Background: The November 2010 Joint Commission Sentinel Event Alert on the prevention of suicides in medical/surgical units and the emergency department (ED) mandates screening every patient treated as an outpatient or admitted to the hospital for suicide risk. Our aim was to develop a suicide risk assessment tool to (1) predict the expert psychiatrist’s assessment for risk of committing suicide within 72 hours in the hospital, (2) replicate the recommended intervention by the psychiatrist, and (3) demonstrate acceptable levels of participant satisfaction.
Methods: The 3 phases of tool development took place between October 2012 and February 2014. An expert panel developed key questions for a tablet-based suicide risk questionnaire. We then performed a randomized cross-sectional study comparing the questionnaire to the interview by a psychiatrist, for model derivation. A neural network model was constructed using 255 ED participants. Evaluation was the agreement between the risk/intervention scores using the questionnaire and the risk/intervention scores given by psychiatrists to the same patients. The model was validated using a new population of 124 participants from the ED and 50 participants from medical/surgical units.
Results: The suicide risk assessment tool performed at a remarkably high level. For levels of suicide risk (minimal or low, moderate, or high), areas under the curves were all above 0.938. For levels of intervention (routine, specialized, highly specialized, or secure), areas under the curves were all above 0.914. Participants reported that they liked the tool, and it took less than a minute to use.
Conclusions: An expert-based neural network model predicted psychiatrists’ assessments of risk of suicide in the hospital within 72 hours. It replicated psychiatrist-recommended interventions to mitigate risk in EDs and medical/surgical units.