Comparing two approaches to implementing suicide risk identification models [abstract] Abstract uri icon
Overview
abstract
  • Background: Machine learning models using electronic
    health record data to identify individuals at risk for
    attempting or completing suicide are being developed and
    readied for implementation. A relatively new innovation
    in suicide prevention efforts, the implementation of these
    models warrants study; guidelines for acceptable and safe
    implementation do not yet exist.
    Methods: Guided by the Consolidated Framework for
    Implementation Research, we interviewed 40 clinicians and
    health care administrators at 3 integrated health systems.
    Two sites are currently engaged in implementing suicide risk
    identification models: one site implemented their approach as
    part of a mental health visit, the other site integrated it into an
    existing telephone-based case management program. Clinicians
    and administrators at the third site, planning for future suicide
    risk identification model implementation, also were interviewed
    about their concerns and hopes for this technology.
    Results: Content analysis of interviews revealed clinicians
    across sites broadly support the use of suicide risk
    identification models as another important tool in the suicide
    prevention toolkit. Clinician recommendations included
    early engagement of clinicians in decision-making and
    planning, establishing a clear clinic workflow, and providing
    appropriate, thorough, and ongoing training. Studying two
    different implementation approaches elicited different
    strengths and challenges. For example, the strength of an
    established patient-provider relationship in a visit-based
    approach could be compared to the opportunity to reach
    patients with an outreach approach and the accompanying
    barrier of not having a history with the patients. Other
    barriers included competing demands and time and lack
    of comfort/training in suicide risk assessment. Clinicians
    across sites called for consolidating all suicide-related risk
    information into a standardized and easy-to-access module
    in the electronic health record to enhance efficiency for
    risk identification follow-up. Finally, clinicians expressed concern that some patients would still be missed, and they
    were not sure what to do with patients with chronic suicide
    ideation who are continuously identified.
    Conclusion: Lessons from two sites provided several areas
    of opportunity for improving implementation of machine
    learning models that identify suicide risk.

  • Link to Article
    publication date
  • 2021
  • Research
    keywords
  • Informatics
  • Medical Records Systems, Computerized
  • Models
  • Risk Assessment
  • Suicide
  • Additional Document Info
    volume
  • 8
  • issue
  • 2