Using Anesthesia AIMS Data in Quality Management
Shermeen B. Vakharia, MD, MBA Joseph Rinehart, MD University of California-Irvine, Orange, California
In the medical arena, perceived quality of care is generally related to outcomes. The Institute of Medicine actually defines quality as the “degree to which health services for individuals and populations increases the likelihood of desired health outcomes and are consistent with current professional knowledge.”1 Although seemingly straightforward, the application of this definition to the specialty of anesthesiology is very complex for several reasons. First, patients with similar comorbidities undergoing the same surgical procedure with the same care often have different outcomes, and suboptimal care does not always lead to an undesirable outcome. Such variation is multifactorial and often related to poorly understood processes of care. Second, anesthesia-related severe morbidity and mortality are rare and there are no risk-adjusted outcome models. Therefore “desired outcomes” often implies measuring the impact of anesthesia care on less serious outcomes which occur more often. The current prevalence of paper records makes this data difficult to abstract, measure, and compare. Finally, the expectation of practicing at the level of “current professional knowledge” emphasizes that the most current scientific knowledge be available and applied in real time at the point of providing anesthesia care to the patient, an expectation that is not easily achieved or even defined. Nevertheless, as health care systems strive to provide ever higher quality of care at lower costs they must struggle to meet these challenges regardless of their complexity. In so doing, the concept of quality management (QM)—an umbrella term encompassing quality assurance (QA) practices, performance improvement and quality improvement (QI) processes, and assurance of compliance with regulatory and REPRINTS: SHERMEEN B. VAKHARIA, MD, MBA, DEPARTMENT OF ANESTHESIOLOGY AND PERIOPERATIVE CARE, UNIVERSITY OF CALIFORNIA-IRVINE, 101 CITY DRIVE SOUTH, BLDG. 53, ZOT 1350, ORANGE, CA 92868. E-MAIL: [email protected] INTERNATIONAL ANESTHESIOLOGY CLINICS Volume 52, Number 1, 42–52 r 2014, Lippincott Williams & Wilkins
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professional standards—has moved into preeminence in hospitals and group practices that are serious about ensuring consistent delivery of high-quality care. Toward this end, anesthesia information management systems (AIMS) have in many ways revolutionized the ways QM may be performed. In this review, we will discuss the features of AIMS that make them indispensable for QM practices as well as some of their current limitations, cover the specific ways in which AIMS have been used to enhance QA and QI practices, and briefly discuss some of the potential future enhancements that may make AIMS even more effective as guarantors of quality care. ’
Features of AIMS that Support QM AIMS Data Quality and Accessibility
Anesthesia is easily the most information-intense period of a patient’s clinical care. The very first anesthesia record created by Cushing was born out of the need to objectively quantify the quality and course of anesthesia care2—this record included respiratory rate, pulse, and temperature. Today, however, the abundance of physiological and pharmacologic data generated as a result of technological advances and increased sophistication of monitoring devices make it difficult, if not impossible, to transcribe a complete and accurate account of an anesthetic encounter by hand.3–5 This is especially true during critical events when patient care takes precedence over manual charting of data.6 Recording vital signs and other information on paper is usually done after delivering care to the patient, leading to recall bias. Other limitations of hand-written anesthesia records include: records are often illegible and incomplete; smoothing of physiological data occurs around expected values; the increased workload on the anesthesia provider may divert attention from patient care; data may be lost; and paper charts require labor-intensive manual review and data extraction for QA or outcomes research. AIMS by their very nature overcome many of these limitations of paper records, improving both the quality and accessibility of the intraoperative data. First, physiological data are usually captured directly from monitors, eliminating bias and the need for retroactive recording during busy periods or critical events. Several studies have proven the superiority of automated data collection over hand-written records; the automated data are more accurate, legible, and accessible even from remote locations in real time.4 Automated data capture, however, is not without limitations. Artifacts in monitoring devices may be misinterpreted as extremes of physiological values. Lapses can occur in both automated capture and manual documentation with potentially www.anesthesiaclinics.com
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serious patient safety and medicolegal implications. Several practices have reported a significant decrease in such documentation errors with the use of independently operating custom software that can generate reminders with appropriate visual or messaging alerts to the anesthesia provider.7,8 Not all AIMS provide alerts for network failures or disconnections, and although this is a rare occurrence, there is a concern for loss of data. Finally, the quality of manual data entry is only as good as the information entered by the end user. Proper configuration with structured text and dropdown menus minimizes the need for free-text entry, which can reduce documentation errors and the time spent by the provider in interacting with the computer, leaving more time for patient care.9,10 Another obvious improvement of AIMS over a paper record is the accessibility of the data. As case data are invariably stored in a database of some kind, pulling large batches of data or searching through any number of anesthetics is only a matter of building a query and allowing the system time to perform the search. This makes data collection for QA analysis much less labor intensive and in most cases a regular report with desired data can easily be generated from the system and sent to appropriate personnel. Further, performing QI studies on new measures is much less difficult because even if not previously tracked by QA personnel, the data already exists in the system and merely needs to be pulled for whatever work is being done. Decision Support
Clinical decision support (CDS) is a knowledge management system that “provides health care providers, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care.”11 Decision support is one of the areas in which AIMS are most rapidly evolving, moving them beyond mere anesthesia record keepers. CDS systems can be passive or active and are designed to manage provider behavior through a number of different pathways (Table 1). Passive CDS includes reference guides such as the emergency airway algorithm and ACLS protocols, as well as pre-made order sets for specific functions such as postoperative anesthesia care. This group also includes many things providers probably do not even consider “decision support,” but nevertheless clearly influence the way they behave, such as document templates or graphical information displays.12 For example, a preoperative H&P form is a type of decision support because it indicates the pieces of information the provider should seek and document. Most modern AIMS are equipped with at least some basic decision support tools for things such as weight-based drug-dose calculations and drug allergy alerts. www.anesthesiaclinics.com
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Table 1.
