Anesthesia Information Management Systems - Wiley Online Library

MOUNT SINAI JOURNAL OF MEDICINE 79:154–165, 2012

154

Anesthesia Information Management Systems: Past, Present, and Future of Anesthesia Records Bassam Kadry, MD,1 William W. Feaster, MD, MBA,1 Alex Macario, MD, MBA,1 and Jesse M. Ehrenfeld, MD, MPH2 1

Stanford University School of Medicine, Stanford, CA 2 Vanderbilt University, Nashville, TN

OUTLINE ORIGINS OF ANESTHESIA RECORD BENEFITS OF ANESTHESIA INFORMATION MANAGEMENT SYSTEMS AS PUBLISHED IN PEER-REVIEWED JOURNALS ANESTHESIA INFORMATION MANAGEMENT SYSTEMS’ INFLUENCE ON PERIOPERATIVE DOCUMENTATION AND PATIENT COMMUNICATION HEALTHCARE INFORMATION TECHNOLOGY AND DOWNSIDE OF POOR SYSTEMS DESIGN CONSUMER TECHNOLOGIES: RAISING EXPECTATIONS FOR ANESTHESIA INFORMATION MANAGEMENT SYSTEMS ANESTHESIA INFORMATION MANAGEMENT SYSTEMS: HELPING CLINICIANS CONVERT DATA INTO KNOWLEDGE UTILIZING ANESTHESIA INFORMATION MANAGEMENT SYSTEMS FOR COMPARATIVE EFFECTIVENESS AND TRANSLATIONAL RESEARCH CHALLENGES OF USING ANESTHESIA INFORMATION MANAGEMENT SYSTEMS DATA FOR ANALYSIS CONCLUSION

Address Correspondence to: Jesse Ehrenfeld Center for Evidence-Based Anesthesia Vanderbilt University Nashville, TN Email: [email protected]

Published online in Wiley Online Library (wileyonlinelibrary.com). DOI:10.1002/msj.21281 © 2012 Mount Sinai School of Medicine

ABSTRACT Documenting a patient’s anesthetic in the medical record is quite different from summarizing an office visit, writing a surgical procedure note, or recording other clinical encounters. Some of the biggest differences are the frequent sampling of physiologic data, volume of data, and diversity of data collected. The goal of the anesthesia record is to accurately and comprehensively capture a patient’s anesthetic experience in a succinct format. Having ready access to physiologic trends is essential to allowing anesthesiologists to make proper diagnoses and treatment decisions. Although the value provided by anesthesia information management systems and their functions may be different than other electronic health records, the real benefits of an anesthesia information management system depend on having it fully integrated with the other health information technologies. An anesthesia information management system is built around the electronic anesthesia record and incorporates anesthesia-relevant data pulled from disparate systems such as laboratory, billing, imaging, communication, pharmacy, and scheduling. The ability of an anesthesia information management system to collect data automatically enables anesthesiologists to reliably create an accurate record at all times, regardless of other concurrent demands. These systems also have the potential to convert large volumes of data into actionable information for outcomes research and quality-improvement initiatives. Developing a system to validate the data is crucial in conducting outcomes research using large datasets. Technology innovations outside of healthcare, such as multitouch interfaces, near-instant software response times, powerful but simple search

MOUNT SINAI JOURNAL OF MEDICINE

capabilities, and intuitive designs, have raised the bar for users’ expectations of health information technology. Mt Sinai J Med 79:154–165, 2012. © 2012 Mount Sinai School of Medicine Key Words: anesthesia information management systems, electronic medical record, operating room management, outcomes research.

155

understand for those not accustomed to its particular layout. Documentation of a patient’s anesthetic is quite different from the documentation associated with an office visit or surgical procedure. Ultimately, the goal of the anesthesia record is to capture a patient’s response to anesthesia and surgery by recording the procedures, physiologic changes, key events, and pharmacologic administration that occur throughout the perioperative period.

Although the traditional paper anesthesia record has a unique format and structure that is generally accepted by anesthesiologists, it may be challenging to

The goal of the anesthesia record is to capture a patient’s response to anesthesia and surgery by recording the procedures, physiologic changes, key events, and pharmacologic administration that occur throughout the perioperative period. A

ORIGINS OF ANESTHESIA RECORD The paper anesthesia record has not changed fundamentally since its earliest inception in the 1890s by Dr Harvey Williams Cushing and Dr Ernest Amory Codman (Figure 1).1,2 At the time, they documented all the signs they could measure: pulse, temperature, and respirations. Their instinct to quantify their efforts to help manage the patient is an important principle–optimal management depends on accurate measurement.3 – 5 Since then, the anesthesia record has evolved to incorporate physiologic metrics measured by newer technologies (eg, pulse oximetry, end-tidal carbon dioxide [CO2 ]). However, the B

