Experiences of Older Mental Health Patients and Their Care Partners Using a Proxy Account to Access Open Notes: Qualitative Interview Study
Bibliographic record
Abstract
BACKGROUND: Older patients with serious mental illnesses such as cognitive disorders often rely on family members or spouses (care partners [CPs]) to meet their health care needs. CPs frequently lack essential information to fully understand the patients' illnesses and effectively support their treatment. Open Notes provide patients with digital access to their health care professionals' clinical notes and are associated with many positive outcomes, such as increased adherence and empowerment. However, older patients who use Open Notes may encounter use barriers such as limited digital literacy. Recent developments allow CPs to access Open Notes (proxy access) and receive valuable information, which holds significant potential for improving the care of older patients. OBJECTIVE: This study explored the experiences, barriers, and opportunities of older mental health patients and their CPs related to using Open Notes. Furthermore, influencing factors and interdependencies were identified. METHODS: Older patients (n=10) and their CPs (n=10) were provided with web-based proxy access to clinical documentation through a web-based patient portal. In-depth qualitative interviews (N=20) were conducted to explore experiences with this access. Data analysis was conducted in accordance with the constructivist grounded theory approach. RESULTS: The prerequisites for using Open Notes with proxy access were sufficient digital literacy on the part of the patient or CP, as well as the establishment of a trusting relationship between patients and CPs. Access to Open Notes enabled patients and CPs to gain a deeper understanding of the illness and its treatment while also facilitating enhanced contact with health care professionals. This resulted in greater involvement in the treatment process but may also prompt changes in relationship dynamics-CPs are better equipped to support patients in their health care but may also tend to monitor or control them through Open Notes. As a result, the introduction of Open Notes was accompanied by mixed feelings. CONCLUSIONS: It is of utmost importance to provide older patients with comprehensive access to Open Notes to preserve their health autonomy. However, the involvement of CPs through proxy access is of great value in improving the care of older patients, especially those with cognitive impairments.
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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 | 0.009 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.006 | 0.005 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.007 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".