Development and Pilot Testing of a Data-Rich Clinical Reasoning Training and Assessment Tool
Bibliographic Data
| ID | 21612965 |
|---|---|
| Authors | Jason Waechter (0000-0003-1179-3977, University of Calgary, corresponding author), James G Allen (0000-0002-5249-9355, University of North Dakota), Jon Allen (0000-0002-2722-7235, J. Allenis professor, Department of Medicine, University of North Dakota School of Medicine and Health Sciences, Grand Forks, North Dakota.), Chel Hee Lee (0000-0001-8209-8176, University of Calgary), Laura Zwaan (0000-0003-3940-1699, L. Zwaanis assistant professor, Erasmus Medical Center, Institute of Medical Education Research Rotterdam, Rotterdam, the Netherlands.) |
| Year | 2022 |
| Volume | 97 |
| Issue | 10 |
| Pages | 1484-1488 |
| Publication date | 2022-10-01 |
| Peer Reviewed | Yes |
| Open Access | No |
| Type | ARTICLE |
| Venue | Academic Medicine (JOURNAL) |
| Journal identifiers | ISSN: 1040-2446 • E-ISSN: 1938-808X |
| Publisher | Oxford University Press (OUP) (PUBLISHER) |
| DOI | 10.1097/acm.0000000000004758 |
| PMID | 35612911 |
| OpenAlex | W4281492908 |
| Language | EN |
| Citations received | 2 |
| References cited | 10 |
PROBLEM: Clinical reasoning is a core competency for physicians and also a common source of errors, driving high rates of misdiagnoses and patient harm. Efforts to provide training in and assessment of clinical reasoning skills have proven challenging because they are either labor- and resource-prohibitive or lack important data relevant to clinical reasoning. The authors report on the creation and use of online simulation cases to train and assess clinical reasoning skills among medical students. APPROACH: Using an online library of simulation cases, they collected data relevant to the creation of the differential diagnosis, analysis of the history and physical exam, diagnostic justification, ordering tests; interpreting tests, and ranking of the most probable diagnosis. These data were compared with an expert-created scorecard, and detailed quantitative and qualitative feedback were generated and provided to the learners and instructors. OUTCOMES: Following an initial pilot study to troubleshoot the software, the authors conducted a second pilot study in which 2 instructors developed and provided 6 cases to 75 second-year medical students. The students completed 376 cases (average 5.0 cases per student), generating more than 40,200 data points that the software analyzed to inform individual learner formative feedback relevant to clinical reasoning skills. The instructors reported that the workload was acceptable and sustainable. NEXT STEPS: The authors are actively expanding the library of clinical cases and providing more students and schools with formative feedback in clinical reasoning using our tool. Further, they have upgraded the software to identify and provide feedback on behaviors consistent with premature closure, anchoring, and confirmation biases. They are currently collecting and analyzing additional data using the same software to inform validation and psychometric outcomes for future publications
Formative assessment · Mathematics education · Medical education · Summative assessment · Workload · Clinical Reasoning and Diagnostic Skills · Computer Science · Innovations in Medical Education · Medicine · Psychology · Simulation-Based Education in Healthcare
| Unique citing works | 2 |
|---|---|
| Citations per year | 0,67 |
| Citation span | 2023 - 2026 (4) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 2 |