Fig 1. Ether chart and a modern-day anesthetic record. (A) The original Harvey Cushing Ether Chart from 1895. (B) Modern-day paper anesthesia record. Note the similarities in the metrics measured and in the layout of the 2 documents. (Harvey Cushing Ether Chart courtesy of Massachusetts General Hospital Archives and Special Collections.) DOI:10.1002/MSJ

156

B. KADRY ET AL.: ANESTHESIA INFORMATION MANAGEMENT SYSTEMS

original format remained unchanged: a longitudinal record of physiologic and pharmacologic data along with fundamental patient demographic information, anesthetic procedures, and key surgical or anesthetic events. There is no other clinical setting in which such an abundance of physiologic and pharmacologic data is collected minute-to-minute. The hyperacute perioperative environment necessitates such attention to detail, given that a patient’s clinical condition can change within seconds. As with all patient data collection, the sampling frequency of physiologic data is dependent on the rate of change of the patient’s clinical condition. The more critical the condition, the more important it is to have a higher sampling frequency of meaningful data such as vital signs, key laboratory values, or neurologic data (eg, pupil size).

There is no other clinical setting in which such an abundance of physiologic and pharmacologic data is collected minute-to-minute. Often during anesthesia, providers do not have sufficient time to confirm a diagnosis prior to making a management decision. For example, if a patient has

Having ready access to physiologic trends (ie, a sudden decrease in saturation of peripheral oxygen coupled with an increase in peak airway pressures) and metrics is essential to allowing anesthesiologists to make proper diagnoses and treatment decisions. an intraoperative pneumothorax, there may not be enough time to obtain a chest radiograph to confirm the diagnosis. The anesthesiologist must assess increased airway pressures, tachycardia, hypotension, and decreased breath sounds and proceed with chesttube insertion if needed. Making the right treatment decision depends on making the correct diagnosis through clinical interpretation of the available data. Therefore, having ready access to physiologic trends (ie, a sudden decrease in saturation of peripheral oxygen [SpO2 ] coupled with an increase in peak airway pressures) and metrics is essential to allowing DOI:10.1002/MSJ

anesthesiologists to make proper diagnoses and treatment decisions. In addition to serving as official documentation of the anesthetic provided, the anesthesia record is designed to illustrate trends graphically, thereby making changes over time more obvious to the clinician. Anesthesia providers routinely monitor the following continuous and frequently obtained metrics and waveform data: • • • • • • • • • • • •

Heart rate. Pulse oximetry. Blood pressure. Electrocardiogram. End-tidal CO2 . Respiratory rate. Airway pressure. Tidal volume. Temperature. Volatile anesthetic concentration. Central venous pressure. Pulmonary artery pressure.

Although the paper anesthesia record has served well as a structure to temporally capture large amounts of physiologic, pharmacologic, and procedure data, it has a number of limitations3,6 – 8 : • Recall bias occurs because the anesthesiologist cannot simultaneously write down the vital signs and deliver care to the patient. This limits the record’s accuracy and can result in smoothing of vital-sign data. • Data can only be queried for outcomes research or quality-assurance purposes through manual chart review. • Records are often illegible or difficult to read. • Records may be lost or be otherwise inaccessible. • Incomplete documentation can limit charge capture and billing opportunities. • Less medicolegal protection. Anesthesia information management systems (AIMS) solve many of these problems. An AIMS interfaces with multiple systems, but at its core is the automated electronic anesthesia record. An automated anesthesia record allows for the collection of physiologic data on a longitudinal record. An AIMS is built around the electronic anesthesia record and incorporates anesthesia-relevant data pulled from disparate systems within the hospital enterprise, such as laboratory, billing, imaging, communication, pharmacy, and scheduling systems. In addition to serving as an information hub for anesthesia providers, it allows for the documentation of key


perioperative events (eg, ‘‘incision,’’ ‘‘aortic crossclamp,’’ or ‘‘end of procedure’’) to ultimately reflect the patient’s entire procedural course.

An anesthesia information management system is built around the electronic anesthesia record and incorporates anesthesia-relevant data pulled from disparate systems within the hospital enterprise, such as laboratory, billing, imaging, communication, pharmacy, and scheduling systems. The documentation of physiologic metrics by computers has been utilized since the 1970s.9 The most obvious benefit of an automated anesthesia record is the ability to free up the anesthesia provider from manually transcribing physiologic and ventilator data.10 Not only is the quality of data more accurate,

The most obvious benefit of an automated anesthesia record is the ability to free up the anesthesia provider from manually transcribing physiologic and ventilator data. The ability of an anesthesia information management system to collect data automatically enables anesthesiologists to reliably create an accurate record at all times, regardless of the concurrent demands on the provider. but also it can be seamlessly recorded in real time during clinically sensitive times such as induction, emergence, resuscitation, and stabilization. Manual recall and documentation of physiologic metrics on paper records is inaccurate.11 – 14 Furthermore, during times of clinical instability, requirements for patient care often prohibit the manual transcription of data. However, the ability of an AIMS to collect data automatically enables anesthesiologists to reliably create an accurate record at all times, regardless of the concurrent demands on the provider. Finally, this more accurate and complete measurement in many

157

cases results in better management. This is true for both the individual care of patients as well as the management of entire operating suites. As a result, AIMS have gained significant importance because of their potential to improve operating-room efficiency, quality, and safety.6,15 – 18

BENEFITS OF ANESTHESIA INFORMATION MANAGEMENT SYSTEMS AS PUBLISHED IN PEER-REVIEWED JOURNALS Anesthesia information management systems have been shown to have benefits in 7 key areas: • • • • • • •

Improved Improved Improved Improved Improved Improved Improved

cost containment. operations management. reimbursement. quality of care. safety. translational research. documentation.