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Passive Versus Active Clinical Decision Support in AIMS
Element
Passive Support
Active Support
Billing compliance
Automated coding and Advisories for elements needed for detection of billable events billing; automated reminders to providers for documentation requirements (attestations, etc.) Documentation Templates with required Automated scanning and provider fields notification of documentation deficiencies Hemodynamics Graphical information Patient analysis and treatment displays recommendations Patient In-process identification of Treatment recommendations management patients at high-risk for based on real-time monitored specific outcomes patient data Antibiotic redosing reminders; Medication In-process interaction and spontaneous recommendations administration allergy checking, for specific patients verification of dosing (eg, PONV prophylaxis) Protocol Algorithm references Active pathways which guide compliance providers through required steps AIMS, anesthesia information management system; PONV, postoperative nausea and vomiting.
Active CDS is different from passive CDS, in that the latter is typically encountered by the provider in the course of provider-initiated actions, whereas active CDS usually involves interruptions or unsolicited alerts from the system about an action the provider should consider taking but has not.13 The obvious benefit of active CDS is that compliance with protocols and recommended care actions, as well as general vigilance, can be increased through reminders to care providers14; the potential risk is alarm fatigue and that interruptions during care may do more harm than good if poorly implemented.15,16 Despite the risks, active CDS tools have been shown to improve patient care in a number of clinical studies.17–21 Real-time diagnostic applications such as detection of critical events22 and predictive applications of CDS23 will have a major impact on the future practice of anesthesiology. Limitations of Current AIMS Implementations in QM
Despite the many benefits that AIMS bring to QM processes, existing AIMS implementations are still subject to some limitations. First, as previously noted, the quality and accuracy of provider-entered data are dependent on the provider, especially when it comes to nonstandard data such as free-text fields. Text templates may reduce variability of these fields but may also decrease accuracy of recorded data if providers do not update text to reflect the actual events (as opposed to the stock text of the template). In fact, AIMS in some cases www.anesthesiaclinics.com
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may increase the risk of inaccuracies in data when providers prioritize moving through required data entry pathways quickly as opposed to focusing on accuracy of data.24 Timeliness of event recording may also be imprecise in these cases. On a larger scale, the heterogeneity of existing AIMS and the different ways they record data and events limits the ability of these systems to interact with external systems. Each AIMS developer must build a unique interface for each external system and vice versa, so even facilities using the same AIMS vendor may find it difficult to share data. Efforts such as the Anesthesia Quality Institute (AQI) initiative may help bring standardization to some elements of anesthesia records. However, in the absence of an authoritative standards body, this situation is unlikely to change substantially in coming years. ’
Using AIMS Data for QM Quality Assurance
Critical incident reporting is essential for retrospective review and analysis to bring about change in practice and improve patient safety. This process is the crux of traditional departmental QA. As noted, an AIMS database provides a more accurate, complete, and objective record for retrospective review and planning QI projects at the departmental level than paper charts. Tracking and trending of events can point to system issues that may need to be corrected. Moreover, specific QA indicators can be recorded in AIMS through voluntary or mandatory self-reporting, and several studies have shown that mandatory in-pathway reporting improves event detection.25,26 An additional consideration is the confidentiality of the QA data. Vigoda et al8 showed a dramatic increase in the anesthesia events reported by creating a mandatory stop before AIMS record closure in the PACU, and this hard stop directed the provider to a QA reporting system outside of AIMS for reporting of critical events. The advantage of this system design is that the clinical event data can be kept confidential and protected from discovery in case of litigation. Several studies indicate that automated detection of certain critical events in AIMS is far superior to voluntary reporting.27–29 Electronic scanning for physiological and laboratory parameters outside of predefined ranges lead to much higher rates of detection of anesthesia events compared with voluntary reporting.29 Many AIMS have integrated perioperative modules (scheduling, anesthesia preoperative, intraoperative, postoperative, and even perioperative nursing and PACU modules) that provide means for assessment, risk identification, and linking of intraoperative and PACU care to events. The most efficient systems for QM are enterprise-wide systems www.anesthesiaclinics.com
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or AIMS integrated with the hospital EMR where all the data goes into 1 repository. Such systems provide additional information on patient’s preoperative risk factors and laboratory values, outcomes beyond the PACU, as well as information such as length of stay in the ICU and hospital. As discussed, however, currently AIMS are highly customized at each facility and there is no national standard, so comprehensive and integrated hospital-wide systems are rare.
