decision Support Systems
Knowledge based Systems-Expert Systems
Healthcare Management and Public Health Systems
Health Promotion and Patient Education
Decisions to be made are based on data Here is a simplified example. Every bit of this process produces data and data is also communicated and transformed.
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What services does Health Informatics involve?
Data processing- (health is a data intense industry). Includes collection, processing, transformation, presentation & use
Communication – main emphasis should be on supporting communication between professionals
Knowledge based services-Includes online knowledge-base services, on-line practice guidelines, drug lists, decision-support and reminder systems
Applications of Health Informatics
Medication dispensing/ordering
Imaging equipment
Purchasing equipment
Clinical Pathways
Support and inform managers to make better decisions
Labour management
Resource allocation and planning
Patient scheduling
Risk management
Budget analysis
Training
Research
Patient Assessment
National database
Monitoring patients
Quality Assurance
Tracking patients in hospital
Donor databases
Stock management
Devices
Tracking sterile supplies
Monitors
Mobile computing
Analysers
Imaging systems in Health Impossible without the use of computers Computers are used to: – Develop an image from specific measurement – Reconstruct the image for optimal extraction of a particular feature – Improve image quality by image processing – Store and retrieve-present images X-rays, ultrasound, computational tomography, MRI etc 11
Telemedicine Telemedicine (tele”= from distance) is the delivery of health-related services and information from distance. Telemedicine could be as simple as two health professionals discussing over the telephone about a patient, or more sophisticated as using videoconferencing to between providers at facilities in two countries…or even as complex as robotic technology. Telehealth: a broader term Telehealth is an expansion of telemedicine. It encompasses
preventive
promotive
curative aspects
Today telehealth addresses an array of technology solutions, simple or more complex. For example, physicians use email to communicate with patients, order drug prescriptions and provide other health services. Clinical uses of telehealth
Transmission of medical images for diagnosis
Groups or individuals exchanging real time health services or education live via videoconference
Transmission of medical data for diagnosis or disease management (remote monitoring)
Advice on disease prevention and promotion of good health by patient monitoring and follow-up.
Other indirect uses of tele-health Distance education including continuing medical education of health professionals and patient education Administrative meetings among telehealth networks, supervision, and presentations Heathcare system integration Patient movement and remote admission Research
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Questions for discussion 1. Briefly explain in your own words why health informatics is not just “computer applications in hospitals”. 2. “Health Informatics is an interdisciplinary field”. Please refer to three disciplines, with a brief explanation. 3. Refer to five reasons why health informatics is essential for quality healthcare services. 4. Of the roles involved in health informatics, which one do you find more intriguing and why is that? 5. Please draw (or hand-draw) an Entity Relationship Diagram for the following process: Medical doctors treat patients. Each doctor can treat more than one patient but each patient has his/her personal doctor. For some of the patients, one or more medicines are prescribed.
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Chapter
2. Successful Integration of e-health
The Health sector nowadays
Health and education are two major consumers of the public purse
In the new era healthcare systems have to be built upon principles of: Cost containment A lot of data-Data doubles every five years Team based care and interprofessional collaboration
Technological push vs. demand pull which is driven by users themselves
Clinical focus to the care provision-the patient and his well being is in the centre of care. Emphasis is moving from administrative to clinical information systems. Health services should be driven by supporting clinical needs and not financial management
Improving quality of care equally important Cost control is a major driving force-planning resources
Multi-vendor systems which are heterogeneous, raising interoperability issues in an era where healthcare is moving towards integration
Distributed structures (independent clinics/labs) – and care provision BUT at the same time strong autonomy of the existing sub-systems.
Increased dependence on automation, data intensive processes are automated
People know more about healthcare (more resources & the internet)
Tension between demand for increased quality of care vs. reduction in costs (efficiency vs. costeffectiveness). Demographic changes, such as ageing of population, awareness and chronic diseases are some of the factors related with the increased demand.
Information Systems in healthcare The introduction of Information Systems in hospitals drastically changed the management of patient care. Increased processing power, local area networks & use of standards led to many possibilities in terms of access & availability of health information.
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Understanding the barriers and major challenges for successful implementation The big question: Although integration of hospital information systems can lead towards efficient use of resources and effective services, the improvement of quality care and rise in productivity has not drastically increased in all cases. WHY? Technology is there, experts are there. The workflow process within the hospital should be thoroughly examined, in order to identify factors related with the impending the successful introduction of Information Systems. Four different areas, towards Successful Implementation- “L.I.F.O”
Legacy information systems (older systems) IT issues and challenges Fear of healthcare professionals Organizational changes Implications for healthcare organisations include...
Unnecessary duplication of tests and investigations
Valuable time spent to track down information
At least 20% of healthcare professionals time is spent reading, writing, sorting & searching through notes
Healthcare not provided as efficiently & cost effectively as possible
Prerequisites for successful integration and implementation
Secure, reliable always online hospital information systems
Systems designed so that the patient is at the centre of care
Promotion of the cost-effective use of IT
Connect and manage distributed information systems
Delivering healthcare is a multiprofessional activity
Movement away from hospital (tertiary) to community (primary) – based shared care (Access to records by GPs)-all levels of care
Ability to share information between care providers
The right information in the right place, in the right format, to the right person, at the right time 15
Integration of the up-to-date IT technology for shared care
Efficiency vs. cost-effectiveness
Demand for increased quality of care vs. reduction in costs
The introduction of e-health applications implies lots of changes for healthcare professionals User interfaces change– data entry is done with radically different methods A new way of information management, analytics and aggregation services Integration of IT into routine clinical practice-everyday working life changes for clinicians The paperless hospital- no paper anymore?? Interconnected computers-is data everywhere?? Organizational Challenges
Integrating new e-health services into the on-going process
Resources (human-location-money)
Maintenance of the systems
Training staff on the new systems
New systems sometimes are obvious to patients
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Provided services should be constantly evaluated The evaluation should be performed taking into account feedback from all categories of directly or indirectly involved with the newly introduced systems: the end-users (health professionals, patients), IT specialists, management etc.
Seminars should regularly be organized regarding use of the new technology, standards, interoperability issues etc.
Training, support and encouragement of the employees
Information systems co-developed by many different companies may be related with implementation issues.
In some cases, users’ reactions are not taken into account by the administration, bearing a limitation to the success of the information system
Questions for discussion 1. Briefly describe the current situation in the health sector today. 2. What are the four most significant barriers towards the successful implementation of e-health today? If you were a healthcare manager, how would you prioritize your strategies to encounter these problems? 3. Refer to five reasons why health informatics is essential for quality healthcare services. 4. Discuss about the possible mid-term benefits for the everyday practice of a medical doctor who starts using e-health applications for the healthcare management of his patients. 5. Briefly explain two of the prerequisites for the successful implementation of hospital information systems 6. Discuss about the current process of healthcare information systems development. What would you expect by an external contractor to do, if you were a healthcare manager? 7. Discuss alternative methods of healthcare information systems development and integration. Can open source solutions be used in large settings? Which may be the benefits and limitations of such solutions?
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Chapter
3. Health Informatics Applications: Clinical Decision Support Systems
The diagnostic process Clinicians have the knowledge and experience to know which questions to ask to the patient, laboratory tests to order and they can assess the results relative to associated risks. Even for a known diagnosis, there are challenging decisions that put into test the doctor’s knowledge and experience: Should I treat the patient or is this something that needs no medication/surgery? If the patient should receive treatment, which is the most appropriate? How should I use the patient’s response to therapy to guide me determine whether what to do next or even reassess the validity of the diagnosis? Requirements for good decision making (1) Accurate data (2) Relevant knowledge (3) Problem-solving skills Good data and an extensive knowledge base still are not enough for a good decision; good problemsolving skills are equally important. Clinicians must develop selection and interpretation skills, they should be able to take into account the sensitivity and specificity of the results and to assess the urgency of a situation.
Additional unneeded data will confuse rather than clarify and when it is imperative to use tools (computational or otherwise) that permit data to be summarized for easier cognitive management. The operating room and intensive-care units are an example; patients are continuously monitored, numerous data are collected, and decisions have to be made on an emergent basis. (E. Shortliffe)
Definition of a clinical decision-support system A clinical decision-support system is any computer program designed to help healthcare professionals make clinical decisions. In a sense, any computer system that deals with clinical data or knowledge is intended to provide decision support.
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Types of decision-support functions We can consider three types of decision-support functions, ranging from generalized to patient specific. 1. Tools for Information Management
Health-care information systems and information-retrieval systems manage information.
Specialized knowledge-management workstations provide tools for storing and retrieving clinical knowledge, browsing through that knowledge and augmenting it with information for clinical problem solving.
Information-management tools provide the data and knowledge needed by the clinician, but they generally do not help her to apply that information to a particular decision task.
Interpretation is left to the clinician, as is the decision about what information is needed to resolve the clinical problem.
2. Tools for Focusing Attention
Clinical-laboratory systems that flag abnormal values
Systems that provide lists of explanations for abnormalities and pharmacy alert systems (i.e. possible drug interactions)
Such programs are designed to remind the user of diagnoses or problems that might otherwise have been overlooked.
Typically, they use simple logics, displaying fixed lists as a response to a definite or potential abnormality.
3. Tools for Providing Recommendations Such programs provide custom assessments or advice based on sets of patient specific data. They may be based on simple algorithms, decision theory, cost–benefit analysis etc. A brief history of decision support systems Starting during the late 1960s, F. T. de Dombal and his associates at the University of Leeds studied the diagnostic process and developed computer-based decision aids using Bayesian probability theory!! deDombal’s approach was successful! In one system evaluation (de Dombal et al., 1972), physicians filled out data sheets summarizing clinical and laboratory findings for 304 patients who came to the emergency room with sudden abdominal pain. Clinicians’ diagnoses: correct in only 65% to 80% of the 304 cases Program’s diagnoses were correct in 91.8% of cases. 19
The MYCIN system It is a rule-based system. Rules are conditional statements that indicate what conclusions can be reached if a specified set of conditions is true. The following rule MYCIN concludes probable bacterial causes of infection if the five conditions in the premise are all found to be true for a specific patient.
Source: Biomedical Informatics: Computer Applications in Health Care and Biomedicine (E. Shortliffe, J. Cimino)
HELP System It is an integrated hospital information system developed at LDS Hospital,Salt Lake City. HELP generates alerts when abnormalities in the patient record are noted. HELP adds to a conventional medical-record system a monitoring program and a mechanism for storing decision logic in “HELP sectors” or logic modules. Thus, patient data are available to users who wish to request specific information, and the usual reports and schedules are automatically printed or otherwise communicated by the system In addition, there is a mechanism for eventdriven generation of specialized warnings, alerts, and reports. Actually HELP’s developers originally created a specialized language named PAL for writing medical knowledge in HELP sectors.
Decision Support Systems Characterization Based on five dimensions: (1) the system’s functions
(4) the low level decision-making process
(2) how advice is offered
(5) human–computer interaction
(3) the consultation style *excellent decision-making capabilities alone do not guarantee system utility or acceptance Decision-support programs generally fall into two categories: Those that assist healthcare workers with determining what is true about a patient (usually what the correct diagnosis is) Those that assist with decisions about what to do for the patient (usually what test to order, whether to treat, or what therapy plan to institute) Many systems assist clinicians with both activities 20
Clinicians want decision support systems to be decision oriented & not machine knowledge oriented Diagnostic programs should leave to the user the task of deciding what data to gather or requires a fixed set of data for all patients. But we cannot view making a diagnosis as separable from choosing from the available options for data collection and therapy. Physicians believe that the majority of questions about which they seek consultation deal with what they should do rather than with what is true about a patient given a fixed data set. How often should the clinician be interrupted? Practitioners generally do not request assistance from such systems, but instead receive automatically. One challenge is to avoid generating excessive numbers of warnings for minor problems. Otherwise, such “false-positive” advisory reports can blunt the usefulness of the important warnings. Artificial Neural Networks There is interest in the use of artificial neural networks as the basis for automated medical diagnosis.
Artificial neural networks (ANNs) are computer programs that perform classification, taking as input a set of findings that describe a given case and generating as output a set of numbers, where each output corresponds to the likelihood of a particular classification that could explain the findings. The program performs this function by propagating calculated weights through a network of several layers of nodes.
The network structure is same for any class of decision problem BUT the weights associated with each of the nodes, are tuned so that the network generates the correct classification for any set of inputs. The values for the weights are determined in incremental fashion when a network is trained on a large collection of previously classified examples during a period of supervised learning. Like statistical pattern-recognition methods, artificial neural networks translate a set of findings into a set of weighted classifications consistent with those findings. An observer cannot explicitly understand why an artificial neural network might reach a particular conclusion. Artificial neural networks may have significant advantages, however, when the correct diagnosis may depend on interactions among the findings that are difficult to predict. Construction of Decision-Support Tools Acquisition and Validation of Patient Data Serious challenges associated with properly structuring and encoding what was said. Otherwise, spoken input becomes a large free text database that defies semantic interpretation. 21
Many workers believe that some combination of speech and graphics, coupled with integrated datamanagement environments that will prevent the need for redundant entry of the same information into multiple computer systems within a hospital or clinic, are the key advances that will attract busy clinicians and other health workers to use computer-based tools. There are several controlled medical terminologies that healthcare workers use to specify precise diagnostic evaluations (e.g., the International Classification of Diseases and SNOMED-CT), clinical procedures (e.g., Current Procedural Terminology). Modeling of Medical Knowledge Creation of a computer-based decision support system requires substantial modeling activity Decide what clinical distinctions and patient data are relevant Identify concepts and relationships among concepts that bear on the decision-making task Find a problem solving strategy that uses relevant clinical knowledge
Representing the 3d nature of human anatomy and other knowlwdge into decision making Among the research challenges is the need to refine the computational techniques for encoding the multimodal knowledge used in problem-solving by clinicians. Physicians use “mental models of the three-dimensional relationships among body parts and organs” when they are interpreting data or planning therapy. Representing such anatomical knowledge & performing spatial reasoning by computer is particularly challenging.
Human beings have a remarkable ability to interpret changes in data over time, assessing temporal trends and developing models of disease progression or the response of disease to past therapies. This cognitive process must be taken into consideration while building decision support systems. Researchers continue to develop computer-based methods for modeling such tasks. The human has the ability to detect changes over time
Integration of Decision-Support Tools with existing Health Information Systems Successful introduction of decision-support tools is likely to be tied to these tools’ effective integration with routine clinical tasks. Innovative research on how best to tie knowledge-based computer tools to programs designed to store, manipulate, and retrieve patient-specific information is still ongoing As hospitals and clinics increasingly use multiple small machines optimized for different tasks, 22
however, the challenges of integration are tied to issues of networking and systems interfaces. It is in the electronic linking of multiple machines with overlapping functions and data needs that the potential of distributed but integrated patient data processing will be realized. Examples of Modern Implementations The Internist-1/QMR project The goal of the original Internist-1 project was to model diagnosis in general internal medicine. Internist-1 contained knowledge of almost 600 diseases and of nearly 4,500 interrelated findings, or disease manifestations (signs, symptoms, and other patient characteristics). On average, each disease was associated with between 75 and 100 findings. The task of diagnosis would be straightforward if each disease were associated with a unique set of findings. For each of these findings, they assigned a frequency weight (FW) and an evoking strength (ES), two numbers that reflect the strength of the relationship between the disease and the finding.
Examples of frequency weight (FW) and an evoking strength (ES). Source: Biomedical Informatics: Computer Applications in Health Care and Biomedicine (E. Shortliffe, J. Cimino)
Quick Medical Reference (QMR) System An expert consultation system that provides advice much as Internist-1 did (using essentially the same knowledge base and scoring scheme). It also serves as an electronic textbook, listing the patient characteristics reported in a given disease or, conversely, reporting which of its 600 diseases can be associated with a given characteristic. Finally, as a medical spreadsheet, it can tracks characteristics and determines the implications. In the commercially available version of QMR, many of the consultation features of Internist-1 were removed (there was an argument on this). So, the QMR product did not ask questions directly of the user in order to pursue a diagnosis and did not 23
attempt to evaluate whether more than one disease might be present at a given time. The DXplain System One of the most extensively used patient-specific decision-support tools. It is used at a number of hospitals and medical schools, but also for clinical consultation. DXplain takes advantage of a large data base of the crude probabilities of over 4500 clinical manifestations associated with over 2000 different diseases-a knowledge base considerably larger than that of QMR. It adopts a modified form of Bayesian reasoning for diagnoses using a custom based algorithm. It has been implemented both as a stand-alone version and an internet service. A Guideline-Based Patient-Management Architecture: The EON system EON consists of problem-solving components that share a common knowledge base of protocol descriptions. The protocol domain model is created with the Protégé system (see below) and defines the format of the protocol knowledge base. The same model also defines the schema for the database mediator, a system that channels the flow of patient data between the problem-solving components and an archival relational database. The entire architecture is integrated into a clinical information system.
Protégé ontology editor & Knowledge Acquisition System (http://protege.stanford.edu/) Developers first create a general model of the concepts and relationships that characterize a particular application area. A module in the Protégé system takes as input such a model and generates as output a customized tool based on that model that developers can use to enter detailed knowledge bases.
A screen from a Protege knowledge-acquisition tool for entry of breast cancer protocols. The protocol specifies the knowledge required to carry out a clinical trial that compares the effects of conventional chemotherapy with those of high dose chemotherapy followed by bone-marrow transplantation.
Future Directions for Clinical Decision-Support Systems Decision-support research and development will continue Distributed databases and decision-support systems will provide more effective communication 24
among all participants in the healthcare system. New systems will provide possibilities to process complex, multimodal data and knowledge. The better understanding of the complex and changing nature of medical knowledge, the clearer it becomes that practitioners will always be required as elements in a cooperative relationship between physician and computer-based decision tool.
Humans v.s Machines There is no evidence that machines will ever equal the human mind’s ability to deal with unexpected situations, integrate audio-visual data that reveal subtleties of a patient’s problem, or to deal with social and ethical issues that are often key determinants of proper medical decisions. Considerations such as these will always be important to the human practice, and practitioners will always have access to information that is meaningless to the machine.
Questions for discussion 1. Discuss the significance of Clinical Decision Support Systems in clinical decision making and their limitations in terms of actually being capable to “replace” health professionals. 2. Which are the three most common types of clinical decision support systems? In your opinion is it possible for them to coexist in a unified application environment? 3. “Clinicians want decision support systems to be decision oriented & not machine knowledge oriented”. Explain the above statement in your own words.