Each benefit depends on the particular perspective of the AIMS end user. The most obvious benefit to frontline clinicians is the automatic collection of vital signs, so that the anesthesiologist can focus on patient care during sensitive times such as induction, emergence, and stabilization. To a departmental manager, an AIMS can help improve operations, scheduling, staffing, or even billing collections. For a qualityimprovement officer, an AIMS can serve as a robust platform for continuous quality improvement. Finally, the contemporaneous and objective physiologic data captured by an automated anesthesia record may provide a more credible medicolegal defense than a handwritten record generated by the anesthesia provider after the fact when an adverse outcome has occurred. Table 1 outlines the potential benefits of AIMS as demonstrated in the peer-reviewed literature.

ANESTHESIA INFORMATION MANAGEMENT SYSTEMS’ INFLUENCE ON PERIOPERATIVE DOCUMENTATION AND PATIENT COMMUNICATION A well-designed preoperative assessment tool is another important aspect of anesthesia care. Not only does it link preoperative anesthesia documentation DOI:10.1002/MSJ

158


Table 1. Potential Benefits of AIMS by Area. Improved cost containment16,48,49 Decrease drug cost/utilization More accurate accounting of anesthesia supplies and medications Tool for controlling resource management in the operating room Improved operations management10,50 Decrease workload on billing personnel when reviewing anesthesia records Improve anesthesia department’s administrative role in the perioperative setting Improve staff scheduling Generate a real-time surgical whiteboard to improve situational awareness Facilitate reductions in staffing costs OR modeling for administrative decision support Improved reimbursement10,50,51,52,67 Enhance anesthesia billing and charge capture Increase hospital reimbursement Merge financial systems with clinical documentation to gain efficiencies Improved quality of care12,52 – 61 Automatic risk calculation and anesthetic management recommendations Artificial intelligence to prevent adverse intraoperative events Facilitate implementation and adherence to departmental protocols Implement evidence based medicine (e.g. adherence to clinical guidelines for antibiotic prophylaxis, administration of beta blockade, venous thromboembolism prophylaxis) Tool to provide point-of-care clinical decision support Can provide timely clinical feedback to impact clinical behavior Improved safety6,62 – 66 Automatic notification of or location errors Development of drug diversion surveillance Help avoid blood transfusion reactions Enhance situational awareness Improved translational research44 – 46,53,68 Helps develop evidence-based medicine guidelines from data sets of empiric clinical practice Link intraoperative data to outcomes data (e.g. National Surgical Quality Improvement Program) Share data through national research consortiums (e.g. the Multicenter Perioperative Outcomes Group or Anesthesia Quality Institute) Improved documentation7,10,13,50,52,69 More accurate capture of clinical data Automatic real-time notification of missing documentation entries Enhanced legal fortification Creation of more legible anesthetic records Support risk management activities

to a patient’s electronic health record (EHR), but the preoperative assessment of the patient and the communication of this assessment to other providers through a preoperative note is also vital to administering a safe anesthetic. If anesthesia providers have to spend time accessing different data sources and clinical information systems and rely on manually transcribing information from one computer system into another, then the likelihood of achieving increased quality and efficiency in this process is compromised. Having an anesthesiologist spend more time entering preoperative information into the computer than participating in face-to-face discussions with the patient is not reassuring for a nervous patient awaiting surgery. A formidable challenge that anesthesiologists face is establishing trust with a new patient in a short period of time in a stressful DOI:10.1002/MSJ

environment. Patients are scared, and a thoughtful discussion of the anesthetic plan and options can serve as an anxiolytic and may also help thwart legal action in the event of an adverse outcome.19,20 Bedside computer terminals may be convenient for data entry, but if they are improperly used, there is risk of diluting the communication experience.21 – 23

Bedside computer terminals may be convenient for data entry, but if they are improperly used, there is risk of diluting the communication experience. Simple modifications to workflow and welldesigned documentation tools can help overcome this challenge. For example, using an AIMS it


is possible to automatically send preoperative information to the anesthesia provider assigned to the case the night before. Another possibility is to use mobile devices to enhance acquisition and transfer of patient information. This is helpful, because one of the easiest methods anesthesiologists can use to help alleviate patient anxiety is to convey prior knowledge of the patient’s history during the initial encounter, and this can be facilitated by an AIMS.24 – 26

