Evaluating the Impact of Prescription Fill Rates on Risk Stratification Model Performance

Research output: Contribution to journalArticle

Abstract

Background: Risk adjustment models are traditionally derived from administrative claims. Prescription fill rates-extracted by comparing electronic health record prescriptions and pharmacy claims fills-represent a novel measure of medication adherence and may improve the performance of risk adjustment models. Objective: We evaluated the impact of prescription fill rates on claims-based risk adjustment models in predicting both concurrent and prospective costs and utilization. Methods: We conducted a retrospective cohort study of 43,097 primary care patients from HealthPartners network between 2011 and 2012. Diagnosis and/or pharmacy claims of 2011 were used to build 3 base models using the Johns Hopkins ACG system, in addition to demographics. Model performances were compared before and after adding 3 types of prescription fill rates: primary 0-7 days, primary 0-30 days, and overall. Overall fill rates utilized all ordered prescriptions from electronic health record while primary fill rates excluded refill orders. Results: The overall, primary 0-7, and 0-30 days fill rates were 72.30%, 59.82%, and 67.33%. The fill rates were similar between sexes but varied across different medication classifications, whereas the youngest had the highest rate. Adding fill rates modestly improved the performance of all models in explaining medical costs (improving concurrent R 2 by 1.15% to 2.07%), followed by total costs (0.58% to 1.43%), and pharmacy costs (0.07% to 0.65%). The impact was greater for concurrent costs compared with prospective costs. Base models without diagnosis information showed the highest improvement using prescription fill rates. Conclusions: Prescription fill rates can modestly enhance claims-based risk prediction models; however, population-level improvements in predicting utilization are limited.

LanguageEnglish (US)
Pages1052-1060
Number of pages9
JournalMedical Care
Volume55
Issue number12
DOIs
StatePublished - Jan 1 2017

Fingerprint

Prescriptions
Risk Adjustment
Costs and Cost Analysis
Electronic Health Records
Medication Adherence
Primary Health Care
Cohort Studies
Retrospective Studies
Demography
Population

Keywords

  • administrative claims
  • electronic health records
  • fill and refill rates
  • medication adherence
  • predictive modeling
  • risk stratification
  • the Johns Hopkins ACG System

ASJC Scopus subject areas

  • Public Health, Environmental and Occupational Health

Cite this

Evaluating the Impact of Prescription Fill Rates on Risk Stratification Model Performance. / Chang, Hsien Yen; Richards, Thomas M.; Shermock, Kenneth M.; Dalpoas, Stacy Elder; Kan, Hong J.; Caleb Alexander, G.; Weiner, Jonathan P.; Kharrazi, Hadi.

In: Medical Care, Vol. 55, No. 12, 01.01.2017, p. 1052-1060.

Research output: Contribution to journalArticle

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abstract = "Background: Risk adjustment models are traditionally derived from administrative claims. Prescription fill rates-extracted by comparing electronic health record prescriptions and pharmacy claims fills-represent a novel measure of medication adherence and may improve the performance of risk adjustment models. Objective: We evaluated the impact of prescription fill rates on claims-based risk adjustment models in predicting both concurrent and prospective costs and utilization. Methods: We conducted a retrospective cohort study of 43,097 primary care patients from HealthPartners network between 2011 and 2012. Diagnosis and/or pharmacy claims of 2011 were used to build 3 base models using the Johns Hopkins ACG system, in addition to demographics. Model performances were compared before and after adding 3 types of prescription fill rates: primary 0-7 days, primary 0-30 days, and overall. Overall fill rates utilized all ordered prescriptions from electronic health record while primary fill rates excluded refill orders. Results: The overall, primary 0-7, and 0-30 days fill rates were 72.30{\%}, 59.82{\%}, and 67.33{\%}. The fill rates were similar between sexes but varied across different medication classifications, whereas the youngest had the highest rate. Adding fill rates modestly improved the performance of all models in explaining medical costs (improving concurrent R 2 by 1.15{\%} to 2.07{\%}), followed by total costs (0.58{\%} to 1.43{\%}), and pharmacy costs (0.07{\%} to 0.65{\%}). The impact was greater for concurrent costs compared with prospective costs. Base models without diagnosis information showed the highest improvement using prescription fill rates. Conclusions: Prescription fill rates can modestly enhance claims-based risk prediction models; however, population-level improvements in predicting utilization are limited.",
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