Using language models to integrate clinical decision support and note taking: a qualitative study
Bibliographic record
Abstract
Introduction: Prior to designing a novel user interface which would integrate clinical decision support from the Acute COPD Exacerbation Prediction Tool (ACCEPT) with physician note-taking, we conducted an exploratory qualitative study to understand physician attitudes, experiences, and expectations for this kind of functionality. Methods: We conducted semi-structured interviews with practicing physicians recruited through convenience sampling from five teaching hospitals in Canada and the US. We asked questions about how formal risk assessment is used, experience and trust in prediction models, EHR integration, and dictation practices. We qualitatively analyzed interview transcripts using affinity diagrams to produce themes highlighting relevant topics. Results: We interviewed ten physicians (70% female, 4 respirologists, 4 cardiologists, 2 residents) whose clinical experience ranged from 2 to 38 years. Our affinity analysis revealed five high-level themes. All participants were routinely but infrequently using prediction models that required manual data entry outside of the EHR system, mostly to determine malignancy risk for pulmonary nodules, pre-operative mortality risk assessment, or statins eligibility. Reasons for using models included risk communication with patients or colleagues, and automating mundane tasks. Frustration with data organization, retrieval, and letter writing in the EHR system was common. Conclusions: Participants were receptive to decision support software interfaces that would simplify data retrieval and letter writing, but were also willing to accept some inconvenience to use a truly useful risk score, especially if they only used it occasionally.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.035 | 0.051 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.010 | 0.012 |
| Scholarly communication | 0.006 | 0.007 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".