A Suicide Prevention Digital Technology for Individuals Experiencing an Acute Suicide Crisis in Emergency Departments: Naturalistic Observational Study of Real-World Acceptability, Feasibility, and Safety
Bibliographic record
Abstract
BACKGROUND: Emergency departments (EDs) are the front line in providing suicide care. Expert consensus recommends the delivery of several suicide prevention evidence-based interventions for individuals with acute suicidal ideation in the ED. ED personnel demands and staff shortages compromise delivery and contribute to long wait times and unnecessary hospitalization. Digital technologies can play an important role in helping EDs deliver suicide care without placing further demands on the care team if their use is safe to patients in a routine care context. OBJECTIVE: This study evaluates the safety and effectiveness of an evidence-based digital technology (Jaspr Health) designed for persons with acute suicidal ideation seeking psychiatric crisis ED services when used as part of routine ED-based suicide care. This study deployed Jaspr Health for real-world use in 2 large health care systems in the United States and aimed to evaluate (1) how and whether Jaspr Health could be safely and effectively used outside the context of a researcher-facilitated clinical trial, and (2) that Jaspr's use would be associated with improved patient agitation and distress. METHODS: Under the auspices of a nonsignificant risk device study, ED patients with acute suicidal ideation (N=962) from 2 health care systems representing 10 EDs received access to Jaspr Health as part of their routine suicide care. Primary outcome measures included how many eligible patients were assigned Jaspr Health, which modules were assigned and completed, and finally, the number of adverse events reported by patients or by medical staff. Secondary outcome measures were patient agitation, distress, and satisfaction. RESULTS: The most frequent modules assigned were Comfort and Skills (98% of users; n=942) and lethal means assessment (90% of patient users; n=870). Patient task completion rates for all modules ranged from 51% to 79%. No adverse events were reported, suggesting that digital technologies can be safely used for people seeking ED-based psychiatric services. Statistically significant (P<.001) reductions in agitation and distress were reported after using the app. Average patient satisfaction ratings by site were 7.81 (SD 2.22) and 7.10 (SD 2.65), with 88.8% (n=325) and 84% (n=90) of patients recommending the app to others. CONCLUSIONS: Digital technologies such as Jaspr Health may be safely and effectively integrated into existing workflows to help deliver evidence-based suicide care in EDs. These findings hold promise for the use of digital technologies in delivering evidence-based care to other vulnerable populations in complex environments.
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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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".