HEALTHCARE INFORMATION TECHNOLOGY AND DOWNSIDE OF POOR SYSTEMS DESIGN The move to adopt ‘‘paperless’’ hospital information systems that create an EHR is prevalent in today’s healthcare environment. However, the information overload that these electronic records can generate can also create an obstacle to achieving the goal that the EHR was intended to address in the first place: to improve the quality of care and patient safety.27,28 Furthermore, increased regulation to improve quality has paradoxically taken valuable resources away from direct patient care and shifted them toward fulfilling administrative mandates to be considered compliant with regulatory bodies. The preoperative anesthesia note can suffer the same fate as other electronic documents within the patient’s chart. Even though healthcare information technology (HIT) is proposed to be a solution to an inefficient paper-based system, the reality is that one computer keystroke can generate volumes of clinically irrelevant information. Scrolling for pages on a computer screen that takes a few minutes to log into in a time-sensitive environment can be as much of a barrier to achieving quality care as sifting through piles of paper. At least handwritten notes, when legible and available, focus on pertinent information and can be written immediately. This problem is exacerbated when the electronic chart has to be printed for transfer of care to another clinician, service, or institution. Interoperability in theory is able to address this issue28 ; however, very few systems today are truly interoperable. Nonetheless, if HIT is to succeed, especially in the area of provider documentation, it must improve the natural clinical workflow and take into account human factors that influence access to clinically relevant information. The hope is that HIT will ultimately enable clinicians to make better decisions at the point of care by having faster, easier, and more complete access to pertinent patient information. However, simply being

159

paperless does not equate to providing more efficient access to information.

Scrolling for pages on a computer screen that takes a few minutes to log into in a time-sensitive environment can be as much of a barrier to achieving quality care as sifting through piles of paper. Thus, being paperless does not equate to providing more efficient access to information. CONSUMER TECHNOLOGIES: RAISING EXPECTATIONS FOR ANESTHESIA INFORMATION MANAGEMENT SYSTEMS A usability gap exists between HIT and technology in other industries.29 – 35 Human-factors engineering takes into account human capabilities in the design of a new technology.36 – 38 The discipline is building traction in the technology industry, where the consumer experience defines the success of the product or service. Novel innovations such as multitouch interfaces, near-instant software response times, powerful but simple search capabilities, and intuitive designs have raised the bar for users’ expectations of technology. As AIMS evolve, it is likely that this gap will narrow and more robust functional elements will be adopted. The challenge will be integrating various technologies into the overall AIMS architectures. Some future AIMS enhancements may include: • Automatic escalation of care notifications for extremes in physiologic metrics. • Automatic customized reports of outcomes of interest to authorized clinicians. • Automatic risk stratification of patients based on known risk factors. • Automatic quality-assurance screening and reporting. • Use of color, text size, and stylized text features to distinguish computer-populated data from information manually entered by clinicians. • Dynamic user interfaces that adjust based on data inputs. • More usable output of printed anesthetic records. • Easy-to-use Web-based data warehouse queries by authorized clinicians. DOI:10.1002/MSJ

160


Knowledge Pyramid

Fig 2. Knowledge Pyramid illustrates how clinical data is converted into actionable information to treat a life-threatening intraoperative pneumothorax. Abbreviations: ACS, acute coronary syndrome; BP, blood pressure; Ddx, differential diagnosis; HR, heart rate; O2 Sat, oxygen saturation; PAP, peak airway pressure; PTX, pneumothorax; RR, respiratory rate.

• Informed consent applications that can be accessed by patients at home or from bedside terminals. • Integration of AIMS with hospital communication systems. • Provision of digital files of anesthetic care that can be uploaded into a personal health record. • Real-time cost tabulation of anesthetic case. • Real-time handheld remote monitoring and reporting. • Real-time location systems for tracking of patients, clinicians, and equipment. • Web-based preoperative interview applications.

ANESTHESIA INFORMATION MANAGEMENT SYSTEMS: HELPING CLINICIANS CONVERT DATA INTO KNOWLEDGE Converting data into action is a process that has been extensively studied in other information-science disciplines.18,39 – 43 Clinicians utilize the principles behind the ‘‘Knowledge Pyramid’’ when deriving a differential diagnosis and management strategy. Figure 2 illustrates how this principle is used to manage an intraoperative pneumothorax. Data are the foundation for knowledge. However, data by definition are without context and DOI:10.1002/MSJ

are only useful if they are converted to information. Knowing how to use the information depends on appropriate knowledge structure. Ultimately, with

