Data driven health informatics

Chapter 1: Introduction to the Discipline Chapter 2: The Nature of Healthcare Data

Data driven health informatics Digital Lecture Companion

DIMITRIOS ZIKOS PhD Health Informatics, MSc, BS Nursing

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. A disease is very often a result of interactions of a patient in their environmental and psychosocial context. Therefore, when evaluating health, many considerations should be made, and a large number attributes need to be collected, stored and retrieved during decision making. The information is produced and communicated during the above mentioned procedures: there are multiple users that share this information in a hospital and outside of the hospital. Very often, same data are accessed concurrently by different health providers, who will need this data for different purposes. Information changes dynamically during the care provision, for example when a new exam is prescribed or when the treatment plan of a patient is altered. It is also important to understand that the health care information derives from the combination of many atomic health data, which have to be assessed together to produce useful information for the clinical decision making. The majority of health related information coming from the analysis of numerical, Boolean data but also from images, video and sound data.

Why Health Informatics?    

Health Informatics provides information to make decisions Better information leads to better decisions Health care, management, planning and policy all need good information Health care, health management, health policy and health planning all depend on having good information to make decisions.

Categories of Information in Healthcare In terms of the scope of the use of information, there are four categories of health care information: clinical, administrative, financial and population health information, all used in various ways to facilitate decision making. There are some important considerations related to health care data: 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. A physiological measurement of 150 is simply a number, we do not know what this figure refers to and how we can measure it. When we place this number within a context, in other words, when we label it, it becomes an 1 Dimitrios Zikos: Data Driven Health Informatics

information that can be used by the clinician. If this measurement refers to the diastolic blood pressure of a patient, measured in mmHg, then the health professional will have acquired information about a patient’s blood pressure. This is a fact that we learned about this patient and we can assess this information and compare with clinical expectations for this specific patient, and make evidence based decisions about the treatment plan. Access & delivery methods of information: Since the 90s there was a slow but steady transition from traditional to electronic methods to store, access and communicate health care information. There have been different architectures for the implementation of hospital information systems over the past four to five decades, and the transition to new architectures generally followed the evolution of information systems, in general. The recent approach is based on dedicated private cloud services that can be accessed by health professionals. This is an important evolution step, from the traditional views to access to personalized services. The information in health care is dynamic: patient information constantly changes during a hospitalization and needs to be up to date. Health professionals need to constantly reassess any new input for informed decisions. Methods of utilizing data to inform clinical decisions A health professional utilizes four critical methods involving data, to make decisions: (i) medical knowledge base (ii) information coming from patient (iii) experience and judgment. (iv) application of data mining methods on historical data for knowledge acquisition. The medical knowledge base is acquired via studying, revisiting, reviewing the medical knowledge of a health professional’s area of expertise. It is not limited to the university acquired knowledge, but also involves continuous education, reviewing recent literature and publications, attending conferences and scientific meetings. Information coming from patient: includes and is not limited to, the patient history, medical exams, vital signs, radiology tests. Experience, medical judgment: we refer to the clinician putting together all the different pieces of a puzzle, including the three components above, to decide on a diagnosis for a patient and consequently which one would be the treatment options. Doctors, during the medical judgment have learned how to do a differential diagnosis, which is the process of differentiating between two or more conditions that share similar signs or symptoms. For nurses, the nursing assessment is part of the nursing process and is a systematic procedure that nurses follow. It involves the collection and analysis of the available patient data, and is not limited to the physiological, but also involving psychological, sociocultural, and lifestyle factors too.

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Data mining for knowledge acquisition: algorithms are applied on large datasets to predict health related events for a patient, like the diagnosis, the prognosis, and the optimal treatment plan. This component is the most recent addition to the clinical decision making mechanism, and still remains unexplored or, at best, underutilized in most hospitals. These systems should serve as an additional input for the health professional, who in their turn, will be expected to use this extra input for more accurate and less error prone medical decisions. It is generally agreed, in the literature that these systems can neither act autonomously, nor can dictate to the health professional, the appropriate practices. Decision making systems, on the contrary, should be designed as an extension to the human cognitive process that takes place during decision making.

Healthcare professionals combine their knowledge, experience and patient information, and eventually will also consult model predictions which use historical data, to make clinical decisions

Definition of Informatics and ICT In order to understand the significance of Health Informatics science, we first need to define Informatics. Informatics (from the French ‘informatique’) includes the: 

Science of information: defines what is considered as information, and how information is acquired systematically by utilizing raw data. The science of information studies the analysis, collection, classification, manipulation, storage, retrieval, movement, dissemination, and protection of information.

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Information processing: how the information can be used, what methods can be applied to health care information to transform it into useful and usable knowledge.

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The engineering of information systems: it involves the engineering of systems which involve input of data and transformation to information, with the use of appropriate methods. The term also refers to the complementary networks of hardware and software that people and organizations use to collect, filter, process, create and also distribute data.

Informatics studies the structure, behavior, and interactions of natural and artificial systems 3 Dimitrios Zikos: Data Driven Health Informatics