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Chapter
4. Electronic Prescribing
Introduction $5.00 – Average cost for a paper chart $2,000 – Average cost to a physician for prescription related callbacks and rework 9 million – Ambulatory prescription errors annually 150 million – Phone calls from pharmacies to physician offices annually for incomplete or illegible prescriptions Prescribing is prone to errors: Adverse drug events (ADE)
Wrong dosage
One of the top leading causes of death
Missed drug-drug interaction
A significant proportion of ADE deaths are attributed to prescription error
Missed drug-allergy interactions Medication errors account for ambulatory deaths as well
Illegible handwriting Definition of e-prescribing
Using software to both generate and transmit the prescription (Rx) Electronic receipt of, and response to, Rx refill requests New Rx must stay in a federally mandated digital form the entire time as it travels from the physician office through clearinghouses to the pharmacy E-prescribing can serve as a pathway to full EHRs, acting as a bridge that allows prescribers to become more technologically proficient and comfortable using electronic systems to support patient care. E-Prescribing systems can be standalone or integrated a stand-alone prescribing-focused solution an integrated module within an Electronic Health Record Goal of E-Prescribing (e-Rx) Reduce medication errors and improve patient safetyHelps prevent wrong medication or dosage from being dispensed 26
Convenience for patients as the Rx arrives before they do Save time for doctors and pharmacies Can help reduce costs because formulary availability increases the use of less expensive medications
Consider refill workflow… Refill request is very time consuming for physicians
Pharmacies call or fax office Reception answers phone or retrieves fax Nurse or assistant pulls chart Doctor reviews Nurse or assistant calls or faxes pharmacy back Nurse or doctor updates chart Can happen many more times per day for each doctor Chronic patients coming back to refill (get their medicines) initiate a very cumbersome process for the healthcare system
Components of an e-Rx system
Drug-drug interaction checking
Drug dosage checking
Drug-allergy interaction checking
Access to a current medication list
Access to formularies and patient benefit information
Access to patient fill histories
It may also include
Drug–condition and drug-age checking
Benefits of e-prescription systems Benefits for Patients Complete prescription history available Improved efficiencies Pharmacy of choice Secure Prescription available for pickup 60 minutes after it is received Adjudication to the Patient’s Prescription Insurance continues as it does today providing an accurate co-pay amount at the Point-of-Sale 27
Higher levels of patient confidence & satisfaction Potential to reduce medication errors (by as much as 55%) Benefits for Physicians Improved efficiency Reduced number of callbacks from pharmacies Refill Renewal Authorizations completed in seconds More time on patient care and other key activities Prevent medication errors Promote increased use of generic drugs Improves quality of care because of clinical decision support Improve patient compliance with medication recommendations Benefits for Pharmacists All necessary prescription information is transmitted and received Saves Time Reduces Transcription Errors Improves Patient Services Improves Record Keeping Reduces Turnaround Time on Renewal Requests and Authorizations Improves Efficiency – Allows more Pharmacist – Patient Interaction
Questions for discussion 1. Only one of the following errors is caused by an illegible eRx prescription: wrong dosage, incombatible medicines, medicines causing allergies. Which is this and how does an eRx address the issue? 2. eRx systems have many benefits for Pharmacists. Refer to three of these benefits. 3. Visit http://dnrmaps.wi.gov/imf/imf.jsp?site=webview to navigate through many interactive GIS health maps. What did you find most interesting here?
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Chapter
5 Public Health Informatics and Geographical Information Systems .
Public Health Informatics “Public Health Informatics” (PHI) is the science of applying information technology to serve the specialized needs of public health. It is the systematic application of information and computer science and technology to public health practice, research, and learning. It is one of the sub-domains of Health informatics. Like the very closely related term Geographic Information Systems, this term, Public Health Informatics, has been added to MeSH (Medical Subject Headings) recently, in 2003. In the United States, public health informatics is practiced by individuals in public health agencies at the federal and state levels and in the larger local health jurisdictions. Additionally, research and training in public health informatics takes place at a variety of academic institutions.
Public health informatics belongs to the Macro Scale applications of Health Informatics
Important Principles that Public Health Informatics should take into consideration The primary focus of public health is to promote the health of populations and not the health of specific individuals. The primary strategy of public health is prevention of disease and injury by altering the conditions or the environment that put populations at risk. Public health activities are not restricted to particular social, behavioral, or environmental contexts. 29
Public health interventions reflect the policy context in which public health is practiced. Geographic Information Systems
Important tools that are used in Public Health Informatics.
GIS have many potential applications in studying geographically differentiated health issues, and changes (ie cardiovascular disease in a given community at a given period)
GIS systems integrate information from different sources
Therefore the range of questions which can be addressed expands rapidly as the number of data sets safely linked together becomes higher
Data Linkage in GIS
Health organizations regularly collect and store a vast array of data from multiple sources, much of which is linkable through some kind of unique person/patient identifier.
These data sets can in turn be linked to many other data sets that contribute to “the big picture” from different government, public and private entities, using location as main key/common linker.
According to the US FGDC, geographic location is a key feature of 80-90% of all government data.
GIS Applications in Healthcare There are two broad types of GIS applications geography of disease
geography of healthcare systems
Health outcomes and epidemiology applications: the more recent real time applications of GIS in health and environmental surveillance/monitoring Healthcare services applications: the health service, resource allocation Use of features from both categories can be used for epidemiological & healthcare delivery applications! Data to be used in GIS are not always easy to obtain Easy to obtain
Not so easy to obtain
base geographic data sets
data on chronic diseases
Demographic
utilization of healthcare services
socioeconomic/deprivation
environmental data sets
lifestyle data sets 30
Possible problems that exist in healthcare GIS
data quality
barriers to access (data protection/ privacy)
geographical coverage
availability of spatio-temporal references Table below shows some examples of parameters taken into account in Public Health GIS Systems Health Outcomes
Patient Factors
Clinic Factors
Community Factors
Asthma
Age
Location
Fast food sales
Diabetes
Gender
Capabilities
Poverty / Economic Hardship
HDL/LDL
Race/ethnicity
Processes
Education level
Immunizations
Co-morbidities
Fresh fruit & vegetable consumption
Obesity
Medications
Traffic
Hypertension
Language
Recreation / parks
Smoking
Insurance
Safety / crime
Alcohol
Urban / Rural
Blood Pressure
WEDSS (Wisconsin Electronic Disease Surveillance System) (www.dhs.wisconsin.gov/wiphin/WEDSS.htm) WEDSS is a secure, web-based system designed to facilitate reporting, investigation, and surveillance of communicable diseases in Wisconsin. It is designed for public health staff, infection control practitioners, clinical laboratories, clinics, and other disease reporters. PHIN (Public Health Information Network) is CDC Initiative (www.cdc.gov/phin/index.html) Other Projects o
Behavioral Risk Factor Survey
o
Family Health Survey
o
PRAMS (Pregnancy Risk Assessment Monitoring System)
o
WISH (Wisconsin Interactive Statistics on Health) – Online data queries for the public
The website found on the link http://dnrmaps.wi.gov/imf/imf.jsp?site=webview allows users to navigate through many interactive GIS health maps 31
Chapter
6. Databases and data types in healthcare
Databases and Types of Data structure Databases are collections of data with a specific well defined structure and PURPOSE.
In hospitals databases are the “spinal cord” of hospital information systems
Databases in healthcare are the collection of health data.
Programs to develop & manipulate these data are called Database Management Systems (DBMS)
ARE THESE DATABASES? An excel file with names and medication of patients within a hospital A nurses agenda with to do’s A schedule of the shifts for next week A list of the medicines available The medical record of a patient One would say that databases are structured collections of data so the list in this textbox is not definite, but rather the way these data are organized.
Types of Data Structures Flat Data
Object-oriented data
Hierarchical Data
NoSQL databases
Relational Data All data submitted into electronic health records are most of the times based on relational, objectoriented or, in few recent cases, on noSQL databases. Flat Files A flat file can be a plain text file, usually containing one record per line or it can be a binary file. The majority of the existing software includes easy access to flat data files. For simple data flat databases work. They waste computer storage by requiring it to keep information on items non logically available Flat databases are not “complicated query friendly” 32
Hierarchical Models Data models in which the relationship between higher and lower items are inherited. An example of an hierarchical structure: folders-subfolders on our computers
Does this structure actually facilitate the real life health process?
Pros: Actions on “parents” save time since they affect all “children” Cons: In the real healthcare world most relationships are not hierarchical. The Relational Database Model Major Elements The database is a collection of tables, which represent entities and relationships The table name is the relationship title Columns represent the characteristics of the entity Rows represent data
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Relational Databases The principles of relational databases can be summarized to the following points: Data in a relational database are values stored in the database. Data alone are useless. Relational databases are composed by a set of tables Each table includes records, which are the table rows and fields, which called table columns Fields can be of various data types. They can be alphanumeric, numeric, date-time, Boolean etc Using keys we access a table record. The key which uniquely identifies a record is the primary key Index the physical mechanism which improves the database efficiency. This is part of the physical structure of the database and is not at all related with keys which are part of the logical structure. In a relational database we call view, a virtual table composed by a sub-set of the actual tables. In relational databases, there exist one-to-one, one-to-many or many-to-many relationships. The term “data integrity” describes the accuracy, validity and unity of the data.
An example of a relational database
Advantages of the Relational Schema Databases can be examined by many different perspectives No need to enter missing information for variables that are not logically possible Easy to modify because adding new entities involves adding new tables and not altering old ones (Granted that the database is adequately normalized)
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Normalization in Relational Databases Normalization is the process where insufficiently normalized schemes are split into smaller schema with more desirable characteristics. With normalization, we succeed to minimize anomalies during data entry, update and deletion. Normalized forms provide the methodological framework to analyze the database schema based on the database keys and the functional dependencies.
Every characteristic belongs to the entity it characterizes.
Every characteristic only exists once in a database.
Keys fully define the records
Each value of the same characteristic is stored into the database only once
Normalization-Easy to Remember Rules
Database categories found in healthcare Distributed Databases in healthcare Data are kept in different settings and different computers. Since data produced are huge, the replication and distribution of databases improves database performance at the healthcare settings. Distributed databases need to address the location of the data AND audit log, that is a chronological record of the destination and source that provide documentary evidence of the sequence of activities that have affected at any time a specific procedure. Possible Cons
Data loss is limited to nodes affected and this is critical for healthcare
Since they are decentralized, they are more flexible and allow different units to update and maintain their own data
Large Healthcare Utilization Databases
They are used to study the use and outcome of treatments
Their huge size allow the study of rare events
Since they are representing the clinical routine care, they can address real world effectiveness and utilization patterns
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BLOBS-Binary Large Object Files Very frequent in healthcare settings Images (ct, mri) Audio (heartbeat seq.) Video (ultrasounds…) The dilemma: should we move these data to data warehouses or keep them in their source? Data-less databases They are distributed databases which have been set-up without any data, until such a need arises. They may be useful in healthcare Less expensive than centralized registries (it requires no equipment and little personnel) The use of the system does not require vague and time-independent patient consents The system does not require duplication of data in different databases Object Oriented Data Models They are more efficient Use of real-life objects (entities) They use SQL Much higher programming flexibility since there is the possibility to integrate the database with object oriented programming languages (i.e. java, C# etc) Not yet fully standardized but this is ongoing
Example of object oriented model 36
Questions for Discussion 1. “Distributed databases may be a better fit for healthcare.” Do you agree with this statement? Briefly explain your answer. 2. A relational database which supports a Health Information System has data of different data types. Provide health data examples of five different data types.
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Chapter
7. Hospital Information Systems
Situation in healthcare today in terms of information management Many errors and problems in terms of information management in hospitals nowadays Incorrect reports, e.g. lab report, may lead to erroneous and even harmful treatment decisions Repeated examinations or lost findings have to be searched for, the costs of health care may increase Information should be documented adequately, enabling health care professionals to access the information needed and to make sound decisions In general clinical patient-related information should be available on time, and it should be up-to-date and valid. Systematic information processing is the key factor for raising quality and reducing costs, there therefore information processing in a health care institution should be managed systematically. Adoption of IT today in healthcare, in the US Medical errors account for more deaths than breast cancer, HIV and motorcycle accidents. In comparison US lags behind compared with health outcomes of other industrialized nations. (Infant mortality, lifespan, etc.) US has highest healthcare costs per capita in world Healthcare is at least a decade behind in adoption of e-health technologies Systems-Information Systems-Hospital Information Systems What is a “System” A system is a set of interacting or interdependent components forming an integrated whole or a set of elements ('components') & relationships which are different from relationships of the set or its elements to other elements or sets. Systems may consist of subsystems, which may have their own subsystems and so on. All subsystems work together to exchange data for a specific purpose. The elements of a system (humans – machines – procedures) determine its internal environment. What is outside is called external environment. Those two environments are in constant communication exchanging data (input-output) 38
Components of a System and their interaction. Can you think of healthcare examples for each component?
Information System An information system (IS) - is any combination of information technology and people's activities that support operations, management and decision making. The term is frequently used to refer to the interaction between people, processes, data and technology. It refers not only to the ICT of an organization, but also to the way in which people interact with the technology to support business processes An information system transforms data (input) to information (output) Information systems do not have to be computerized. Most modern high complexity information systems cannot easily be implemented without computer and telecommunications support.
Information Systems in Healthcare
Information Systems are everywhere in healthcare: in hospitals, clinics, public health settings etc.
Their features vary, according to the specific scope of each setting.
Comprehensive, integrated information systems designed to manage the medical, administrative, financial and legal aspects of a hospital and its service processing.
The significance of healthcare information systems Information processing is an important quality factor, but an enormous cost factor as well. It is also becoming a productivity factor. 39
Information processing should offer a holistic view of the patient and of the hospital. A hospital information system can be regarded as the memory and nervous system of a hospital Integrated Processing of data in healthcare The integrated processing of information is important because: All groups of people and all areas of a hospital depend on its quality The amount of information processing in hospitals is considerable Health care professionals frequently work with the same data
Hospital Information System (HIS) is an integrated computer assisted system which stores, manages and recalls information related with the clinical and administrative healthcare expectations of in a hospital. A comprehensive definition of Hospital Information Systems
It is a collection of systems and equipment which manage all hospital information in order to Support health professionals to be efficient during their everyday practice Improve quality of health services provided to the patients Reduce the cost of care Related Terms and Historical Background Related terms There are several titles and acronyms for similar approaches to managing the flow and storage of information in hospital routine services. Hospital Information System (HIS) Healthcare Information System Clinical Information System (CIS) Patient Data Management System (PDMS) Historical Background Traditional approaches used to encompass paper-based information processing as well as resident work position and mobile data acquisition and presentation. Since the 60s, computers started being used in hospitals for: Financial management 40
Clinical Laboratories Diagnosis and coding Analysis of ECG Beginning of 70s: there was introduced the term “Integrated Hospital Information System”.
Priorities change as health systems and technologies change: moving from “automation” to the “integration”
Scope and Requirements of a Hospital Information System
Common registry for all patient information
Access to information from any place at any time
Availability of the right information without waiting times
Recognizes the health professional as the main user, but, at the same time, it places the patient at the centre of care-meets the user needs
Integrates new information using multiple diverse sources of information
Cost efficient since it facilitates the better management of resources in healthcare settings
Tools for cost and quality of care assessment
It should be the base for enhanced applications
Backwards compatibility: we need to move old data into the new systems\
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Main Better care
Cost containment
Secondary
Improvement of communications Smaller waiting times Better Decision Making Smaller Length of Stay Less administrative workload Better use of resources Reduction of staff costs
Two primary goals of a Hospital Information System is to improve patient care and efficiently manage recourses
Main elements of a Hospital Information Systems Humans (users): those (healthcare professionals, administrative staff etc), who produce the information and use it for decision making during their everyday practice Data: raw data to be processed based on the needs of the users Procedures: series of guidelines that describe how humans will act under specific circumstances Support human activities Ensure that the right information reaches the right person at the right time Define the way that information will be transformed Equipment (software-hardware): for collection, storage, communication, editing of data/ information Network technologies required to support a Hospital Information System High speed wired networks
Intranets
Wireless Networks
Video conferencing
Internet-VoIP (Voice over IP)
Bar-code scanning
Web servers (client-server)
Speech recognition
Architecture of a Hospital Information System Supported in client-server architectures for networking and processing. Most work positions for HIS currently are resident types. Mobile computing began with wheeled PC stands, and now tablet and Smartphone applications A cloud computing alternative is not recommended, as data security of individual patient records services are not well accepted by the public. HIS is composed of one or several software components with extensions, as well as of a large variety of 42
sub-systems in medical specialties. Some of the most important sub-systems are the following: Laboratory Information System (LIS) Policy and Procedure Management System Radiology Information System (RIS) Picture archiving and communication system (PACS)
Structure (Architecture) of a Hospital Information System. Notice that the clinical subsection is core element
A HIS is nowadays service oriented: different services are used by many different health professionals
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Services (Applications) of an Integrated Hospital Information System These address all processes, administrative and clinical, of a hospital. The most important of these applications which are supported by dedicated components of an HIS are the following: Patient registration
Pharmacy systems
Billing systems
Imaging systems
Appointment systems
Telemedicine
Computerized Physician Order Entry (CPOE)
Library resources
Lab systems
Decision Support Systems
EHR (Electronic Health Records)
Use of Standards in Hospital Information Systems Existing standards and guidelines should be integrated into Hospital Information Systems; otherwise these standards will be of limited usefulness. Universal medical vocabularies are used to “speak the same language” Standards for data exchange (ie Health Level 7) to communicate data between different information systems Standard formats for medical records (like openEHR), laboratory data, medical images etc Standardization of medical literature formats (Like Medical Subject Headings-MeSH) Health care standards - treatment guidelines (Structured guidelines describing how to make a clinical intervention appropriately) Implications about management involvement and integration with external providers If the hospital management decides to invest in systematic information processing it decides to manage the hospital information system in a systematic way. The management of a hospital information system forms and controls the information system, and it ensures its efficient operation Integration of information processing should consider not only information processing in one health care organization, but also information processing among different institutions (such as integrated health care delivery systems)
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Keep in mind that…
The integrated processing of information is important because: all groups of people and all areas of a hospital depend on its quality, the amount of information processing in hospitals is considerable, and health care professionals frequently work with data The systematic processing of information: contributes to high-quality patient care, and reduces costs Information processing in hospitals is complex and therefore we need: the systematic management and operation of hospital information systems, and medical informatics specialists responsible for the management and operation of hospital information systems
Questions for Discussion 1. The scope of a Hospital Information System is the Quality of Care and the Cost Containment. How are these two goals satisfied with the successful implementation of an integrated HIS? 2. Modern Hospital Information Systems are distributed and service oriented. A.What is the meaning of the above statement? B. Describe the benefits of this approach in comparison with the horizontal development. 3. Why is it so important for Health Information Systems to be backwards compatible and upgradeable?
