Guidelines for Patient-Centered Documentation in the Era of Open Notes: Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Patients in the United States have recently gained federally mandated, free, and ready electronic access to clinicians' computerized notes in their medical records ("open notes"). This change from longstanding practice can benefit patients in clinically important ways, but studies show some patients feel judged or stigmatized by words or phrases embedded in their records. Therefore, it is imperative that clinicians adopt documentation techniques that help both to empower patients and minimize potential harms. OBJECTIVE: At a time when open and transparent communication among patients, families, and clinicians can spread more easily throughout medical practice, this inquiry aims to develop informed guidelines for documentation in medical records. METHODS: Through a series of focus groups, preliminary guidelines for documentation language in medical records were developed by health professionals and patients. Using a structured focus group decision guide, we conducted 4 group meetings with different sets of 27 participants: physicians experienced with writing open notes (n=5), patients accustomed to reviewing their notes (n=8), medical student educators (n=7), and resident physicians (n=7). To generate themes, we used an iterative coding process. First-order codes were grouped into second-order themes based on the commonality of meanings. RESULTS: The participants identified 10 important guidelines as a preliminary framework for developing notes sensitive to patients' needs. CONCLUSIONS: The process identified 10 discrete themes that can help clinicians use and spread patient-centered documentation.
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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 | 0.060 | 0.076 |
| 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 | 0.011 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".