Data by definition are without context and are only useful if they are converted to information. experience, wise users of information know how to use data efficiently and effectively. So in the example of a pneumothorax (Figure 2), one can see how the data elements can be used to first assess the clinical situation, then determine a diagnosis of highest likelihood and initiate appropriate treatment. In anesthesia, the process of converting data into knowledge is continuous and is the basis of vigilant anesthetic care. Often, data are collected and in the process more data are needed to determine whether a patient is safe or in danger. For example, in Figure 3, the patient’s heart rate is 135 beats per minute with a systolic blood pressure of 65 mm Hg. While this may seem ominous, a key piece of data is missing: the patient’s age. This example illustrates the importance of each data element. In particular, it shows how knowledge and wisdom help refine which data elements are pertinent or irrelevant when determining an action plan. As EMRs become more prevalent, the new challenge will be converting


161

Fig 3. Utilizing data to drive action. Abbreviations: DBP, diastolic blood pressure; ETCO2 , end-tidal carbon dioxide; ETSev, end-tidal concentration of sevoflurane; HR, heart rate; PAWP, peak airway pressure; Sat%, saturation percentage; SBP, systolic blood pressure; Vt, tidal volume.

large volumes of data into meaningful information. Ultimately, the approach to doing so will utilize the same principles behind the Knowledge Pyramid.

UTILIZING ANESTHESIA INFORMATION MANAGEMENT SYSTEMS FOR COMPARATIVE EFFECTIVENESS AND TRANSLATIONAL RESEARCH The wealth of data stored in AIMS can offer insight into the efficacy of perioperative care, and several national efforts have attempted to leverage this capability. The American College of Surgeons recognized this potential and partnered with the Anesthesia Patient Safety Foundation to study the feasibility of including perioperative AIMS data into the American College of Surgeons National Surgical Quality Improvement Program.44 These data would link the preoperative patient risk factors, surgical factors, and adverse events following surgery. The technical challenge of communicating both datasets is profound. Linking both records depends on using interoperability standards such as Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT) and Health Level 7 Working Group for Generation of Anesthesia Standards (HL7 WG GAS), as demonstrated by Walsh et al.44 The Multicenter Perioperative Outcomes Group based at the University of Michigan aggregates deidentified AIMS data from multiple institutions to create a powerful research database.45,46 The American Society of Anesthesiologists, through

its Anesthesia Quality Institute (AQI), has also created a data warehouse to collect anesthesia data and claims data, though often currently derived from billing claims data and paper medical records rather than from an AIMS. Both organizations are working together to import AIMS data directly into the AQI data warehouse. The benefit of aggregated AIMS data, as opposed to other clinical data, is its accuracy, granularity, and sheer volume. Such efforts to combine AIMS data from across institutions can help utilize the large volumes of available data to determine targets for future research. The difficulty is delineating which anecdotal methodologies are efficacious. The discovery process is resource-intensive and time-consuming. Moreover, research funding is becoming more limited. Therefore, organizations like the Multicenter Perioperative Outcomes Group or the AQI that create tools to assess relationships through queries of large datasets can guide researchers to targets worthy of exploration. The quicker the process, the faster anecdotal information can be proven ineffective or be incorporated as evidence-based medicine.

CHALLENGES OF USING ANESTHESIA INFORMATION MANAGEMENT SYSTEMS DATA FOR ANALYSIS There are several limitations of using any large dataset for robust analytics. Sometimes important information may be hard to quantify and is consequently DOI:10.1002/MSJ

162


overlooked.47 For data that are measured, confirming their validity is important, because decisions based on inaccurate data can have bad consequences. Creating a system of checks and balances to purge false data is essential to minimize poor data analysis. For example, if a pulse oximetry reading is 75% but the pulse oximetry waveform is poor, blood pressure is normal, end-tidal CO2 is normal, and there is no change in ventilation, then it is very likely that the pulse oximeter reading is artifactual 1. Whereas in a clinical situation this seems obvious, to a research analyst without the benefit of a clinical context it may not. Developing a system to validate data is therefore an important step in conducting outcomes research using large datasets. Another significant challenge for large datasets is that the distribution of data should ideally represent the population at large, or at least the population of interest.

Developing a system to validate data is an important step in conducting outcomes research using large datasets. Another significant challenge for large datasets is that the distribution of data should ideally represent the population at large, or at least the population of interest. The validation process may take time but is important for further studies to be conducted using a subset of the data. For example, the distribution of the age of patients in the dataset should resemble the distribution of the entire population. Otherwise, the application of new insights as to how to optimally treat patients as derived from the dataset may not be applicable to the population at large. Confirming that the dataset is valid will help assess the generalizability of outcomes research using large datasets. Identifying thresholds or definitions is very challenging when conducting retrospective outcomes research. For example, troponin levels are a good indicator of a myocardial event; but intraoperatively, anesthesiologists do not typically use this information because they must act immediately. Anesthesiologists often do not have the time to wait for a laboratory value, but they do have echocardiograms and other vital signs that can be used to establish a diagnosis and guide therapy. However, troponin levels are often used to quantify whether or not DOI:10.1002/MSJ