that store, process and communicate information. It is obvious that the term informatics does not refer to computational methods and the computer science, although many of the methods that informatics employs, can be computer science methods. Information and Communication Technology (ICT) – The communication technologies and services that are used in various applications. The term describes these computer, communication and multimedia technologies that can be used to receive, process, store, display and disseminate information. ICT is an umbrella term and often is used to describe the communication within applications in a specific domain, like for example “ICT in Health Care”. Key Elements of Informatics Acquisition: capture data produced during health care provision. There are different ways that this is achieved in a clinical environment: 1. Observation and clinical examination: doctors and nurses collect crucial information about patients using the medical and nursing evaluation procedures, which include systematic steps to assess a patient condition from a medical and nursing perspective. Doctors also perform the clinical examination which includes palpation, inspection, percussion and auscultation. This way, doctors use their natural senses and tools which amplify our natural senses abilities while examining a patient. 2. Talking with the patient, talking with other health professionals. Especially the interactions between nurses and doctors are of great importance, taking into consideration that nurses spend a considerable amount of time with the patient and are able to do nursing observations which can provide an invaluable input to the doctor. A nurse can typically see small changes or events to a patient’s condition, like for example loss of appetite, skin colour changes, change of consciousness. The above can be significantly important and the physician would not be in place to notice, timely. There is evidence that a hospital environment valuing the interprofessional collaboration between health professionals, is an invaluable factor and significant contributor to quality health care services and patient satisfaction. 3. Physiological measurements: may be very simple, such as the measurement of body temperature with a clinical thermometer, and the measurement of blood pressure, or they may be more complicated, for example measuring how well the heart is functioning by taking an ECG (electrocardiograph.). Typically, the majority of physiological measurements are performed by nurse practitioners. 4. Laboratory tests and radiology examinations: laboratory tests involve the analysis of samples extracted from patients (i.e. blood, urine, tissue). Typically, a laboratory test is part of a regular check-up, but during a patient hospitalization, these are usually performed to 4 Dimitrios Zikos: Data Driven Health Informatics

help shape a diagnosis. The analysis of enzyme concentration, blood elements, anti-body tests, urine ketone concentration, are some typical examples. 5. Radiology exams: include a variety of imaging techniques such as X-ray radiography, ultrasound, computed tomography (CT), nuclear medicine including positron emission tomography (PET), and magnetic resonance imaging (MRI) which are used to diagnose and/or treat diseases. Storage (and retrieval): involves the storage of data in physical medial, so that it can be retrieved. The storage of healthcare related data is nowadays achieved via (i) the direct entry of information into Electronic Medical Records, and consequently the storage of data in relational (in most of the cases) databases, on the system backend (ii) using sensors, which perform measurements and then send the data via a communication module to interoperable systems (iii) via scanning handwritten documents and using optical character recognition (OCR) technologies. Storing patient information in portable devices is not a recommended practice, for data privacy reasons. These devices (e.g. bed monitors, tablets) should send the measurements to the main system via wireless technologies, without storing the data locally. Information retrieval during the clinical practice involves healthcare professionals navigating and using search tools found in graphical user interfaces of Electronic Medical Records. Healthcare professionals are end users and have no direct access to the data, and no access to database querying engines. A typical functionality of modern systems is the support of advanced reports which visualize the clinical information, transforms the data into useful representations with longitudinal data insights. Communication: data moves from the point of data collection to storage, for analysis, and finally, back to the point of data use. Communication of health care data within and across subsections of a hospital information system, involves the use of communication protocols and interoperability standards. Interoperable systems should not only be technically compatible but should also achieve seamless data exchange. Manipulation: data usually needs to be manipulated, to be combined with other data, and aggregated, for statistical and healthcare analytics purposes. Data manipulation can be as simple as the calculation of a patient age from the age of birth, or it can refer to more advanced applications where data science methods are applied on data to generate prediction models. Data manipulation may also refer to different representations of data for the end user: it may be possible for the healthcare professional to view information about a patient, based on a sequential time-line, which represents each clinical intervention and event according to the time it occurred. At any given point, though, a healthcare professional may opt for viewing the same exact information for that patient, based on the location of the clinical interventions and events. For example, the output provides details about events, classified by the location, e.g. events that occurred in a hospital ward, in the radiology 5 Dimitrios Zikos: Data Driven Health Informatics

department, in the surgery, etc. Display: refers to the way that data may be displayed so that it can be easily understood and used. Displaying information does not only refer to the physical output devices (monitors, printers etc.), but primarily addresses the presentation of the information to the user via user friendly interfaces, successful human computer interactions and functional dialogue systems.

From data to information and new knowledge: the knowledge circle

The discipline has evolved during the last decades The area of health informatics is evolving and is nowadays considered to be a well-defined scientific area with multidisciplinary nature. When mainframe computers were introduced in hospitals halfway through the 20th century, the obvious benefits were not solely information science related, and could rather be summarized in (i) moving away from paper records which are prone to physical damage and take up a lot of storage space (ii) being able to share data, since the mainframe could be accessed with the use of dummy terminals from more than one user at a time and (iii) calculations for cost estimation and budgeting purposes became easier, since they would actually be performed by processing units rather than manually. Actually, the latter, for many, has been one of the most significant motivators for decisions involving investments for computer mainframes back in the day. Later on, with advancements in computer science and information technology science, the storage and retrieval of the majority of clinical information started to rely on computer systems and computer networks; this is when there appeared the first early hospital information systems. These information systems have soon been covering a wide spectrum of functions within a hospital. Specialized medical devices appeared in the market, often offering advanced networking capabilities, for their time. Many such examples can be found in medical imaging. These systems were accompanied by standards defining their specifications in detail, and protocols that each manufacturer should follow. Within this context, one could see new evolving sub-domain, or application domain of computer science, and therefore the medical computer science became a well-defined domain on its own. 6 Dimitrios Zikos: Data Driven Health Informatics

Still one of the biggest challenges of hospitals was ahead, and was primarily related to the management of the enormous amounts of data that were stored and retrieved during the clinical care. Efforts then started to focus on how it would be possible to develop user friendly systems which could make the entry, retrieval and presentation of healthcare data seamless, and how this data could be transformed into clinically meaningful information for healthcare professionals. During the decades of the 1980s and 1990s, the direction of the scientific community was to study and develop novel electronic medical record frameworks which would facilitate the above priorities.

