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. 2022 Jan 1;146(1):123-131.
doi: 10.5858/arpa.2020-0702-EP.

Hot Seat Diagnosis

Affiliations

Hot Seat Diagnosis

Rachel Han et al. Arch Pathol Lab Med. .

Abstract

Context.—: Competency-based medical education relies on frequent formative in-service assessments to ascertain trainee progression. Currently at our institution, trainees receive a summative end-of-rotation In-Training Evaluation Report based on feedback collected from staff pathologists. There is no method of simulating report sign-out.

Objective.—: To develop a formative in-service assessment tool that is able to simulate report sign-out and provide case-by-case feedback to trainees. Further, to compare time- versus competency-based assessment models.

Design.—: Twenty-one pathology trainees were assessed for 20 months. Hot Seat Diagnosis by trainees and trainee assessment by pathologists were recorded in the laboratory information system. In the first iteration, trainees were assessed by using a time-based assessment scale on their ability to diagnose, report, use ancillary tests, comment on clinical implications, and provide intraoperative consultation and/or gross cases. The second iteration used a competency-based assessment scale. Trainees and pathologists completed surveys on the effectiveness of the In-Training Evaluation Report versus the Hot Seat Diagnosis tool.

Results.—: Scores from both iterations correlated significantly with other assessment tools including the Resident In-Service Examination (r = 0.93, P = .04 and r = 0.87, P = .03). The competency-based model was better able to demonstrate improvement over time and stratify junior versus senior trainees than the time-based model. Trainees and pathologists rated Hot Seat Diagnosis as significantly more objective, detailed, and timely than the In-Training Evaluation Report, and effective at simulating report sign-out.

Conclusions.—: Hot Seat Diagnosis is an effective tool for the formative in-service assessment of pathology trainees and simulation of report sign-out, with the competency-based model outperforming the time-based model.

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