The editors and publisher would like to thank Dr. James Caldwell for contributing to this chapter in the previous edition of this work. It has served as the foundation for the current chapter.
Anesthesia providers produce and record extraordinary amounts of physiologic, pharmacologic, and care management information. Since the previous edition of this text was published in 2011, there has been exponential growth in the use of computerized anesthesia information management systems (AIMS) both as a stand-alone system and as part of an overall patient care electronic health record (EHR). In the late 1990s, only a handful of academic anesthesia practices had an AIMS installation, with even fewer in private practice settings. However, by 2007 approximately 44% of academic medical centers had completed or were in the process of implementing AIMS. A 2014 follow-up survey estimated that 84% of U.S. academic medical centers would have an AIMS installed by the end of that year. The prediction was that within a few years, few anesthesia trainees would graduate from residency having used a paper anesthetic record. EHRs will likely incorporate the growing number of adjunct electronic devices and other software, combining all into the global term health information technology , or health IT . Given the enormous impact of health IT on patient care, anesthesia providers must have an understanding of these technologies including their potential benefits and hazards. The scientific discipline that serves as the foundation of health IT is medical informatics (the branch of information science that relates to health care and biomedicine), which encompasses health informatics, medical computer science, and computers in medicine.
Given their special skills and knowledge, anesthesia providers should be key players in the development, assessment, selection, and deployment of perioperative health IT. Anesthesia teams now need a working knowledge of the applicable theory and practice of medical informatics. In this chapter, several key health IT topics for the anesthesia provider will be reviewed, with a focus on AIMS, including some considerations for managing the procurement and operation of information technology in an anesthetic practice.
History of Anesthesia Documentation and Aims
The origins of the modern AIMS date back to the creation of the paper record in 1895 by neurosurgeon and physiologist Harvey Cushing and his medical school classmate E.A. Codman. As pioneers of anesthesia quality improvement, Codman and Cushing had challenged each other to improve their anesthesia practice. In support of this goal, they were the first to collect and review physiologic data using written anesthesia records just 50 years after the discovery of anesthesia. About the same time, Cushing and others began to employ newly invented automated hemodynamic monitors with paper-based recordings, including noninvasive arterial blood pressure measurements. Over the subsequent 50 years, the anesthetic record maintained the same basic format for representation of hemodynamics, albeit with a slow and steady increase in the amount and types of data recorded. These two innovations—documentation of significant events during actual anesthesia and surgery coupled with automated real-time recordings of hemodynamic vital signs—formed the foundation of the modern AIMS.
The late 1970s and early 1980s saw the rollout and initial evaluation of the computerized anesthesia automated record keeper (AARK), but commercialization and widespread adoption were slowed by the limited availability of cheap and reliable computer hardware and software. Yet, many benefits of AARKs became apparent, even within the limitations of this nascent technology. AARKs corrected limitations of paper records such as recall bias, illegible records, missing data or whole records (with regulatory and billing implications), and the lack of an audit trail for medical/legal purposes. Clinical studies of AARKs also revealed that they produced a more accurate record of hemodynamic variables than handwritten charts. For instance, handwritten anesthetic records had increased “data smoothing” (i.e., recorded data were often approximated, leading to less variation between individually recorded data points) as compared to AARKs.
The 1990s and early 2000s heralded a proliferation of advanced computer hardware and software, such as local area networks, the Internet, digital hemodynamic monitors, medical communication protocols such as Health Level Seven International (HL7), and a significant reduction in the cost of computer processing power. Coupled with the voracious demand for more data that paper records could not satisfy, the relatively simple AARKs evolved into full-fledged AIMS, with numerous additional capabilities.
