Novel Precision Education Tools to Enhance Anesthesiology Workforce Development

Anesthesiology residency programs bear a critical responsibility to develop physicians who can safely provide care to a wide range of patients undergoing a broad spectrum of procedures. Currently, residency programs rely on time in training, required specialty rotations, and procedure logs to align the future anesthesiology workforce with the growing demands of effective practice management. Our research, funded by a FAER Research in Education Grant (REG) and titled “INSPECT” (Improvement in the Scope and Precision of Educational Cases for Trainees) supports a consortium of programs using data analytics to develop new precision education tools for learners and programs. Precision education tools could support individualized clinical training and allow more tailored workforce development in response to changing regional and national health care needs.

Regulatory bodies such as the Accreditation Council for Graduate Medical Education (ACGME) adjust residency program requirements periodically to adapt to an evolving health care landscape (asamonitor.pub/3x8hNPc). Precision education tools that leverage available large health care datasets could complement existing processes by providing contemporary data and support more rapid response to changing practice patterns. For example, there has been a recent proliferation in catheter-based cardiac valve interventions (Int J Cardiol 2019;292:68-72). Transcatheter aortic valve replacement (TAVR) was first approved in the United States in 2011, but was limited to patients deemed inoperable. Indications rapidly expanded to lower-risk populations such that in 2019 TAVRs exceeded surgical aortic valve replacement volumes (SAVR) in the U.S. (J Am Coll Cardiol 2020;76:2492-516). With optimized data structures and analytic tools, training programs could more efficiently identify trends in health care delivery as exemplified by the rapid shift in the treatment of aortic stenosis. In response, programs may implement measures such as modification of clinical assignments or other educational interventions to better prepare the emerging workforce.

Because residency management decisions are distributed across individuals and physical locations, a means to identify and effectively share knowledge regarding a trainee’s current skills and prior experiences would be valuable for facilitating safe care and optimizing learning. ACGME case logs combined with clinical rotations have been used to ensure adequate clinical training and provide some description of a resident’s current capabilities. However, there are significant limitations with existing approaches for tracking clinical experience. For example, if a resident completed a neuroanesthesia rotation and logged intracerebral cases, it is assumed that they understand aspects of elevated intracranial pressure management even if they did not actually experience this challenge. Current required self-reported case logs track experience with 12 specific surgical procedure types to help ensure breadth of exposure. That is far fewer than the hundreds of unique procedure types with which a typical resident is expected to develop competency. Self-reported case logs also suffer limitations due to omissions and misclassifications in reporting. Time on clinical rotations may not guarantee sufficient depth and breadth of clinical experience. As examples, a resident may develop the skills of effective double lumen tube placement but may not have experienced less common scenarios such as lung isolation in a patient with a difficult airway or the management of refractory hypoxia during single lung ventilation.

Large electronic health records (EHRs) could be leveraged to mitigate the challenges of practice management through workforce development via graduate medical education. There is a substantial body of literature using the standardized and quality controlled Multicenter Perioperative Outcomes Group (MPOG) database to support scientific discovery, identify optimal clinical care, and facilitate quality improvement. However, the use of this data to similarly develop educational insights and to improve the quality of anesthesiology education is underdeveloped. Our FAER REG explores the feasibility and utility of MPOG repository data analysis for anesthesiology GME. This pilot project will generate descriptive data regarding the hemorrhage resuscitation experience of more than 2,000 anesthesiology residents across 37 U.S. academic training centers. Unmeasured by current training paradigms in the U.S., hemorrhage resuscitation was chosen as the target experience due to its universal importance in anesthesia care and the multiple competencies required to effectively manage it. Preliminary single-center data demonstrated substantial variability in trainee exposure to hemorrhage resuscitation. During a second phase of this study, data visualizations will be designed to allow programs to understand and track trainee experience. It is anticipated that this will improve consistency in training across residents, identify gaps and strengths in trainees and training programs, and provide insights into the optimal sequencing of clinical care experiences.

Residency programs prepare trainees for independent practice through a curriculum that includes the provision of progressive clinical care challenges with diminishing direct supervision during training progression. Disruptions to the clinical curriculum occur from predictable (e.g., resident leave of absence, key surgeon departure) and less predictable (e.g., global pandemic) events. Individualized data on clinical experience may allow mitigation of such disruptions. As a proof a concept and template for future development, INSPECT will support programs and learners by generating actionable information regarding hemorrhage resuscitation for anesthesiology GME. Hemorrhage resuscitation was chosen as an exemplar clinical experience, but many other formative experiences are worthy of future study. With new tools, programs can better meet the ever-evolving challenges of practice management and balance the needs of learners with the responsibility to the patients we serve.