Predicting Healthcare Utilization Using a Pharmacy-based Metric With the WHO’s Anatomic Therapeutic Chemical Algorithm

BACKGROUND: Automated pharmacy claim data have been used for risk adjustment on health care utilization. However, most published pharmacy-based morbidity measures incorporate a coding algorithm that requires the medication data to be coded using the US National Drug Codes or the American Hospital Formulary Service drug codes, making studies conducted outside the US operationally cumbersome. OBJECTIVE: This study aimed to verify that the pharmacy-based metric with the World Health Organization (WHO) Anatomical Therapeutic Chemical (ATC) algorithm can be used to explain the variations in health care utilization. RESEARCH DESIGN: The Longitudinal Health Insurance Database of Taiwan's National Health Insurance enrollees was used in this study. We chose 2006 as the baseline year to predict the total cost, medication cost, and the number of outpatient visits in 2007. The pharmacy-based metric with 32 classes of chronic conditions was modified from a revised version of the Chronic Disease Score. RESULTS: The ordinary least squares (OLS) model and log-transformed OLS model adjusted for the pharmacy-based metric had a better R in concurrently predicting total cost compared with the model adjusted for Deyo's Charlson Comorbidity Index and Elixhauser's Index. The pharmacy-based metric models also provided a superior performance in predicting medication cost and number of outpatient visits. For prospectively predicting health care utilization, the pharmacy-based metric models also performed better than the models adjusted by the diagnosis-based indices. CONCLUSIONS: The pharmacy-based metric with the WHO ATC algorithm and the matching ATC codes were tested and found to be valid for explaining the variation in health care utilization

Actuarial science · Business · Data mining · Diagnosis code · Environmental health · Family medicine · Health care · Metric (unit) · Operations management · Population · Computer Science · Machine Learning in Healthcare · Medicine · Pharmaceutical Practices and Patient Outcomes · Pharmacovigilance and Adverse Drug Reactions · Pharmacy

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