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Chapter
8. Electronic Health Records (EHR)
The need of EHR: Continuity of Care Aim of healthcare systems today is the continuity of care- quality, access, efficiency, in all settings and also covering all levels of care (primary, secondary, tertiary). Healthcare services are distributed across clinical environments (Healthcare professionals-Medical doctors, nurses and other staff) and administrative and public health services (Health managers & Health authorities, Epidemiologists etc).
Towards this direction, Electronic Health Records aim to integrate information about the management of patient care to support the above requirements.
Electronic Health Record (EHR) Digitally stored healthcare record (or part of it) for the whole life of a patient, aiming to support the continuity of patient care (quality, access, efficiency) education and research.
An EHR system includes Longitudinal collection of electronic health information for and about persons, where health information is defined as information pertaining to the health of an individual or health care provided to an individual Direct electronic access to individual and population-level information only by authorized users; Provision of knowledge and decision-support that enhance the quality, safety, and efficiency of patient care; 46
Supports efficiently all processes for health care delivery.
Other similar terms have been used, with different meanings in terms of their scope and orientation. Choose one word from each column to create “new” terms.
Important Functions Electronic Health Records should have Support of both non episodic i.e. patient history (applicable across visits e.g. allergies) versus episodic data (applicable with one visit) Efficient data entry of all orders and documentation by authorized clinicians. Ideally documentation includes clinical reasoning and rationale. Automation and follow up of the typical clinician’s workflow Support of electronic signatures to avoid non-repudiation Additional support of data collection for non clinical uses, such as billing, quality management, reporting and public health disease monitoring Access to knowledge sources at any point within the clinical workflow For subsequent encounters, access to relevant information from previous hospitalizations All care related data and patient functional status are in coded form. Patient problem list, patient history, physical exam, allergies, vital signs, immunizations, medications, orders, diagnostic results & images Access to the patient information needed with integrated views, specialty specific forms, diagrams and flagging information outside of normal limits 47
Tools to manage order communication and monitor completion process For ambulatory (out of hospital) care, EHR gathers data to support regulatory requirements Include decision support tools to guide and critique medication administration Recommendations and alerts tailored to the individual patient condition Evidence of patient outcomes related to patient condition and treatment and care delivery Real-time surveillance and alerting Can accept information from external systems and data capture devices (eg bar code scanners etc) Supports reporting for the evaluation of healthcare services, the compliance & process standards Integrates EHR information with financial information and other external data such as patient satisfaction for purposes of analyzing process and practice performance Supports data modeling for evaluation of potential changes Captures health related data needed to predict allocation of resources Provide access to tools and displays that can be customized to end user preferences Provides tools to facilitate teamwork and coordination
Reasons that we keep records in healthcare Direct healthcare provision to the patient
Research and Education
Management of patient care and resources
Public health policies
Assessment of the Quality of care
Financial management
Ultimate Goals of Electronic Health Records Improve patient safety Support the delivery of effective patient care Facilitate management of chronic conditions Improve efficiency Disadvantages of traditional paper records available only once at a time and in one place handwritten entry and transferring mistakes from the data source legibility issues-misunderstanding 48
Increased volumes of paper inadequate follow-up of the health status difficult to gather data for research purposes
Different Levels of application for Electronic Health Records Various Health professionals: Doctors, Nurses, Patients Various Settings: Hospital settings, groups of clinicians, primary care settings, rehabilitation centres and other specialized services Geographical Areas: country, region, city…
Electronic Health Records: Different Users, different levels of application
Core Functions of EHR Health information and data-Patient support
Electronic communication and connectivity
Result management
Administrative processes
Decision support
Reporting and health of population
Order entry/order management
Main uses of Electronic Health Records 49
Patient Care Decision making support
Management and development of care plans
Risk assessment for individual patient
Setting up guidelines for prevention
Risk factors for patients can be tracked down
Support of nursing care
Facilitate healthcare provision according to clinical guidelines
Tracking down provided services (medication and treatments)
Patient satisfaction can be measured Management of Care and Quality assessment
Case-mix and practices
Basis for usability studies
Analysis of disease severity
Quality assurance
Clinical practice guidelines
Risk assessment
Resource management and workload assessment
Secondary uses of Electronic Health Records
Research and education
Public health policies
Tool for management of the finances
Main Benefits of Electronic Health Records Time
Quality of care
timely access to health data
decision making support
quick data retrieval for research purposes
tools for distributed care
Money
Research and Education
better management of health resources
clinical & epidemiologic research, education of patients, training of health professionals
Reimbursement faster and more efficient Benefits for pharmacists
Medical prescriptions (Rx) based on a specific predefined plan Rx The right Rx firsthand-no comebacks 50
On site assessment of possible drug interactions less Adverse Drug Effects improved Drug Utilization Review Benefits for Primary Care: Minimizing total costs -personal record administrators decrease by 50%
-time for health professionals decreases by 15%
-cost to transfer data decreases by 50%
-costs due to bad practices decreases by 5%
Benefits for nursing care Patients admitted to hospital when this is required Dynamic disease management and hospital procedures Less unneeded laboratory tests, x-rays etc
Functions of an Electronic Health Record Functions include support of management of care, information management (hospital) and the direct clinical care (Patient). The diagram below presents all the functions of an Electronic Health Record.
Table. Expected Benefits from the implementation of Electronic Health Records 51
Benefit
Explanation
Time
Errors
No more time spent to transcribe data
less time required to share data
Less administrative workload for patient & nursing staff
easier access to medical data
Reduced Length of Stay
Less -medication errors -errors due to data transcription
Access to knowledge
Good practices enabled by the clinical staff Variability of nursing, medical behaviors minimized
Increase in Productivity
More efficient use of existing resources (including clinicians)
Classification, terminology, Codification Since the reason that we move from the traditional record to the E.H.R is not simply the record keeping but the support of more procedures like: Disease Making, Research, Communication-data sharing, Management, Statistical surveys; that is why there is the need for common Classifications, Terminologies and Codifications.
ClassificationDifferent concepts and their relationship TerminologyMedical terms CodificationCommon codes
Classifications Placing objects in groups (or classes) based on their relationships, we achieve classification. Classification is based on a priori knowledge of the field of knowledge (ie diagnoses, medical procedures etc) and is the key for new knowledge. Classifications can be Single or Multi-axis 52
Single: ICD10 disease classification Multi-axis: organ-anatomy-etiology-morphology-dysfunction-diagnosis… Characteristics of a good classification Completely covers the field of interest Contains no overlapping classes Is suitable for its scope Is homogenous-one principle by level Includes well defined criteria for the class limits If followed by clinical guidelines for their application in a clinical environment Has a desirable detail level (a good balance) SNOMED- Systematized Nomenclature of Human and Veterinary Medicine (a multi-axis classification) Example: Tubeculosis (D-14800) can be classified as: Lung (T-28000) + Granuloma (M-44000) + Tubeculosis Mycobacterium (L-21801) + Fever (F-03003)
The Axes of SNOMED Classification
Other classification systems MeSH-Medical Subject Headings: Classification of the international medical library UMLS-Unified Medical Language System: Intelligent retrieval of biomedical information from various sources Thesauri and Codification 53
Thesaurus: list of terms and their synonyms Codification: replacing a concept with combination of numbers and/or letters
Numeric
Mnemonic
Juxtaposition codes
Meta, the UMLS metathesaurus and some of the medical vocabularies and classifications included
ASTM Continuity of Care Record - a patient health summary standard based upon XML, the CCR can be created, read and interpreted by various EHR systems, allowing easy interoperability between otherwise disparate entities. ANSI X12 (EDI) - A set of transaction protocols used for transmitting virtually any aspect of patient data. Has become popular in the United States for transmitting billing information, because several of the transactions became required by the Health Insurance Portability and Accountability Act (HIPAA) for transmitting data to Medicare. CEN - CONTSYS (EN 13940), a system of concepts to support continuity of care. CEN - EHRcom (EN 13606), the European standard for the communication of information from EHR systems. CEN - HISA (EN 12967), a services standard for inter-system communication in a clinical information environment. DICOM - a heavily used standard for representing and communicating radiology images and reporting HL7 - HL7 messages are used for interchange between hospital and physician record systems and between EMR systems and practice management systems; HL7 Clinical Document Architecture (CDA) documents are used to communicate documents such as physician notes and other material. IHE - Integrating the Healthcare Enterprise; while not a standard itself, IHE is a consortial effort to integrate existing standards into a comprehensive best-practice solution ISO - ISO TC215 defined the EHR, and produced a technical specification describing the requirements for EHR Architectures. openEHR - next generation public specifications and implementations for EHR systems and communication, based on a complete separation of software and clinical models.
Many standards, classification systems & thesauri exist in healthcare. Here are examples of the most common
Architecture of an Electronic Health Record 54
Time oriented Feb 21, 2013
Shortness of breath, cough, and fever. Very dark feces.
Exam: RR 150/90, pulse 95/min, Temp: 39.3 C. Rhonchi, ESR 25 mm, Hb 7.8, occult blood feces +.
Chest X-ray: no atelectasis, slight sign of cardiac decompensating.
Medication: Amoxicillin caps 500 mg twice daily
Feb 28, 2013
No more cough, slight shortness of breath, normal feces.
Exam: slight rhonchi, RR 160/95, pulse 82/min.
Keep Aspirin at 32 mg per day. Hb 8.2, occult blood feces.
Source oriented Visits Feb 21, 2013 Shortness of breath, cough, and fever. Very dark feces. Exam: RR 150/90, pulse 95/min, Temp: 39.3 oC. Rhonchi, abdomen not tender. Mar 4, 2013 No more cough, slight shortness of breath, normal feces. Exam: slight rhonchi, RR 160/95, pulse 82/min. Medication: keep Aspirin at 32 mg /day. Laboratory tests Feb 21, 2013: ESR 25 mm, Hb 7.8, occult blood feces +. Mar 4, 2013: Hb 8.2, occult blood feces. X-rays Feb 21, 2013: Chest X-ray: no atelectasis, slight sign of cardiac decompensation. Problem oriented (SOAP) Subjective- based on patients description Objective- based on findings by health professionals Assessment- diagnosis and lab tests Plan- therapeutic plan, medication Problem 1: Acute bronchitis 55
Feb 21, 2013 S: Shortness of breath, cough, and fever. O: Pulse 95/min, Temp: 39.3C.Rhonchi. ESR 25 mm, Chest X-ray: no atelectasis, sign of cardiac decompensation. A: Acute bronchitis. P: Amoxicillin caps, 500 mg twice daily.
Mar 4, 2013 S: No more cough, slight shortness of breath. O: Pulse 82/min. Slight rhonchi. A: Sign of bronchitis minimal.
Problem 2: Shortness of breath Feb 21, 2013 S: Shortness of breath. O: Rhonchi, RR 150/90, Chest X-ray: no atelectasis, slight sign of cardiac decompensation. Α: Minor sign of decompensation. A “Subjective-Objective-Assessment-Plan” EHR Representation
Questions for Discussion 1. Out of the functions of an Electronic Health Record, refers to those that are related with Patient Safety. Please explain your answer. 2. Which are the advantages of the use of Electronic Health Records over the traditional handwritten records? 3. Among other functions, Electronic Health Records support (i) research and (ii)education of patients. How are both of the above functions achieved? 4. Can you describe the benefits we are expected to have in terms of time, if we introduce a new Electronic Health Record system? 5. ‘ICD-10 is a single classification while SNOMED is a multi-axis classification.’ Is the above statement correct? Please explain your answer.
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Chapter
9. Telemedicine
Why telemedicine today? During the last decades, there have been many social, political and financial changes which have changed the way that the health and social is provided to patients. Those changes are demographic, financial, request for improved quality of life and of social equity nature. Leading factors for the development of telemedicine Social Needs: Improving the quality of health services Demographic changes: aging population, increase in people with chronic diseases, increase in people who cannot move easily, lack of staff in remote locations Financial Needs: increased cost of providing health services (e.g. chronic patients), cost sophisticated medical / healthcare equipment, new directions - markets in health Definition of Telemedicine Telemedicine transfers Medical Information through technology. It is the use of telecommunications to provide medical information and services. It may be as simple as having two health professionals discussing about a patient case using their mobile phones or as complex as satellite technology for consultation during a robotic surgery. Telehealth is a broader view of telemedicine, which views specifically on curative measures. Provides solutions for the needs of healthcare facilities Allows patients to be seen when traditional health care cannot be given due to distance, location, or lack of medical centers. Combines preventive, promotive, and curative aspects. Telemedicine Goals
Transmission of digitized audio, video and images Accessibility, improved quality of care in rural and isolated communitieslinkage with urban areas Delivery of information and services at home Telemonitoring Reducing travel time and expenses for patients and health professionals New health business opportunities
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Benefits from the use of Telemedicine
Improving quality of services Patient monitoring Faster advice, diagnoses Ease of access to knowledge Cost reduction of offered services (after initial installation)
Telemedicine… Makes doctor’s offices and medical facilities close to one another. Can be used in remote parts of the world or in places as close as a correctional facility, helping to eliminate risks and costs (ie transportation of prisoners to a medical center) Allows a surgeon/physician to be at two places at once. Allows treatment for patients at home Who benefit most
Populations living in isolated places Patients with disabilities at their homes Telemedicine does not only refer to patient-doctor interactions!
Types of telemedicine Real Time Telemedicine requires the presence of both parties at the same time and a communications link that allows a real-time interaction. Video Conferencing is one of the most common forms of technologies in Real Time telemedicine Store-and-Forward Telemedicine involves acquiring medical data (like medical images) and then transmitting them to a doctor for assessment offline, later on. Advantages are that it does not demand the presence of both parties at the same time & does not require expensive equipment Video Conferencing Equipment Tele-Otoscope-allows a remote physician to 'see' inside a patient's ear wireless otoscope, wireless receiver, television with video input. Tele-stethoscope-allows the consulting remote physician to hear the patient's heartbeat wireless stethoscope, wireless receiver, television with video Input
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Telemedicine uses ICT Information Digital editing - image and signal analysis Hospital Information System-Electronic Patient Record Decision Support Systems Telecommunications Satellite communications Computer Networks - Internet Wireless Networks Communication technologies in telecare: PSTN - ISDN: for voice services (past!), broadband networks, optical fibers, wireless mobile networks: (GPRS - General Packet Radio Service, EDGE - Enhanced Data Rates, 3G - 3rd Generation, etc.), satellite communications, Interaction with multimedia services in CATV, Voice Over IP, Other internet services
History of telemedicine Generations of Telemedicine First generation (1950-1989): Transmission of radiology images Second generation (1989-200x): Better features, remote monitoring, transmission of digital images Third generation (200x-): videoconferencing systems, interactive communication (synchronous), etc. Through the early 1960: first telemetric medical programs in NASA's manned spacecraft to monitor the physiological functions of astronauts. 1964: first TV program with telemedicine interaction to provide medical care. Closed circuit television link between Nebraska Psychiatric Institute and Hospital of Norfolk, 112 miles away. 1967: Interactive video linkage between Massachusetts Hospital & International Airport, Boston. 70s in the USA-the first wave Large-scale telemedicine demos involving satellite programs to connect paramedics in remote villages in Alaska and Canada with hospitals. Common features of these programs: accurate communication infrastructure, restrictions on humanmachine interaction, most of them supported by the U.S. government High cost-benefit ratio: A “crash” of Telemedicine in America around mid 70's. 90s in the USA-second wave
Second huge wave of popularity telemedicine: New crisis in health care in the province, telecommunications revolution, political decisions. 59
Telemedicine program WellCare (1994) The Massachusetts General Hospital (MGH) in Boston, and the company WellCare (Paris) launched the largest private telemedicine program including various hospitals. In 1994 teleradiology services performed transmissions between MGH and Saudi Arabia (more than 6,000 medical images for diagnosis and teleconsultation-approximately 600 cases).
Services/Applications of Telemedicine Teleconsultation It is the remote access to special knowledge or special event or collaborative diagnosis cooperation decision (eg teleradiology).Refers to sharing images and other patient data between physician and one or more specialists. The diagnosis is made by a physician and specialists help him come up with more accurate diagnosis. Interactive Consultation-example
A typical example is the communication between a large urban hospital and a rural health setting The patient does not have to travel to the “big city” Also may be crucial in emergency events (ie teleconsultation in the case of a heart attack) Almost all specialties are conductive to this kind of consultation (internal medicine, rehabilitation, pediatrics, cardiology, neurology etc)
Telediagnosis Patient diagnosis from a remote doctor (e.g. telecardiology). Sharing of images & other patient data between doctor and patient A doctor locally, provides additional information. “Collaborative diagnosis”: cooperation between remote health professionals for a diagnosis Telecare Use of remote sensing data to provide remote assistance to patients (e.g. patients with diabetes). Devices provide care instructions from a distance Telemonitoring Remote monitoring of a patient in a non-hospital environment Telemetry devices monitor vital signs and inform accordingly Distance Learning
continuing medical education for healthcare professionals without having to travel to central sites. Tele-education / clinic session remotely Remote education for patients Simulation applications (eg surgical operation sims). 60
Online seminars, interactive video conferencing, instant access to training materials.
Distant Learning refers to both the patient education and the training of health professionals Components of telecare
Databases Playback: Sound, video, printers Contact: Navigators, software video conferencing, e-mail, etc.
