Original Article Risk Factor Model to Predict a Missed Clinic Appointment in an Urban, Academic and Underserved Setting Orlando Torres, MD, MS, 1 Michael B. Rothberg, MD, MPH, 2 Jane Garb, MS, 3 Owolabi Ogunneye, MD, MRCP, 1 Judepatricks Onyema, MD, 4 and Thomas Higgins, MD, MBA 1 Abstract In the chronic care model, a missed appointment decreases continuity, adversely affects practice efficiency, and can harm quality of care. The aim of this study was to identify predictors of a missed appointment and develop a model to predict an individual’s likelihood of missing an appointment. The research team performed a retrospective study in an urban, academic, underserved outpatient internal medicine clinic from January 2008 to June 2011. A missed appointment was defined as either a ‘‘no-show’’ or cancellation within 24 hours of the appointment time. Both patient and visit variables were considered. The patient population was randomly divided into derivation and validation sets (70/30). A logistic model from the derivation set was applied in the validation set. During the period of study, 11,546 patients generated 163,554 encounters; 45% of appointments in the derivation sample were missed. In the logistic model, percent previously missed appointments, wait time from booking to appointment, season, day of the week, provider type, and patient age, sex, and language proficiency were all associated with a missed appointment. The strongest predictors were percentage of pre- viously missed appointments and wait time. Older age and non-English proficiency both decreased the like- lihood of missing an appointment. In the validation set, the model had a c-statistic of 0.71, and showed no gross lack of fit (P = 0.63), indicating acceptable calibration. A simple risk factor model can assist in predicting the likelihood that an individual patient will miss an appointment. (Population Health Management 2014; xx:xxx–xxx) Introduction A missed clinic appointment (whether cancelled at the last minute or ‘‘no-show’’) decreases continuity, ad- versely affects practice efficiency, and can harm quality of care. In a primary care practice, missed appointments result in lost revenue, increased cost to deliver health care, and decreased patient satisfaction. 1–3 Many factors have been identified previously as contribut- ing to missed appointments. These can be divided into patient, appointment, and/or environmental characteristics such as age, race, insurance status or payer, behavioral comorbidities, co- morbidities index, patient beliefs, immediate symptoms, pre- viously kept visits, provider type (attending vs. resident), continuity of care, specialty, appointment wait times, time of the day, day of the week, weather, transportation methods, and even reminder methods. 2,4–28 No single factor can adequately predict an individual’s likelihood of missing an appointment and evidence for some factors, especially demographic and socioeconomic factors, is conflicting. 2 Observational studies have attempted to identify those factors that explain a missed appointment. Norris et al most recently provided a comprehensive literature review. 2 They found that 4 factors alone have the greatest association with missed appointments in an adult population: appointment wait time (or lead time), patient age, financial payer, and patient rate of previously missed appointments. Multiple studies also agree that these are potent independent predic- tors, 2,4–17 and have proposed models to predict a missed ap- pointment using the derivation and validation techniques. 5–7 A number of interventions to decrease missed appoint- ments have been tried and described in the literature. An 1 Department of Medicine, Baystate Medical Center/Tufts University School of Medicine, Springfield, Massachusetts. 2 Department of Medicine, Medicine Institute, Cleveland Clinic, Cleveland, Ohio. 3 Department of Epidemiology and Biostatistics, Baystate Medical Center/Tufts University School of Medicine, Springfield, Massachusetts. 4 Department of Medicine, Orlando Regional Medical Center, Orlando Health, Orlando, Florida. POPULATION HEALTH MANAGEMENT Volume 0, Number 0, 2014 ª Mary Ann Liebert, Inc. DOI: 10.1089/pop.2014.0047 1