The Demand for Data
In 2001, the Anesthesia Patient Safety Foundation (APSF) endorsed and advocated “the use of automated record keeping in the perioperative period and the subsequent retrieval and analysis of the data to improve patient safety.” There were also demands for anesthesia and perioperative data for such purposes as compliance documentation, research, quality assurance, and the streamlining of billing and administrative functions. However, U.S. federal government action may have most catalyzed the rapid pace of EHR adoption in this country in the 21st century. The Health Information Technology for Economic and Clinical Health (HITECH) Act, enacted as part of the American Recovery and Reinvestment Act of 2009, encouraged the adoption and appropriate use of health IT, including provisions for monetary incentives and penalties.
In 2011, the U.S. Department of Health and Human Services (HHS) Centers for Medicare & Medicaid Services (CMS) initiated the Medicare and Medicaid EHR Incentive Programs. Their Meaningful Use (MU) criteria encourage U.S. health care providers and organizations to adopt health IT through a staged process, via variable payments or penalties. For ongoing MU compliance, organizations must—by 2017—satisfy Stage 3 rules, which consolidate and update many of the Stage 1 and 2 requirements, as well as add requirements for privacy and security practices and the electronic submission of clinical quality measure (CQM) data for all providers ( Box 3.1 ). Reporting compliance within the MU system is complex. For instance, there are specific reporting, incentive, and hardship exemption rules that may apply to anesthesia providers. Advice from the American Society of Anesthesiologists, HHS, Office of the National Coordinator for Health Information Technology (ONC), and health IT professionals may help navigate these requirements. The requirements are dynamic, and in early 2016, in response to stakeholder feedback, the federal government was developing the Advancing Care Information program. This new program’s intent is to simplify or replace the MU program, focusing on improving interoperability (see later) and creating user-friendly technology designed to support physician workflows. Up-to-date information about federal guidelines and requirements for health IT is available online.
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Protect patient health information
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Electronic prescribing (eRx)
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Clinical decision support (CDS)
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Computerized provider order entry (CPOE)
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Patient electronic access to health information
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Coordination of care through patient engagement
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Health information exchange (HIE)
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Public health and clinical data registry reporting
Discrete data collection and reporting within a health care organization is often cited as a key reason to implement health IT. Reporting supports analysis of workflows; guides efforts at utilization, scheduling, and resource management improvements; permits the measurement of costs, quality, and clinical outcomes; satisfies compliance regulations; serves research studies; and may be required by external public and private agencies. Important data will often reside across multiple systems, leading to the rise of the Data Warehouse, a central repository of integrated data, pooled from one or more separate sources.
Although local reporting has great potential, these local data are leading to the creation of national and international large databases, termed data registries. Several observational data registries are focused on the fields of anesthesia and perioperative care: the Anesthesia Quality Institute (AQI), National Anesthesia Clinical Outcomes Registry (NACOR), the data registry of the Multicenter Perioperative Outcomes Group (MPOG), the Society for Ambulatory Anesthesia (SAMBA) database (SAMBA Outcomes Registry, SCOR), the Pediatric Regional Anesthesia Network, and the Society for Cardiovascular Anesthesiologists Adult Cardiac Anesthesia Module. These data registries can receive data directly from health IT, but several issues make sharing data from local health IT difficult. First, a significant investment of time and other resources is required to map local clinical concepts to the registry data schema. Another barrier to full harvesting of the information contained within these datasets is the inconsistency among the varieties of clinical taxonomies—a universally agreed-upon anesthesia “data dictionary” has yet to appear. A third issue is the missing or inaccurate data in health IT anesthesia documentation. This problem may be intractable without significant expense of resources or technological advances, because clinicians cannot be expected to be high-quality data-entry personnel while simultaneously administering anesthesia and caring for patients. Finally, much of health IT data is not discrete, structured, or categorized and rather is represented in plain text; that is, natural/human language. Until natural language processing (NLP, a field of artificial intelligence in which computer software understands human languages) matures, much of this information cannot be used to great extent.
