Using Discrete Form Data in the Electronic Medical Record to Predict the Likelihood of Psychiatric Consultation

Abstract

Background: Manually screening for mental health needs in acute medical-surgical settings is thorough but time-intensive. Automated approaches to screening can enhance efficiency and reliability, but the predictive accuracy of automated screening remains largely unknown.

Objective: The aims of this project are to develop an automated screening list using discrete form data in the electronic medical record that identify medical inpatients with psychiatric needs and to evaluate its ability to predict the likelihood of psychiatric consultation.

Methods: An automated screening list was incorporated into an existing manual screening process for 1 year. Screening items were applied to the year's implementation data to determine whether they predicted consultation likelihood. Consultation likelihood was designated high, medium, or low. This prediction model was applied hospital-wide to characterize mental health needs.

Results: The screening items were derived from nursing screens, orders, and medication and diagnosis groupers. We excluded safety or suicide sitters from the model because all patients with sitters received psychiatric consultation. Area under the receiver operating characteristic curve for the regression model was 84%. The two most predictive items in the model were "3 or more psychiatric diagnoses" (odds ratio 15.7) and "prior suicide attempt" (odds ratio 4.7). The low likelihood category had a negative predictive value of 97.2%; the high likelihood category had a positive predictive value of 46.7%.

Conclusions: Electronic medical record discrete data elements predict the likelihood of psychiatric consultation. Automated approaches to screening deserve further investigation.

Keywords: electronic medical records; prediction models; proactive integrated C-L; quality improvement.