From “Computers in Medicine” to specializations of health informatics

The term ‘Medical Informatics’, while it precedes heath informatics, is still broadly used nowadays and refers to the medical applications of health informatics, but is regarded to be a subdomain of the latter. Today, there are many well defined sub-areas of health informatics, which independently develop methods and new knowledge for their own areas of specialization: dental informatics, nursing informatics etc. The recent direction of health informatics is the focus on the integration of smart data analytics and data mining algorithms for clinical and administrative decision making. This direction is driven by recent technological and computer science advancements which make it possible to analyze huge volumes of data with novel machine learning methods, to provide accurate predictions and estimations which can be extremely useful during decision making. Eventually, in the coming years, we will be witnessing the integration of predictive algorithms into Electronic Health Records, which will be providing recommendations for patients at the point of care. These recommendations will be a result of the analysis of enormous amounts of historical patient data, in order to identify useful patterns, for the diagnosis, therapeutic plan and prognosis of a patient, with the ultimate goal to improve the quality of care in a patient centered health-care system. 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 7 Dimitrios Zikos: Data Driven Health Informatics

communications to support health and health related disciplines such as medicine, nursing, pharmacy and dentistry’ 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 studies the resources, devices and methods required, in order to optimize the:

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Acquisition Storage Retrieval Use

of information in health. Health Informatics is an Interdisciplinary field combining health, computer science, statistics and engineering. It is recognized as a 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.

Health Informatics uses information to improve health care. One needs to understand how we define the concept of ‘improving healthcare’, and whether this concept is a quantifiable one. This is a requirement, in order to meaningfully introduce health informatics applications. We can understand, though, in the healthcare domain, that the ‘improvement’ concept is related to making more informed decisions which drive better patient outcomes, and encourage safe and error-free provision healthcare of healthcare services, but also to lead to improved hospital efficiency and an increase of revenue. As an interdisciplinary field, 8 Dimitrios Zikos: Data Driven Health Informatics

health informatics applies technology & information to enhance healthcare delivery, biomedical research. It is closely bonded with fostering education of health professionals and the public. Health informatics can provide tools and methods for e-health literacy, that is to reach out large populations for disease prevention and health promotion, through targeted and personalized interventions. Health informatics methods and systems, are also becoming important for the education of health professionals and healthcare administrations. Health informatics studies the process where 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. Each one of those groups may be utilizing the same data which have been transformed and processed in order to facilitate the strategic goals of different professional groups in health care, and of patients as well. Related terms Consumer Health Informatics: both healthy individuals & patients want to be informed on medical topics. MediQoC is one example of such systems, and will be discussed in appendix. This system provides to the user the opportunity to navigate through Medicare healthcare providers, by typing in their symptoms, in order to find appropriate and safe health care services. The appendix describes the platform and the methods that have been used for the development of MediQoC. Health knowledge management: can prove to be extremely useful in an overview of latest medical journals, best practice guidelines or epidemiological tracking. Nowadays there is a wealth of new medical knowledge coming out every day. Hundreds of journal research papers appear and it is virtually impossible for a health care professional to catch up with this knowledge flow, in its raw form. Knowledge management tools provide categorized content, classified on the basis of areas of interest, by the nature of findings, or by the impact of the published results, making it easier for the researcher and the healthcare professional to navigate through medical knowledge. 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. Nursing Informatics: the "science and practice that integrates nursing, its information and knowledge, with management of information and communication technologies to promote the health of people, families, and communities worldwide." (IMIA Special Interest Group on Nursing Informatics, 2009). The application of nursing informatics knowledge is empowering 9 Dimitrios Zikos: Data Driven Health Informatics

for all healthcare practitioners in achieving patient centered care. Public Health Informatics: Chapter 18 discusses the principles of Public Health Informatics in detail. Bioinformatics: an interdisciplinary field that develops methods and software tools for understanding biological data. As an interdisciplinary field of science, bioinformatics combines computer science, statistics, mathematics, and engineering to analyze and interpret biological data. 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

Mobile Health (m-health) M-Health is 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. There are three main categories of modalities which are used for data collection in m-Health applications: (i) integrated mobile sensors (accelerometers, gyroscopes, and GPS), (ii) specialized biometric sensors (blood glucose, heart rate) and (iii) manual or semi-automated data entry. Mobile devices using modern communication technologies help nurses and doctors in their everyday practice

E-Health E-Health is a broad term for healthcare practice which is supported by electronic processes and communication. It is a relatively recent term and can encompass a range of services in healthcare and information technology. It is not clearly defined (for example some use it instead of healthcare informatics, others use the term describing healthcare practice using the Internet). A study published in the Journal of Medical Internet Research found 51 definitions for e-health. E-health offers a broader coverage of electronic/digital processes in health, often including m-health applications too. 10 Dimitrios Zikos: Data Driven Health Informatics

Health informatics tools and methods Health Informatics are not just “Computers in Healthcare”. It develops advanced methods to develop and integrate seamlessly into the clinical practice components such as: clinical guidelines, medical terminologies, clinical dictionaries and nomenclatures, information and communication systems, decision support and recommendation systems. Health Informatics ≠ IT: Information Technology in hospitals is not Health Informatics. Information technology is focused on the development of hardware & software, which are undeniably invaluable vehicles for the health informatics science, to implement the methods of health informatics. Read the message to the left and discuss: (i)

Whether and how health informatics would still exist as a discipline without the use of computers (ii) How the development of information technologies and the progress in computer science have helped the domain of health informatics develop better methods and become an established scientific area The IT sector is very important enabling field for advanced health informatics applications and methods. The introduction of ICT technologies has sky rocketed the discipline of health informatics.

Health informatics is not quite new, as you might think. Since healthcare services started to become more systematic, the need for information management was eventually recognized as an important priority. Here is some evidence: 

The first version of the International Classification of Diseases (ICD) was initiated in the year 1893. Ten updates followed, and we still use the same classification system, in its 10th edition

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The first structured clinical guidelines appeared several decades before the widespread use of computers

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Hospital information management methods were evident before the 70s in hospitals, whereas there are still hospitals in many developing countries around the globe, which do not use computerized EHR systems. It needs to be understood, though, that, before the introduction of computers, these information management methods, were typically limited to file maintenance & life cycle management of paper-based files, other media & medical records.