Clinical knowledge Communications Material Logs: Scanners, Cameras, etc. Storage: Optical, magnetic media
Areas of Application Tele-Radiology One of the most popular apps and the first appeared. Radiographs are obtained in one location and transmitting them to one or more remote locations, where displayed on a screen or printed. The images can be from scratch in digital form or digitized retrospectively. The images are stored in a retrieval system (eg PACS) or sent straight. Telepathology Images of pathology may be sent from one location to another for clinical consultation Telesurgery Application to inaccessible places: Remote areas (remote islands, mountainous areas, space, etc.) Anatomical locations difficult to access (e.g. brain surgery) Enhancing surgical skill: Using robotic systems for greater accuracy Video Conferencing • •
Very popular among hospitals Customized hardware and software at two locations (patient-carer or carer-carer)
www.polycom.com www.onlinetelemedicine.com Various other Examples of Applications Interface hospitals to share knowledge and data 61
Ambulance first broadcast information during the evacuation of the patient Health professionals in remote parts communicate with central hospitals Telemetry systems installed at home chronic patients Robotic surgery in inaccessible places Emergency care
Telemedicine Programs DIABTel System: Telemonitoring & telecare services to diabetic patients in remote non-hospital settings (Barcelona, Spain). American TeleCare’s Personal Telemedicine System: healthcare providers, using two way interactive systems at centralized work stations, virtually walk patients at home through simple tests, including blood pressure, pulse, temperature, and respiratory rate checks. iPath telemedicine program (http://sourceforge.net/projects/ipath/) Free open platform for "case based collaboration", especially designed for medical applications (telemedicine, etc). iPath provides a sort of medical BBS to discuss/consult cases online. Sjunet (Sweden): Connects 80 hospitals, 800 primary health centers, 900 pharmacies & private institutes. Sjunet uses private network (security) based on optical fibers (fast) and supports videoconferencing and collaboration EVISAND (Spain): it is operating since 2000 in three provinces of Andalusia. Evisand provides teleconsultation for cardiology, dermatology, pediatrics, psychiatry, ophthalmology, radiology, neurosurgery to professionals & emergency services. Possible Challenges and Barriers for successful implementation
Some States still do not allow out-of-state physicians to tele-practice in other states Medicare and Medicaid have restrictions for the reimbrushment of telemedicine services Insurance companies will also not reimbrush telecare practices (ie in California) Technology issues have been overcome and the main barrier in the past has been network restrictions
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Some important Requirements
Interoperability Compatibility (or technical interoperability) Scalability augment modular telecare equipment on existing systems Portability: porting applications to different platforms keeping the cost low Reliability and Availability
Critical issues for successful implementation
Analysis of user requirements (not technology driven) to improve acceptance. Organization's administration commitment User centered design (eg friendly GUI) Motivation of users (health professionals and patients) Providing tools for the appropriate user training Integrating security and confidentiality Analysis of legal consequences, Cost Benefit Analysis
Installation and evaluation of telemedicine systems Telemedicine brings about organizational changes New service changes the clinical routine and common clinical protocols Increased workload of the doctor in the early stages of implementation Increased number of patients and data (need to provide tools for automated decision-making) Difficulty to evaluate telecare (patients not in controlled environment) Telehealth Nursing Telehealth nursing is the practice of nursing over distance using telecommunications technology (National Council of State Boards of Nursing, 1997). The delivery, management, and coordination of care and services provided via telecommunications technology within the domain of nursing (American Association of Ambulatory Care Nursing, 2004). It involves the use of electromagnetic channels (e.g., wire, radio, and optical) to transmit voice, data, and video communications signals. It is defined as distance communications, using electrical or optical transmissions, between humans and/or computers (Skiba, D.J. & Barton, A.J., 2000). Leveraging technology and nursing expertise to provide quality nursing care, to delivering nursing expertise to those who need care, and to improving health and patients’ outcomes.
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Special issues in telemedicine Homecare Development of means of communication, not only restricted to traditional methods (telephone)
Using sensors and wireless data transmission to healthcare workers Alerting systems Providing personal secure, support to daily activities Providing information to patients Monitoring patients remotely (eg blood pressure monitors, glucose control, automatic analyzers) Remote therapeutic devices
Telemonitoring/teleconsultation of diabetic patients
Using tablet PCs Recording measurements (sugar, medications, meals, notes) Monitoring level taking nutrients Monitoring glucose level (reports, graphs) Categorize records (eg the time) Representation of information in an easy and fast way Communicate with other computers for data transfer
Sensors for distance disease monitoring A sensor is a converter that measures a physical quantity and converts it into a signal which can be read by an observer or by an (today mostly electronic) instrument. Potential in tele-monitoring Integration of available specialized medical technology with wireless networks (ie wearable accelerometers with integrated wireless cards for patient monitoring) Save on medical expenses, time (less face-to-face appointments), more participants in clinical trials Operation in buildings results in further interference due to walls, etc. decreasing reliability What are the advantages of sensors in telecare? No “Human Error” – no “forgotten” readings More Accurate and Reliable Real Time Monitoring of the patient An important time advantage- all readings are on time Better for the patient-no messing around with him all the time 64
Automatically measures trends using temporal functions Low power-low cost
Sensors in home tele-care for rehabilitation
Wearable Smart Clothes
A new report projects wearable wireless sensors for fitness and wellness monitoring will approach 80 million devices by 2016, growing at a 46% CAGR from 2010 to 2016. Using the rapidly improving wireless communication technologies and advanced sensors available today, many companies and universities are proposing solutions for healthcare applications.
Wearable Clothes: the example of Lifeshirt (vivometrics) A noninvasive system based on plethysmography (measuring changes in volume within an organ or whole body usually resulting from fluctuations in the amount of blood or air it contains) Wearable: provides constant monitoring by measuring and storing respiratory and cardiac parameters Creates health profile during normal daily activities. Users wearing a light garment can be washed in normal washing. Embedded sensors collect data on cardiopulmonary function. The system may include an electronic calendar of patients where they can save user data. Lifeshirt improves the speed and quality of sleep studies, since patients can be monitored from home. 65
Can be combined with optional peripherals and monitor functions such as electrocardiogram, EMG, leg movement, body temperature, blood oxygen saturation, blood pressure etc The person being monitored can even indicate symptoms itself, activities and medication taken in a portable handheld computer) directly connected to the vest.
Lifeshirt
Telesurgery Definition: reprogrammable multifunction operator to achieve various tasks [Robotics Institute of Carnegie Mellon University in the U.S.] Communication between surgeons in remote areas Use of robotic systems remotely What is required: Increased telecommunications infrastructure Specialized software systems Specialized hardware Virtual Reality
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The robot arm Consists of the: (i) Mechanical Part (base, associations, joints, motors, sensors, action tool) (ii) Controller: Computer (Hardware)-Software (Software) The robot arm is also characterized by the degrees of Freedom (human hand has 7 degrees of freedom) Variables: Position and Orientation The Surgical Robot Surgeon / Master Console (Software - Hardware) Robotic Arms / Slaves Kwoh - 1985 - Puma 560 - Neurosurgery Biopsy Ph.Mouret - 1990 - Laparoscopic Cholecystectomy Dr. Hap Paul, DVM / William Barger, MD with Russell Taylor (IBM) - early 90s- Robodoc - hip replacement John Wickham, MD (urologist) / Brian Davies, PhD - early 90s- Probot - transurethral prostatectomy Defense Advanced Research Projects Agency / DARPA (USA) - 1996 - Under the Stanford Research Institute - SRI) Kenneth Salisbury, PhD, Mark Raibert, PhD, Robert Playter, PhD (MIT) - The Phantom - Force Feedback System
Minimally Evasive Surgery: Laparoscopic Surgery
Uses endoscopes - camera Problems: (i) small working area (ii)limited field of view (iii) degradation of natural senses 2 Solutions: (i) Technical assistance surgeon (ii) Replacement by an automatic machine Computer Assisted Surgery (CAS) Using artificial senses, sight and touch three dimensional and three-dimensional imaging and diagnostic equipment. Robotic Surgery - Evolution of endoscopic surgery Depending on the degree of involvement of the surgeon in surgery have: Supervisory-controlled systems Planning in advance of the surgical procedure 67
Telesurgical systems Handling arms from distance (ie Da Vinci) Shared-control systems
Examples of Robotic Systems Robodoc (computer controlled) 1992 - Integrated Surgical Systems - orthopedic surgery (total hip replacement, total knee replacement) 1. Preoperative planning system (ORTHODOC) 2. Surgical Assist System (ROBODOC) Minerva (computer controlled) 1991 - University of Lausanne (EPFL) - Neurosurgery Surgery - Use inside the scanner (handicap) and use of stereotactic frame of reference - 1 arm- 5 degrees of freedom Acrobot (Surgical navigation system) 1999 - spin-out company of Imperial College - Knee Surgery - The surgeon manipulates the arms (two) with a handle / lever attached to the system. Its entirety: Acrobot Modeller, Acrobot Planner, Acrobot Navigator, Acrobot Sculptor Neuroarm (computer controlled) 2007 - Dr. Garnette Sutherland - Neurosurgery Surgery - compatible with MRI. It features: Robotic arms Quickly switch gears Force sensors, tactile Functions such as Beak and using soft tissue biopsies, bone separation from surrounding tissues, sections at the level of tissues, sutures, suctions etc. Two monitors, two touch screens, stereoscopic projection, two control knobs with force feedback Special safety mechanism 2 robotic arms-7 degrees of freedom Endoscopic Systems Aesop (controlled by pedals or voice commands) - Computer Motion Inc. 1993 - Aesop 1000 1996 - Aesop 2000 1997 - Aesop 3000 Aesop HR - networking ability via Hermes: The third hand of the surgeon in minimally invasive surgery!- 1 Arm, Cost: $ 80,000 Endoassist (controlled by a special headband) - Prosurgics 68
Use: Camera Handling Particular accuracy laparoscope (eg Prostatectomy, mitral valve repair)- 1 Arm
Control and Teleconsultation Socrates - Computer Motion (Construction Company) Integrated system of telecommunications equipment, medical devices and networked robotic systems to provide an affordable and efficient way of working and mentoring from a distance. Hermes - Computer Motion & Stryker New open system architecture that allows connectivity and voice control various devices needed to perform minimally invasive surgeries Functions: Control of surgical instruments, surgical table setting and lighting, taking pictures or video of the surgical field etc. Master-Slave
Zeus (1995 - Computer Motion Inc) 6 degrees of freedom, FDA (2001), CE, 975.000 $ Interventions: Cardiac surgery, general surgery, obesity surgery (bariatric), urology, neurosurgery etc. Parts: surgeon control console, three robotic arms on the operating table and a computer - controller Features: Feedback power, eliminating flicker, using a camera with voice commands from the central arm, MicroWrist and others Da Vinci SiHD (Last edition - 2009) New features: High precision depth perception Console with touchscreen (video, audio) Improved Ergonomic Attach a second console (second surgeon) Ceiling vision system Countries that have installed Da Vinci Systems: United States, Austria, Belgium, Canada, Denmark, 69
France, Germany, Italy, India, Japan, Netherlands, Romania, Saudi Arabia, Singapore, Sweden, Switzerland, United Kingdom, Australia, Turkey, Greece and Czech Republic. Strength and Touch Feedback in Robotic Surgery Systems with such capability have been developed The importance of feedback force is large in the case of robotic surgical systems. Whenever a surgical instrument touches some tissue in the patient, the surgeon must have his hands feel the resistance of the particular tissue. Otherwise, the absence of this sense can lead to tissue damage. The surgeon presses the web with the robotic arm without knowing the amount of pressure is actually exerted. The most popular haptic feedback devices are PHANTOM Series systems, which can be part of a masterslave system. 1993 - SensAble Technologies - Dr. Kenneth Salisbury and Thomas Massie Ethical/Legal Issues Security, privacy and confidentiality, in telemedicine consultation Advocate for safe and effective use of telehealth technology. Serve as well informed resources for consumers and technology developers for the safe use of technology to meet healthcare needs. Monitor outcomes of care resulting from telehealth nursing practice. Ensure confidentiality and patient privacy in all telehealth encounters.
Questions for Discussion 1. Synchronous and asynchronous telemedicine services: can you provide one example for each of these two types of communication in telemedicine? 2. How can telehealth improve the quality of care? Provide three important factors. 3. What is the difference between teleconsultation and telediagnosis? 4. Discuss the organizational considerations you should address for the successful implementation of telehealth services in a health system. 5. How are sensors related with telehealth? Refer to 3 common sensors that are used in home telecare services
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10. Basic Concepts of Medical Imaging
Image analysis was firstly introduced in the 1950s at academic institutions such as MIT, as a branch of artificial intelligence and robotics. It can be defined as the quantitative or qualitative characterization of 2D or 3D digital images. 2D images are, for example, to be analyzed in computer vision and 3D images in medical imaging. In image analysis what primarily happens is the extraction of meaningful information from images; mainly from digital images by means of digital image processing techniques. Image analysis tasks can be as simple as reading bar coded tags or as sophisticated as identifying a person from their face.
Computer image analysis contains the fields of computer or machine vision, and imaging, and makes use of pattern recognition, digital geometry and signal processing.
Examples of image analysis techniques 2D and 3D object recognition Image segmentation Motion detection (Single particle tracking, Skeletal tracking etc) Video tracking Medical scan analysis 3D Pose Estimation
Applications of Digital Image Analysis Medicine: detecting cancer in an MRI scan Microscopy: counting the germs in a swab. Remote sensing: such as detecting intruders in a house Machine vision: automatically count items Astronomy: such as calculating the size of a planet. Materials science: determining if a metal weld has cracks. Security: detecting a person's eye color or hair color. Robotics: avoid steering into an obstacle. metallography: determining the mineral content of a rock sample Optical character recognition: automatic car license plate detection 71
Techniques to analyze images There are many different techniques used in automatically analyzing images. Each technique may be useful for a small range of tasks However there still aren't methods of image analysis that are generic enough for wide ranges of tasks, compared to the abilities of a human's image analyzing capabilities From Physiology to Information Processing Understanding image medium Tissue density, blood flow, perfusion, cardiac motion Physics of imaging Transmission of X-rays, emission of gamma rays, MR imaging Imaging instrumentation Collecting the data, signal-to-noise ratio, resolution General Performance Measures Positive: Object was observed Negative: Object was not observed True Positive-False Negative-True Negative-False Positive Sensitivity-Specificity-Accuracy
Biomedical imaging informatics is the study of methods for generating, manipulating, managing, and integrating images in many biomedical applications. 72
What is a medical image A geometrical distribution of a certain physical/ psychological property (ies) Modalities: several images from a certain region
Image construction Extract images of a certain physical property or anatomical structure This procedure is non invasive most of the times When we talk about image geometry aspects we basically refer to two features: image projection image tomography Image modalities X-RAY (CT/radiography) MRI (Magnetic Resonance Imaging) PET (Positron Emission Tomography) U/S (Ultrasound)
…are the most common kinds of imaging techniques in healthcare
Pre-processing and Post-processing of images Pre-processing the process of designing the best fit protocol for the acquisition of the raw image “A raw image is minimally processed data from the image sensor” Preprocessing also includes the reconstruction of the image from the raw data Noise reduction in raw image level Post processing Noise reduction in produced image (i.e. jpg file) Image enhancement in specific image areas of interest Image partitioning to meaningful areas (based on anatomic structures or based on pathologic areas) Other areas Computer Aided Diagnosis 73
Virtual surgery Multimodal Image Fusion Various types of images based on… Number of channels
Anatomical or Functional Imaging
A. Single channel-CT, PES, U/S
Anatomical: static distribution a certain physical/ anatomical structure
B. Multiple Channels acquired-MRI
Physiological/functional: i.e. metabolism
Geometry of an image How is the image projected? Can we “see” the depth?
Projected: a single line in the object will be mapped into a single point at images (i.e. x-ray)
Tomography: cross section-many slides are produced (i.e. CT)
It can be either 2D or 3D
X-ray transmission How does this work?
Absorption coefficient (μ) of x-ray protons are displayed as image
Differential X-ray absorption by tissues produces varying densities that enable images to characterize normal-abnormal structures.
Geometry of an image?
Projection and Tomography
Very good resolution
Contrast?
Good contrast for hard tissue (bones)
Low contrast for soft tissue (muscle, tumors)
Ultrasounds How do ultrasounds work?
oscillating sound pressure wave with a frequency greater than the upper limit of the human hearing range)
Based on sound wave reflection (Ultrasound is an 74
•
Reflection times differ based on the tissue type
Behind bones or air?
No view!
• Very safe for health • Average resolution • Is used for anatomical representation and movement of anatomic elements Types of Ultrasound Images Sonography (2d imaging) Ultrasound-based diagnostic imaging technique used for visualizing subcutaneous body structures (tendons, muscles, joints, vessels, internal organs) for possible pathology. Doppler-color Doppler Sonography can be enhanced with Doppler measurements, which employ the Doppler effect to assess whether structures (i.e blood) are moving towards or away from the probe, and its relative velocity. i.e. Blood flow-heart monitoring
Doppler shifts of sound frequency are used to evaluate blood flow in many organs and in major vessels.
Change of wavelength caused by motion of the source
The MRI Imaging Spin density and relaxation times Properties of proton H+ spin are imaged Multichannel images are produced MRIs depict energy fluctuations of certain atomic nuclei (usually hydrogen) when they are aligned in a magnetic field and then perturbed by a radiofrequency pulse. In specific, in MRI image the following three metrics are measured: (I)Proton Density (II) Spin-Lattice Relaxation Times (III)Spin-Spin Relaxation Times 75
Not only for diagnosis but also for monitoring! There is no risk for the patient The image resolution is very high Since the data acquisition is parametric there is great flexibility to how to manipulate and present different modalities as an output MRI is very safe BUT cannot be performed to patients with electrical or magnetic prosthetics Very high contrast for soft tissues, but contrast is not so high in the case of hard tissue (ie bones) Other tests based on the same technology: Fmri: functional MRI, blood flow, MRA: magnetic resonance angiography
How to deal with the contrast issue In some cases clinicians can enhance the contrast by injecting contrast fluid/agents into the human element to be imaged (ie in angiography)
DSA: Digital Subtraction Angiography Comparison between two different x-rays For example the one has been produced with fluid injection and the second image without contrast fluid injection Roles for Imaging in Biomedicine Imaging is a central part of the healthcare process
diagnosis
treatment planning
image-guided treatment
estimation of prognosis.
Plays important role in medical communication and education, as well as in research. Detection and diagnosis Detection of medical abnormalities during the diagnostic process [Mammograms are often used for the detection of breast cancer] 76
Detection and diagnosis are very closely tied: an imaging test is not just able to detect but also to narrow the range of possibilities Examples of Detection and Diagnosis Examples based on simple image methods (ie just photographing the area of interest)
Ophthalmology: retinal photography
Pathology: specimen viewing, light microscopy
Dermatology: view skin lesions after having taken images of high resolution
The visible-light spectrum is also responsible for the production of the endoscopic images Assessment and Planning Using mefical images clinicians can better assess patient’s health progression of a disease Examples Analysis of cardiac status by assessing the heart’s size and motion Ultrasound to assess fetal size and growth, as well as development
Example of feedback using imaging
Computed tomography is used frequently to determine approaches for radiation therapy.
Precise calculations of radiation-beam configuration can be determined to maximize dose to the tumor while minimizing absorption of radiation by surrounding tissues.
This calculation is often performed by simulating alternative radiation-beam configurations.
