MétaCan
Menu
Back to cohort
Record W4408346670 · doi:10.2196/63713

Patient–Health Care Professional Communication via a Secure Web-Based Portal in Severe Mental Health Conditions: Qualitative Analysis of Secure Messages

2025· article· en· W4408346670 on OpenAlexvenueno aff
Eva Meier-Diedrich, Carolyn Turvey, Jonas Wördemann, Justin Speck, Mareike Weibezahl, Julian Schwarz

Bibliographic record

VenueJMIR Formative Research · 2025
Typearticle
Languageen
FieldHealth Professions
TopicElectronic Health Records Systems
Canadian institutionsnot available
Fundersnot available
KeywordsPreprintInternet privacyMental healthQualitative researchComputer securityQualitative analysisWorld Wide WebComputer scienceMedicinePsychiatrySociology

Abstract

fetched live from OpenAlex

BACKGROUND: Patients' web-based access to their medical records and secure messaging (SM) via patient portals is becoming increasingly prevalent worldwide. SM offers several potential benefits, including improved health outcomes and increased patient engagement. However, SM also raises concerns about effects on the therapeutic relationship and may be constrained by factors such as limited digital literacy and access to digital devices. Evidence on the use of SM in mental health is limited, and results are inconclusive. OBJECTIVE: This study aimed to examine (1) the purposes for which health care professionals (HCPs) and patients with psychiatric disorders use SM to communicate and (2) the specific use patterns associated with both patients and HCPs. METHODS: The secure messages (n=274) of 38 patients with psychiatric disorders and 4 HCPs (psychiatrists) from 3 psychiatric outpatient clinics in Brandenburg, Germany, was analyzed using thematic analysis. The data selected for this study represent a subsample from a larger study comprising a total of 116 patients. The subsample consists of the patients and HCPs who used SM. RESULTS: A total of 274 messages were analyzed: 22.3% (61/274) were initial notes from HCPs, 44.5% (122/274) were patient responses, and 33.2% (91/274) were HCP replies. Patients sent between 1 and 15 messages (mean 4.16, SD 3.42) and logged in 1 to 42 times (mean 10.78, SD 9.38). Most messages were sent during the day, although some were also sent at night and in the early morning. Regarding the purposes of SM, 4 core functions of SM were identified: reporting and feedback, interpersonal uses, intrapersonal uses, and organizational uses. Both patients and HCPs used SM to share treatment-relevant information and elicited feedback on treatment and medication. Furthermore, secure messages included expressions of gratitude by the patients, in addition to well-wishes and emotional support from the HCPs. SM allowed patients to reflect on their treatment and provide self-encouragement. Finally, secure messages were used to address organizational aspects such as scheduling, appointments, and administrative tasks. CONCLUSIONS: SM in outpatient mental health care is multifaceted and holds the potential to enhance therapeutic contact and improve access to care by enabling quick, low-threshold communication between patients and HCPs, allowing treatment-related concerns to be addressed promptly and effectively. However, the asynchronous nature of SM also poses new challenges, particularly in managing acute mental health crises and in setting boundaries to prevent HCPs from being perceived as constantly available. Therefore, specific training for HCPs-both during medical education and in clinical practice-is essential, along with clear guidelines on handling crises and managing sensitive information.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.012
metaresearch head score (Gemma)0.024
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.012
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0120.024
Meta-epidemiology (narrow)0.0000.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.002
Science and technology studies0.0050.006
Scholarly communication0.0030.003
Open science0.0010.005
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0020.000

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.

Opus teacher head0.063
GPT teacher head0.579
Teacher spread0.516 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
Domainnot available
GenreEmpirical

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".

Quick stats

Citations5
Published2025
Admission routes1
Has abstractyes

Explore more

Same venueJMIR Formative ResearchSame topicElectronic Health Records SystemsFrench-language works237,207