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Health Informatics health care levels of interest

Single hospital department

The hospital needs

A large area or a district

The healthcare system at a national level

Health informatics also has application in the community health. The central objective of community health is to improve the health characteristics of biological communities, in geographical areas or within groups of people with common characteristics. Health informatics, therefore contributes with specialized programs, methods and tools that are used in population based healthcare services, for health promotion, disease prevention and syndromic surveillance.

Who is involved in Health Informatics Clinical personnel – they need suitable information in caring for patients. They want the data that is being generated during the health provision, to be transformed into information that will facilitate more informed and personalized clinical decisions. At the same time, they want to have direct, non-delayed access to patient information during the everyday practice, therefore the data retrieval interval should be minimal. Nonclinical Staff: educators, administrators, research scientists – they need relevant data and information to perform their tasks. Hospital databases store information that can be invaluable if utilized properly. A large hospital counts thousands of patient admissions each year and detailed data for each of the admissions. In a span of 10 years, for instance, there is expected to have been collected a very large dataset of history admissions. This dataset can be used in research, specifically in retrospective epidemiologic studies, or for the development of predictive algorithms that will be applied to new cases. Health care administrators utilize historical data to overview the evolution of the cost of care, the most common case-mix profiles and changes over time, identify new challenges, measure the efficacy and cost effectiveness of practices over time and evaluate the quality of health care provision, identify gaps and alterable factors which contribute to the improvement of health outcomes. Health educators, often use de-identified subsets of historical hospital data to provide to their students, real examples and also to investigate patient cases during the medical specialty practice. 12 Dimitrios Zikos: Data Driven Health Informatics

Information science and IT professionals use computer technologies to manage information so as to fulfill the needs and requirements of other end users. It is easy to generate and print-out a clean looking report with information for some patient, which can be reviewed by a health practitioner and/or archived. External parties (I): policy makers. The aggregation and analysis of hospital and patient data from multiple health care providers will provide feedback to health care policy makers for planning of health resources and assessment of the direction of the healthcare system in the level of a province, state, or even the whole country. External parties (II): insurance companies. Payers have access to de-identified data and they recently apply advance data mining methods to these data: Insurance companies will not happily accept to pay for services which were evidently not required for the treatment of a condition, or for complications to the treatment due to malpractices and medical errors. There should be noted though that medical doctors should always independently do their best to achieve optimal outcomes for their patients. In a broader sense all the groups below are served by Health Informatics     

Patients The Community Health care providers (MDs, nurses) Primary Care & General Practitioners Management in Hospitals

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Government Bodies and Policy makers Facility and operational management Healthcare researchers Healthcare educators and students

As we mentioned earlier, 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 (i.e. EMR, Hospital Information Systems-HIS) and integrated methods to support healthcare. These applications are ruled by specific standards and protocols.

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Top Left: Knowledge Areas of health informatics Top Right: Technologies in health informatics Bottom Left: Applications Architecture Bottom Right: Standards used in health informatics

The improvement of the Quality of Care is the ultimate goal of Health Informatics: this requires the establishment of inteprofessional collaboration between different specializations and proper education and training of users to be prepared to use new technologies effectively. Interprofessional Collaboration Education Healthcare Professionals Patient Safety Quality of Care

Presupposes AND Reinforces

Health Informatics

Areas of interest for Health Informatics Health informatics is interested in a wealth of areas. The most important ones are:           

Communication systems and networks in health care Modeling, classification and coding systems and integration in electronic medical records Healthcare Information systems Electronic Health Record systems Decision Support Systems (DSS), for clinical and administrative decision making Knowledge based Systems and expert systems Bio signal and image processing Tele-care and telemedicine and use of remote patient monitoring and patient education Medical education and clinical consultation Healthcare management, public health systems and health promotion Patient education

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Health Care decisions are based on information The figure below presents a typical example of a decision making process in a hospital. Every step of this process produces data and, then, this data is communicated and transformed to useful information.

Services of Health Informatics 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, such as on-line practice guidelines, drug lists, decision-support and reminder systems Examples of Health Informatics applications      

Medication ordering Purchasing equipment Clinical pathways Labour management Patient scheduling Research

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Quality assurance Medical devices Monitors Imaging equipment Clinical decision support Resource allocation

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Risk management Patient assessment Monitoring patients Stock management Mobile health care

Imaging systems in Health Imaging systems in health are impossible without use of computers. Computers are used to:

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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.

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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. Tele health is an expansion of telemedicine. It encompasses preventive, promotive and curative aspects. Today tele health 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. Some important clinical uses of tele health include (i) the transmission of medical images for diagnosis (ii) groups or individuals exchanging real time health services or education live via videoconference (iii) transmission of medical data for diagnosis or disease management (remote monitoring) (iv) advice on disease prevention and promotion of good health by patient monitoring and follow-up. More applications of tele-health

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Distance education including (Continuing medical education and patient education) Administrative meetings through telehealth networks, supervision, and presentations Healthcare system integration Patient movement and remote admission Research

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 three reasons why health informatics is essential for quality healthcare services. 4. Out of the many different roles involved in health informatics, which one do you find more intriguing and why?

