Predictive Model-Driven Hotspotting to Decrease Emergency Department Visits: a Randomized Controlled Trial.

BACKGROUND: Emergency department (ED) visits contribute substantially to health care expenditures. Case management has been proposed as a strategy to address the medical and social needs of complex patients. However, strong research designs to evaluate the effectiveness of such interventions are limited. OBJECTIVES: To evaluate whether a community-based case management program was associated with reduced ED utilization among complex patients. DESIGN: Patients whose risk exceeded a threshold were randomly assigned to a group offered case management or to the control group. Assignment occurred at five intervals between November 2017 and January 2019. Program effectiveness for all assigned patients was assessed using an intention-to-treat effect. Program effectiveness among those who received treatment was assessed using a local average treatment effect, estimated using instrumental variables. Both estimators were adjusted for baseline characteristics using linear models. PARTICIPANTS: Adults over age 18 with at least one health care encounter with Michigan Medicine or St. Joseph Mercy Health System between June 2, 2016, and November 27, 2018. INTERVENTIONS: Intervention arm participants (n = 486) were offered coordinated case management across medical, mental health, and social service organizations. Control arm participants (n = 409) received usual care. MAIN MEASURES: The primary outcome was the number of ED visits in the 6 months following randomization into the study. Secondary outcomes were 6-month counts of inpatient and outpatient visits. KEY RESULTS: Of the 486 patients assigned to the intervention, 131 (27%) consented to receive case management. The intention-to-treat effect on ED visits was + 0.14 (95% CI: - 0.27 to + 0.55). The local average treatment effect among those who consented and received case management was + 0.53 (95% CI: - 1.00 to + 2.05). Intention-to-treat and local average treatment effects were not significant for secondary outcomes. CONCLUSIONS: The community case management intervention targeting ED visits was not associated with reduced utilization. Future case management interventions may benefit from additional patient engagement strategies and longer evaluation time periods. TRIAL REGISTRATION: Clinicaltrials.gov Identifier: NCT03293160.

Towards a Learning Health System to Reduce Emergency Department Visits at a Population Level.

High utilizers of the Emergency Department (ED) often have complex needs that require coordination of care between multiple organizations. We describe a Learning Health Systems (LHS) approach to reducing ED visits, in which an intervention is delivered to a cohort of high utilizers identified using population-level data and predictive modeling. We focus on the development and validation of a random forest model that utilizes electronic health record data from three health systems across two counties in Michigan to predict the number of ED visits each resident will incur in the next six months. Using 5-fold cross-validation, the model achieves a root-mean-squared-error of 0.51 visits and a mean absolute error of 0.24 visits. Using time-based validation, the model achieves a root-mean-squared error of 0.74 visits and a mean absolute error of 0.29 visits. Patients projected to have high ED utilization are being enrolled in a community-wide care coordination intervention using twelve sites across two counties. We believe that the repeated cycles of modeling and intervention demonstrate an LHS in action.