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

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.

Automated Screening to Enhance Proactive Consultation-Liaison Psychiatry Services in Acute Medicine Units: Evaluation of Service Outcomes.

Objective: Proactive consultation-liaison (C-L) psychiatry aims to meet the mental health needs of medical-surgical populations-many of which go unmet by the conventional C-L model-through systematic screening and integrated care. We implemented an automated screening list to enhance case identification of an existing proactive C-L service and evaluated service metrics along with clinician- and patient-reported outcomes.Methods: Service outcomes were evaluated using historical and contemporary comparison data. Adjusted difference-in-difference analyses were used to determine change in consult characteristics, mean length of stay (LOS), and scores on Hospital Consumer Assessment of Healthcare Providers and Systems (HCAHPS). Practitioners and nurses were surveyed regarding service satisfaction, perceived safety, and burnout.Results: During the intervention, the consult rate was 3-fold higher than at baseline. Change in time to consultation was equivocal. Overall mean LOS was not reduced, but observed LOS was 1.2 days shorter than expected among non-COVID patients receiving psychiatric consultation (P = not significant). Mean patient-rated hospital satisfaction on HCAHPS was 1 point higher on intervention units during the intervention. Surveys revealed broad satisfaction with this model among practitioners and improved perception of safety among nurses.Conclusions: Proactive C-L psychiatry enhanced by automated screening was associated with improved service utilization and evidence suggestive of LOS reduction among those most likely to receive direct benefit from this model of care. Further, both patient and clinician ratings were improved during the intervention. Proactive C-L psychiatry provides benefits to patients, clinicians, and health systems and may be poised to achieve the Triple Aim in health care.Prim Care Companion CNS Disord 2024;26(2):23m03647. Author affiliations are listed at the end of this article.