Patient and Health Care Provider Experiences With Suicide-Related Tele–Mental Health Evaluations in the Emergency Department: Multiphase Qualitative Study
Bibliographic record
Abstract
BACKGROUND: Suicide is one of the most pressing public health issues in the United States, inflicting a devastating toll on families, communities, and society. Individuals with suicide risk often visit emergency departments (EDs), but the setting has chronic shortages in psychiatric care staffing, which results in gaps in best practices, prolonged length of stay for patients, and unnecessary inpatient admissions. To improve behavioral health care and suicide prevention practices, we implemented telehealth-based mental health evaluations with enhanced suicide care at 2 EDs in Massachusetts. Little is known about patient experiences and perceptions toward the appropriateness of telehealth for emergency mental health evaluations in the context of suicide prevention. OBJECTIVE: The goal of our qualitative study was to understand patient and health care provider experiences with the Telehealth to Improve Prevention of Suicide (TIPS) program and to gain insight into aspects of the implementation process. METHODS: We conducted 25 semistructured qualitative interviews with 10 patients who received a tele-mental health evaluation and 12 clinicians, including behavioral health and ED providers, whose clinical workflows included the new telehealth implementation. We used methods for rapid qualitative analysis and were guided by key implementation of a priori domains outlined in the Practical, Robust Implementation and Sustainability Model framework. RESULTS: Patients and health care providers reported their perceptions of the patient care experiences and recommendations related to implementation. Patients' perspectives were highly varied, with several factors and priorities contributing to their views on tele-mental health in this setting. Overall, patients valued transparency and informed decision-making, which extended to having the option to choose between an in-person or telehealth evaluation. Health care providers generally felt that in-person evaluations were preferable; however, given the long wait times and staffing concerns, telehealth evaluations offered a strong alternative. Both patients and health care providers reported several recommendations for future implementation efforts, including increased support and information, communication throughout the process, and improving overall psychiatric care in the ED. CONCLUSIONS: Given current shortages in behavioral health care, emergency tele-mental health evaluations could provide an opportunity to reduce wait times and support the delivery of best practice suicide-related care. However, their implementation has the potential to exacerbate existing issues related to patient autonomy, therapeutic alliance, and care transitions. Our study contributes to filling a gap in knowledge related to patient and health care provider experiences of this telehealth service and describes factors that impact implementation, which may inform future care advances by clinicians and administrators.
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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 | 0.015 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.005 | 0.004 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.001 | 0.002 |
| 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".