Images as a guidance to procedures Images provide real-time guidance with virtual-reality methods. In minimally invasive surgery, imaging can provide a localizing context for visualizing and orientating the endoscopic findings and this is of great importance for the surgeon Such minimally invasive surgery can be conducted at a distance telepresence Manipulation of the endoscope can be controlled by a robotic device with haptic feedback telerobotics Communication In medical decision-making, clinicians use image concurrently with textual reports and discussions of interpretations. Clinicians tend to observe medical images while discussing to make an assessment 77
about their patients. Imaging is important to communication and images to be a desirable component of a multimedia electronic medical record. Communicating digital images enables remote viewing, interpretation and consultation in telemedicine (teleradiology, telepathology, and teledermatology) Education and Training Medical diagnosis and treatment depends on imaging and therefore specific “image interpretation” skills are required. Images can provide educational support: Online and hard copy databases of pathological and physiological images Great opportunities for clinical practice in simulated environments Atlases and three-dimensional models Example Medical students can be trained in endoscopy techniques by using a model and video images along with visual feedback that correlate with the human interventions
Research Imaging is involved in many aspects of research. (i.e. structural modeling of DNA and proteins, 3d-views. Images obtained in molecular or cellular biology to follow the distributions of fluorescent or radioactively tagged molecules. As an example, quantitative study of morphometrics, or growth and development, depends on imaging methods.
The Radiologic Process and Its Interaction The radiology department and the radiologist Radiology departments: engaged in all aspects of the healthcare process (detection and diagnosis, treatment, follow-up and prognosis assessment) Involved in acquiring and managing images, interpreting, and communicating those interpretations. Through imaging, healthcare personnel obtain information that can help to diagnose, plan therapy, and follow-up The Radiology department produces the images, and the radiologist provides primary analysis and interpretation of radiologic findings. 78
Thus, radiologists play a direct role in clinical problem-solving and in diagnostic-work-up planning. Radiologists play a primary role in treatment [another term: Interventional radiology] Understanding the clinical steps 1. Evaluation of a clinical problem 2. Relevant clinical history is acquired 3. The imaging procedure is carried out images are acquired. 4. The radiologist reviews images 5. The radiologist creates a report to communicate the results to the referring clinician (& makes suggestions for further evaluation as needed) Also:
-Quality control and monitoring
-Continuing education and training
Image Management and Display from the past to today Many medical imaging studies were until recently recorded and stored on film Even images produced by digital CT and MRI scans, were often transferred to film after the technologist optimized them for viewing Film storage requires a large amount of space and film is expensive Digital acquisition of images Dramatic reduction of the physical space requirements, material cost, and manual labor of traditional film-handling tasks through on-line digital archiving, rapid retrieval of images, high-speed transmission of images Dissemination of images along with reports for viewing by referring physicians throughout a healthcare enterprise has been made possible by advances in image compression and in internetbased web technology. Picture-archiving and communication systems (PACS) play very significant role towards this direction Image Acquisition The primary requirement for PACS is that it must obtain images in digital form Image procedures are scheduled through a Radiology Information System (RIS)*, and patientidentification, schedule information is transmitted to a modality workstation through a DICOM RIS gateway 79
The images produced are transmitted through a DICOM gateway to an auto routing server, which is responsible for sending the images where they are needed and for managing workflow Images can be viewed on interpretation workstations and invoke special processing functions through servers (e.g., for three-dimensional rendering etc)
Multiple images from a particular exam need to be associated, and both prior and other associated studies and reports may need to be available.
This linkage is accomplished by a validation server, which is able to query for and retrieve information from the RIS as well as from the PACS archives Coordinating the association of image and non image information
* Radiology Information Systems (RIS) are the backbones of all procedures in a radiology department
Storage Requirements On-line digital archiving of image data for a busy radiology department requires vast amounts of storage. Size depends on:
Contrast and spatial resolution required
Number of images or the size of the data sets
Whether raw or processed data are stored
Whether data-compression techniques are used
Size example CT image
512 Χ 512 pixels.
Full dynamic range: each pixel is represented by 12 bitsonce manipulated (contrast, brightness) 8bits
A typical CT examination consists of 40 to 80 cross-sectional slices.
60 Χ 512 Χ 512 Χ 12 bits = 180 million bits = 21 mb must be saved.
CT scanning is becoming increasingly higher in resolution, with slice thickness decreasing, and thus with many more slices obtained to cover a given field, such as a patient’s chest.
A single-view chest X-ray (or CR) image consists of 2,048 Χ 2,560 Χ 12 bits
Considering that a typical radiology department performs 250 examinations per day, and nominally assuming 30 megabytes per study, then, in an average day, approximately 7.5 gigabytes of data
300 days per year: more than 2tb
Data compression and prior selection or preprocessing of image data can reduce storage 80
requirements considerably.
Lossless compression uses simple run-length encoding (RLE) or variations: assigns the shortest codes to most frequently occurring values. Maximum compression ratios: 2:1 or 3:1.
Lossy compression uses methods to filter the image’s frequency spectrum and to encode data selectively at various frequencies more compactly and to eliminate other frequencies (jpeg): 20:1
Wavelet compression is accepted as a superior method for image compression. “Wavelets are basis functions for representing discrete data or continuous functions” Wavelet series provide multi-resolution representations for data, organizing them into a hierarchy according to spatial frequency and spatial position. DICOM: Standardization of Communication Formats for image transmission It is self explanatory why TCP/IP and seven-layer Open Standards Interconnect (OSI) protocol are used for the communication of medical images A very important medical imaging specific format, developed as an outgrowth of work by the American College of Radiology (ACR) and the National Equipment Manufacturers Association (NEMA), is known as Digital Imaging and Communications in Medicine (DICOM), has been adopted to a large extent worldwide for both radiological and other medical images
DICOM: a standard format for transmitting image information, including patient, exam, and study series information. DICOM is a prerequisite for PACS to succeed DICOM is intended to ensure that equipment (acquisition devices, archive nodes, interpretation consoles, review workstations, servers doing special processing) can be interfaced with the network and that the data can be recognized and interpreted correctly. DICOM adopts an object-oriented model and consists of definitions of information objects, service classes, and network protocols. DICOM 3.0 is a complex multipart standard consisting of 13 parts, and specifications. Display Capabilities Radiologists usually interpret an examination by comparing multiple images from both the current examination and previous or correlative studies. Flexibility is crucial: radiologists organize and reorganize the images to display temporal sequence, to reflect anatomic organization, and to compare preintervention versus postintervention Many medical images (i.e. mammography) require very high resolution for microcalcification 81
detection.
Example of digital Mammography An advantage for digital mammography is the ability to preprocess images with computer-aided detection (CAD) algorithms for highlighting potential nodules and microcalcifications. Interpretation consoles o have advanced considerably (Gray scale manipulation, histogram equalization, edge enhancement, image subtraction etc),
Radiology Information Systems (RIS) The workiflow of a radiology department illustrate the many tasks in producing and managing clinical images. All tasks are supported by Radiology Information Systems. Filmless radiology department is still limited by the fact that film mammography has not yet been supplanted by digital modalities to a significant extent. For example mammography has the highest resolution demands (50 micron), and digital mammography has only recently become available commercially. Nowadays, soft copy radiology interpretation at workstations has largely overcome early technical difficulties and user resistance. Management of work flow in a radiology department is a complex activity and includes: maintenance of digital archive scheduling of examinations registration of patients performance of examinations review and analysis of studies by radiologists creation of interpretations transcription of dictated reports Integration of Images with Radiology Information Systems A RIS either: Includes services which have been incorporated in functionality of the overall Hospital Information System OR They have been developed as separate systems that operate independently and have limited interface to an HIS. Gradually, as integrated delivery networks have forced the development of more highly modular and 82
comprehensive software architectures Since demands for both image and information access have spread enterprise-wide, a new level of integration is required Display considerations
4,000 Χ 4,000 image on a 1,000 Χ 1,000: each displayed pixel summarizes the value in a corresponding 4 Χ 4 pixel area
How value of that pixel is computed determines image quality on the reduced image.
Subsampling (e.g., every fourth pixel), or averaging, does not work well.
A function that computes the brightness level that the eye would detect in that a 4 Χ 4 area on a higher resolution monitor is the optimal way to determine the value of the single pixel on the lower resolution monitor.
The question of optimal resolution has not yet been satisfactorily answered. Teleradiology Workstation and network capacity for interpretation, acquisition and transmission of radiological examinations over a distance has rapidly grown. Radiology departments enable radiologists to provide coverage from home, especially for interpretations of CT and MRI examinations. Teleradiology: provision of remote interpretations-is increasing as a mode of delivery of radiology Wide image distribution and access not only for radiology but also for other procedures-clinical departments becoming multimedia capable Remote interpretation by imaging specialists Review and consultation among clinicians, for surgical planning, teaching etc DICOM and Interoperability (HL7) Integration among PACS, RIS, and HIS has been aided by the evolution of DICOM and HL7. Different imaging modality devices transmit images to common PACS archives, and enable images to be manipulated on common workstations, while maintaining the association between images and the series and exams of which they are part, for specific patients. RISs and PACS must communicate using both DICOM and HL7 standards, to fully information. (There is a joint group to resolve areas of interface and overlap)
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The Example of Surgical Planning and Image-Guided Therapy Images modeled and reconstructed in projections, with certain layers removed or enhanced or colored to enable abnormalities to be visualized clearly. The projections are conformed to the exact perspective of a surgeon looking at an operating field by reconciling position on a head mounted display with coordinates the patient Minimally invasive techniques like focused ultrasound, cryosurgery, and thermal ablation can be monitored in real time through tissue changes on MRI scans.
Brain Map Information Systems Growing number of examples of image-based information systems for brain mapping that have been funded by the Human Brain Project and other efforts. Visualization-based approaches for mapping the location of cortical language sites onto the surface of the brain, and relating these sites to other measures of language activation, such as those obtained from functional MRI. The cost factor Image management and PACS development have cost benefits for radiology departments Direct acquisition of high quality digital images Ability to conduct distant consultations Teleradiology services Expectations and Future trends
Higher spatial, contrast, and temporal resolution, up to the physical limits.
Trend toward three-dimensional data.
Image physiological function and genetic and molecular expression
Widespread access to images throughout health-care delivery networks, as well as across wider geographic areas for teleradiology services
Sophisticated user interfaces will be combined with anatomical knowledge: deformable 3d models
Soft copy interpretation of radiology images: radiologists will be comfortable with the process
Integration of RIS and PACS
Healthcare personnel throughout an enterprise will have on-line access to the images
Integration into the enterprise network for distribution of their images and for teleconsulting
Image-guided surgery and advances in minimally invasive therapy as imaging is integrated in real time with the treatment process. 84
Questions for Discussion 1. What is a medical image? Which are the two most important image geometry aspects of medical images? 2. Which is the most common multi-channel imaging modality in healthcare? Briefly describe the principles it is based on. 3. What is doppler sonography and why is it preferable for specific observations? Which are those observations? 4. What is DICOM? Which is the scope of DICOM and what do we mean when we say that PACS is prerequisite for the existence of DICOM? 5. Which service of telehealth has advanced significantly through the evolution of medical imaging? How is this service beneficial for (i) the clinician (ii) the patient?
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11. Security & Privacy of Data in Healthcare-HIPAA Security Rules
What is Personal Protected Health Information (PHI) Information that should be protected and considered private since possible acquisition by unauthorized parties may identify the health status of individuals. The tables below presents the Protected Health Information.
Patient Name
Certificate/License Number
Address: city, county, zip code (more than 3 digits) or other geographic codes
Any vehicle or device serial number
Web URL, IP Address
Dates directly related to patient
Finger or voice prints
Telephone, Fax Number
Photographs
E-mail addresses
Social Security Number
Any other unique id number, characteristic, or code (generally available or not)
Medical Record Number
Health Plan Beneficiary Number
Age greater than 89 (>90 year old population is small narrowing down selection would be easy)
Account Number
How Information Exchange has evolved New security concerns have appeared during the past decades. Those concerns have to do with the interconnection of different interested parties related to the provision of services and exchange of data through potentially unsecure networks. Examples include: Patient care: instant access to current, correct, readable data from anywhere Insurance and billing business processes use health data coming from hospitals Data is transferred to other external health organizations-flying through networks The medical prescriptions are electronic Telemedicine is exploding! Notification of infectious diseases to state and federal authorities Security Issues in the Real World Healthcare Environment Information Systems in health organizations are integrated and share information among several people and access to them is not adequately controlled Laboratories and other departments are equipped with systems of different architectures and 86
therefore it is not easy to implement a universal security infrastructure Family doctors are not networked. Their PCs are also unprotected. Such a non-security conscious environment encourages poor security practices Healthcare is a “High Security Environment” Healthcare is at high risk of attack or data exposure. Health information systems contain confidential information (e.g. patient records, financial, administrative information) or have important, sensitive organizational roles (e.g. accounting, payrolls, quality indicators and reporting). US Government Security Regulations • Privacy Act (1974) • Health Insurance Portability and Accountability Act-HIPAA (1996) • Electronic Signature Act (2000) • Various State Security and Privacy laws and regulations
Health Insurance Portability & Accountability Act (HIPAA) Privacy & Security Rules HIPAA Security Standards: the Security Rule HIPAA aims to protect the confidentiality, integrity, and availability of electronic protected health information (ePHI). The so called Security Rule of HIPAA addresses three areas (i) administrative (ii) physical (iii) technical aspects of ePHI. The rules apply to the security (keep secure) and integrity (keep intact) of electronically created, stored, transmitted and processed personal health information. Administrative access to data (HIPAA) According to HIPAA, healthcare facilities will monitor logon attempts to the network. Inappropriate logon attempts should be reported to the respective departmental level security designee. All computer systems that have been installed in the hospital are subject to audit. The same applies to the access to the hospital intranet, which will also be monitored. As far as the access to Protected Health Data is concerned, this should only be granted to authorized individuals. Installation of software without prior approval is prohibited and disclosure of ePHI via electronic means is forbidden without authorization. Last but not least, all computers should be manually logged off automatically when the authorized user is not in front of the computer, for any reason (ie returning from the screensaver back to the desktop should require a password). Confidentiality-Integrity-Availability 87
“Systems and applications in healthcare should operate effectively and provide appropriate confidentiality, integrity, and availability”. Both security experts and users should realize the level of risk of harm resulting from unauthorized access, loss, misuse or modification. Confidentiality Confidentiality is commonly applied to conversations between doctors and patients. Legal protections prevent physicians from revealing discussions with patients. This physician-patient privilege only applies to physician-patient secrets during medical care. The maintenance of this confidential relationship should therefore be preserved when such data is communicated with electronic telecommunications.
Definition of Confidentiality Data or information is not disclosed to unauthorized persons or processes. The information contained in the message is kept private and only the sender and the intended recipient can read it. The rule dates back to at least the Hippocratic Oath, which reads: Whatever, in connection with my professional service, or not in connection with it, I see or hear, in the life of men, which ought not to be spoken of abroad, I will not divulge, as reckoning that all such should be kept secret.
Availability “Health data or information is accessible and usable upon demand by an authorized person.” Data must be protected against threats and hazards that may deny access to data or render the data unavailable when needed. For this purpose there must exist appropriate backup in the event of a threat, hazard, or natural disaster. Health systems should also provide appropriate disaster recovery and business continuity plans for departmental operations involving ePHI. Integrity “Data or information has not been altered or destroyed in an unauthorized manner.” Integrity is the verification that the information contained in the message is not tampered with, accidentally or deliberately, during transmission. In order to achieve this, health organizations should ensure that health data must be protected against improper destruction or alteration and provide appropriate backup in the event of a threat, hazard, or natural disaster. Authenticity Authenticity verifies that the people with whom we are corresponding actually are who they claim to be. An “authentic” system should ensure that the data, transactions, communications or documents produced and transmitted (electronic or physical) are genuine. It is also important for authenticity to validate that both parties involved (sender-recipient) are who they claim to be. Creating, Changing and Handling Passwords as healthcare professionals 88
We use usernames and passwords to Authenticate and Authorize (the “two A’s”) the user. Risks related with passwords and address potential issues like theft, sniffing, brute force attacks and others. For the above reasons, “One Time Password Devices” have been used for access to health information systems by health professionals, like the “RSA SecurID” system. Such small devices address many username/password concerns using various access techniques (time based/event based etc) but they are only good for authentication of the user. User id and password are critical to ePHI security. Users must access the healthcare facility information utilizing their username and password; password sharing is prohibited. Users are personally responsible for access to information utilizing their password and are subject to disciplinary action. The most important rules that should be taken into consideration when handling passwords are summarized in the frame below: Do not keep an unsecured paper record of passwords Do not post passwords in open view e.g. on your monitor Do not share passwords with anyone Do not include passwords in automated logon processes Do not use “weak” passwords. Passwords should be at least 6 characters long & must contain components from at least 3 of the 4 following categories: Upper case- Lower case-Numerals-Keyboard symbols Passwords must be changed every 90 days. Guidelines for a good password
Examples of strong and weak passwords Notice that strong passwords should be “meaningless”, they should contain a combination of numbers, letters and symbols and a couple of capital letters. They are also longer than 8 characters. On the contrary, weak passwords are either too short and/or are real dictionary words.
To remember a password, apply your own rules: e.g. “this was an informatics class” gives this password: !twaic@ Card/token systems Related risk: People would leave tokens behind 89
Card-swipe systems Related risk: People would leave systems logged on after they left Biometric systems
Related risk: Expensive-possible failure to log off
Alternative logon methods and related risks
Change of Employee Status Administrative directors are responsible for informing the IT administrator of employment status changes. Upon termination of employment the employee’s network and PC access is terminated; that means that they can no longer access the system using their passwords. All ePHI & computer equipment of the former employee (laptops, PDAs) should be retrieved. For the above mentioned reasons, the use of a prior employee’s user-ids and passwords should be strictly forbidden. “Generic” user-ids are strictly forbidden and a new clean account should be provided to the new employee. Role Based Access Control Individual users should not be assigned rights – too difficult to track and change as roles evolve Users should belong to groupsGroups should be granted access rights. Different categories of healthcare professionals are granted different access rights Policy should be established for regular audits and updates of group membership (i.e. yearly) Malicious Software, Backup and Reporting Pirated software, “viruses,” “worms,” “trojans,” “spyware,” peer-to-peer file exchange software E-mail attachments should be tested for malicious software. Nowadays most security software has dedicated features to ensure “safe email” All software installed in hospitals must be approved by a department level security officer. Installation of personal/downloaded software is not allowed since it may provide some security holes to the potential intruder. Suspicious software should be reported the IT technical support personnel immediately. Approved anti-virus software must be installed and kept up to date on all computer systems (even portable ones) AND Home computers and other equipment utilized to access the hospital network.
Social Engineering Online discharge summaries are available to everyone in hospital. A little information is enough to know more about a person. Criminals use patient info to blackmail the patient’s relatives; or the staff may use patient data to hunt victims (very extreme case).