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Chapter

2. The Nature of Data in Healthcare

Healthcare is Data-Intensive As we already discussed in Chapter 1, healthcare is a data intensive process. Many processes run at the same time producing new data, literally every single second. Some of these data are high resolution, uncompressed images (x-rays, CT-scans) which take up a lot of storage space and need to be further processed to become clinically useful. Multiple records of same data are often created for each patient and these records are stored and maintained with older measurements and observations. These longitudinal considerations are extremely important for a patient evaluation, since they help clinicians reassess their treatment plan, and make more accurate patient prognoses. Reference data is data that defines the set of permissible values to be used by other data fields. For example, the attribute icd-10 diagnosis uses as reference data a table of 60,000 diagnoses codes one can pick up from. Unfortunately, not all data are drawn from reference data. In many cases, data produced during the clinical care are free text, like in the case of a nursing assessment. Very frequently, data comes from processing of other data: for example, clinicians would want to know the in-hospital mortality ratio for a given disease when they need to provide treatment to a patient with that same diagnosis. In our example the in-hospital mortality ratio will be calculated by the formula NX’ / NX where NX’ = Count of in-hospital deaths for patients with a medical diagnosis x and NX = count of admissions of patients with a diagnosis x. To make our example more interesting, let us now assume that the clinician wants to know if this diagnosis x is a high risk one. In other words we now want to investigate if disease x should lead the clinician to the conclusion of this patient being a high-risk case for in-hospital mortality. For now, we will define high risk disease for in-hospital mortality, as a disease which causes deaths at a higher ratio compared to the death ratio of the whole patient population. The Standardized mortality ratio1 would be calculated by the formula (NX’ / N) / (NA’/NA), where NX’ = Count of in-hospital deaths for patients with diagnosis x NX = Count of admissions of patients with diagnosis x

Standardized Mortality Ratio (SMR) is a ratio between the observed number of deaths in a study population and the number of deaths would be expected, based on the age- and sex-specific rates in a standard population and the age and sex distribution of the study population. 1

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NA’ = Count of in-hospital deaths for all hospital admissions NA = Count of all hospital admissions This information is easily calculated from data which has already been collected and stored into the Electronic Medical Record. The data has been collected retrospectively, for the needs of the clinical care of past patients. The health care professional will therefore be using historical data in order to understand and evaluate the health of a current patient, at a present time. When data is used for purposes other than the ones these have been collected for, we call this use secondary use of data. Discuss in class Which of the following health data use is secondary data use? “Reviewing the five most recent measurements of the blood pressure of a patient, to see if the new medication schema works well” “A afternoon shift nurse reading through the nursing assessment notes of the nurse who just finished her morning shift, for a given patient” “The estimation of bed availability in a specific hospital department, via the count of patient admissions and dates, count of patient discharges and dates, and bed capacity, for that given department” “A warning indicating that a given patient is at risk of developing in-hospital infections, through the analysis of similar case-mixes of past patients”

The Nature of Data in Healthcare We will now discuss about the nature of health data that is generated in a hospital. This section explains the most important categories of data that are produced during the clinical care. Different users, with different roles and responsibilities in a hospital, often have access to these data and utilize them to make decisions. For example, medical doctors would be reviewing the laboratory test and radiology test results, the physical examination, the routine ward measurements, the feedback from the nursing staff as well as the patient history, in order to combine this information with their cognitive skills, knowledge and experience, to assess the health condition of a patient and conclude a diagnosis and an appropriate treatment plan. Prior to the hospital admission: data collected during the triage phase. During the triage phase, health professionals determine the priority of patients' treatments based on the severity of their condition. It result in determining the order and priority of emergency treatment, transport and destination for the patient. Before the hospital admission: demographics, initial evaluation of a patient, source of admission, health insurance information 18

Discussing with the patient and their caregivers: patient demographics, family history, occupational history, allergies, pathology by system, past diseases and surgical operations and information about the social health, all provide to the physician extremely important input for the patient assessment Routine ward measurements: e.g. vital signs (blood pressure, respirations per minute, temperature, pulses per minute), fluid balance (from fluid intake-output) Physical examination: including percussion, observation, auscultation, palpation Laboratory tests: blood tests, urine analysis etc. These data have been ordered by the medical doctor in charge and Laboratory Information System (LIS) receives this order, and as soon as the samples arrive at the hospital laboratories, there is a variable required time for each test to be processed. As soon as this is done, the results are uploaded via the LIS and the Electronic Medical Record would then be updated with the laboratory test result. The physician will then be notified and timely review the results, in order to make informed decisions. Radiology department: medical images, segmentation and handling of images using DICOM systems, assessments from radiologists. Pharmacy: including Rx, (re)-stocking and ordering Patient assessment: medical diagnosis, ordering of laboratory examinations, decisions of the appropriate medication. During a patient hospitalization, there is typically only one primary patient diagnosis. This is the diagnosis which, in the majority of the cases, is considered to be the main reason that led to the decision for a patient admission to the hospital. Secondary diagnoses, are either pre-existing diseases (usually chronic conditions) or were diagnosed during the hospital stay. Tracking down medication: dosage, method of administration (e.g. intravascular, intramuscular) and time-intervals. Nurses will be responsible for the management of the medication administering to the patient, according to the physician guidelines. Discharge data: discharge destination, discharge method, discharge outcome(s). Data produced by the patient: patient experiences surveys have recently become the norm and it is widely recognized that the patient feedback does matter. The “Hospital Consumer Assessment of Healthcare Providers and Systems Survey” (HCAHPS) is the most commonly used patient experiences survey and is widely used by many healthcare providers. The author of this book was member of the working group to adapt the survey to other languages2. Staff records: including but not limited to information about personnel shifts, department capacity, distribution of human resources, hours of leave and other. Squires A, Bruyneel L, Aiken L, Van den Heede K, Brzostek T, Busse R, Ensio A, Schubert M, Zikos D, Sermeus W. Cross-cultural evaluation of the relevance of the HCAHPS survey in five European countries. Int J Qual Health Care. 2012; 24(5):470-5 2