AIMS—A Conduit for National Registries
Despite the variances between vendors and even within vendors between hospitals, AIMS nevertheless help organize data and facilitate electronic transfer of relevant data to national registries. The AQI was chartered by the American Society of Anesthesiologists with the goal of collecting electronic data from all anesthesia practices in the United States and establishing the National Anesthesia Clinical Outcomes Registry.30 The participating practices can access their own quality and practice management data as well as benchmark their practices to their peer groups to improve quality at the local level. Currently, the bulk of AQI data comes from billing data which is generally present in digital format. However the ultimate goal is to collect detailed perioperative data from AIMS on risks, anesthesia care, and outcomes. The hope is that this data sharing can help identify and disseminate best practices and advocate for public health interests. With regard to establishing standards among AIMS vendors, the AQI, in collaboration with The Multicenter Perioperative Outcomes Group (MPOG—a consortium of practices that share AIMS data for outcomes research), is developing a common dataset and terminology for outcomes.31,32 This standardization of terminology is essential for comparing outcomes at a national level.
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Using AIMS for Performance Improvement
Since the Institute of Medicine released its landmark report in 2006 discussing pay-for-performance,33 health care systems have been increasingly focused on ensuring high-quality care and meeting designated outcome targets. The entire health care system in the United States is under more intense scrutiny than ever, especially as costs have risen without a concomitant improvement in perceived quality. AIMS play a critical role in performance improvement processes from the individual provider to hospital and health care system levels. www.anesthesiaclinics.com
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Adherence to Clinical Practice Guidelines
Under the Affordable Care Act in the United States, the Centers for Medicare and Medicaid implemented the Hospital Value-Based Purchasing program in 2012. This program links payments to quality of care measures with the goal of preventing medical errors and decreasing cost of care; payments will be based on specific process measures and patient satisfaction. Several of the perioperative performance measures are from the Surgical Care Improvement Project focusing on reducing postoperative complications related to surgical site infection, venous thromboembolism, and adverse cardiovascular events. For example, one of the guidelines pertains to administration of antibiotics within 60 minutes before surgical incision. Tools in AIMS in the form of prompts, visual alerts, and provider-specific feedback have enabled providers to achieve high levels of compliance with antibiotic administration34,35 to satisfy this requirement. At least 1 practice achieved nearly 100% compliance with the use of alerts, email messaging along with a hard stop, without which the AIMS anesthesia record could not be signed off.36 Similar point-of-care decision support tools can facilitate compliance with administration of perioperative b-blockers, maintaining normothermia, and VTE prophylaxis. This consistently high level of performance would be difficult to achieve with paper records. Even if not linked to financial incentives, clinical practice guidelines may standardize care and decrease variation, thereby improving patient safety and decreasing cost of care. Clinical practice guidelines include “systematically developed statements” on best practices that are based on current medical evidence,37 but a variety of barriers have hindered their widespread use.38 AIMS can facilitate removal of some of the barriers related to awareness, familiarity, and environment by bringing clinical guidelines directly to the point of care. For example, Kooij et al20 demonstrated that the use of real-time decision support can assist with risk identification and improve compliance with postoperative nausea and vomiting guidelines in the PACU. Real-time decision support algorithms integrating patient-specific and case-specific recommendations based on practice guidelines have been shown to improve patient care and provider performance in a variety of settings. Examples include prevention of postoperative respiratory complications by automated risk detection and notification of respiratory therapist for patients at risk for obstructive sleep apnea,39 an alert system for potentially insufficient anesthesia,40 and guidance for intraoperative hemodynamic management and early detection of critical events.41–43 Team Performance in the Operating Room
Some perioperative systems have the added advantage of retrieving information from disparate systems and integrating this information onto a single dashboard for display on a large screen in the operating www.anesthesiaclinics.com
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room. Such dashboards display aggregate information on patient identity, planned procedure, critical labs, allergies, preoperative risks, vital signs trends, personnel information, case milestones, patient data and disposition, and time elapsed since the last dose of antibiotics. A dashboard like this heightens the situational awareness of the entire surgical team, improves team communication, and enhances patient safety by displaying critical information. Provider Performance
AIMS can simplify data abstraction and reporting on quality measures in clinical practice, compliance with the federally mandated Physician Quality Reporting System (PQRS), or medical staff credentialing. The PQRS requires anesthesiologists to report on 3 quality measures—timely antibiotic administration, central line infection prevention practices, and postoperative normothermia—and is tied to negative payment adjustment for providers who do not report on these metrics.44 AIMS also allow providers to track and compare their performance with their peers as a QI tool.34–36,45 Ongoing Professional Practice Evaluation (OPPE) is a Joint Commission standard that requires medical staff to collect provider-specific quality metrics and initiate interventions, if needed, to target areas of deficiency.46 The use of AIMS data to fulfill OPPE requirements can save time and resources as opposed to traditional credentialing methods. ’
Unrealized Potential
Despite the many benefits that AIMS have brought to health care, the full potential of these systems is yet to be realized. For example, although CDS is being implemented at many levels, there is far greater opportunity to improve practice with more intelligent systems.47 Analytic systems may be able to detect adverse events as they evolve and provide a level of vigilance beyond simple vital signs range alarms.48 QA processes could be dramatically enhanced by the implementation of wide-spread automatic event reporting; several studies have shown that automated capture is a significant improvement over voluntary forms of reporting.26,49,50 Cross-system communication, especially the sharing of data with other institutions and national bodies (such as the AQI and MPOG programs), will allow individuals and hospitals alike to compare their own performance to national averages to maintain exceptional practices while directly targeting deficiencies for interventions.51 There is a great deal of untapped potential in data management and analysis, thanks to the rise of AIMS, and the coming years should see a rapid expansion in both the prevalence and sophistication of their use in QM practices. www.anesthesiaclinics.com
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Conclusions
AIMS have revolutionized QM practices in many ways—indeed, running an efficient and effective anesthesiology QM program today more or less requires one. With the increasing national focus on quality and outcomes, we expect the majority of anesthesiology practices will find it not just cost effective but essential to transition to AIMS from paper records in the coming years if they wish to meet reporting requirements. Significant gains could be made by focusing developmental efforts on cross-platform integration and increasing the sophistication of both real-time data analysis and decision support within these systems.
