Reducing Physical Therapy Consults for Patients With High Functional Mobility in the Acute Medical Inpatient Setting: A Difference-in-Difference Analysis

Abstract

ObjectiveTo evaluate the effectiveness of clinical decision support for reducing misallocation of physical therapy (PT) consults. DesignA prospective quasi-experimental study. Between October 2018 and November 2021, routinely documented data on functional status and physical therapy referrals were collected from electronic medical records. SettingHospital Medicine and General Internal Medicine service lines at a large quaternary academic medical center. Participants20,810 adult patients hospitalized on any of the included treatment (hospital medicine) or control (general internal medicine) service lines. Main Outcome MeasureThe primary outcome was “change in proportion of misallocated PT consults” measured as likelihood of PT consults for patients admitted with high functional mobility scores. Changes in the primary outcome from the pre-intervention to post-intervention period were compared in the control and treatment groups using propensity score–weighted difference-in-differences multivariable logit regression adjusting for clinically relevant covariates. InterventionThe intervention period was measured for 20 months and consisted of a clinical decision support tool embedded in the daily note templates for hospital medicine providers. The tool provided education on patient mobility scores and their relation to need for PT consult. The tool was rolled out without any further announcements or education. ResultsOur cohort included 20,810 unique admissions (mean age 58.9, 55% women, 83% Black). Post-intervention, the likelihood of PT referrals for patients with high baseline mobility (AM-PAC >18) decreased by 7.3% (P<.001) for the treatment group compared with control, adjusted for age, sex, race, ethnicity, length-of-stay, and mobility change. ConclusionMobility score-based clinical decision support can decrease unneeded PT consults in the inpatient setting. This could help allocate therapy time for at-risk patients while also having a positive effect on health care systems.