Early prediction of potentially preventable events in ambulatory care sensitive admissions from clinical data

Ambulatory care sensitive conditions (ACSCs) are characterized as health conditions for which good outpatient care can potentially prevent the need for hospitalization, or for which early intervention can prevent complications or more severe disease. Currently, there are 16 identified ACSCs within the US health system: diabetes short-term complication, perforated appendix, diabetes long-term complication, pediatric asthma, chronic obstructive pulmonary disease, pediatric gastroenteritis, hypertension, congestive heart failure, low birth weight rate, dehydration, bacterial pneumonia, urinary tract infection, angina admission without procedure, uncontrolled diabetes, adult asthma, and lower-extremity amputation among patients with diabetes. Potentially preventable acute health events (PPEs) for such diagnosis codes represent a straightforward opportunity for reducing medical costs while concomitantly improving quality of care. While claims data have previously been used to predict future health outcomes of patients, we report here a novel approach, using data mining techniques, towards supplementing such data with patients' electronic health records (EHR) to develop a clinical decision support system that satisfactorily predicts the onset of PPEs in a large population of patients.