Iman Mohammadi|Huanmei Wu|Ayten Türkcan|Tammy Toscos|Bradley N. Doebbeling
Data Analytics and Modeling for Appointment No-show in Community Health Centers
Indiana University – Purdue University Indianapolis|Indiana University – Purdue University Indianapolis|Northeastern University|Parkview Health|Arizona State University
objectives using predictive modeling techniques developed compared appointment noshow prediction models better understand appointment adherence underserved populations methods materials collected electronic health record ehr data appointment data including patient provider clinical visit characteristics 3year period patient data came urban system community health centers chcs 10 facilities sought identify critical variables logistic regression artificial neural network nave bayes classifier models predict missed appointments used 10fold crossvalidation assess models ability identify patients missing appointments results following data preprocessing cleaning final dataset included 73811 unique appointments 12392 missed appointments predictors missed appointments versus attended appointments included lead time time scheduling appointment patient prior missed appointments cell phone ownership tobacco use number days since last appointment models relatively high area curve 3 models eg 086 nave bayes classifier discussion patient appointment adherence varies across clinics within healthcare system data analytics results demonstrate value existing clinical operational data address important operational management issues conclusion ehr data including patient scheduling information predicted missed appointments underserved populations urban chcs application predictive modeling techniques helped prioritize design implementation interventions may improve efficiency community health centers timely access care chcs would benefit investing technical resources needed make data readily available means inform important operational policy questions