A comprehensive and patient-centered service model is required to ensure the health of patient populations. A patient’s ability to access outpatient care is a key driver to prevent emergency department (ED) visits and hospital admissions. Cooper University Health Care first used patient data sets focused on key diversity, equity and inclusion (DEI) measures to understand the patient and their reasons for not attending appointments. Data gathered through EHRs regarding patient detail information measured against multiple categories was pulled over the course of a one-year period. The data demonstrated clear areas for interventions to address social determinants of health (SDoH) and improve patient show rates. . In this session, attendees will learn how identification, analysis and deployment of a predictive analytics tool used to identify patients likely not to show to their appointment is a solution that can support patients and organizations for their own objectives.
Learning Objectives:
Discover measurement methodology and intervention tactics to support beter patient show rates
Apply predictive analytic modeling design options and positive drivers that impact provider schedules
Employ EHR tools that facilitate awareness and patient-centered communication
.jpg "Karl Lane, MSQMS, LSSMBB, CSM photo")