S.B.V. is a member of the Surgical Information Systems Anesthesia Advisory Board. J.R. declares that there is nothing to disclose.
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References
1. Shaneyfelt TM. Building bridges to quality. JAMA. 2001;286:2600–2601. 2. Molnar C, Nemes C, Szabo S, et al. Harvey Cushing, a pioneer of neuroanesthesia. J Anesth. 2008;22:483–486. 3. Reich DL, Wood RK Jr, Mattar R, et al. Arterial blood pressure and heart rate discrepancies between handwritten and computerized anesthesia records. Anesth Analg. 2000;91:612–616. 4. Thrush DN. Are automated anesthesia records better? J Clin Anesth. 1992;4:386–389. 5. 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. 6. Egger Halbeis CB, Epstein RH. The value proposition of anesthesia information management systems. Anesthesiol Clin. 2008;26:665–679; vi. 7. 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; table of contents. 8. Vigoda MM, Gencorelli F, Lubarsky DA. Changing medical group behaviors: increasing the rate of documentation of quality assurance events using an anesthesia information system. Anesth Analg. 2006;103:390–395; table of contents. 9. Ehrenfeld JM, Rehman MA. Anesthesia information management systems: a review of functionality and installation considerations. J Clin Monit Comput. 2011;25:71–79. 10. Muravchick S, Caldwell JE, Epstein RH, et al. Anesthesia information management system implementation: a practical guide. Anesth Analg. 2008;107:1598–1608. 11. Office of the National Coordinator for Health Information Technology: Clinical Decision Support. 2013. Available at: http://www.healthit.gov/policy-researchersimplementers/clinical-decision-support-cds. Accessed August 6, 2013. 12. Michard F. Decision support for hemodynamic management: from graphical displays to closed loop systems. Anesth Analg. 2013;117:876–882. 13. Rothman B, Leonard JC, Vigoda MM. Future of electronic health records: implications for decision support. Mt Sinai J Med. 2012;79:757–768. www.anesthesiaclinics.com
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14. Bright TJ, Wong A, Dhurjati R, et al. Effect of clinical decision-support systems: a systematic review. Ann Intern Med. 2012;157:29–43. 15. Bates DW, Kuperman GJ, Wang S, et al. Ten commandments for effective clinical decision support: making the practice of evidence-based medicine a reality. J Am Med Inform Assoc. 2003;10:523–530. 16. Westbrook JI, Coiera E, Dunsmuir WT, et al. The impact of interruptions on clinical task completion. Qual Saf Health Care. 2010;19:284–289. 17. Schneider E, Franz W, Spitznagel R, et al. Effect of computerized physician order entry on radiologic examination order indication quality. Arch Intern Med. 2011;171: 1036–1038. 18. Epstein RH. Postoperative nausea and vomiting, decision support, and regulatory oversight. Anesth Analg. 2010;111:270–271. 19. Mack EH, Wheeler DS, Embi PJ. Clinical decision support systems in the pediatric intensive care unit. Pediatr Crit Care Med. 2009;10:23–28. 20. 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; table of contents. 21. Spalding SC, Mayer PH, Ginde AA, et al. Impact of computerized physician order entry on ED patient length of stay. Am J Emerg Med. 2011;29:207–211. 22. Renardel de Lavalette GR, Groenboom R, Rotterdam E. Formalisation for decision support in anaesthesiology. Artif Intell Med. 1997;11:189–214. 23. Murthy BV, Lake SP, Fisher AC. Evaluation of a decision support system to predict preoperative investigations. Br J Anaesth. 2008;100:315–321. 24. Goldberg SI, Shubina M, Niemierko A, et al. A weighty problem: identification, characteristics and risk factors for errors in EMR data. AMIA Annu Symp Proc. 2010;2010:251–255. 25. Peterfreund RA, Driscoll WD, Walsh JL, et al. Evaluation of a mandatory quality assurance data capture in anesthesia: a secure electronic system to capture quality assurance information linked to an automated anesthesia record. Anesth Analg. 2011;112:1218–1225. 26. Simpao AF, Pruitt EY, Cook-Sather SD, et al. The reliability of manual reporting of clinical events in an anesthesia information management system (AIMS). J Clin Monit Comput. 