Trial reportJAMA network open2025
Risk Model-Guided Clinical Decision Support for Suicide Screening: A Randomized Clinical Trial.
Trial report in JAMA network open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05312437 (A Pragmatic Randomized Controlled Trial of Machine Learning-driven Clinical Decision Support to Improve Suicide Risk Screening in Ambulatory Care Settings), which is not on this map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
A Pragmatic Randomized Controlled Trial of Machine Learning-driven Clinical Decision Support to Improve Suicide Risk Screening in Ambulatory Care Settings
Who cites it
6 citing papers in PubMed.
- Diagnostic accuracy of machine learning approaches for suicide‑related outcomes: a meta‑analysis.Annals of general psychiatry · 2026Article
- Research Domain Criteria and Deaths by Suicide in the National Violent Death Reporting System.JAMA network open · 2026Article
- External Validation of Epic's Risk of Opioid Abuse and Overdose Model Among Primary Care Patients in Three Health Systems.Journal of general internal medicine · 2026Article
- Clinical guidelines on self-harm and suicide prevention: taking uncertainty into account in the evidence base.BMJ mental health · 2026Article
- Barriers and Solutions to Efficient Health Care AI Implementation.JAMA network open · 2025Article
- Enhancing Suicide Attempt Risk Prediction Models with Temporal Clinical Note Features.Applied clinical informatics · 2024Article
Corrections and comments
- Update of
Authors and funding
21 authors.
Funding
Abstract
Importance: Suicide prevention requires risk identification, intervention, and follow-up. Traditional risk identification relies on patient self-reporting, support network reporting, or face-to-face screening. Statistical risk models have been studied and some have been deployed to augment clinical judgment. Few have been tested in clinical practice via clinical decision support (CDS). Barriers to effective CDS include potential alert burden for a stigmatized clinical problem and lack of data on how best to integrate scalable risk models into clinical workflows. Objective: To evaluate the effectiveness of risk model-driven CDS on suicide risk assessment. Design, Setting, and Participants: This comparative effectiveness randomized clinical trial was performed from August 17, 2022, to February 16, 2023, in the Department of Neurology across the divisions of Neuro-Movement Disorders, Neuromuscular Disorders, and Behavioral and Cognitive Neurology at Vanderbilt University Medical Center, an academic medical center in the US Mid-South. Patients scheduled for routine care in those settings were randomized at visit check-in. Follow-up was completed March 16, 2023, and data were analyzed from April 11 to July 24, 2023. Analyses were based on intention to treat. Interventions: Interruptive vs noninterruptive CDS to prompt further suicide risk assessment using a real-time, validated statistical suicide attempt risk model. In the interruptive CDS, an alert window via on-screen pop-up and a patient panel icon were visible simultaneously. Dismissing the alert hid it with no effect on the patient panel icon. The noninterruptive CDS showed the patient panel icon without the pop-up alert. When present, the noninterruptive CDS displayed "elevated suicide risk score" in the patient summarization panel. Hovering over this icon resulted in a pop-up identical to the interruptive CDS. Main Outcomes and Measures: The main outcome was the decision to assess risk in person. Secondary outcomes included rates of suicidal ideation and attempts in both treatment arms and baseline rates of documented screening during the prior year. Manual medical record review of every trial encounter was used to determine whether suicide risk assessment was subsequently documented. Results: A total of 561 patients with 596 encounters were randomized to interruptive or noninterruptive CDS in a 1:1 ratio (mean [SD] age, 59.3 [16.5] years; 292 [52%] women). Adjusting for clinician cluster effects, interruptive CDS led to significantly higher numbers of decisions to screen (121 of 289 encounters [42%]) compared with noninterruptive CDS (12 of 307 encounters [4%]) (odds ratio, 17.70; 95% CI, 6.42-48.79; P < .001) and compared with the baseline rate the prior year (64 of 832 encounters [8%]). No documented episodes of suicidal ideation or attempts occurred in either arm. Conclusions and Relevance: In this randomized clinical trial of interruptive and noninterruptive CDS to prompt face-to-face suicide risk assessment, interruptive CDS led to higher numbers of decisions to screen with documented suicide risk assessments. Well-powered large-scale trials randomizing this type of CDS compared with standard of care are indicated to measure effectiveness in reducing suicidal self-harm. Trial Registration: ClinicalTrials.gov Identifier: NCT05312437.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.