Backup and recovery 90
A system must ensure recovery from any hardware or software damage within a reasonable time (based on how critical this function may be). Each department must determine (i) data criticality and (ii) potential threats and must have a plan for backup, disaster recovery, and business continuity in case of an emergency. In healthcare health professionals cannot be disconnected from their patients’ data even for a single second. Backup data must be stored in an off-site location; so that in the case a disaster (ie flood, fire) destroys all equipment, data are somewhere else intact. It is also assumed that data coming from the backup must be maintained with the same level of security as the original data. Incident reporting According to HIPAA, known and suspected security violations must be reported to the Administrative Director or their designee. Security incidents must be fully documented to include time/date, personnel involved, cause, mitigation, and preventive measures. Physical Damage and theft Electronic assets must be protected from physical damage and theft. Electronic devices containing ePHI should be secured behind locked doors, when possible. Special security consideration should be given to portable devices (laptops, smart cell phones, digital cameras, DVDs, USB “drives,”) to protect against damage and theft. ePHI must never be stored on mobile devices or storage media unless there exist: Power-on or boot passwords and auto-log off Encryption of stored data e.g. True Crypt® Physical safeguards also must provide appropriate levels of protection against fire, water, and other environmental hazards such as extreme temperatures and power outages/surges. Technical safeguards “Technology, policy and procedures that protect electronic health information and control access.” Technological solutions are required to protect ePHI where applicable. Examples include data encryption and secure data transfer over the network. All wireless networks require security protocols and encryption. All electronic transmission of ePHI must be encrypted. Encryption must be achieved through software approved by the IT Department Security designee, e.g. TrueCrypt®
Have you heard or packet sniffers and honeypots? 91
Packet sniffers are programs or a hardware that can intercept and log traffic passing over a digital network or part of a networks: can reveal a lot about health networks and HIPAA compliance Lure potential intruders with a Honeypot: a trap to detect/counteract attempts at unauthorized use of information systems. It consists of a computer, data, or network site that appears as part of a network, but is actually isolated & monitored
“Packet Sniffers” and “Honeypots”
You can visit the following address to further read about HIPAA security and privacy rules and requirements http://www.hhs.gov/ocr/privacy/hipaa/understanding/summary/index.html
Data Encryption Data encryption is the method of using algorithms and mathematical calculations to transform plain text into ciphered text, to make it non-readable for unauthorized parties. To decrypt an encrypted message the recipient must use a special key that transforms text back to the original version. No particular encryption technology-no matter how ‘strong’ it may be can ever, ensure that information remains secure. Instead, a variety of circumstances need to be taken into consideration to ensure that personal information is protected against unauthorized access. Data encryption is a requirement for many data transactions. Earlier, pre-Internet era, people rarely used encryption. Nowadays with banking, online shopping and other services data encryption is a primary requirement. Connecting to a secure server with a web browser automatically encrypts data to prevent intruders. In case one attempts to capture encrypted information successfully, it will be scrambled and unreadable, since the intruder does not have the reverse algorithm to read the data. Data Encryption algorithms are constantly advancing Many types of data encryption software algorithms 64-bit encryption was considered strong enough, but nowadays at least 128-bit solutions are used A newest standard called Advanced Encryption Standard (AES) allows a maximum of 256-bits. Symmetric and Asymmetric Encryption Symmetric Encryption A single key that is shared by the pair of users who want to communicate a message. It is also called ‘Secret Key encryption’, since the key has to remain secret, because its acquisition is just enough to retrieve the original message. Encrypt with the key-decrypt with the same key Restrictions of symmetric encryption include that: 92
It does not scale very well We do not want to have intruders grab it through the network or the internet. This is simply because this key is enough to reveal all our data If we “loose it” we need to get another key Many people have the key and this increases the risk of having some of them losing it On the other hand, the benefits of symmetric encryption may be summed-up to the following points: There is no overhead-very fast encryption process Diversity-the method can be used together with other encryption methods
Symmetric encryption: during one to one transactions we only have to give one key to each end. Things are complicated though if the data exchange addresses more pairs of health professionals.
Asymmetric Encryption It is called “Public key cryptography” and is a relatively newer technology. The idea of asymmetric algorithms was first published in 1976 (Diffie and Hellmann). In asymmetric encryption there are used 2 different keys: 1. Private Key (should be kept secret)-none else needs this but the message sender 2. Public Key (can be seen by anyone!). One can post it anywhere (even on facebook!) and it is safe The private key is the only one that can decrypt data in asymmetric cryptography and there is no way one can “retrieve” or reverse engineer the private key if they have the public key in their possession What does it the public key decrypt? It decrypts the data encrypted by the private key! 93
Symmetric and asymmetric encryption methods can be used in combination together, in order to provide fast, efficient and secure encryption to the sensitive health data
Symmetric vs. Asymmetric encryption in terms of complexity “How many keys?” Assumption: 6 users, one by one transactions Symmetric: 1 public key is shared to 6 people so we need to exchange 1 public key for each transaction.
Total keys to exchange = (n-1) + (n-2) + … + (n-(n-1)) = 15
Asymmetric: each new user should just have 2 more keys (a public and a private) to securely communicate data with all the rest
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So total keys to exchange are 2n=12
Common Encryption Protocols and Algorithms Transport Layer Security and Secure Sockets Layer Strong encryption like TLS (Transport Layer Security) and SSL (Secure Sockets Layer) will also keep data private (but they can't always ensure its security)
Websites that uses these types of encryption may be verified with the procedure of checking the digital signature on its certificate that in turn must be validated by an approved Certificate Authority.
Advanced Encryption Standard (AES) AES is based on “substitution-permutation network” and is based on a 4×4 column-major order matrix of bytes. Most AES calculations are done in a special finite field. AES has a fixed size of 128 bits, and a key size of 128, 192, or 256 bits. The key size specifies the number of repetitions of transformations that convert the input (plaintext) into output (ciphertext) Number of cycles of repetition:
10 cycles of repetition for 128-bit keys.
12 cycles of repetition for 192-bit keys.
14 cycles of repetition for 256-bit keys.
Each round consists of processing steps, including one that depends on the encryption key itself. A set of reverse rounds are applied to transform cipher text back into the original plaintext using the same encryption key. AES supports key lengths of 128, 192 and 256 bits Triple-DES (predecessor of AES) supports a key length of 112 and 168 bits. Triple-DES keys of 112 bits are also no longer typically used for storage of sensitive information. 95
Digital Certificates To use asymmetric encryption, there must be a way for people to discover other public keys. The typical technique is to use digital certificates A certificate is a package of information that identifies a user through his id information (ie name, user's e-mail address and the user's public key). During a secure encrypted communication, both ends send a query over the network to the other party, which sends back a copy of the certificate. The other party's public key can be extracted from the certificate. A certificate is also used to uniquely identify the holder
Considerations about encryption Encrypted data should always stay encrypted when not used Encryption keys must be of sufficient length to resist attempt to break the encryption Secure authentication of users: “Prior to decrypting, authorized users must be securely authenticated (robust passwords): only authorized users can decrypt data”. No unintended creation of unencrypted data: “No file containing decrypted data should keep existing after a user had accessed encrypted data and viewed or updated it in decrypted form”. Identified, authorized and trained users: “Health information security professionals determine which users have access to encrypted information on a given mobile device or on mobile media”. Major challenges in encryption For any encryption approach, there are two major challenges: Key distribution: how we convey keys to those who need them to establish secure communication. Key management: for large numbers of keys, we should preserve them safely & available as needed.
Implications about Encryption of ePHI Personal health information must be 24-h accessible If an encryption system makes data unreadable when a user is unavailable (e.g. death, illness etc), or when a user forgets a password, then that encryption is unsuitable for healthcare environments. Products from well-known vendors provide centralized management of passwords; remote password resets etc to facilitate the efficient management media without fearing loss of data. 96
Encryption systems must backup the encrypted data files on a regular basis. Poorly designed encryption systems may leave temporary file copies of encrypted data in unencrypted form on disks/mobile devices
Threat and Risk Assessment Encryption must take into consideration loss or theft of a portable device, staff errors, lack of training, hackers, etc. To judge these threats to the data a systematic approach is required. Therefore to ensure that an encryption technology is properly deployed we carry out a Threat and Risk Assessment (TRA). •
Secure Encryption Keys
•
Secure Authentication of Users
•
No Unintended Creation of Unencrypted Data
•
Good encryption algorithm must be used
•
Encryption keys must be protected and managed effectively
Lessons learned about encryption
Questions for Discussion 1. The need for privacy of health data existed since the early steps of medical science. In your opinion which are the two major challenges of privacy, due to the extensive use of networks and computer technology in healthcare? 2. Out of the three major security and privacy requirements (Confidentiality-Integrity-Availability), breaking which two may pose a direct threat to the patients’ life? Explain your answer. 3. Rate the passwords in terms of strength (weak-average-strong). Underline your answer. anne@ dad 1!gtRs@ login 01081068 1dallas! I!l@p$f!A
weak weak weak weak weak weak weak
average average average average average average average
strong strong strong strong strong strong strong
4. In your own words describe the symmetric and asymmetric encryption in brief. Which method(s) would you implement into a health information system and why?
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5. Ten (10) users exchange data on a network. When a new 11th employee arrives, how many keys will he/she need in order to be able to exchange data with his 10 co-workers if the network implements (a) symmetric encryption (b) asymmetric encryption?
Appendix I. Successful Implementation of e-health: a case report “Assessment of Hospital Information Systems Implementation” The case report shows that different roles appreciate the IS implementation based on their own perspective. Workflow changes, participation in the decision making, user support and professionalism are factors affected by the introduction of a new IS, though not in the same manner for administrative, clinical, technical staff and external providers. Incentive to perform this case study: a qualitative assessment of Hospital Information Systems implementation and of the integration level in the case of three hospitals. The assessment pinpointed to the nature of the problems.
Design, implementation and level of integration were investigated using a questionnaire, based on literature evidence that success & failure factors are not only technical, but also related to the existing organizational models, education, managerial and evaluation issues.
The hospital workflow process was examined in detail to identify factors related with the impending the successful introduction of IS. The case study was performed in 2 mid-sized general hospitals and one oncology hospital. Hospital A has an integrated administrative, financial and clinical IS. Hospital B was in process of getting Integrated IS Hospital C has non-integrated subsystems, without a clinical section.
The qualitative assessment included employee perceptions analysis (2 phases)
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The assessment questionnaire QUESTIONS IN RELATION WITH… 1.
Human resources and IT services within the hospitals
2.
Existence of Administrative, Clinical & Pharmacy and LIS subsections
3.
System specifications
4.
Use of coding systems to achieve integration
5.
Role of hospital management in terms of planning and financing
6.
Existence of contracts with developer, also investigated
7.
Education & training of the IS users, user support during implementation, user motivators and external consultants
8.
Contracted agreements between supplier - hospital management
9.
Hospital workflow, changes due to the new system & parameterizations according to the workflow needs
10. Data migration (old to new system) 11. Coding standards 12. Users’ reluctance to use the system 13. Specialized IT staff 14. Needs analysis, feasibility & risk analysis prior to implementation 15. Evaluation in terms of patient and employee satisfaction
Phase 1: the Assessment Questionnaire
Regarding the role of hospital management, its active participation was only reported in Case A
Implementation, utilization of key employees & adoption of a long term plan
With an exception of case A, the other 2 hospitals did not contract a full task agreement with the developer to commit to the provision of IS support.
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Hospital B also recruits an external consultant.
Case B regularly proceeds into support & maintenance contracts.
Regarding workflow adjustments & data migration, in hospital A, typical workflow has been altered due to the introduction of the IS.
In cases B and C, there was followed a reverse procedure, where the IS was adjusted to adapt to the existing workflow.
Only in Hospital A, changes due to the new IS were managed by the IT dept. & working group
In hospital C data was successfully transferred to the new IS while maintenance of the old one remained possible.
In hospital B the old system was reported to be interoperable with the new one in terms of data exchange.
User training, education & support by IT staff, was considered a high priority only in case A & partially in case B.
IT employees believe that major barriers during implementation include lack of central planning difficulties in acceptance-incorporation of IT non use of standards.
In two out of the three hospitals there is common belief that
specific “human interests” existed
there is insufficient IT staffing & health informaticians
Phase 2: Open Interviews During interviews with the IT employees, there have been identified 3 broad categories of problems, namely resource related, human factors & organizational/planning issues.
Human factors- indifference of users & lack of motivators to support the process & use the system 100
Resource related- inadequate number of staff in the IT department, inadequate working space & financial resources to effectively support the IS utilization.
Organizational issues- lack of concrete IS scope specification & consistent IS introduction plan
IT department staff not utilized by the management
Phase 2: More in depth observation of Case C This phase of the study includes observation and discussion sessions with four different groups of employees: GROUP 1: representatives of the supplier company GROUP 2: members of the hospital management GROUP 3: employees working in hospital depts. (nurses, MDs) GROUP 4: hospital IT department staff GROUP 1
•
According to the supplier company, it contacted the IT department to be informed about the hospital structure & facilities also collaborating with head employees regarding the coding of hospital material.
•
All potential IS users were invited to a presentation.
•
A special computer training room for user training with real scenarios, was prepared.
We did what we were asked to do. We got our money, everything is fine…
GROUP 2
•
The administrative personnel, expressed concern that the decision to introduce an integrated IS has been solely taken by the hospital manager
•
The hospital management activities & financial dept. strategy were also reported to be a cumbersome process with unnecessary bureaucracy
GROUP 3
Clinical nurses & MDs working in departments reported that it was not until late when they were informed about the new system. Despite having been invited to presentations of the IS benefits, most were found to be
distrustful about the success of the system.
Skepticism may be related with the expected workflow changes, while most reactions were found within nursing departments.
GROUP 4 Employees working at the hospital IT department reported to be positive towards the introduction of an 101
integrated IS in their hospital and supported the process. The IT department employees also assisted with the development of a training program, alongside with the contribution of the hospital’s education office and nursing management. So they were willing to provide assistance and support but they were not given the opportunities to actively participate
Further brainstorming Inadequate IT department personnel cannot support the development process efficiently. IT departments should play a particularly important role in the context of increasing needs for informational organization of hospitals. This is a hospital IT specialist. He wants-he cannot.
Appendix II. The challenge of Big Data in healthcare Despite the fact that there are many sources of information which could be used by clinicians for decision making, they still don't have access to all information. This lack of complete information affects decision making, treatment & outcomes. Health information systems unable to recognize clinicians as the main users. Especially, they do not succeed in foreseeing the clinicians need for more and up-to date information. Even those systems that use multiple sources, there sources often include outdated information. In addition healthcare costs continue to increase and simply implementing new systems without thinking of how to integrate diverse and distributed data, is not expected to solve the problem New systems adoption is slow and clinicians continue to lose valuable time hunting for information. It has been estimated that they actually waste 20-40% of their time for such procedures. Patient registration systems are not connected and as a result, each time a patient visits a new setting, their information has to be reentered into the system. Re-entering demographic and other registration information is error prone, time consuming and is related to added employee burden. Communication is also not timely and inconsistent, as a result.
Definition of “Big Data”
Big data is a collection of large and complex data sets which are difficult to process using common database management tools or traditional data processing applications. The challenges related with the big data management, include capture, storage, search, sharing & analysis.
The trend to larger data sets is due to the additional information from related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to 102
Identify trends in the health of an individual of a specific population
prevent diseases and organize health promotion activities
determine and improve quality of research
O’Reilly Radar defines Big Data as: “it is the data that exceeds the processing capacity of conventional database systems. The data is too big, moves too fast, or doesn’t fit the limitations of database architectures” ZDNet definition is as follows “big data refers to the tools, processes and procedures allowing an organization to create, manipulate, and manage very large data sets and storage facilities.” Some of the most common processes related with Big Data include:
Big Science (Genetic research) Radio Frequency Identification (RFID) implementations Data from Sensor networks Social networks and their data Internet text, web logs, internet indexing
Storage and Warehouses Risk Management & Modeling 360 View of the Customer Email Analysis
Problems related with the Big Data phenomenon Limitations due to large data sets in many areas: meteorology, genomics, connectomics, complex physics simulations, biological and environmental research. Data sets grow in size: information-sensing mobile devices, remote sensing, software logs, cameras, microphones, radio-frequency identification readers, wireless sensor networks
Historical Background Extremely large data volumes were originally an issue for supercomputers, nuclear physics, meteorology, space travel. Late in the 20th century airline and bank operations, entered the “big data family”, while during the mid 90s of the previous century, the Human Genome Project was initiated and this was the first large scale project to use Big Data in healthcare! Later on there were started to be used Big Data in finance, research, marketing and entertainment. Nowadays big data is considered more as a challenge and an opportunity and less as an issue which should be diminished and provides great potential for most industry sectors.
The Healthcare Data Explosion The volume of global digital data is increasing exponentially, from 130 exabytes in 2005 to 7,910 exabytes in 2015. By 2020, it is expected to be 35 zettabytes. This is almost four piles of CDs reaching Mars from earth! 103
Specifically for healthcare, in 2012 worldwide digital healthcare date was estimated to be equal to 500 petabytes and is expected to reach 25,000 petabytes in 2020. In other words worldwide healthcare data is expected to grow to 50 times the current total. megabyte (MB)
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gigabyte (GB)
109
terabyte (TB)
1012
petabyte (PB)
1015
exabyte (EB)
1018
zettabyte (ZB)
1021
yottabyte (YB)
1024
Low storage medium prices + Faster CPUs + Easier and automated access to Information --------------------------------------------------------------------------------------= Big Data An interesting equation simplifying the reasons which contributed to the big data phenomenon
The data in healthcare is unstructured In healthcare more and more data come from: a. converting existing data to electronic form: personal medical records, radiology images, clinical trial data, FDA submissions, human genetics and population data b. generating new types of data: 3D imaging, sensor readings, genomics etc Lots of data in healthcare are so called “unstructured data”. Historically, the point of care generated mostly unstructured data: office medical records, handwritten nurse and doctor notes, hospital admission and discharge records, paper prescriptions, radiograph films, MRI, CT and other images. Structured data is can be easily stored, queried, recalled, analyzed and manipulated. Structured data include electronic accounting and billings, actuarial data, some clinical data, laboratory instrument readings and data generated by the ongoing conversion of paper records to electronic health and medical records. When we refer to unstructured data we refer to those sources of data which share the following characteristics:
Is full of unneeded/confusing information
Is often times unclear and “dirty”
Is often full of valuable information hidden within the mess 104
Has potential to be useful only if combined ! Discuss possible examples of such data and the healthcare process they are related with.
Big Data and Healthcare Organizations Factors increasing the amount of electronic healthcare information As we have already discussed before, big data is emerging because hospitals and health systems are collecting large amounts of data on patients every single day. These data comes for a variety of settings: clinical, billing, scheduling etc. In the past, a lot of that data was not leveraged to make patient care and hospital operations better, but recently, there has been a shift to change that. The main factors for this explosion of electronic healthcare information include:
Electronic health records increased electronic data generated New challenges appeared for the better understanding of the health status of a population, since public health decisions should be based upon different health, environmental and socioeconomic characteristics.