2012;26:437–439. 27. 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. 28. Benson M, Junger A, Michel A, et al. Comparison of manual and automated documentation of adverse events with an Anesthesia Information Management System (AIMS). Stud Health Technol Inform. 2000;77:925–929. 29. Sanborn KV, Castro J, Kuroda M, et al. Detection of intraoperative incidents by electronic scanning of computerized anesthesia records. Comparison with voluntary reporting. Anesthesiology. 1996;85:977–987. 30. Anesthesia Quality Institute. National Anesthesia Clinical Outcomes Registry. 2013. Available at: http://www.aqihq.org/introduction-to-nacor.aspx. Accessed November 11, 2013. 31. Kheterpal S. Clinical research using an information system: the multicenter perioperative outcomes group. Anesthesiol Clin. 2011;29:377–388. 32. McCormick P, Krol M, Reich D. Sharing data with hospital information systems. Available at: http://www.aqihq.org/data-sharing.pdf. Accessed July 8, 2013. 33. Institute of Medicine (US). Committee on Redesigning Health Insurance Performance Measures Payment and Performance Improvement Programs. Rewarding Provider Performance: Aligning Incentives in Medicare. Pathways to Quality Health Care. Washington, DC: National Academies Press; 2007. xxi, p. 248. www.anesthesiaclinics.com
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34. O’Reilly M, Talsma A, VanRiper S, et al. An anesthesia information system designed to provide physician-specific feedback improves timely administration of prophylactic antibiotics. Anesth Analg. 2006;103:908–912. 35. 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; table of contents. 36. 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. 37. Graham R, Institute of Medicine (US). Committee on Standards for Developing Trustworthy Clinical Practice Guidelines. Clinical Practice Guidelines We Can Trust. Washington, DC: National Academies Press; 2011. xxxiv, p. 266. 38. Cabana MD, Rand CS, Powe NR, et al. Why don’t physicians follow clinical practice guidelines? A framework for improvement. JAMA. 1999;282:1458–1465. 39. 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. 40. Mashour GA, Esaki RK, Vandervest JC, et al. A novel electronic algorithm for detecting potentially insufficient anesthesia: implications for the prevention of intraoperative awareness. J Clin Monit Comput. 2009;23:273–277. 41. Dexter F, Penning DH, Lubarsky DA, et al. Use of an automated anesthesia information system to determine reference limits for vital signs during cesarean section. J Clin Monit Comput. 1998;14:491–498. 42. 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. 43. Krol M, Reich DL. Development of a decision support system to assist anesthesiologists in operating room. J Med Syst. 2000;24:141–146. 44. Centers for Medicare and Medicaid Services: Physician Reporting Quality System. Available at: http://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/ PQRS/?gclid=COD5rp6R57oCFapxQgodrWgAZg. Accessed November 15, 2013. 45. Vakharia S, Faizy Y, Kain ZN. Changing practitioner behavior through provider performance dashboards: the next frontier. American Society of Anesthesiologists. 2012:A587. 46. Ehrenfeld J., Anesthesia Information Systems: clinical and operational impact. Anesthesiology News, 2010: p. 1-8. 47. Chau A, Ehrenfeld JM. Using real-time clinical decision support to improve performance on perioperative quality and process measures. Anesthesiol Clin. 2011;29:57–69. 48. Cannesson M, Rinehart J. Innovative technologies applied to anesthesia: how will they impact the way clinicians practice? J Cardiothorac Vasc Anesth. 2012;26:711–720. 49. McColm D, Karcz A. Comparing manual and automated coding of physicians quality reporting initiative measures in an ambulatory EHR. J Med Pract Manage. 2010;26: 6–12. 50. Silas R, Tibballs J. Adverse events and comparison of systematic and voluntary reporting from a paediatric intensive care unit. Qual Saf Health Care. 2010;19:568–571. 51. Kadry B, Feaster WW, Macario A, et al. Anesthesia information management systems: past, present, and future of anesthesia records. Mt Sinai J Med. 2012;79:154–165.
