Unveiling the Impact of Occupational Therapy on Acute Care Outcomes: A Machine Learning Approach.

OBJECTIVE: To examine the effects of occupational therapy (OT) services on patient discharge outcomes. DESIGN: This is a retrospective cohort study. We developed decision tree algorithms to investigate how 3 measures of OT service delivery-(1) the total number of completed OT visits, (2) the total minutes of all completed OT visits, and (3) the proportion of hospitalized days that included an OT visit (frequency)-affect outcomes. SETTING: Acute care settings. PARTICIPANTS: The target population was patients admitted to and discharged from the Cleveland Clinic between 2017 and 2021, who received at least 1 OT session and stayed in the hospital for <30 days, with an orthopedic physician designated as the primary treating provider (N=36,300). INTERVENTIONS: Not applicable. MAIN OUTCOME MEASURES: Reduced 30-day readmission rates, increased rates of home discharge, and improved daily activity abilities after hospital discharge. RESULTS: Our prediction models for predicting our outcomes showed good to excellent performance metrics (accuracy, 0.69-0.91). Our model demonstrated that variations in the total number, minutes, or frequency of OT sessions are associated with the likelihood of readmission within 30 days after hospital discharge and minimal detectable improvements in daily activities for subgroups of patients with orthopedic conditions. CONCLUSIONS: Our study suggests that the effective use of limited OT services may require prioritizing certain subgroups of patients for providing more frequent OT sessions to optimize the impact of OT services on patient outcomes.

Duke Scholars

Author Joshua Kurt Johnson Orthopaedic Surgery, Physical Therapy