[Users Choice] Code Calc Di Phil Adams E Carl Bulger
DESCRIPTION OF PROGRAM/INTERVENTION, INCLUDING ORGANIZATIONAL CONTEXT (E.G. INPATIENT VS. OUTPATIENT, PRACTICE OR COMMUNITY CHARACTERISTICS): The project is implemented in a large urban, academic family medicine practice located on the campus of a tertiary care center in Philadelphia. This site serves approximately 27,000 patients, the majority of whom are age 18 and older, female, either African American or White, and commercially insured. Clinical data consists of diagnosis groups built from ICD-9 and ICD-10 codes for a range of chronic diseases. Healthcare utilization encompasses in-network primary care visits, specialist visits, emergency department visits and hospitalizations.
METHODS: Our study population included adults living in West Philadelphia, and were Medicaid patients at one of two Penn internal medicine practicesan intervention and control clinicwithin the same building. Intervention clinic patients with a scheduled appointment received a telephone reminder 2days before and were offered free transportation to-and-from clinic using Lyft. Up to 3 attempts were made to contact the patient. For those who accepted, staff pre-scheduled a Lyft ride using a web-based dispatch tool, circumventing the need for patients to have a smartphone or app. To return home, patients called staff to dispatch a Lyft ride after their clinic visits. Control clinic patients received an appointment reminder 2days before appointments as part of usual care. For both clinics, we collected appointment attendance data from a period before (Aug 4 – Sep 16, 2016) and during (Oct 1221, 2016) the intervention. The primary outcome was appointment show ratethe proportion who attended among those called. We employed a difference-in-difference analytic approach using logistic regression with robust standard errors to compare show rate changes at the two clinics between the two data collection periods. We adjusted for patient demographics, zip code, and provider types (resident, attending, or mid-level). Our approach accounts for the influence of secular trends in the intervention clinic and time-invariant differences between clinics.
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