Preparing Patients and Clinicians for Open Notes in Mental Health: Qualitative Inquiry of International Experts
Bibliographic record
Abstract
BACKGROUND: In a growing number of countries worldwide, clinicians are sharing mental health notes, including psychiatry and psychotherapy notes, with patients. OBJECTIVE: The aim of this study is to solicit the views of experts on provider policies and patient and clinician training or guidance in relation to open notes in mental health care. METHODS: In August 2020, we conducted a web-based survey of international experts on the practice of sharing mental health notes. Experts were identified as informaticians, clinicians, chief medical information officers, patients, and patient advocates who have extensive research knowledge about or experience of providing access to or having access to mental health notes. This study undertook a qualitative descriptive analysis of experts' written responses and opinions (comments) to open-ended questions on training clinicians, patient guidance, and suggested policy regulations. RESULTS: A total of 70 of 92 (76%) experts from 6 countries responded. We identified four major themes related to opening mental health notes to patients: the need for clarity about provider policies on exemptions, providing patients with basic information about open notes, clinician training in writing mental health notes, and managing patient-clinician disagreement about mental health notes. CONCLUSIONS: This study provides timely information on policy and training recommendations derived from a wide range of international experts on how to prepare clinicians and patients for open notes in mental health. The results of this study point to the need for further refinement of exemption policies in relation to sharing mental health notes, guidance for patients, and curricular changes for students and clinicians as well as improvements aimed at enhancing patient and clinician-friendly portal design.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".