New reimbursement models need large amounts of information to accurately understand what occurs with patients.
Practice becomes evidence-based in healthcare Practice becomes predictive in healthcarehistorical data are used in predictive models New technologies existing: capture devices, sensors, mobile applications
Current infrastructure and challenges The current infrastructure of health organizations reflects the importance of the issue. Big data is difficult to be used with relational databases, desktop statistics and traditional visualization packages. They require instead "massively parallel software”, on tens to thousands of servers. The Big Data Issue is for some organizations, facing hundreds of gigabytes of data for the first time may are in front of a need to reconsider data management options. But for others, it may take tens or hundreds of terabytes before data size becomes a significant consideration. What is common for both cases though is that big data is not just about storing huge amounts of data; it is the ability to mine and integrate data, extracting new knowledge from it to inform and change the way providers, even patients, think about healthcare. Without aggregating, managing and analyzing big data, the healthcare industry would be in information overload, which would me sometimes meaningless and often unexploited. Healthcare organizations will keep collecting massive volumes of data, so aggregating & analyzing it will be a continual challenge. 105
It is very important for decision making in healthcare to manage big data and get the most out of it
Four important challenges related to big data in healthcare
Different healthcare related sectors and professionals all need better access to big data
Healthcare Professionals want real-time access to patient, clinical and other relevant data to support improved decision-making and facilitate better quality of care
Researchers and Epidemiologists want new tools to improve the data workflow e.g., predictive modeling, statistics and algorithms that improve the design and outcome of their experiments and epidemiologic studies.
Pharmaceutical companies want to better understand the causation of various diseases and the factors related with the pharmaceutical response, in order to find more targeted drugs, and design successful clinical trials to introduce new medicines into the market.
Medical device companies collect data from hospital based and home based devices for monitoring of the patient safety and also to predict possible adverse events. Therefore they need this data, to integrate it with old and new forms of personal data and make more safe and accurate medical devices for diagnosis and therapy.
Patients want their everyday use of technology to flow into their medical care and to have better control of their own data
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Benefits of Big Data in Healthcare
Big data and health surveillance Clinical Statistics: Admission count, Readmission count and readmission rate Financial Statistics: Total direct cost by total admission and by readmission Operational Statistics: Counts of different length of stay periods We have to keep in mind that for all the above processes, we use big data to convert health data into useful and meaningful information utilizing more usable formats and appropriate visualizations. Some useful indicators that can be calculated with higher accuracy if we have access to big data platforms, include: Mortality: mortality rate (number of cases of a specific disease in a given population) Average direct cost by total admission and by readmission only Risk Assessment of Patients Timely recognition of epidemics in a population Probability of having a patient readmitted to a hospital
Better point-of-care decisions Physicians have more information and they can take care of patients providing personalized care. Valid and relevant data are produced and these are more current. Useful visualizations and predictive functions facilitate the better point of care decisions. Nurses also benefit from big data, since nursing care is related not only to the assessment of the patients’ clinical needs, but also to the understand and focus on the psychological and social problems of the patient. ”Physicians make the right decisions at the right time” Example 1-Prediction of patients who have nosocomial infections NorthShore University Health System, Evanston, implemented predictive modeling in clinical decisions. Using large data sets, there were developed models to identify which patients are most likely to be carriers of a threatening microorganism, Methicillin-Resistant Staphylococcus Aureus. By implementing the results of that modeling into the Electronic Medical Records, the health providers working in the hospital were also receiving alerts when a patient who meets the characteristics of being a high-risk carrier, was admitted. This implementation had very high success rates, reaching around 90% of correct predictions. 107
Example 2-Reduced readmissions Predictive models for the likelihood of readmission within 30-days data from the EMR into data warehouse have also been developed Patient's risk of being readmitted in 30 days is computed and then feed that data back into the EMR, risk (high, medium or low) of being readmitted in 30 days. Messages are sent to primary care practices. The messages alert the patients' primary care providers of their high risk and if they have any follow-up appointments scheduled. Two examples of predictive modeling for better point of care decisions
Operational improvements An important issue which the challenge of big data comes to address in the United States is the cost and quality of services. Rather than just collecting data, hospitals can analyze and use it to inform decisions in the organization. Big data also provides hospitals performance metrics with which it is possible to compare operational efficiencies of healthcare organizations. An example can be the case of individual employees, who can now see their ranked performance among many others and increase their motivation to achieve better results. Population health management Big data informs population health management as findings from predictive models can be shared with providers across the care continuum. This diverse data offers providers the ability to use information and discover patterns in patient populations that may not have been possible before. Big Data and Medical-Epidemiologic Research Big data advances clinical research towards new knowledge discovery, since more information is analyzed regarding patient care and disease. Therefore, studies can be completed faster. Recent growth of biomedical research data from genomics, imaging and electronic health records gives great research possibilities. Cost benefits of Big Data in Healthcare Big Data can enable more than $300 billion savings per year in US healthcare with two-thirds of that through reductions of around 8% to national healthcare expenditures. Clinical operations and R&D are two of the largest areas for potential savings, with $165 billion and $108 billion in waste respectively.
The four Big Data “V”s Volume: data warehouses: moving from terabytes to exabytes Velocity: complex real-time transactions 108
Variety: diverse data sources/integration of data, structured or not Veracity: relevance, use for decision making, meaningfulness
Velocity Velocity describes the constant flow of new data accumulating at unprecedented rates and the speed needed to retrieve, analyze, compare and make decisions using the output has changed. In some medical situations, real-time data (trauma monitoring for blood pressure, bedside heart monitors, operating room monitors etc.) are actually a matter of life or death. Future applications of real-time data in the ICU (detecting infections as early as possible) could reduce patient morbidity and mortality or even stop hospital disease outbreaks. Being able to perform real-time analytics against such high-volume data in motion could revolutionize healthcare.
An example of big Velocity is the case of “Credit Suisse”. Their business included the Processing of 1,000,000,000 transactions during working hours. These extravagant volumes of transactions raised the demand for: in-memory architecture for performance, ondisk resiliency for availability and distributed architecture for data coherency An example of big Velocity
Veracity - Data of varying quality, relevance and meaning Traditional data management assumes that warehouse data which is stored in warehouses is certain, clean, and precise; but data is sometimes uncertain, imprecise or wrong. Data quality issues are a particular concern in healthcare. Veracity issues are unique to healthcare: are diagnoses, treatments, prescriptions, procedures, outcomes correctly captured?
Technologies in healthcare related with big data Data from sensors accurate measurements more measurements no need for humans to make the measurements Digital Medical Images Standards for storage and communication of medical images Interoperability with electronic health records a shared research warehouse enterprise 109
Knowledge Decision Databases Vast wealth of existing medical knowledge Robotic Devices Surgical Robots Intelligent adaptive patient rehabilitation robots Health professionals and public health authorities take advantage of the benefits of: easy to use tools, analytics providing useful indicators, as well as and various visualizations.
Tools, analytics and visualizations are at the availability of health professionals
From data collection to wisdom The five steps of Big Data analysis The analysis of big data usually involves the selection of data over huge numbers of records. It is worth mentioning that issues such as the data quality and the data being up-to-date, should also be considered. Step two involves mapping of the datasets in order extract interesting and useful information from each. Towards transforming data into meaningful information, step three involves shuffling and sorting the intermediate results in order to prepare them for aggregation and data reduction, which is step four. This is the last step before the generation of the final output (step 5)
Transforming Data to Information and vice versa 110
Transforming unstructured data into structured data and information is an important step to enabling data-driven healthcare.
Tools based on data mining, cluster analysis, statistics, data visualizations, artificial intelligence machines, text analytics, and Natural Language Processing (NLP) to mine data for patterns and meaning.
Predictive analytics: knowing what to expect helps healthcare managers and health professionals make better decisions
Other issues Data Security and Privacy Concerns about data security, unintentional exposure or loss of data to unauthorized parties are expected to exist since we have lots of multi-centric data moving across networks and therefore private health information should be protected. There is still resistance to moving healthcare data to the cloud. The idea of putting PHI in the cloud is still immature: use of the Internet, cloud computing and pooling of data all raise the data security risks. Healthcare data contains details of a person’s life and it must be protected with the highest security possible. Who owns the data? Although most people would assume that they own their own healthcare data, this may not always be the case. These concerns have led to patient groups (eg e-patient movement), where patients help each other to become active participants in their own care alongside doctors. 111
A peer-reviewed, open access journal: Journal of Participatory Medicine: to advance the participatory medicine among healthcare professionals & patients. Society of Participatory Medicine is a cooperative model of healthcare that encourages active involvement by both patients and healthcare professionals
Examples of Big Data Applications and Services Many companies, regardless their specialization on healthcare, develop and advance data management platforms, data storage solutions and frameworks. These include:
Traditional vendors like IBM, Cisco Systems, Oracle Smaller organizations Individual developers Platform companies like Google, Amazon Open source groups: Linux Foundation, Apache Foundation (Hadoop), Mozilla Foundation
NoSQL Databases and Big Data Many of the above solutions and architectures are based on NoSQL databases, which differ from the traditional relational databases in terms of their flexibility and distributional nature (ie. They are scalable). NoSQL databases refer to a large family of databases which do not use the same structure but they do share some basic principles, which may be shortlisted as follows: NoSQL databases are not “A.C.I.D” (Atomicity, Consistency strict, Isolated, Durable)
They allow an easier and faster development
They better deal with the requirements of Big Data and Web Scale
They use flexible data models
They are way more scalable
“Big Tables” are used in many NoSQL systems. An example of DBMS based on “Big Tables” is Hbase, which is open source. Big Tables are distributed storage systems, which scale huge arrays of data among distributed servers. These tables map three values into a byte array, namely the Row key, Column Key and Timestamp. Tables are split upon ~200 mb of size chunks.
Distributed computing-Some dogmas maybe reconsidered
“The network is reliable”
“The network is secure”
“Latency is zero”
“Topology doesn’t change”
“Bandwidth is infinite”
“There is one administrator” 112
“Transport cost is zero”
“The network is homogeneous”
Big Data Platforms and Frameworks in Healthcare
The National Institute of Health (NIH) NIH aims to play a very important lead role in addressing the complex issues of big data. Their strategic plan includes the involvement of
Stakeholders in the research community
Government agencies
Private organizations
NIH is involved in scientific data generation, management and analysis.
Data repositories: the example of tranSMART Public domain (open source license) Offers a data repository with
Clinical observations
Clinical trial outcomes
Adverse events
Gene expression and metabolism data
Patient demographics
Software Frameworks: the example of Apache Hadoop Apache Hadoop is a software framework (NOT a database) that supports data-intensive applications
Enables applications to work with thousands of nodes
It uses both the CPU and disc of single commodity boxes (or nodes)
Boxes can be combined into clusters, while new boxes can be added as needed without changing data formats, the way data is loaded etc.
“Send the application to the data rather than data to the application”
IBM Big Data Platform Includes 1. traditional Big Data technologies (ie Netezza) used to address the more traditional Big Data problems 2. NoSQL-like technologies that include velocity and variety capabilities; 113
Institute for Health Metrics & Evaluation (IHME) IHME gathers large distributed data sets globally for data analysis and health measurement data from disparate sources including censuses, surveys, vital statistics, disease registries, hospital records. Aim is to support policy decisions in order to improve population health. The most recent project of IHME has a title “The Global Burden of Disease.” Some of the questions seeking answers include the following: What are the world’s major health problems? How well is society addressing these problems? How do we best dedicate resources to maximize health improvement?
A University of California, Santa Cruz Initiative (2012) In this $10.5 million project, which is the world’s largest repository for cancer genomes, a huge database with biomedical information is structured, which will allow to get a complete molecular characterization of cancer.
Healthx
“Develops and manages online cloud based portals for health healthcare companies”
“Focuses on: enrollments, claims management, business intelligence”
“Vast data: benefits, physician, prescription information etc”
“It is based on Apache Cassandra with Hadoop”
Sickweather LLC Sickweather LLC scans social media (Facebook, Twitter) to track outbreaks of disease, offering forecasts to users, similar to weather forecasting to keep individuals aware of outbreaks in their area.
Humedica Inc Humedica (a medical informatics company) connects clinical and patient information across varied settings and time periods to generate longitudinal and comprehensive views of patient care. They claim to provide accurate and detailed predictive models by the normalizing data to produce more accurate and precise inputs over longer periods of time.
Practice Fusion “Cloud-based EMR platform for medical practices that also aggregates population data across multiple sites to 114
improve clinical research and public health analysis“ Includes e-prescribing, labs, meaningful use, charting and scheduling. Recent projects of “Practice Fusion” are on cancer and heart disease. Practice fusion analyzes aggregated data from the EMR and public health to monitor health on a population level. These data include:
Health Population Surveillance and Education e.g. flu, asthma Drug surveillance Public Health Research Care Plan Best Practices
Self-Care and Big Data Examples Humetrix’s iBlueButton® is a mobile health information exchange app system to access and exchange medical records. It combines the convenience of mobile phones with Big Data and gathers medical information, tracks sleep, manages diabetes, heart disease and asthma, understand behavior patterns and motivations for prevention. Asthmapolis collects data from patients and provides them with feedback which helps them better manage their asthma. A mobile sensor tracking device attaches to asthma inhalers to monitor the time and location of events. Asthmapolis aggregates real-time data for epidemiologic and public health use.
Sensors and big data: the example of ZEO “ZEO, Inc. is analyzing over a million nights of data to help consumers improve their sleep.” The personal sleep coach device tracks the quality of users’ sleep and gives personalized advice on how to improve sleep. ZEO have shared sleep data with Universities to proceed to the 360 degree understanding of of sleep. The limitation of this project is that the sleep data needs to be combined with blood pressure, weight, heart rate, and other measures to aggregate and republish it.
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Appendix III. Data Mining for Decision Making in Healthcare Data is expanding: big data, many different sources of data. Many different kinds of data-different nature are in existence: these data in healthcare are inter-related. “Human beings exist in balance with their environment and external factors affecting their health should be considered too” On the other hand nowadays advances in computers are important and therefore data mining is now possible. Computers are cheaper, larger, and faster: disk storage, memory, processors. Parallel computing architectures and advance affordable networks are there, while exhaustive searches due to (i) processing power and (ii) data integration using distributed databases are now possible.
Definitions of Data mining Data mining originated from statistics and machine learning as an interdisciplinary field. It is defined as: The process of finding previously unknown patterns and trends in data, to build predictive models The process of data selection, exploration and building models using vast data to uncover unknown patterns. Data mining emerged in the middle of 1990’s. Actually the term “Data Mining” was only registered as term for 2010 Medical Subject Headings (MeSH) in late 2009 Data mining has been used extensively by financial institutions, marketers, retailers and manufacturers. In other words it is a new approach to data analysis and knowledge discovery Data mining has advanced since, including pattern recognition, database design, artificial intelligence, visualization, etc
“data mining is the analysis of (often large) observational data sets to find unsuspected relationships and to summarize the data in novel ways that are both understandable and useful to the data owner” MIT’s Technology Review: “…one of the 10 emerging technologies that will change the world”
Unlike statistics, data mining, without a hypothesis, explores data that have been collected in advance, and discovers hidden patterns from data. In short, data mining is a process of producing the general (i.e., knowledge or an evidence based hypothesis) from the specific data. Three of the most common Data Mining Functions
Clustering into groups of similar characteristics (unsupervised)
Classification into known classes; e.g. diagnosis (supervised)
Detection of associations
”90% of patients with flu also have fever” 116
Data Mining vs. Traditional Statistics
Limitations of conventional statistics Manual hypothesis testing: not practical with large numbers of variables and huge datasets Assumptions on linearity, probability distribution, made in conventional statistics may not be valid User is the one who specifies the variables and methods this may influence resulting models
Sampling vs. Entire Population
Statistics uses a sample of data from a population
Data mining typically uses data for entire population
Since data mining clusters data and discovers hidden patterns, data mining should, use all the population data. In statistics, use of a sample data provides almost the identical statistical significance as would occur if the researcher used the entire population data. Sampling tests large data sets when most computer systems or manual calculations (especially during preprocessing era) could not handle an entire population data sample. Statistics use “conservative” analysis strategies, data mining is flexible about methods to be used Statistics handles numeric, while data mining can handle other kinds of data, e.g. medical images Statistics are primarily based on mathematics, while many data mining approaches also adopt heuristics to resolve problems, especially in discrete data.
Deductive vs. Inductive Statistics are based on predefined hypotheses (deductive), while data mining is abductive. In statistics, a hypothesis is built and then data is collected to test the hypothesis. Use of knowledge obtained from data mining can directly help healthcare providers provide better health services. Statistics has been the main data analysis method in most scientific fields. The second step of data mining is data understanding and this requires statistics
The patient is warm. But if he has an infection, then it would be unsurprising that patient is warm. Therefore, by abductive reasoning, the possibility that patient is warm is reasonable. Example of Abductive Reasoning
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Need for Data Mining in Medicine
How is Healthcare unique in terms of applying data mining techniques? 1. Nature of medical data: noisy, incomplete, uncertain 2. Lots of data collected due to computerization (text, graphs, images,…) 3. Too many disease attributes available for decision making 4. Increased demand for health services: Greater awareness, increased life expectancy …) 5. Overworked physicians and facilities: Stressful work conditions in ICUs, etc.
Data mining provides novel biomedical knowledge to support clinical decision-making (e.g. diagnosis, treatment, prognosis prediction) as well as administrative decision-making (e.g., staffing estimates, insurance, demographic trends, quality assurance, etc.) in healthcare delivery.
Data Mining supports the whole spectrum of medical procedures Diagnosis: Classify patterns of diseases in multivariate patient attributes Treatment: Select from available treatment methods the most suitable one based on various criteria Prognosis: Predict future outcomes based on previous data and present conditions
Why will healthcare benefit from the application of data mining Health insurance companies try to reduce money loss due to fraud by using data mining methods Transactions are too voluminous and complex to be processed by traditional methods. Data mining improves decision-making since it is used for the discovery of trends and patterns in large amounts of different data. Financial motivators in health industry need for healthcare organizations to make decisions based on the analysis of clinical and financial data. Information gained from data mining is expected to maintain a high level of care and to improve organizational planning: healthcare organizations that perform data mining have better predictions about their mid and longterm requirements
“Data is a great asset to health organizations, provided that they are transformed into information.”