Table 2. Principles for Meaningful Outcomes Research70,71 Comprehensive data collection Validation of data Clear definition of outcomes of interest (defined numerators) Comprehensive collection of entire population (verified denominators) Accurate relational mapping Logical and reproducible analysis

a patient had a myocardial event. Therefore, an intraoperative myocardial event may be difficult to ‘‘catch’’ retrospectively using an AIMS record exclusively. More creative means to ‘‘catch’’ an event may depend on a temporal mapping of heart rate, blood pressure, electrocardiographic changes, beta-blocker or nitroglycerin administration, or cardiology consultation in recovery. Moreover, the integration of the AIMS record with data from an inpatient laboratory system might help link a surgery with a myocardial event by evaluation for increased troponin levels in the postoperative period. Anesthesia information management systems that are part of a complete enterprise suite offer a compelling value proposition due to their ability to capture and analyze data across all care settings. Furthermore, even among experts, reaching consensus on specific definitions is difficult. This makes it difficult when analyzing large datasets, because specific thresholds need to be identified. Meaningful outcomes research requires agreement on the process of data collection, data validation, and clear definitions of outcomes, as shown in Table 2.

Meaningful outcomes research requires agreement on the process of data collection, data validation, and clear definitions of outcomes.

CONCLUSION Although the paper record is relatively easy to use, the potential value brought by an AIMS moves far beyond the simple automation of the paper anesthesia record. To achieve all of the potential benefits of the AIMS, however, it cannot exist as a stand-alone system. It must either be integrated within an EHR or be able to automatically and bidirectionally communicate with one. The expansion of HIT without interoperability will likely convert inadequate paper documentation into more


legible, but still incomplete and ineffective, digital documentation. In the future, AIMS will likely be more intuitive to use, interoperable with hospitalbased EHRs, and incorporate useful tools such as decision support. These advances will likely reduce barriers to AIMS adoption and accelerate their adoption.

163

15.

16.

DISCLOSURES

17.

Potential conflict of interest: Nothing to report. 18.

REFERENCES 1. Bruce SS, Bruce JN. Harvey Cushing, neurosurgical pioneer. Curr Surg 2005; 62: 138–140. 2. Molnar C, Nemes C, Szabo S, et al. Harvey Cushing, a pioneer of neuroanesthesia. J Anesth 2008; 22: 483–486. 3. Blumenthal D. The future of quality measurement and management in a transforming health care system. JAMA 1997; 278: 1622–1625. 4. Cerese J. Leveraging performance measurement and management: the quality and efficiency edge. Top Health Inf Manage 2001; 22: 73–78. 5. Kimberly JR. Assessing ‘‘quality’’ in health care: issues in measurement and management. Int J Qual Health Care 1997; 9: 161–162. 6. Balust J, Macario A. Can anesthesia information management systems improve quality in the surgical suite? Curr Opin Anaesthesiol 2009; 22: 215–222. 7. Ehrenfeld JM. Anesthesia Information Management Systems: a guide to their successful installation and use. Anesthesiol News 2009; 9: 1–7. 8. Vigoda MM, Lubarsky DA. Failure to recognize loss of incoming data in an anesthesia record-keeping system may have increased medical liability. Anesth Analg 2006; 102: 1798–1802. 9. Daub D, Destunis S, Halbach M, et al. First experiences with a documentation system via display terminals. Acta Anaesthesiol Belg 1975; 23(suppl): 200–204. 10. Vigoda MM, Feinstein DM. Anesthesia information management systems. Adv Anesth 2008; 26: 121–136. 11. Benson M, Junger A, Fuchs C, et al. Using an anesthesia information management system to prove a deficit in voluntary reporting of adverse events in a quality assurance program. J Clin Monit Comput 2000; 16: 211–217. 12. Junger A, Hartmann B, Benson M, et al. The use of an anesthesia information management system for prediction of antiemetic rescue treatment at the postanesthesia care unit. Anesth Analg 2001; 92: 1203–1209. 13. Reich DL, Kahn RA, Wax D, et al. Development of a module for point-of-care charge capture and submission using an anesthesia information management system. Anesthesiology 2006; 105: 179–186. 14. Sanborn KV, Castro J, Kuroda M, et al. Detection of intraoperative incidents by electronic scanning

19.

20.

21.

22.

23.

24.

25.

26.

27.

28.

29.