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Hospital Budgeting: resources allocation, revenue projections, planning ahead for the fiscal year. Hospital budgeting is not a trivial process by any means and requires multidisciplinary work of people who know the healthcare market, understand health economics and reimbursement challenges, and health professionals who can foresee the healthcare delivery challenges. Payments: including patient Diagnosis Related Groups (DRGs), insurance information, Medicare or Medicaid information, and any other payer data. A Diagnosis-Related Group (DRG) is a statistical system of classifying any inpatient stay, into groups for the purposes of payment. The DRG classification system divides possible diagnoses into more than 20 major body systems and subdivides them into almost 500 groups for the purpose of Medicare reimbursement3. Hospital Quality of Care Evaluation and Quality improvement data: we will devote a separate chapter for this very important topic, to discuss the strategic goals of the healthcare system, discuss the dimensions of the quality of care and patient safety and strategies for the healthcare system to assess the quality of health care delivery. Hospital Consumer Assessment of Healthcare Providers and Systems Survey (HCAHPS) HCAHPS is a standardized survey instrument and data collection methodology that has been in use since 2006 to measure patients' perspectives of hospital care. A partnership of public and private organizations led by the Federal government, specifically the Centers for Medicare & Medicaid Services (CMS)- Opens in a new window and the Agency for Healthcare Research and Quality (AHRQ)- Opens in a new window, created HCAHPS (pronounced "H-caps") to publicly report the patient’s perspective of hospital care. The HCAHPS results posted on Hospital Compare allow consumers to make fair and objective comparisons between hospitals and with state and national averages on important measures of patients' perspectives of care. The survey asks a random sample of recently discharged adult patients to give feedback about topics like how well nurses and doctors communicated, how responsive hospital staff were to patient needs, how well the hospital managed patients' pain, and the cleanliness and quietness of the hospital environment. Patients are the best sources of information on these topics. Source: Medicare.gov

Data Types in Healthcare It is essential to understand that the data being collected and stored in Electronic Medical Records did not come from typing-in free text in computer textboxes. For the majority of the cases, any data comes from reference data, or in other words data dictionaries, which, as mentioned earlier in this chapter, are predefined lists specifying the acceptable input.

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Gillian I. Russell, Terminology, in FUNDAMENTALS OF HEALTH LAW 1, 12 (American Health Lawyers Association 5th ed., 2011.

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The most obvious example of derived data is that of the medical diagnoses. Since 2014, every healthcare system in the United States started to use the 10th version of the International Classification of Diseases (ICD). This is an enormous list of approximately 68,000 different codes, following a hierarchical organization. Each code represents a medical condition. The doctor needs to decide which code would accurately describe the patient condition, and to select the appropriate ICD-10 code for that condition. There are several levels of depth in ICD-10 and the diagnosis often does not reach the deepest level for a condition. ICD is not the only classification system in use. For the vast majority of in-hospital data, there is existing one or more classification systems, which standardizes the data entry and retrieval process. Chapter 7 will cover some of the most important classification systems and standards which are used by the major health care providers in the United States. Numeric data, such as physiological measurements and laboratory test results are captured and entered into the Electronic Medical Records without any modifications and without any use of reference data. In this section, we will discuss health care data of various data types. Numeric Data Most of numeric data are clinical data “produced” by those directly involved in healthcare. Numeric data allow a much more efficient data manipulation and therefore a more effective use of data to produce aggregated information and make other simple of more advanced calculations. Numbers often come from clinical measurements in hospitals, like, for instance, vital sign measurements. These data have to be entered into the system indicating the exact value of the measurement. Acceptable decimal precision varies, according to the nature of the data and the precision of the measurement device, if used. Many numeric data in everyday practice are derived data. For example, the 24h fluid intake and fluid output are used to calculate the fluid balance of a patient. Laboratory examination results are very often in numeric format. These are often accompanied by the reference normal values. An Electronic Medical Record, nowadays, is expected to have those normal values integrated. Therefore, during data entry, when a laboratory test result for a patient is off bounds, this should be indicated with the use of a different color and potentially a notification should be generated. It should be mentioned, though, that very often, there are different normal value bounds for different age groups and patient gender. This should be taken into account in such implementations, which should automatically recognize the patient demographics and present personalized notifications for a patient, which are in accordance with patient attributes such as gender and age group. Comparison of new examination results with previous results of the current and recent hospital stays is of uttermost importance for health care professionals, so that they can better assess the disease progression, re-evaluate the therapeutic plan and make a more informed assessment of the patient prognosis. It is therefore, important, for comparisons to be made, and the result of these comparisons should be notified to the physician. We will use, in our 21

example hypertension, which is a very common condition and the major risk factor for strokes. A female 77-year-old patient was recently admitted to the hospital for uncontrolled hypertension and was found to have a blood pressure of 210/120 mm Hg. She was therefore admitted as an emergency hypertension case. As soon as the patient was admitted, she received hydralazine IV, and was been monitored with the use of a bedside monitor. Nurses have been checking the monitor every hour and updating the patient record with the blood pressure value. Few days, later the female patient was measured with a 140/85 mmHg blood pressure, which, while still higher than normal, when evaluated with a temporal insight, would indicate a significant improvement. Examples of laboratory tests that produce numeric data are numerous. Numeric data are also often accessed and utilized by professionals indirectly involved in patient care. An example can the number of vacant beds in a hospital department. Again this may be a number which has been calculated from other primary data: ‘Bed capacity’ MINUS ‘Number of patients currently hospitalized’. Some numeric data is used by the hospital quality department or healthcare policy makers. Usually these data are in the form of indicators: for example, the mortality and morbidity during the month June 2012, and the number of cases of infectious diseases divided by the number of patients admitted during a set time period. Boolean Data: Boolean data types in health care can only have two values (usually denoted true and false), which represent the truth values of logic and Boolean algebra. Boolean data should not be confused with categorical data with two categories (for example gendermale/female) Examples of Boolean Data in Healthcare Patient History Related Were there any cases of a specific disease on a family member of the patient? Did the patient undergo a surgical operation in the past? Does the patient receive any drugs? Admission of Patients: Did the patient arrive at the hospital with an ambulance? Was the admission an emergency one? Laboratory Test Results: Existence of SMA Antigens, Glucose found in an Urinalysis Medical Evaluation: Does a patient have a specific symptom? Non-directly related with the patient care: Does the patient have an insurance plan?