Despite such challenges, there is significant potential for local and national registries with respect to quality improvement and health care research. These data can help describe the current state of clinical care and allow for benchmarking of process and outcome measures across multiple organizations, as well as sharing of lessons learned. Pooled data can also be analyzed to explore the relationships between specific patient care factors and clinical outcomes, especially when these outcomes are rare, although there are concerns that such observational, large cohort studies have significant shortcomings compared to traditional prospective randomized controlled trials. But large datasets—often called big data —have helped big business in other fields visualize novel customer-product interrelationships and devise new strategies. Perhaps, big data techniques will be a cost- and time-effective way to augment prospective interventional studies and basic science research in anesthesia. Some anticipated uses of big data include modeling the risk of complications for perioperative patients and sending such information back to the EHR systems to inform clinical decision support (CDS) rules, possibly predicting problems before they actually occur. New computer techniques, such as machine learning or cognitive inference computing, may be able to use big data to draw conclusions from data in ways humans cannot.
Professional Performance Data Reporting with Health IT
Electronic reporting of professional quality is a specific use of health IT data that is responsible for many reporting initiatives. The Physician Quality Reporting System (PQRS) receives quality information from individual eligible professionals and group practices for CMS. PQRS quality measures are designed to help eligible professionals and group practices assess their performance across a range of quality domains. In 2019, CMS plans to merge several current quality and value-based assessment systems (including MU and PQRS) into either Merit-based Incentive Payment Systems (MIPS) or advanced Alternative Payment Models (APMs) stemming from the recent Medicare Access and CHIP Reauthorization Act of 2015 (MACRA).
Quality measure reporting is recognized as a critical feature of an EHR. Some systems give the option of recording quality documentation within the EHR itself. Conversely, perhaps this reporting should be conducted outside the EHR to reduce the risk of unwanted legal discovery. An alternative to direct documentation is membership in a CMS-approved qualified clinical data registry that has an option for collection and submission of PQRS quality measures data on behalf of individual providers. The AQI is currently designated as both a Patient Safety Organization, which meets criteria established in the Patient Safety Rule of the HHS and a qualified clinical data registry. Qualified clinical data registries and patient safety organizations have a high level of medicolegal discovery protection to encourage accurate reporting. Because MPOG is also a 2015 qualified clinical data registry via its Anesthesiology Performance Improvement and Reporting Exchange registry (ASPIRE), NACOR and MPOG participants can leverage their participation in these data registries to also satisfy federal reporting requirements.
Features of the Electronic Health Record in Anesthesia and Perioperative Care
The EHR is a longitudinal electronic record of patient health information generated by one or more encounters in any care delivery setting. Although there are significant realized and potential advantages of using EHRs for patients, providers, and the health care organization ( Box 3.2 ), there are also many potential pitfalls. Careful design may make the difference between an effective EHR and a failed project. Because the fundamental purpose of the EHR is to support required clinical and administrative activities, the EHR should be intuitive and guide users as well as provide access to the right information at the right time to meet the needs of modern health care.
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It provides legible documentation.
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Information is accessible anywhere inside or outside facility; accessible via mobile technology; accessible by patients and providers.
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Data entry is traceable (an audit trail).
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It offers better completeness and accuracy of information.
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Information is current, and data repository has the same information no matter how it is accessed.
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It decreases paperwork.
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It may improve care quality, reduce errors, improve coordination of care.
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It increases clinical efficiency, if constructed properly.
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It may eventually reduce overall health care costs.
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It facilitates research.
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It can facilitate teaching and learning.
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Automates many processes. Can apply rules and logic to 100% of documentation sessions. It never sleeps.
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It offers administrative efficiencies—including improving charge capture.
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Can provide real-time alerts, prompts, notifications, reminders.
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Patients can access their own health information.
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Health IT vendor is certified by CHPL and supports provider and organization attestation for Meaningful Use.
CHPL, Certified health IT product list.
