Artificial intelligence based assessment of clinical reasoning documentation: an observational study of the impact of the clinical learning environment on resident documentation quality
AI can be used to assess complex skills such as clinical reasoning in authentic clinical notes that can help elucidate the potential impact of the CLE on resident clinical reasoning documentation quality. Future work should explore residency program and systems interventions to optimize the CLE.
Authors
- Daniel J. Sartori (ORCID: https://orcid.org/0000-0003-3138-7183)
- Matthew Haller (ORCID: https://orcid.org/0000-0003-3536-8270)
- Benedict Guzman (ORCID: https://orcid.org/0000-0002-2791-1491)
- Jesse Burk‐Rafel (ORCID: https://orcid.org/0000-0003-3785-2154)
- David J DiTullio (ORCID: https://orcid.org/0000-0002-3862-5255)
- Ilan Reinstein (ORCID: https://orcid.org/0000-0001-7360-4436)
- Kevin Hauck
- Verity E. Schaye (ORCID: https://orcid.org/0000-0003-0816-4037)
Institutions
- NYU Langone Health (US)
- New York University (US)
Publication Details
- Journal
- BMC Medical Education
- Published
- 2025-04-22
- DOI
- https://doi.org/10.1186/s12909-025-07191-x
- Citations
- 6
- Primary Topic
- Clinical Reasoning and Diagnostic Skills
- Type
- article
- Field-Weighted Citation Impact
- 9.09