30.

of computerized anesthesia records: comparison with voluntary reporting. Anesthesiology 1996; 85: 977–987. Benson M, Junger A, Fuchs C, et al. Use of an anesthesia information management system (AIMS) to evaluate the physiologic effects of hypnotic agents used to induce anesthesia. J Clin Monit Comput 2000; 16: 183–190. Coleman RL, Sanderson IC, Lubarsky DA. Anesthesia information management systems as a cost containment tool. CRNA 1997; 8: 77–83. Eden A, Grach M, Goldik Z, et al. The implementation of an anesthesia information management system. Eur J Anaesthesiol 2006; 23: 882–889. Ehrenfeld JM, Epstein RH, Bader S, et al. Automatic notifications mediated by anesthesia information management systems reduce the frequency of prolonged gaps in blood pressure documentation. Anesth Analg 2011; 113: 356–363. Hickson GB, Jenkins AD. Identifying and addressing communication failures as a means of reducing unnecessary malpractice claims. N C Med J 2007; 68: 362–364. Huntington B, Kuhn N. Communication gaffes: a root cause of malpractice claims. Proc (Bayl Univ Med Cent) 2003; 16: 157–161. McCord G, Pendleton BF, Schrop SL, et al. Assessing the impact on patient-physician interaction when physicians use personal digital assistants: a Northeastern Ohio Network (NEON) study. J Am Board Fam Med 2009; 22: 353–359. Anderson JG, Rainey MR, Eysenbach G. The impact of CyberHealthcare on the physician-patient relationship. J Med Syst 2003; 27: 67–84. Rouf E, Whittle J, Lu N, et al. Computers in the exam room: differences in physician-patient interaction may be due to physician experience. J Gen Intern Med 2007; 22: 43–48. Klafta JM, Roizen MF. Current understanding of patients’ attitudes toward and preparation for anesthesia: a review. Anesth Analg 1996; 83: 1314–1321. Nuebling M, Kindler CH, Langewitz W. Preoperative visits in anesthesia: communication patterns and sequences: application of an explorative analysis. Patient Educ Couns 2004; 54: 379–381. Orliaguet G. Preoperative information for paediatric patients: the anaesthesiologist’s point of view [article in French]. Ann Fr Anesth Reanim 2006; 25: 451–457. Boyer L, Renaud MH, Limousin S, et al. Perception and use of an electronic medical record system by professionals of a public psychiatric hospital [article in French]. Encephale 2009; 35: 454–460. Van Vleck TT, Stein DM, Stetson PD, et al. Assessing data relevance for automated generation of a clinical summary. AMIA Annu Symp Proc 2007; 761–765. Alamantariotou K, Zisi D. Consumer health informatics and interactive visual learning tools for health. Int J Electron Healthc 2010; 5: 414–424. Bos L, Carroll D, Marsh A. The impatient patient. Stud Health Technol Inform 2008; 137: 1–13. DOI:10.1002/MSJ

164


31. Cohen SB, Grote KD, Pietraszek WE, et al. Increasing consumerism in healthcare through intelligent information technology. Am J Manag Care 2010; 16(12 suppl HIT): SP37–SP43. 32. Haselkorn A, Coye MJ, Doarn CR. The future of remote health services: summary of an expert panel discussion. Telemed J E Health 2007; 13: 341–347. 33. Ho J. Consumer health informatics. Stud Health Technol Inform 2010; 151: 185–194. 34. Nash M, Pestrue J, Geier P, et al. Leveraging information technology to drive improvement in patient satisfaction. J Healthc Qual 2010; 32: 30–40. 35. V¨alim¨aki M, Nenonen H, Koivunen M, et al. Patients’ perceptions of Internet usage and their opportunity to obtain health information. Med Inform Internet Med 2007; 32: 305–314. 36. Elrod J, Androwich IM. Applying human factors analysis to the design of the electronic health record. Stud Health Technol Inform 2009; 146: 132–136. 37. Pelayo S, Bernonville S, Kolski C, et al. Applying a Human Factors Engineering approach to healthcare IT applications: example of a medication CPOE project. Stud Health Technol Inform 2009; 143: 334–339. 38. Santiago O, Li Q, Gagliano N, et al. Improving EMR usability: a method for both discovering and prioritizing improvements in EMR workflows based on human factors engineering. AMIA Annu Symp Proc 2006; 1086. 39. Bonniface L, Green L. Finding a new kind of knowledge on the HeartNET website. Health Info Libr J 2007; 24(suppl 1): 67–76. 40. Fleissner P, Hofkirchner W. The making of the information society: driving forces, ‘Leitbilder’ and the imperative for survival. Biosystems 1998; 46: 201–207. 41. Hollander MJ, Corbett C, Pallan P. Time for a paradigm shift: managing smarter by moving from data and information to knowledge and wisdom in healthcare decision-making. Healthc Q 2010; 13: 49–54. 42. Wilkinson I. Dollar$ & $en$e. Part VI: Knowledge management: the state of the art. Clin Leadersh Manag Rev 2001; 15: 187–189. 43. Wilkinson I. Dollar$ & $en$e. Part IV: Measuring the value of people, structural, and customer capital. Clin Leadersh Manag Rev 2001; 15: 45–49. 44. Walsh JL, Hurrell MJ, Wu H, et al. Mapping anesthesia records to standard term enables inclusion of AIMS data in the NSQIP database. Paper presented at: American Society of Anesthesiologists Annual Meeting; October 18 , 2009; New Orleans, LA. 45. Kheterpal S. Perioperative comparative effectiveness research: an opportunity calling. Anesthesiology 2009; 111: 1180–1182. 46. Ramachandran SK, Kheterpal S. Outcomes research using quality improvement databases: evolving opportunities and challenges. Anesthesiol Clin 2011; 29: 71–81. 47. Rosenzweig P. Robert S. McNamara and the evolution of modern management. Harvard Bus Rev. December 1, 2010. 48. Junger A, Benson M, Quinzio L, et al. An Anesthesia Information Management System (AIMS) as a tool for controlling resource management of operating rooms. Methods Inf Med 2002; 41: 81–85. 49. Lubarsky DA, Sanderson IC, Gilbert WC, et al. Using an anesthesia information management system as DOI:10.1002/MSJ

50. 51.

