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Related Concept Videos

SBAR II: Application of SBAR01:14

SBAR II: Application of SBAR

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SBAR is an effective communication tool used by healthcare professionals to communicate patient information accurately. SBAR stands for Situation, Background, Assessment, and Recommendation. For a better understanding, an example is given below.
SBAR Report from a Nurse to a Health Care Provider
S: "Hello, Dr. Smith. This is Jane, RN, from the Med Surg unit. I am calling to tell you about Ms. White in Room 210, who is experiencing increased pain and redness at her incision site. Her recent...
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Related Experiment Video

Updated: Sep 18, 2025

Implementation of a Real-Time Psychosis Risk Detection and Alerting System Based on Electronic Health Records using CogStack
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Suicide Risk Screening in Jails: Protocol for a Pilot Study Leveraging the Mental Health Research Network Algorithm

Erin B Comartin1, Grant Victor2, Athena Kheibari1

  • 1School of Social Work, Wayne State University, Detroit, MI, United States.

JMIR Research Protocols
|June 25, 2025
PubMed
Summary

This study validates a machine learning (ML) model using administrative data to improve suicide risk detection in jails. The ML approach shows promise for enhancing current screening practices and potentially reducing suicide rates among incarcerated individuals.

Keywords:
health risk behaviorsjailsmachine learningsuicidesuicide prevention

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Area of Science:

  • Public Health
  • Data Science
  • Criminology

Background:

  • Suicide rates are higher in local jails compared to the general population, highlighting the need for improved risk screening.
  • Current jail suicide risk screening methods are often inadequate, lacking validated instruments and clinical oversight, and may not elicit honest responses.
  • Machine learning (ML) models have demonstrated potential in improving the accuracy of suicide risk detection compared to traditional practices.

Purpose of the Study:

  • To assess the feasibility and practicality of employing administrative data and ML modeling for suicide risk detection at jail booking.
  • To validate an existing ML model using comprehensive claims data from individuals booked into two diverse jails.
  • To incorporate hypothesis testing regarding the clinical outcomes of enhanced suicide risk detection methods.

Main Methods:

  • Validation of a pre-existing ML model using administrative, Medicaid, and vital records data from approximately 6000 jail bookings.
  • Utilizing 313 demographic and clinical characteristics from 5 years of historical healthcare data for model validation.
  • Merging jail administrative data (2021-2022), Medicaid records (2016-2023), and vital records (2022-2023) to detect suicide risk.

Main Results:

  • The ML algorithm's performance will be evaluated using the C-statistic and area under the receiver operating characteristic curve.
  • Comparison of the ML model's predictions against current suicide identification practices (PAU) for identified suicide attempts and deaths.
  • Validation of algorithm predictions against actual events is planned for spring 2025, with results anticipated in fall 2025.

Conclusions:

  • The study aims to investigate implementation factors like feasibility, acceptability, and appropriateness to promote jail adoption of the ML model.
  • Hypothesizing that a combined approach using intake screening PAU and the ML model will offer optimal accuracy and practical application.
  • Expected dissemination of findings in 2026, following data collection on implementation factors in summer 2025.