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

• Identifying patients with chronic conditions using pharmacy data in Switzerland

  Open Access•Carola A Huber, Thomas D Szucs et al.•BMC Public Health•2013

  Incluir item

• Health effects of parental deaths among adults in Norway

  Open Access•Øystein Kravdal, Emily M D Grundy•SSM - Population Health•2016

  Incluir item

• The influence of socio-economic status and multimorbidity patterns on healthcare costs

  Open Access•Raymond N Kuo, Mei-Shu Lai•International Journal for Equity…•2013

  Incluir item

• Do short birth intervals have long-term implications for parental health? Results from analyses of complete cohort Norwegian register data

  Emily Grundy, Roberto Irizarry-Rivera et al.•Journal of Epidemiology and…•2014

  Incluir item

• Longitudinal Patterns of Spending Enhance the Ability to Predict Costly Patients

  Julie C Lauffenburger, Jessica M Franklin et al.•Medical Care•2017

  Incluir item

• The poorer cancer survival among the unmarried in Norway

  Open Access•Øystein Kravdal•Social Science & Medicine•2013

  Incluir item

• Adapting a clinical comorbidity index for use with ICD-9-CM administrative databases

  Open Access•Richard A Deyo, R DEYO•Journal of Clinical Epidemiology•1992

  Incluir item

• Smearing Estimate

  Naihua Duan•Journal of the American…•1983

  Incluir item

• A new method of classifying prognostic comorbidity in longitudinal studies

  Open Access•Mary E Charlson, Peter Pompei et al.•Journal of Chronic Diseases•1987

  Incluir item

• Health-Based Risk Adjustment

  Open Access•Femmeke J Prinsze, René C J A Van Vliet•INQUIRY The Journal of Health…•2007

  Incluir item

• Predicting Costs of Care Using a Pharmacy-Based Measure Risk Adjustment in a Veteran Population

  Anne E Sales, Chuan-Fen Liu et al.•Medical Care•2003

  Incluir item

• Coding Algorithms for Defining Comorbidities in ICD-9-CM and ICD-10 Administrative Data

  Hude Quan, Vijaya Sundararajan et al.•Medical Care•2005

  Incluir item

• Construction and Characteristics of the RxRisk-V

  Kevin L Sloan, Anne E Sales et al.•Medical Care•2003

  Incluir item

• Predictive Modeling of Total Healthcare Costs Using Pharmacy Claims Data

  Christopher A Powers, Christina Meyer et al.•Medical Care•2005

  Incluir item

• Development and Estimation of a Pediatric Chronic Disease Score Using Automated Pharmacy Data

  Paul Fishman, Paul A Fishman et al.•Medical Care•1999

  Incluir item

• Predicting Healthcare Costs in a Population of Veterans Affairs Beneficiaries Using Diagnosis-Based Risk Adjustment and Self-Reported Health Status

  Kenneth Pietz, Carol M Ashton et al.•Medical Care•2004

  Incluir item

• Comorbidity Measures for Use with Administrative Data

  Anne Elixhauser, Claudia Steiner et al.•Medical Care•1998

  Incluir item

• The Importance of Comorbidities in Explaining Differences in Patient Costs

  Michael Shwartz, Lisa I Iezzoni et al.•Medical Care•1996

  Incluir item

• The Medicaid Rx Model

  Todd Gilmer, Richard Kronick et al.•Medical Care•2001

  Incluir item

• Diagnostic, Pharmacy-Based, and Self-Reported Health Measures in Risk Equalization Models

  Piet Stam, Pieter J A Stam et al.•Medical Care•2010

  Incluir item

• Risk Adjustment Using Automated Ambulatory Pharmacy Data

  Paul Fishman, Paul A Fishman et al.•Medical Care•2003

  Incluir item

• A Chronic Disease Score with Empirically Derived Weights

  Daniel O Clark, MICHAEL VON KORFF et al.•Medical Care•1995

  Incluir item