52.

53.

54.

55. 56.

57.

58.

59.

60.

61.

62.

63.

64.

a cost containment tool: description and validation. Anesthesiology 1997; 86: 1161–1169. Egger Halbeis CB, Epstein RH. The value proposition of anesthesia information management systems. Anesthesiol Clin 2008; 26: 665–679, vi. Sandberg WS, Sandberg EH, Seim AR, et al. Real-time checking of electronic anesthesia records for documentation errors and automatically text messaging clinicians improves quality of documentation. Anesth Analg 2008; 106: 192–201. Meyer-Jark T, Reissmann H, Schuster M, et al. Realisation of material costs in anaesthesia: alternatives to the reimbursement via diagnosis-related groups [article in German]. Anaesthesist 2007; 56: 353–365. Kheterpal S, Gupta R, Blum JM, et al. Electronic reminders improve procedure documentation compliance and professional fee reimbursement. Anesth Analg 2007; 104: 592–597. Jost A, Junger A, Zickmann B, et al. Potential benefits of Anaesthesia Information Management Systems for multicentre data evaluation: risk calculation of inotropic support in patients undergoing cardiac surgery. Med Inform Internet Med 2003; 28: 7–19. Kheterpal S, Han R, Tremper KK, et al. Incidence and predictors of difficult and impossible mask ventilation. Anesthesiology 2006; 105: 885–891. Ramachandran SK, Kheterpal S, Haas CF, et al. Automated notification of suspected obstructive sleep apnea patients to the perioperative respiratory therapist: a pilot study. Respir Care 2010; 55: 414–418. Wax DB, Beilin Y, Levin M, et al. The effect of an interactive visual reminder in an anesthesia information management system on timeliness of prophylactic antibiotic administration. Anesth Analg 2007; 104: 1462–1466. Rohrig R, Junger A, Hartmann B, et al. The incidence and prediction of automatically detected intraoperative cardiovascular events in noncardiac surgery. Anesth Analg 2004; 98: 569–577. O’Reilly M, Talsma A, VanRiper S, et al. An anesthesia information system designed to provide physicianspecific feedback improves timely administration of prophylactic antibiotics. Anesth Analg 2006; 103: 908–912. Eden A, Pizov R, Toderis L, et al. The impact of an electronic reminder on the use of alarms after separation from cardiopulmonary bypass. Anesth Analg 2009; 108: 1203–1208. Kooij FO, Klok T, Hollmann MW, et al. Decision support increases guideline adherence for prescribing postoperative nausea and vomiting prophylaxis. Anesth Analg 2008; 106: 893–898. Nair BG, Newman SF, Peterson GN, et al. Feedback mechanisms including real-time electronic alerts to achieve near 100% timely prophylactic antibiotic administration in surgical cases. Anesth Analg 2010; 111: 1293–1300. Epstein RH, Dexter F, Piotrowski E. Automated correction of room location errors in anesthesia information management systems. Anesth Analg 2008; 107: 965–971. Epstein RH, Gratch DM, Grunwald Z. Development of a scheduled drug diversion surveillance system based on an analysis of atypical drug transactions. Anesth Analg 2007; 105: 1053–1060.


65. Jiang M, Xing B, Sun Z, et al. A dynamic blood information management system based on RFID. Conf Proc IEEE Eng Med Biol Soc 2005; 1: 546–549. 66. Gaba DM. Anaesthesiology as a model for patient safety in health care. BMJ 2000; 320: 785–788. 67. Muravchick S, Caldwell JE, Epstein RH, et al. Anesthesia information management system implementation: a practical guide. Anesth Analg 2008; 107: 1598–1608. 68. Kheterpal S, Woodrum DT, Tremper KK. Too much of a good thing is wonderful: observational data

165

for perioperative research. Anesthesiology 2009; 111: 1183–1184. 69. Ehrenfeld JM, Rehman MA. Anesthesia information management systems: a review of functionality and installation considerations. J Clin Monit Comput 2011; 25: 71–79. 70. Chumney EC, Simpson KN. Methods and Designs for Outcomes Research. Bethesda, MD: American Society of Health-System Pharmacists; 2006. 71. Choy YC. Critical incident monitoring in anaesthesia. Med J Malaysia 2006; 61: 577–585.

DOI:10.1002/MSJ