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Shermeen B. Vakharia, MD, MBA Joseph Rinehart, MD University of California-Irvine, Orange, California
In the medical arena, perceived quality of care is generally related to outcomes. The Institute of Medicine actually defines quality as the “degree to which health services for individuals and populations increases the likelihood of desired health outcomes and are consistent with current professional knowledge.”1 Although seemingly straightforward, the application of this definition to the specialty of anesthesiology is very complex for several reasons. First, patients with similar comorbidities undergoing the same surgical procedure with the same care often have different outcomes, and suboptimal care does not always lead to an undesirable outcome. Such variation is multifactorial and often related to poorly understood processes of care. Second, anesthesia-related severe morbidity and mortality are rare and there are no risk-adjusted outcome models. Therefore “desired outcomes” often implies measuring the impact of anesthesia care on less serious outcomes which occur more often. The current prevalence of paper records makes this data difficult to abstract, measure, and compare. Finally, the expectation of practicing at the level of “current professional knowledge” emphasizes that the most current scientific knowledge be available and applied in real time at the point of providing anesthesia care to the patient, an expectation that is not easily achieved or even defined. Nevertheless, as health care systems strive to provide ever higher quality of care at lower costs they must struggle to meet these challenges regardless of their complexity. In so doing, the concept of quality management (QM)—an umbrella term encompassing quality assurance (QA) practices, performance improvement and quality improvement (QI) processes, and assurance of compliance with regulatory and REPRINTS: SHERMEEN B. VAKHARIA, MD, MBA, DEPARTMENT OF ANESTHESIOLOGY AND PERIOPERATIVE CARE, UNIVERSITY OF CALIFORNIA-IRVINE, 101 CITY DRIVE SOUTH, BLDG. 53, ZOT 1350, ORANGE, CA 92868. E-MAIL: [email protected] INTERNATIONAL ANESTHESIOLOGY CLINICS Volume 52, Number 1, 42–52 r 2014, Lippincott Williams & Wilkins
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professional standards—has moved into preeminence in hospitals and group practices that are serious about ensuring consistent delivery of high-quality care. Toward this end, anesthesia information management systems (AIMS) have in many ways revolutionized the ways QM may be performed. In this review, we will discuss the features of AIMS that make them indispensable for QM practices as well as some of their current limitations, cover the specific ways in which AIMS have been used to enhance QA and QI practices, and briefly discuss some of the potential future enhancements that may make AIMS even more effective as guarantors of quality care. ’
Features of AIMS that Support QM AIMS Data Quality and Accessibility
Anesthesia is easily the most information-intense period of a patient’s clinical care. The very first anesthesia record created by Cushing was born out of the need to objectively quantify the quality and course of anesthesia care2—this record included respiratory rate, pulse, and temperature. Today, however, the abundance of physiological and pharmacologic data generated as a result of technological advances and increased sophistication of monitoring devices make it difficult, if not impossible, to transcribe a complete and accurate account of an anesthetic encounter by hand.3–5 This is especially true during critical events when patient care takes precedence over manual charting of data.6 Recording vital signs and other information on paper is usually done after delivering care to the patient, leading to recall bias. Other limitations of hand-written anesthesia records include: records are often illegible and incomplete; smoothing of physiological data occurs around expected values; the increased workload on the anesthesia provider may divert attention from patient care; data may be lost; and paper charts require labor-intensive manual review and data extraction for QA or outcomes research. AIMS by their very nature overcome many of these limitations of paper records, improving both the quality and accessibility of the intraoperative data. First, physiological data are usually captured directly from monitors, eliminating bias and the need for retroactive recording during busy periods or critical events. Several studies have proven the superiority of automated data collection over hand-written records; the automated data are more accurate, legible, and accessible even from remote locations in real time.4 Automated data capture, however, is not without limitations. Artifacts in monitoring devices may be misinterpreted as extremes of physiological values. Lapses can occur in both automated capture and manual documentation with potentially www.anesthesiaclinics.com
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serious patient safety and medicolegal implications. Several practices have reported a significant decrease in such documentation errors with the use of independently operating custom software that can generate reminders with appropriate visual or messaging alerts to the anesthesia provider.7,8 Not all AIMS provide alerts for network failures or disconnections, and although this is a rare occurrence, there is a concern for loss of data. Finally, the quality of manual data entry is only as good as the information entered by the end user. Proper configuration with structured text and dropdown menus minimizes the need for free-text entry, which can reduce documentation errors and the time spent by the provider in interacting with the computer, leaving more time for patient care.9,10 Another obvious improvement of AIMS over a paper record is the accessibility of the data. As case data are invariably stored in a database of some kind, pulling large batches of data or searching through any number of anesthetics is only a matter of building a query and allowing the system time to perform the search. This makes data collection for QA analysis much less labor intensive and in most cases a regular report with desired data can easily be generated from the system and sent to appropriate personnel. Further, performing QI studies on new measures is much less difficult because even if not previously tracked by QA personnel, the data already exists in the system and merely needs to be pulled for whatever work is being done. Decision Support
Clinical decision support (CDS) is a knowledge management system that “provides health care providers, patients, or other individuals with knowledge and person-specific information, intelligently filtered or presented at appropriate times, to enhance health and health care.”11 Decision support is one of the areas in which AIMS are most rapidly evolving, moving them beyond mere anesthesia record keepers. CDS systems can be passive or active and are designed to manage provider behavior through a number of different pathways (Table 1). Passive CDS includes reference guides such as the emergency airway algorithm and ACLS protocols, as well as pre-made order sets for specific functions such as postoperative anesthesia care. This group also includes many things providers probably do not even consider “decision support,” but nevertheless clearly influence the way they behave, such as document templates or graphical information displays.12 For example, a preoperative H&P form is a type of decision support because it indicates the pieces of information the provider should seek and document. Most modern AIMS are equipped with at least some basic decision support tools for things such as weight-based drug-dose calculations and drug allergy alerts. www.anesthesiaclinics.com
Using Anesthesia AIMS Data in Quality Management
Table 1.
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Passive Versus Active Clinical Decision Support in AIMS
Element
Passive Support
Active Support
Billing compliance
Automated coding and Advisories for elements needed for detection of billable events billing; automated reminders to providers for documentation requirements (attestations, etc.) Documentation Templates with required Automated scanning and provider fields notification of documentation deficiencies Hemodynamics Graphical information Patient analysis and treatment displays recommendations Patient In-process identification of Treatment recommendations management patients at high-risk for based on real-time monitored specific outcomes patient data Antibiotic redosing reminders; Medication In-process interaction and spontaneous recommendations administration allergy checking, for specific patients verification of dosing (eg, PONV prophylaxis) Protocol Algorithm references Active pathways which guide compliance providers through required steps AIMS, anesthesia information management system; PONV, postoperative nausea and vomiting.
