The Design and Implementation of an Automated System for Logging Clinical Experiences Using an Anesthesia Information Management System

Simpao, Allan F.; Heitz, James W.; McNulty, Stephen E.; Chekemian, Beth; Brenn, B. Randall; Epstein, Richard H.

doi:10.1213/ane.0b013e3182042e56

Cited by 28 publications

(27 citation statements)

References 9 publications

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“…Many specialties, including anesthesiology, use case logging systems to track clinical experience, and these logs are used as a surrogate for performance in routine care. 2,3,19 But, case logs do not contain evaluative information regarding clinical performance. Simulation has come to play a major role in technical and non-technical skill evaluations as another means of performance assessment.…”

Section: Discussionmentioning

confidence: 99%

“…2,3 However, to our knowledge there are no descriptions of an automated, near-real time performance feedback tool that provides residents and program directors with data on objective clinical performance concerning the quality of patient care that residents deliver. The increasing adoption of electronic record keeping in the perioperative period opens the possibility to develop real time or near-real time process monitoring and feedback systems.…”

Section: Introductionmentioning

confidence: 99%

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Automated Near–Real-time Clinical Performance Feedback for Anesthesiology Residents

et al. 2014

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Background Anesthesiology residencies are developing trainee assessment tools to evaluate 25 milestones that map to the 6 core competencies. The effort will be facilitated by development of automated methods to capture, assess, and report trainee performance to program directors, the Accreditation Council for Graduate Medical Education and the trainees themselves. Methods We leveraged a perioperative information management system to develop an automated, near-real-time performance capture and feedback tool that provides objective data on clinical performance and requires minimal administrative effort. Prior to development, we surveyed trainees about satisfaction with clinical performance feedback and about preferences for future feedback. Results Resident performance on 24,154 completed cases has been incorporated into our automated dashboard, and trainees now have access to their own performance data. Eighty percent (48 of 60) of our residents responded to the feedback survey. Overall, residents ‘agreed/strongly agreed’ that they desire frequent updates on their clinical performance on defined quality metrics and that they desired to see how they compared to the residency as a whole. Prior to deployment of the new tool, they ‘disagreed’ that they were receiving feedback in a timely manner. Survey results were used to guide the format of the feedback tool that has been implemented. Conclusions We demonstrate the implementation of a system that provides near real-time feedback concerning resident performance on an extensible series of quality metrics, and which is responsive to requests arising from resident feedback about desired reporting mechanisms.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated Near–Real-time Clinical Performance Feedback for Anesthesiology Residents

et al. 2014

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“…4 Previous investigators have found that Decision Support for Resident OR Assignments ACGME case logs and information derived from AIMS records deviates by at least 5% for the majority of residents. 5 Given the current lack of an automated mechanism to resolve these discrepancies or eliminate them by allowing AIMS records to populate ACGME case logs, we included both sets of metrics within Rhinos' resident profiles. With the current read-only ACGME case-log Web service, this is what is currently feasible.…”

Section: Discussionmentioning

confidence: 99%

Decision Support Using Anesthesia Information Management System Records and Accreditation Council for Graduate Medical Education Case Logs for Resident Operating Room Assignments

Wanderer

Charnin

Driscoll³

et al. 2013

Anesthesia &Amp; Analgesia

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Our goal in this study was to develop decision support systems for resident operating room (OR) assignments using anesthesia information management system (AIMS) records and Accreditation Council for Graduate Medical Education (ACGME) case logs and evaluate the implementations. We developed 2 Web-based systems: an ACGME case-log visualization tool, and Residents Helping in Navigating OR Scheduling (Rhinos), an interactive system that solicits OR assignment requests from residents and creates resident profiles. Resident profiles are snapshots of the cases and procedures each resident has done and were derived from AIMS records and ACGME case logs. A Rhinos pilot was performed for 6 weeks on 2 clinical services. One hundred sixty-five requests were entered and used in OR assignment decisions by a single attending anesthesiologist. Each request consisted of a rank ordered list of up to 3 ORs. Residents had access to detailed information about these cases including surgeon and patient name, age, procedure type, and admission status. Success rates at matching resident requests were determined by comparing requests with AIMS records. Of the 165 requests, 87 first-choice matches (52.7%), 27 second-choice matches (16.4%), and 8 third-choice matches (4.8%) were made. Forty-three requests were unmatched (26.1%). Thirty-nine first-choice requests overlapped (23.6%). Full implementation followed on 8 clinical services for 8 weeks. Seven hundred fifty-four requests were reviewed by 15 attending anesthesiologists, with 339 first-choice matches (45.0%), 122 second-choice matches (16.2%), 55 third-choice matches (7.3%), and 238 unmatched (31.5%). There were 279 overlapping first-choice requests (37.0%). The overall combined match success rate was 69.4%. Separately, we developed an ACGME case-log visualization tool that allows individual resident experiences to be compared against case minimums as well as resident peer groups. We conclude that it is feasible to use ACGME case-log data in decision support systems for informing resident OR assignments. Additional analysis will be necessary to assess the educational impact of these systems.

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“…Automated systems can help alleviate some of the manual data entry required by such a system for anesthesiology residents [4]. Additionally, automated systems have been shown to increase compliance with residency expectations in emergency medicine for the patient care and PBLI competencies [5].…”

Section: Introductionmentioning

confidence: 99%

Automated Case Cancellation Review System Improves Systems-Based Practice

et al. 2015

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The Design and Implementation of an Automated System for Logging Clinical Experiences Using an Anesthesia Information Management System

Cited by 28 publications

References 9 publications

Automated Near–Real-time Clinical Performance Feedback for Anesthesiology Residents

Automated Near–Real-time Clinical Performance Feedback for Anesthesiology Residents

Decision Support Using Anesthesia Information Management System Records and Accreditation Council for Graduate Medical Education Case Logs for Resident Operating Room Assignments

Automated Case Cancellation Review System Improves Systems-Based Practice

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