Data mining applications can help healthcare insurers detect fraud and abuse Healthcare providers can gain assistance in making decisions 118
Healthcare providers in hospitals, and patients can identify effective treatments and best practices Prospective payments may be based on classifying patients into case-mix groups
Research and New Knowledge: data mining helps researchers gain up-to-date biomedical knowledge and they can more easily understand large biomedical datasets. With the use of data mining, there are generated scientific hypotheses from large experimental data, clinical databases, biomedical literature.
Data mining in biomedical and healthcare applications differ from other applications Mining of medical data is involved with privacy and legal issues Quality of data in the biomedical-healthcare fields is inferior. An important reason may be missing values: even patients with the same disease do not always follow the same lab tests (due to different demographics, symptoms, complications) different data sets are being generated Data of temporal nature are very common in biomedicine (time-series attributes). Especially for the clinical sector, dates of examinations and lab tests are very important Health Information Systems are primarily designed for financial and administrative purposes and therefore it is challenging to obtain high quality data for clinical data mining (a lot of missing data) In the United States many hospitals do not use full EMR systems. Medical data that is often incomplete in terms of electronic availability. Even today handwritten records are kept in many health organizations
Also, these are two very important considerations: Health researchers must ensure patient privacy (be in accordance with privacy regulations) Anonymousness of patient data Medical data mining may reveal previously unknown medical errors (suspicious patterns in medical practice), which could lead to lawsuits against doctors.
There is a balance that should be kept between Data quality and availability vs. need to protect patient confidentiality
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Data mining methodologies and algorithms
Descriptive and Predictive Data Mining Two main categories of data mining: -descriptive (or unsupervised learning) -predictive (or supervised learning)
Descriptive data mining Clusters data by measuring the similarity between objects and discovers unknown patterns so that users can understand huge amounts of data. Descriptive data mining is exploratory. [Clustering, association, summarization etc]
Prediction data mining Infers prediction rules (classification/prediction models) from training data and applies the rules to unclassified data 1. Preparation: cleanup, transform, delete attributes, add possible new attributes 2. Split data into a training and a test set 3. Training: Develop model on the training set 4. Testing either by using repeated cross-validation runs or using specific ratio of one dataset 5. Evaluation: See the accuracy of the model on the test set 6. Actual use: Use model on new input data to estimate unknown output [Classification, regression, time series analysis, prediction etc]
Data mining “families” of techniques Description and visualization: understand our dataset and detect hidden patterns in data. Classification: prediction of a target variable that is categorical in nature Association: determine which variables go together (If patients undergo treatment A, there is a 0.60 probability that they will have outcome B) Clustering: group objects, such as patients, in such a way that objects belonging to the same cluster are similar and objects belonging to different clusters are dissimilar.
Classification Classifies data into predefined categorical class labels. To classify data, a classification algorithm creates a classification model consisting of classification rules. In the healthcare, classification is used to define medical diagnosis and prognosis based on health conditions. 120
“Class” in classification, is the feature in a data set, in which we are most interested. It is defined as the dependent variable in statistics.
Classification Steps-Training and Testing Builds a classification model, which includes classifying rules, by analyzing training data Some classifiers use mathematics rather than IF-THEN rules (“IF BloodPressureHigh=yes AND Smoking=yes THEN HeartFailureRisk=yes”) Classifying rules are not 100% true; Rules with 90–95% accuracy are solid rules. The second step, testing, examines a classifier (using testing data) for accuracy or its ability to classify unknown objects (records) for prediction. Testing is very simple and computationally lightweight compared to the training step.
An example of a classifier: Naïve Bayesian classifier Α probabilistic statistical classifier, widely-used in medical data mining “naïve” indicates conditional independence among features For the above reason the computation complexity is greatly reduced Because of this simplicity, it can handle a data set with many attributes Needs a small set of training data for accurate estimations: this is because it only requires the calculation of the frequencies of attributes in the training data set Generally, it produces good accuracy despite the above violations Drawback: its assumption that all attributes are independent one another. (In healthcare patient symptoms and health conditions are strongly related each other)
Other considerations Before applying classification, redundant attributes and irrelevant attributes to class (e.g., sex attribute on prostate) should be identified. These attributes increase noise and slow performance and can be identified using statistical methods (such as correlation analysis) or feature selection methods. The exception and non inclusion of some attributes should be performed with care. We may miss an important relationship between a set of independent variables and a dependent variable.
Example Individually, smoking and an infection might not affect stomach cancer so that they might be eliminated. However, their combination may be significantly related to the cancer noise reduction should be used carefully.
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Weka (version 3.x) provides more than 50 classification algorithms There is no single-best classification algorithm for every biomedical data set. A data set should be split into: training data (~66.6%) and testing data (~33.3%). Output (classification model) is tested with testing data to measure accuracy The top classification algorithms should be selected for future prediction
Sometimes, records in testing data are very hard or very easy to classify. In such cases, classification accuracies are not reliable. For this reason, cross-validation is frequently used so that every record in the data set is used for both training & testing. Evaluation experiments are normally performed 10 times (i.e., 10-fold cross-validation).
Cross-validation Technique to assess how the results of a statistical analysis generalize to an independent data set. One round of cross validation: partition a sample of data into subsets perform analysis on one subset (training set), and validate analysis on other subset (validation set) To reduce variability, multiple rounds of cross-validation are performed using different partitions, and validation results are averaged over the rounds.
Decision trees These actually belong to classification methods. C4.5 (J48 in Weka) is the most widely-used decision tree algorithm Decision tree classifiers construct a hierarchical like a tree structure Building a decision tree is the training step. The method for the tree construction is called Attribute Selection Method (ASM): an attribute is found whose sorting result is closest to the pure partitions by the class in terms of class values and selected attributes become nodes in a decision tree. A drawback occurs when a data set contains many attributes. In this case, the decision tree may be too complex. To resolve the problem, tree pruning approaches are applied to such decision trees [there are various statistical methods to prune the least important branches of the decision tree]
Neural networks Mimic the neurologic functions of the brain using computational nodes
(i)Input (ii)Hidden and (iii) Output layers The most widely used NN is multi-layer perceptron with back-propagation
Each node/neuron is interconnected with other nodes via weighted links
It was the best classification algorithm until the introduction of the decision trees and the Support Vector Machine which is the most widely-used classification algorithm in biomedicine
Link weights are adjusted when the NN is being trained. Nodes are classified into three categories: 122
Interconnecting artificial neurons for solving artificial intelligence problems without creating a model of a real system. The goal of artificial neural networks is good, or human-like, predictive ability.
Source: Wikipedia Commons Disadvantages of Neural Networks They require many parameters, that are empirically determined Its classification performance is sensitive to the parameters selected Very slow training process and computationally heavy (~100 times slower than regression in a statistical analysis package called SPSS) Clinicians find it difficult to understand how its classification decisions are taken and cannot interpret the results easily Classification accuracy inferior to recent classification algorithms (decision tree, SVM)
Support vector machine (SVM) Support Vector Machine method is based on the statistical learning theory and is designed to solve two-class classification problems (e.g., safe therapy vs. risky therapy). When a dataset is represented in a “high dimensional feature space”, it searches for the optimal separating hyperplane where the margin between two different objects is maximal Hyperplanes are decision boundaries between 2 different sets of objects SVM uses support vectors and the margin is determined using the two support vectors. The major advantage of SVM is its classification accuracy.
Support vector machine drawbacks (1) SVM is designed to resolve 2-class classification problems this is resolved by reducing a multiclass problem into multiple binary problems (2) Various functions have different classification accuracy for every data set this is resolved by selecting a right kernel function (3) Training step of SVM is extremely slow and requires extensive computational resources.
Ensembles “Multiple classifiers together for better classification accuracy than the use of one classifier.” Using ensembles gives researchers with reliable prediction results 123
Meta-ensemble approaches (i.e., an ensemble of ensembles) A number of studies show that ensembling improves classification performance in the biomedical and healthcare fields
Example: if Classifiers A, B, and C predict that patient 1 has lung cancer and Classifiers D and E predict that patient 1 doesn’t have lung cancer, then, using a voting strategy, patient 1 is determined to have lung cancer (each classifier may also be differently weighted).
A very famous ensemble method: AdaBoost (adaptive boosting) Received attention in biomedicine because it has very high classification performance and normally outperforms even SVM Uses weighted majority voting: classifiers with good classification results during initial training process have higher weight in final decisions.
Clustering “Grouping similar objects provides users with fundamental information to understand the data.”
Clustering is unsupervised learning and when implemented it observes only independent variables For this reason, clustering may be best used for studies of an exploratory nature, especially with large amount of data, with little known about data. Clustering, groups objects in specific number of clusters: objects in a cluster are similar and objects from different clusters are not similar. Clustering algorithms are categorized into hierarchical and partitional. When very little about data is known, hierarchical clustering should be used first because they do not require to input of k Clustering has been widely used to study genes when very little information is available and when microarray data can be used for clustering genes. Pay attention to outlier values: sometimes they may be assigned as a separate cluster by the algorithm, but this may be in some cases a relevant cluster.
Hierarchical clustering algorithms They merge the most similar two groups of objects based on the pair wise distances between two groups of objects, so that objects are hierarchically grouped The categorization of methods is based on the selection methods of the representative object of each group for similarity calculation. (single-link, complete-link, and average-link.)
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Among the hierarchical algorithms “average-link“ provides the best clustering accuracy in most cases. Main advantage: visualization capability shows how many objects are similar one another. Dendrograms: researchers can reasonably guess the number of clusters, but are poor to visualize if we have too much data. Possible solution: randomly sample the data. Other clustering algorithms cannot provide this very useful feature easily.
Disadvantages of Hierarchical clustering algorithms High complexity, so that the algorithms are very much limited for very large data sets. They also use a huge amount of system memory to calculate distances between objects.
Top row of nodes are individual observations. Remaining nodes are clusters to which data belong Arrows represent the distance (dissimilarity).
Height of each node is proportional to the value of the intergroup dissimilarity between its two daughters (top nodes representing individual observations are all plotted at zero height).
Partitional (or centroid based) clustering algorithms: require a user to input the number of clusters (k) and then relocate objects to k clusters.
Categorized according to
how they relocate objects how they select a cluster centroid (or representative) among objects within a (incomplete) cluster how they measure similarities between objects and cluster centroids. Benefits Superior clustering accuracy as compared with hierarchal clustering algorithms Faster. Can handle large data sets which hierarchal algorithms cannot (i.e. better scalability)
Major drawback: their clustering results depend on the initial cluster centroids (which are random): clustering results are a little different each time the partitional algorithm runs.
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K-means: he most widely-used partitional algorithm 1. randomly selects k centroids (objects) 2.decomposes objects into k disjoint groups (based on the similarity between centroids and objects). A cluster centroid is the mean value of objects in the cluster
Partitional clustering (a) vs. Distributional clustering (b) which is another common clustering method
Expectation-Maximalization (an example of Hierarchical Clustering) Data set is modeled with a fixed number of randomly generated Gaussian distributions. Their parameters are optimized to fit better to the data set. Expectation: expectation of log-likelihood using the current estimate for the parameters Maximization (M): computes parameters maximizing the expected log-likelihood of E step
Considerations we need to make when converting discrete data to numeric
Almost all clustering algorithms only handle numeric data: However, most healthcare databases have a number of discrete/categorical attributes.
Although we can “convert” categorical data into numbers for clustering, this causes serious problems because this distorts distances between categories. Example Three discrete values: A, B, and C are converted into 1, 2, and 3, respectively. In the real world let’s assume that distance between A and B, or B and C is 1, the distance between A and C is 2 This conversion indicates that A and C are more dissimilar than either A and B or B and C, which is not true Few clustering algorithms can handle discrete/categorical data. FarthestFirst (found in Weka), two-step Cluster Analysis (found in SPSS)
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Association Rule Mining So called basket analysis in the market “Discover customers’ hidden sales patterns or relationships among items purchased “ A customer buys bread & butter, and then it is likely that he buys milk also. Using this information, grocery managers can increase their sales, with more targeted advertising campaigns and pricing. In the healthcare, the same methodology is used to discover underlying relationships among symptoms, diseases and other inter-related factors The algorithm requires two user inputs: support and confidence (%), which serve as filters Association rules (sets of transactions) that frequently occur (support) and which are accurate (confidence). This property significantly limits the search for frequent item sets and considerably improves the efficiency of the algorithm. [e.g., if male female prostate (!!) cases are not frequent, which is the case; no association rules related to the disease are generated.]
is a set of binary attributes called items. is a set of transactions called database. Each transaction in D contains a subset of the items in I. A rule is defined as an implication of the form XY where and items X and Y are called antecedent and consequent of the rule respectively.
(intersect). The sets of
Applications and Examples from the Health Industry Data mining is widely used in healthcare fields because of its descriptive and predictive power. Predict health insurance fraud, healthcare cost, disease prognosis, disease diagnosis, and length of stay (LOS) in a hospital Obtain frequent patterns from biomedical and healthcare databases, such as relationships between health conditions and a disease, relationships among diseases, and relationships among drugs.
Step by step business model Business understanding and objectives: how the organization works-which are the main attributes, what questions need answers Data preparation: sampling and data transformation Modeling stage: the actual data analysis (cluster analysis, regression analysis, decision trees etc) Evaluation stage: comparison of models and results from any data mining model by using a common yardstick, such as lift charts, profit charts, or diagnostic classification charts. 127
Deployment: actual implementation of the data mining models.
An ideal data mining package should (1) Support intelligent data preprocessing that automatically selects data for data mining and uses domain knowledge for various data processes (2) Fully automates the knowledge discovery process so that it understands and utilizes existing knowledge in data mining processes for better knowledge discovery.
Data mining applications in the industry-Health care fraud prevention Highmark, a health insurance company built classification models, based on claims, customer and provider data, to identify potential fraud instances. Their fraud detection system aims at real-time analysis to build predictive models that can detect fraud and stop it before it occurs. Highmark has found that conducting decision making regarding fraud is carried out more quickly than before, as the classification system is automated to avoid labor-intensive work. This updating cycle of data mining led to savings of $11.5 million.
Reimbursements: Medicaid and Medicare Receive proper Medicaid and Medicare reimbursements: a decision tree model (Security Blue Reimbursement Model) was built using patient symptoms, health history, demographics to predict the risk for diseases each patient will have and ranks patients based on the risk of 13 diseases. If those patients are detected at an early stage: costs for care can be lowered as providers and insurers do not resubmit claims: this is because reimbursement received from Medicare & Medicaid depends on diagnosis. These decision trees are annually revised because of the growth of the number of diseases modeled. Use of the model has saved Highmark millions of dollars in healthcare reimbursement
Data mining applications in the research areas: Classification applications Classification is the core data mining method in bioinformatics Researchers distinguish between similar diseases if they can have the DNA expression microarray data of sample cells infected with similar diseases and can correctly classify microarray data. Golub et al correctly distinguished acute myeloid leukemia (AML) and acute lymphoblastic leukemia (ALL) using gene expression data and classification algorithms. An example Harper used healthcare datasets to compare classification algorithms. He used Discriminant Analysis (DA), regression models (multiple and logistic), tree-based algorithms (CART), and artificial neural networks. CART (a decision tree algorithm) achieved the best overall accuracies and was the second fastest. 128
Artificial neural networks: inferior accuracies to CART and Regression-the slowest algorithm in the study.
Clustering of DNA microarrays Dr. van’T Veer used DNA microarray data of 98 primary breast tumors to cluster the tumors using hierarchical algorithm.
34% of “relapse” patients in the upper cluster (62 tumors) 70% of “relapse” patients in the lower cluster (36 tumors) (who developed distant metastases within 5 years). Upper cluster is considered to be “poor prognosis tumors” while lower cluster is “good prognosis tumors”. After clustering they also used classification to predict poor prognosis: prediction of cancer outcomes using microarray data was better than prediction using clinical parameters.
Association applications Example 1 Application of association mining to huge data sets (such as national health insurance data), to identify relationships between two drugs, or between diseases Korea Medical Insurance Corporation (KMIC) database was used to help to formulate a government policy on hypertension management The KMIC has healthcare utilization data, demographic, clinical data (e.g., blood glucose) and lifestyle data (e.g., smoking and drinking) through from a nation-wide-health-promotion program. (18,277 subjects were selected from the population of 127,886) Example 2 Antacids: to alleviate the gastric ulcer and relieve heartburn Antacids do not require prescription, but Taiwan NHI reimburses them Interesting to know how they are used with other drugs Analysis of use patterns of antacids using association mining Visit & prescription datasets: 526,693 and 2,574,739 records, respectively. Support=1% and confidence=52.2%, 36 association rules Five most frequently used drug sets with antacids were extracted
Healthcare management examples Group Health Coop. stratifies patients by demographic characteristics and medical conditions to determine which groups use the most resources Arkansas Data Network uses readmission & resource utilization and compares data with scientific evidence to determine best treatments 129
Blue Cross uses emergency department and hospitalization claims data, pharmaceutical records, and physician interviews to identify unknown asthmatics and develop interventions. Seton Medical Center uses data mining to decrease patient length-of-stay, avoid clinical complications, provide information to clinicians etc Sierra Health Services has used data mining to identify areas for quality improvements, including treatment guidelines, disease management groups, and cost management. “Lightweight Epidemiological Advanced Detection Emergency Response System” (LEADERS) analyzes data and statistics to search for patterns that might indicate bio-terrorist attacks
Infection Control
Hospital infection control or as an automated early-warning system in the event of epidemics. A syndromic system is more efficient than a traditional system that is based on diagnosis. An early warning of the global spread of SARS virus is an example of the usefulness of syndromic systems based on data mining [Zikos D, Diomidous M. Integration of data analysis methods in syndromic surveillance systems. Stud Health Technol Inform. 2012;180:1114-6.]
Treatment effectiveness example Data mining applications to evaluate treatment effectiveness By comparing and contrasting causes, symptoms, and courses of treatments, data mining can deliver an analysis of which courses of action prove effective. [For example, the patient group outcomes treated with different drugs for the same disease can be compared to determine which treatments work best]
United HealthCare mined its treatment record data to cut costs and deliver better medicine. It also developed profiles about doctors’ practice patterns to compare these with industry standards.
In 1999, Florida Hospital launched the clinical best practices initiative, to develop a standardized path of care Customer relationship management Determine preferences, usage patterns, patient needs to improve satisfaction. Using data mining, Customer Potential Management Corp. developed an Index that indicates an individual’s trend to use specific healthcare services, defined by 25 diagnostic categories The index was based on millions of transactions and can identify:
patients who can benefit most from specific healthcare services encourage those who most need specific care reach audiences for improved health and long-term patient relationships 130
Detection of fraud and abuse examples Data mining to detect fraud & abuse establish norms & then identify unusual or abnormal patterns of claims by physicians, clinics etc
Medicaid Fraud & Abuse Detection System, recovered $2.2 million & identified 1,400 suspects for investigation after operating for
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