22 From no show to arrived: using machine learning to bolster patient attendance for resident continuity-clinic appointments

Background Resident continuity-clinic (RCC) is a crucial component of ambulatory training in primary care.The no-show rate (NSR) in a large academic center with 60 residents averaged 27% in academic year (AY) 2018, despite an automated phone/text reminder system 3 days prior to appointment, resulting in fragmented care, reduced access and decreased learning opportunities for residents. Objectives To determine whether telephone outreach targeting patients predicted to be at high-risk to no-show can reduce NSR for RCC appointments. Methods A validated machine-learning prediction model developed by data scientists at UPMC for Primary care, generated a daily list of high-risk patients (i.e. =20% risk to no-show). Starting Oct 2018, these patients received a phone-call reminder from a clinical staff, 48 hours prior to their scheduled appointment. The outcomes of the call recorded were confirmed, cancelled, rescheduled, voicemail, not reached. Monthly NSR was tracked from July 2017 through June 2019 and analyzed using control charts. Results Fifty-nine percent (1206/2046) of targeted patients were reached. Of those 89% confirmed and 10% canceled or rescheduled their appointment. The overall no-show rate for RCC appointments in (AY) 2019 decreased to 23%, p Conclusions To our knowledge this is the first study showing that targeted phone outreach for high-risk patients can decrease NSR for RCC appointments, augmenting resident learning opportunities and revenue.