System feature requirements specific to AIMS include the AARK core functions (permanent recording of device data/device integration from hemodynamic monitors, anesthesia machines, and other clinical devices), capture of meta-data such as case events (e.g., in-the-room time; cardiopulmonary bypass time), documentation of preoperative evaluation (including the use of structured data to support reporting and CDS), management of perioperative orders, and integration with the patient’s EHR and other records in various health IT systems. Key targets for integration include the following:
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Medication data (requiring integration with pharmacy systems, which encompasses patient allergies, medication orders, administrations, interactions, formulary, and costs)
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Laboratory and radiology systems (study orders and results, ability to record point-of-care test results)
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Provider orders, notes, and consults
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Nursing assessments including “ins and outs”
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Billing functions (create charges to patient and their insurance plan)
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Patient tracking (integration with admission/discharge/transfer application)
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Perioperative management systems (e.g., case ordering, scheduling, utilization management)
For modular AIMS (components of a larger EHR), this integration may be operationalized via shared databases and routines (e.g., the AIMS module records medication orders and administrations in the enterprise database shared with the pharmacy and other clinical applications). For standalone AIMS, multiple interfaces (hardware and software) may be required to communicate data back and forth between the AIMS and the other health IT systems (described earlier) to avoid a perioperative information “black hole.”
Perhaps the most important EHR feature is reliability. The EHR must be fault tolerant , meaning resistant to diverse challenges such as software “bugs,” hacking, hardware failures, network errors, and even natural disasters. Preparing for business continuity after a failure includes a fail-safe workflow (e.g., paper records with scanning) and redundant data storage. Two common models for protecting data are (1) data mirroring , in which an application on a local workstation works with locally stored data that are automatically copied to remote storage (or a cloud ), and (2) the client-server model in which the local workstation (the client ) works with data stored on a remote computer (the server ). An advantage of data mirroring is that it may be resistant to brief network interruptions. Client-server architectures can simplify system management by centralizing software and data to ease maintenance and backup activities. Box 3.3 shows features that should be available in the EHR.
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Electronic document management
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Scanned document management
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Orders capability (computerized physician order entry, CPOE)
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Physiologic device data importation into EHR
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Exchanging information with other hospital processes and services: admission-discharge-transfer, scheduling, radiology, pharmacy, respiratory therapy, laboratory, blood bank, picture archiving and communication systems (PACS), emergency services
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Integration or communication with rehabilitation and long-term care facilities
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Staffing, concurrency checks
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Procedural documentation
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Templates that channel documentation, ensuring compliance with local organizational, national professional, and government guidelines, practice parameters, standards or requirements.
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Clinical decision-support checklists, alerts, reminders, emergency checklists and protocols
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“Scripting” or “macro” documentation allowing set-up and multi-item documentation for repetitive situations
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Structured handoffs
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Medication management
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Administrative reporting
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Mobile integration
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Charge capture
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Telemedicine
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Facility and professional charge capture and compliance checks and reports
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Patient communication and engagement (patient portals, care instructions, pathway guides, others)
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Structured discrete data (flowsheets, lists, checkboxes, buttons, etc.)
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Categorized data, rather than free text
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Facilitates reporting and data analysis
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Quality and outcomes analysis
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Predictive modeling/analytics
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Ability to export for data registries, population health projects
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Data warehouse
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Patient satisfaction surveys: HCAHAPS, Press-Ganey, others
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Practice management reports
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HCAHAPS, Hospital Consumer Assessment of Healthcare Providers and Systems survey; CDS, clinical decision support.
Health Care Information Privacy and Security
Health care providers are morally and legally obligated to protect the privacy of their patients as well as the security of the EHR. The Health Insurance Portability and Accountability Act (HIPAA) Privacy, Security, and Breach Notification Rules are U.S. regulations that codify this obligation into law. The Privacy Rule sets standards for when and how protected health information (PHI), may be used and disclosed in any medium, including electronic, written, and oral. PHI includes any data that could be used to identify a patient, and when stored in digital form is termed electronic PHI (ePHI) ( Box 3.4 ). The Security Rule requires certain precautions so that access to health IT systems is limited to those with legitimate purposes and proper authorization. The Breach Notification Rule requires health care providers and organizations to report any breach (a loss of patient privacy or failure of health IT security) to HHS, patients, and, in some cases, the media.
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