Alphanumeric Data: also frequently generated during the healthcare process. In most cases, these are data produced after the “mediation” of the human brain (healthcare professional) and also in most cases during the interaction between the healthcare professional and the patient. Data in healthcare can be images: the medical imaging methods below produce data in the form of images. Medical images are generated by medical imaging devices. Medical 22

images are nowadays digitally produced and stored into the storage device of the computer. Images are usually compressed with simple lossless and near-lossless methods and usually require large storage space. These are the six of the most common radiology tests: (i) (ii) (iii) (iv) (v) (vi)

Radiography Computer Tomography (CT) and the High Resolution Computed Tomography (HRCT) Magnetic Resonance Imaging (MRI) Ultrasound-Mammography Nuclear Medicine Imaging Photo acoustic imaging

The most popular standards that are used to store and transmit medical images, are  PACS (Picture archiving and communication system): it is a medical imaging technology which provides economical storage and convenient access to images from multiple modalities (source machine types)4.  DICOM (Digital Imaging and Communications in Medicine): standard for handling, storing, printing, and transmitting information in medical imaging. It includes a file format definition and a network communications protocol. There are other obvious data in the form of images in some healthcare organizations, like a patient photo which is uploaded into the Electronic Medical Record. The trend is to reduce the use of free text data as much as possible: Information in the form of codes is assigned to each separate concept with significant benefits related to the data quality. The advantages of using classification systems are also significant to researchers, since they can save significant amount of time for data preparation, trying to manually merge descriptions of conditions with different wording (different syntactic) but same meaning (same semantic). Chapter 7 outlines the importance of classification systems and discusses a selection of some critical standards. Four important hospital data procedures and the method of information acquisition Clinical Procedure Method of information acquisition Nursing Evaluation Written nursing assessment, plan and follow-up Diagnosis Combination of a series of practices: clinical measurements, laboratory tests, clinical observation Treatment plan Reviewing the patient condition, and comorbidities, past and current medications that the patient takes and patient allergies Patient History Discussion with the patient and/or his family Various Reports Written analysis of events which are sometimes required by the existing legislation

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Choplin R (1992). "Picture archiving and communication systems: an overview". Radiographics. 12: 127–129.

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Classification of Hospital Data based on their Source The table below outlines examples of data types which have been organized by their source. In other words, the table will provide to you an idea about the location where new data is being generated in a hospital. Discuss the importance of each of those hospital locations and to the quality of healthcare services and how each location contributes to the clinical and administrative decision making Hospital Department -Measurements of Vital Signs -Fluid Intake-Output -Clinical Judgments -Clinical Examination

Medical Imaging Labs -Radiography -Computer Tomography (CT) -Magnetic Resonance Imaging (MRI) -Ultrasound-Mammography

Administrative Department -Number of beds available -Routine and Urgent Admissions -Weekly Swift Plans

Financial Department -Salaries to be paid -Diagnoses Related Group Costing -Reporting to the insurance company

Laboratories -Blood Glucose Test results -Enzyme & Protein Blood test results -Urine test results

Supplies Department -Issuing of order notice -Stock and supplies

Primary methods for clinical data collection 1. The patient or their caregiver provides information to health professionals verbally (i.e. medical history). This information is handwritten, since the clinician keeps notes or competes a structured patient history form which is often digital (via handheld devices and tablets). 2. Physical Examination by the medical doctor and nursing evaluation based on the nursing observation. The physical examination is the process by which a medical professional observes a patient thoroughly for signs, indicating a health conditions. It follows the taking of the medical history. Together with the medical history, the physical examination contributes to determining a diagnosis and treatment plan. 3. Manual, direct measurements of health professionals to patients. Examples include the measurement of blood glucose levels using a stick, blood pressure, respirations per minute measurements, fluid outtake (with a catheter). 4. Laboratory examinations prescribed by clinicians 5. Radiology tests

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The fundamental data acquisition methods (talking with patients, physical examination, clinical measurements, laboratory exams and radiology tests) consist of the main source of data to populate Electronic Medical Records. Typically the patient history precedes the physical examination which precedes the physiological measurements. The physical examination is not a static process; health professionals are challenged to constantly assess, and reevaluate the patient condition.

Derived Data Derived data are data elements derived from other data elements using a mathematical, logical, or other type of transformation, e.g. arithmetic formula, composition, aggregation. Modern Healthcare Information Systems should calculate these data automatically. Derived data are useful for:

  

More efficient patient monitoring To assess the quality of care and patient safety (i.e. morbidity indicators) To assess the health status of populations in regional and national level

Four fundamental unique health care data properties In modern health care systems, where proper use of data is especially important for the provision of quality health services, it is important to understand the existence of four fundamental properties of health care data. These properties are of uttermost importance, in that their existence is a requirement for the clinical data to become meaningful (information) and useful (knowledge). 1. Non-atomicity

2. Cognition

3. Sharability

4. Longitudinality

The above four properties have to be in harmonic co-existence so as to form the basis for a successful clinical decision making environment in health care. There are specific organizational (e.g. interprofessional collaboration) and technical considerations (e.g. decision support systems, interoperability standards) that the health care system has to ensure in order to satisfy these four fundamental health data properties. 25

Non-Atomicity: Each piece of health care data should not be assessed independently: Most of the clinically useful information comes by combining multiple data resources and by evaluating this combined information with the clinical knowledge of a health professional. A typical scenario involves a medical doctor who puts together and combines the physical examination, laboratory test result and patient history data, to make a diagnosis. Glucose levels of 125 mm Hg are assessed differently when combined with different demographics information: for some 23 year old patient with Type I diabetes, this is considered a normal value, but this would not be the case for a non-diabetic person.

