Open Notes in Mental Health: A Scoping Review of Stakeholder Experiences and Implications for Clinical Practice
Bibliographic record
Abstract
Background/Objectives: Open Notes—defined as patients’ electronic, portal-based access to clinicians’ narrative documentation within electronic health records (EHRs)—has become routine through policy and portal initiatives. In mental health (MH), transparency intersects with sensitive formulation and risk language, making outcomes contingent on documentation practices, release timing, and reader support. This scoping review mapped empirical evidence on experiences, perceived impacts, and implementation of Open Notes in MH across stakeholders and settings, deriving implications for practice, training, and policy. Methods: A PRISMA-ScR-guided review was conducted with a preregistered protocol on OSF. Eligible studies examined Open Notes in MH settings and reported stakeholder perspectives. Two reviewers independently screened and extracted data, analyzed through inductive narrative thematic synthesis. Results: Twenty-two studies (2012–2025) from the USA, Sweden, Germany, Canada, and international settings included surveys, qualitative interviews, mixed-methods designs, pilot and quasi-experimental implementations, and a Delphi consensus. Patients consistently reported improved comprehension, recall, empowerment, and—in some cases—greater trust. Large surveys identified error detection and patient-initiated corrections as safety mechanisms, while a minority reported worry or feeling judged by wording. Clinicians adapted documentation—modifying tone, wording, or candor—to minimize misinterpretation. Workload effects were generally modest, limited to occasional clarifications. Implementation and expert studies emphasize organizational readiness, training, patient preparation, and privacy-aware portal design as key enablers of safe transparency. Conclusions: In MH, Open Notes function as a communication and engagement tool that strengthens partnership, comprehension, and safety when implemented with attention to risk-sensitive documentation and privacy safeguards.
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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 | 0.014 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".