Medical Students' Perception of Automated Note Feedback After Simulated Encounters.
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| Název: | Medical Students' Perception of Automated Note Feedback After Simulated Encounters. |
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| Autoři: | Bansal SK; Department of Internal Medicine, University of Illinois College of Medicine at Peoria, Peoria, Illinois, USA., Yadav M; Department of Internal Medicine, University of Illinois College of Medicine at Peoria, Peoria, Illinois, USA., Zhou J; Department of Computer Science, University of Illinois, Urbana-Champaign, Illinois, USA., Ebert-Allen RA; Jump Simulation, an OSF HealthCare and University of Illinois College of Medicine at Peoria Collaboration, Peoria, Illinois, USA., Klute RM; Jump Simulation, an OSF HealthCare and University of Illinois College of Medicine at Peoria Collaboration, Peoria, Illinois, USA., Bond WF; Jump Simulation, an OSF HealthCare and University of Illinois College of Medicine at Peoria Collaboration, Peoria, Illinois, USA.; Department of Emergency Medicine, University of Illinois College of Medicine at Peoria, Peoria, Illinois, USA., Bhat S; Department of Electrical and Computer Engineering, University of Illinois, Urbana-Champaign, Illinois, USA. |
| Zdroj: | The clinical teacher [Clin Teach] 2025 Dec; Vol. 22 (6), pp. e70273. |
| Způsob vydávání: | Journal Article |
| Jazyk: | English |
| Informace o časopise: | Publisher: Blackwell Pub Country of Publication: England NLM ID: 101227511 Publication Model: Print Cited Medium: Internet ISSN: 1743-498X (Electronic) Linking ISSN: 17434971 NLM ISO Abbreviation: Clin Teach Subsets: MEDLINE |
| Imprint Name(s): | Original Publication: Oxford, UK : Blackwell Pub., c2004- |
| Výrazy ze slovníku MeSH: | Students, Medical*/psychology , Natural Language Processing* , Educational Measurement*/methods , Formative Feedback* , Education, Medical, Undergraduate*/methods, Humans ; Feedback ; Focus Groups |
| Abstrakt: | Background: Grading medical student patient notes (PNs) is resource-intensive. Natural language processing (NLP) offers a promising solution to automatically grade PNs. We deployed an automated grading system that uses NLP and explored the perceived value of PN feedback. Approach: The automated system graded written notes after two standardized patient encounters by third-year medical students. The system generated an individualized report on 'items found' and 'items not found' in the history, physical examination, and diagnosis sections, which was shared with students for feedback via a web-based interface. By rotation, block students received either the automated case feedback first or the faculty-written model note feedback first (the pre-intervention baseline). Evaluation: After reviewing feedback, students completed surveys for both automated feedback and model note feedback and participated in follow-up focus groups. In total, 44 students received feedback, 37 completed surveys, and 28 participated in focus groups. Qualitative themes that emerged suggested the automated feedback was visually appealing and allowed for easy comparison of items found vs. missing, which would help improve students' documentation skills. Model note appeared trustworthy. Implications: We found automated systems can be a potential tool for formative feedback on note writing activity although in terms of quality it does not surpass the pre-existing feedback methods, such as model note feedback used in our study. Order effects may have influenced these perceptions and the small sample size limits generalizability. Tested software had occasional errors in recognizing a phrase or showing a false positive. (© 2025 The Author(s). The Clinical Teacher published by Association for the Study of Medical Education and John Wiley & Sons Ltd.) |
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| Grant Information: | University of Illinois College of Medicine at Peoria, Dean's Fund for Innovative Curriculum Award |
| Contributed Indexing: | Keywords: automated feedback; medical student; model notes; natural language processing (NLP) |
| Entry Date(s): | Date Created: 20251118 Date Completed: 20251118 Latest Revision: 20251123 |
| Update Code: | 20251123 |
| PubMed Central ID: | PMC12624243 |
| DOI: | 10.1111/tct.70273 |
| PMID: | 41251038 |
| Databáze: | MEDLINE |
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Nájsť tento článok vo Web of Science | Abstrakt: | Background: Grading medical student patient notes (PNs) is resource-intensive. Natural language processing (NLP) offers a promising solution to automatically grade PNs. We deployed an automated grading system that uses NLP and explored the perceived value of PN feedback.<br />Approach: The automated system graded written notes after two standardized patient encounters by third-year medical students. The system generated an individualized report on 'items found' and 'items not found' in the history, physical examination, and diagnosis sections, which was shared with students for feedback via a web-based interface. By rotation, block students received either the automated case feedback first or the faculty-written model note feedback first (the pre-intervention baseline).<br />Evaluation: After reviewing feedback, students completed surveys for both automated feedback and model note feedback and participated in follow-up focus groups. In total, 44 students received feedback, 37 completed surveys, and 28 participated in focus groups. Qualitative themes that emerged suggested the automated feedback was visually appealing and allowed for easy comparison of items found vs. missing, which would help improve students' documentation skills. Model note appeared trustworthy.<br />Implications: We found automated systems can be a potential tool for formative feedback on note writing activity although in terms of quality it does not surpass the pre-existing feedback methods, such as model note feedback used in our study. Order effects may have influenced these perceptions and the small sample size limits generalizability. Tested software had occasional errors in recognizing a phrase or showing a false positive.<br /> (© 2025 The Author(s). The Clinical Teacher published by Association for the Study of Medical Education and John Wiley & Sons Ltd.) |
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| ISSN: | 1743-498X |
| DOI: | 10.1111/tct.70273 |