Active CDS is different from passive CDS, in that the latter is typically encountered by the provider in the course of provider-initiated actions, whereas active CDS usually involves interruptions or unsolicited alerts from the system about an action the provider should consider taking but has not.13 The obvious benefit of active CDS is that compliance with protocols and recommended care actions, as well as general vigilance, can be increased through reminders to care providers14; the potential risk is alarm fatigue and that interruptions during care may do more harm than good if poorly implemented.15,16 Despite the risks, active CDS tools have been shown to improve patient care in a number of clinical studies.17–21 Real-time diagnostic applications such as detection of critical events22 and predictive applications of CDS23 will have a major impact on the future practice of anesthesiology. Limitations of Current AIMS Implementations in QM
Despite the many benefits that AIMS bring to QM processes, existing AIMS implementations are still subject to some limitations. First, as previously noted, the quality and accuracy of provider-entered data are dependent on the provider, especially when it comes to nonstandard data such as free-text fields. Text templates may reduce variability of these fields but may also decrease accuracy of recorded data if providers do not update text to reflect the actual events (as opposed to the stock text of the template). In fact, AIMS in some cases www.anesthesiaclinics.com
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may increase the risk of inaccuracies in data when providers prioritize moving through required data entry pathways quickly as opposed to focusing on accuracy of data.24 Timeliness of event recording may also be imprecise in these cases. On a larger scale, the heterogeneity of existing AIMS and the different ways they record data and events limits the ability of these systems to interact with external systems. Each AIMS developer must build a unique interface for each external system and vice versa, so even facilities using the same AIMS vendor may find it difficult to share data. Efforts such as the Anesthesia Quality Institute (AQI) initiative may help bring standardization to some elements of anesthesia records. However, in the absence of an authoritative standards body, this situation is unlikely to change substantially in coming years. ’
Using AIMS Data for QM Quality Assurance
Critical incident reporting is essential for retrospective review and analysis to bring about change in practice and improve patient safety. This process is the crux of traditional departmental QA. As noted, an AIMS database provides a more accurate, complete, and objective record for retrospective review and planning QI projects at the departmental level than paper charts. Tracking and trending of events can point to system issues that may need to be corrected. Moreover, specific QA indicators can be recorded in AIMS through voluntary or mandatory self-reporting, and several studies have shown that mandatory in-pathway reporting improves event detection.25,26 An additional consideration is the confidentiality of the QA data. Vigoda et al8 showed a dramatic increase in the anesthesia events reported by creating a mandatory stop before AIMS record closure in the PACU, and this hard stop directed the provider to a QA reporting system outside of AIMS for reporting of critical events. The advantage of this system design is that the clinical event data can be kept confidential and protected from discovery in case of litigation. Several studies indicate that automated detection of certain critical events in AIMS is far superior to voluntary reporting.27–29 Electronic scanning for physiological and laboratory parameters outside of predefined ranges lead to much higher rates of detection of anesthesia events compared with voluntary reporting.29 Many AIMS have integrated perioperative modules (scheduling, anesthesia preoperative, intraoperative, postoperative, and even perioperative nursing and PACU modules) that provide means for assessment, risk identification, and linking of intraoperative and PACU care to events. The most efficient systems for QM are enterprise-wide systems www.anesthesiaclinics.com
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or AIMS integrated with the hospital EMR where all the data goes into 1 repository. Such systems provide additional information on patient’s preoperative risk factors and laboratory values, outcomes beyond the PACU, as well as information such as length of stay in the ICU and hospital. As discussed, however, currently AIMS are highly customized at each facility and there is no national standard, so comprehensive and integrated hospital-wide systems are rare.
AIMS—A Conduit for National Registries
Despite the variances between vendors and even within vendors between hospitals, AIMS nevertheless help organize data and facilitate electronic transfer of relevant data to national registries. The AQI was chartered by the American Society of Anesthesiologists with the goal of collecting electronic data from all anesthesia practices in the United States and establishing the National Anesthesia Clinical Outcomes Registry.30 The participating practices can access their own quality and practice management data as well as benchmark their practices to their peer groups to improve quality at the local level. Currently, the bulk of AQI data comes from billing data which is generally present in digital format. However the ultimate goal is to collect detailed perioperative data from AIMS on risks, anesthesia care, and outcomes. The hope is that this data sharing can help identify and disseminate best practices and advocate for public health interests. With regard to establishing standards among AIMS vendors, the AQI, in collaboration with The Multicenter Perioperative Outcomes Group (MPOG—a consortium of practices that share AIMS data for outcomes research), is developing a common dataset and terminology for outcomes.31,32 This standardization of terminology is essential for comparing outcomes at a national level.
