ArticleJournal of general internal medicine2022
Development of a Clinical Reasoning Documentation Assessment Tool for Resident and Fellow Admission Notes: a Shared Mental Model for Feedback.
Article in Journal of general internal medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07037940 (Comparing OpenEvidence and GPT-4 for Diagnostic Reasoning and Management Decisions), which is not on this map. Cited by 19 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Comparing OpenEvidence and GPT-4 for Diagnostic Reasoning and Management Decisions: A Randomized Trial
Who cites it
19 citing papers in PubMed.
- Large Language Model Influence on Diagnostic Reasoning: A Randomized Clinical Trial.JAMA network open · 2024Trial
- Conditional Perplexity Scoring for Large Language Model-Generated Differential Diagnoses in Case Reports: Preliminary Computational Evaluation.JMIR formative research · 2026Article
- Bridging the mentorship divide: how large language models could reshape medical workforce equity.NPJ digital medicine · 2026Article
- Rethinking AI in clinical decision support: a framework for reciprocal human-AI interaction.Frontiers in digital health · 2026Article
- Evaluating the diagnostic reasoning of large language models in complex neuro-ophthalmological cases: a comparative analysis of GPT-o1 Pro, GPT-4o, Gemini, Grok 2 and DeepSeek.BMJ open ophthalmology · 2025Article
- Evaluating large language model performance to support the diagnosis and management of patients with primary immune disorders.The Journal of allergy and clinical immunology · 2025Article
- Artificial intelligence based assessment of clinical reasoning documentation: an observational study of the impact of the clinical learning environment on resident documentation quality.BMC medical education · 2025Observational
- Large Language Model-Based Assessment of Clinical Reasoning Documentation in the Electronic Health Record Across Two Institutions: Development and Validation Study.Journal of medical Internet research · 2025Article
- Transformation and articulation of clinical data to understand students' clinical reasoning: a scoping review.BMC medical education · 2025Article
- Current and future state of evaluation of large language models for medical summarization tasks.npj health systems · 2025Article
- Clinical Reasoning and Knowledge Assessment of Rheumatology Residents Compared to AI Models: A Pilot Study.Journal of clinical medicine · 2024Article
- Developing and Evaluating Large Language Model-Generated Emergency Medicine Handoff Notes.JAMA network open · 2024Article
- Qualitative metrics from the biomedical literature for evaluating large language models in clinical decision-making: a narrative review.BMC medical informatics and decision making · 2024Review
- Influence of a Large Language Model on Diagnostic Reasoning: A Randomized Clinical Vignette Study.medRxiv : the preprint server for health sciences · 2024Article
- Digital Evidence: Revisiting Assumptions at the Intersection of Technology and Assessment.Perspectives on medical education · 2024Article
- A Pilot Longitudinal Clinical Reasoning Curriculum for Pediatric Residents.MedEdPORTAL : the journal of teaching and learning resources · 2024Article
- Assessing the Assessment-Developing and Deploying a Novel Tool for Evaluating Clinical Notes' Diagnostic Assessment Quality.Journal of general internal medicine · 2023Article
- TRainee Attributable & Automatable Care Evaluations in Real-time (TRACERs): A Scalable Approach for Linking Education to Patient Care.Perspectives on medical education · 2023Article
- Development and Validation of a Machine Learning Model for Automated Assessment of Resident Clinical Reasoning Documentation.Journal of general internal medicine · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundResidents and fellows receive little feedback on their clinical reasoning documentation. Barriers include lack of a shared mental model and variability in the reliability and validity of existing assessment tools. Of the existing tools, the IDEA assessment tool includes a robust assessment of clinical reasoning documentation focusing on four elements (interpretive summary, differential diagnosis, explanation of reasoning for lead and alternative diagnoses) but lacks descriptive anchors threatening its reliability.
objectiveOur goal was to develop a valid and reliable assessment tool for clinical reasoning documentation building off the IDEA assessment tool. DESIGN, PARTICIPANTS, AND MAIN MEASURES: The Revised-IDEA assessment tool was developed by four clinician educators through iterative review of admission notes written by medicine residents and fellows and subsequently piloted with additional faculty to ensure response process validity. A random sample of 252 notes from July 2014 to June 2017 written by 30 trainees across several chief complaints was rated. Three raters rated 20% of the notes to demonstrate internal structure validity. A quality cut-off score was determined using Hofstee standard setting. KEY
resultsThe Revised-IDEA assessment tool includes the same four domains as the IDEA assessment tool with more detailed descriptive prompts, new Likert scale anchors, and a score range of 0-10. Intraclass correlation was high for the notes rated by three raters, 0.84 (95% CI 0.74-0.90). Scores ≥6 were determined to demonstrate high-quality clinical reasoning documentation. Only 53% of notes (134/252) were high-quality.
conclusionsThe Revised-IDEA assessment tool is reliable and easy to use for feedback on clinical reasoning documentation in resident and fellow admission notes with descriptive anchors that facilitate a shared mental model for feedback.
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What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.