Algorithmic Prediction of Restraint and Seclusion in an Inpatient Child and Adolescent Psychiatric Population.

BACKGROUND

Restraint and seclusion in an inpatient child and adolescent psychiatric population adversely affects the overall value and safety of care. Due to adverse events, negative outcomes, and associated costs, inpatient psychiatric hospitals must strive to reduce and ultimately eliminate restraint and seclusion with innovative, data-driven approaches.

AIM

To identify patterns of client characteristics that are associated with restraint and seclusion in an inpatient child and adolescent psychiatric population.

METHOD

A machine learning application of fast-and-frugal tree modeling was used to analyze the sample.

RESULTS

The need for restraint and seclusion were correctly predicted for 73% of clients at risk (sensitivity), and 76% of clients were correctly predicted as negative or low risk (specificity), for needing restraint and seclusion based on the following characteristics: having a disruptive mood dysregulation disorder and/or attention-deficit hyperactivity disorder diagnosis, being 12 years old or younger, and not having a depressive and/or bipolar disorder diagnosis.

CONCLUSIONS

The client characteristics identified in the predictive algorithm should be reviewed on admission to recognize clients at risk for restraint and seclusion. For those at risk, interventions should be developed into an individualized client treatment plan to facilitate a proactive approach in preventing behavioral emergencies requiring restraint and seclusion.