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Using AIMS for Performance Improvement
Since the Institute of Medicine released its landmark report in 2006 discussing pay-for-performance,33 health care systems have been increasingly focused on ensuring high-quality care and meeting designated outcome targets. The entire health care system in the United States is under more intense scrutiny than ever, especially as costs have risen without a concomitant improvement in perceived quality. AIMS play a critical role in performance improvement processes from the individual provider to hospital and health care system levels. www.anesthesiaclinics.com
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Adherence to Clinical Practice Guidelines
Under the Affordable Care Act in the United States, the Centers for Medicare and Medicaid implemented the Hospital Value-Based Purchasing program in 2012. This program links payments to quality of care measures with the goal of preventing medical errors and decreasing cost of care; payments will be based on specific process measures and patient satisfaction. Several of the perioperative performance measures are from the Surgical Care Improvement Project focusing on reducing postoperative complications related to surgical site infection, venous thromboembolism, and adverse cardiovascular events. For example, one of the guidelines pertains to administration of antibiotics within 60 minutes before surgical incision. Tools in AIMS in the form of prompts, visual alerts, and provider-specific feedback have enabled providers to achieve high levels of compliance with antibiotic administration34,35 to satisfy this requirement. At least 1 practice achieved nearly 100% compliance with the use of alerts, email messaging along with a hard stop, without which the AIMS anesthesia record could not be signed off.36 Similar point-of-care decision support tools can facilitate compliance with administration of perioperative b-blockers, maintaining normothermia, and VTE prophylaxis. This consistently high level of performance would be difficult to achieve with paper records. Even if not linked to financial incentives, clinical practice guidelines may standardize care and decrease variation, thereby improving patient safety and decreasing cost of care. Clinical practice guidelines include “systematically developed statements” on best practices that are based on current medical evidence,37 but a variety of barriers have hindered their widespread use.38 AIMS can facilitate removal of some of the barriers related to awareness, familiarity, and environment by bringing clinical guidelines directly to the point of care. For example, Kooij et al20 demonstrated that the use of real-time decision support can assist with risk identification and improve compliance with postoperative nausea and vomiting guidelines in the PACU. Real-time decision support algorithms integrating patient-specific and case-specific recommendations based on practice guidelines have been shown to improve patient care and provider performance in a variety of settings. Examples include prevention of postoperative respiratory complications by automated risk detection and notification of respiratory therapist for patients at risk for obstructive sleep apnea,39 an alert system for potentially insufficient anesthesia,40 and guidance for intraoperative hemodynamic management and early detection of critical events.41–43 Team Performance in the Operating Room
Some perioperative systems have the added advantage of retrieving information from disparate systems and integrating this information onto a single dashboard for display on a large screen in the operating www.anesthesiaclinics.com
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room. Such dashboards display aggregate information on patient identity, planned procedure, critical labs, allergies, preoperative risks, vital signs trends, personnel information, case milestones, patient data and disposition, and time elapsed since the last dose of antibiotics. A dashboard like this heightens the situational awareness of the entire surgical team, improves team communication, and enhances patient safety by displaying critical information. Provider Performance
AIMS can simplify data abstraction and reporting on quality measures in clinical practice, compliance with the federally mandated Physician Quality Reporting System (PQRS), or medical staff credentialing. The PQRS requires anesthesiologists to report on 3 quality measures—timely antibiotic administration, central line infection prevention practices, and postoperative normothermia—and is tied to negative payment adjustment for providers who do not report on these metrics.44 AIMS also allow providers to track and compare their performance with their peers as a QI tool.34–36,45 Ongoing Professional Practice Evaluation (OPPE) is a Joint Commission standard that requires medical staff to collect provider-specific quality metrics and initiate interventions, if needed, to target areas of deficiency.46 The use of AIMS data to fulfill OPPE requirements can save time and resources as opposed to traditional credentialing methods. ’
Unrealized Potential
Despite the many benefits that AIMS have brought to health care, the full potential of these systems is yet to be realized. For example, although CDS is being implemented at many levels, there is far greater opportunity to improve practice with more intelligent systems.47 Analytic systems may be able to detect adverse events as they evolve and provide a level of vigilance beyond simple vital signs range alarms.48 QA processes could be dramatically enhanced by the implementation of wide-spread automatic event reporting; several studies have shown that automated capture is a significant improvement over voluntary forms of reporting.26,49,50 Cross-system communication, especially the sharing of data with other institutions and national bodies (such as the AQI and MPOG programs), will allow individuals and hospitals alike to compare their own performance to national averages to maintain exceptional practices while directly targeting deficiencies for interventions.51 There is a great deal of untapped potential in data management and analysis, thanks to the rise of AIMS, and the coming years should see a rapid expansion in both the prevalence and sophistication of their use in QM practices. www.anesthesiaclinics.com
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Conclusions
AIMS have revolutionized QM practices in many ways—indeed, running an efficient and effective anesthesiology QM program today more or less requires one. With the increasing national focus on quality and outcomes, we expect the majority of anesthesiology practices will find it not just cost effective but essential to transition to AIMS from paper records in the coming years if they wish to meet reporting requirements. Significant gains could be made by focusing developmental efforts on cross-platform integration and increasing the sophistication of both real-time data analysis and decision support within these systems.
S.B.V. is a member of the Surgical Information Systems Anesthesia Advisory Board. J.R. declares that there is nothing to disclose.
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