Health care professionals should, therefore, have at their disposal, tools which provide easy access to patient data and generate reports summarizing all the clinical information that is available for a patient, at any given point of the health care provision (e.g. patient history, clinical observations, laboratory test results, medical imaging). Cognition: Health care data should be assessed with human cognitive skills. Differential diagnosis and other cognitive procedures based on knowledge and skill-sets of health professionals are always crucial when new data about a patient becomes known and needs to be assessed. Clinicians acquire medical skills and knowledge and a have a dynamic understanding on how the information they have in their hands can direct them towards specific clinical decisions. This cognitive process is systematic and varies across different categories of health care professionals. Physicians perform differential diagnosis that is the process of differentiating between two or more conditions that share similar signs or symptoms, while, for nursing practitioners, the nursing diagnosis is a clinical judgment about individual, family, or community responses to the health problem. Medical education and continuing professional development are important success factors for this dimension. Shareability: Health care data should be shared across the healthcare system and between different health care professionals, to become more meaningful. No health professional should ever act in an introverted manner within the healthcare system in that respect. 26

An MRI test cannot be solely assessed by the radiologist, but should be shared with the physician who is going to review the MRI to make informed decisions about the patient. One of the most important requirements to seamlessly share data is to achieve a highly interoperable environment. Business, technical and information interoperability, are all invaluable requirements for the fundamental shareability property. Interoperability is the ability of a system to work with other systems without special effort on the part of the health professionals: data should be exchanged across the health care system seamlessly. Health Level 7 is the most important interoperability standard nowadays, and addresses the business, technical and information dimension of interoperability. Interprofessional collaboration is also crucial, since it is not always sufficient for the information to be inserted into the records. Often, health professionals need to discuss to understand qualities of the observations and exchange their insight on the condition of a patient. Longitudinality: Health care data should be assessed with a longitudinal insight. The progression of a disease is not linear, neither are the therapy outcomes. In addition many of the health care procedures are repeated during the course of a patient hospitalization (e.g. measurement of vital signs, blood tests). When these data are reviewed, health care professionals need to recognize any longitudinal changes and patterns over time and assess the disease progression and treatment effectiveness. There are many tools available that can be used to visualize data. Nurses do not need to complete manual charts of the vital signs, since these are auto generated from the data. Longitudinal data can form the basis for predictive modelling of the patient outcomes and the effectiveness of medical treatments. Morning blood glucose levels of 135 mm Hg would seem to be elevated for a given patient, but the clinician would not worry if, for that patient, five preceding daily higher measurements, showed steadily decreasing blood glucose levels day by day. The image on the left shows the blood pressure, pulse rate and pulse pressure record plots of a patient for one month. The physician see that there are quite a few days, during the month where the blood pressure was elevated, and there is a pattern of high blood pressure waves with peaks and lows Source: raywinstead.com/bp

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The four health data properties, namely non-atomicity, cognition, sharability and longitudinality are not unique to healthcare, but their existence is undeniable, and indeed, all four appear in co-existence in virtually any clinical health care environment. Hospital administrators and policy makers can prioritize the organizational and technical requirements that need to be built around, and support these fundamental properties, crucial to the clinical decision making process and ultimately to the quality of health care services.

The information exchange is non-stop across the healthcare system; patients, being the main source of health data are placed in the center of the care

The temporal and spatial nature of health data Healthcare data is of temporal and spatial nature and this has a huge impact to the analysis of a patient case and the treatment plan. The temporal nature of health data is determined by scale of various repeated measurements. The continuous monitoring and reassessment of patients plays a major role and the evaluation of the disease progress over time is in clinician’s everyday schedule. Medication and self-management of a condition for a chronic patient, as well as during rehabilitation sessions, also requires similar considerations to be made. Geography also plays an important role in healthcare with regard to the understanding of various causes in health dynamics. Geographical Information Systems (GIS) are mainly used in public health. The trinity of public health, which is constituted by the individual living inside an interactive environment underlines the importance of geography as far as health and illness are concerned. GIS system can support public health policy through: 28

 The development of map-based applications which visualize health related parameters  Providing location-based information about diseases  Identifying spatial correlations that exist in the data and that can help inform a public policy decision

 Identifying possible relationships that influence the health status within the population Look at the plot on the left and discuss the importance of a longitudinal insight into healthcare observations, for successful clinical decisions. Provide three examples, to indicate the importance of time for the: (i) population surveillance (ii) hospital health care (iii) tertiary care/ patient rehabilitation

Questions for discussion 1. What types of data does a nurse produce during the healthcare practice? What types of data does a nurse need to retrieve from the Electronic Medical Records to make informed decisions? 2. An important attribute of healthcare data is ‘longitudinality’. Why is this property important for decide what would be the optimal treatment plan for a patient? 3. Explain why the entry of a patient diagnosis into an Electronic Medical Record, is considered reference data.

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The author Dimitrios Zikos is an assistant professor at the Central Michigan University. He holds a Bachelor of Science degree in Nursing and a Master’s and Ph.D. degree in Health Informatics, University of Athens, Greece. His research involves clinical and administrative informatics, clinical statistics and data mining, healthcare delivery and clinical care policies for decision making. In 2013, he arrived at the United States as a visiting professor to help the University of Texas at Arlington develop new curricula in Health Informatics and supervised undergraduate and graduate students in health analytics projects. Dr. Zikos has been investigator of national and regional funded projects and co-organizer of an NSF-sponsored annual conference PETRA (Pervasive Technologies Related to Assistive Environments). While overseas, he was researcher and project manager in large scale European Union multi-country e-health projects and partner with an accreditation and research center in Greece for the European Union Network for Patient Safety. This research resulted in translational guidelines for health professionals and the public on patient safety. He served as executive committee member of the Greek Nursing Studies Association (2010–2012) and was an external collaborator of the Centre for Health Services Management & Evaluation (CHESME). Dr. Zikos has also served as visiting faculty of a nursing degree simulation program, which was organized by the Cyprus University of Technology, where he taught health informatics courses. He is the author of numerous peer-reviewed journal and conference papers and reviewer for numerous journal and conference contributions.

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