Identifying patients with cost-related medication non-adherence: a big-data approach

Abstract

Background: Millions of Americans encounter access barriers to medication due to cost; however, to date, there is no effective screening tool that identifies patients at risk of cost-related medication non-adherence (CRN).

Objective: By utilizing a big-data approach to combining the survey data and electronic health records (EHRs), this study aimed to develop a method of identifying patients at risk of CRN.

Methods: CRN data were collected by surveying patients about CRN behaviors in the past 3 months. By matching the dates of patients’ receipt of monthly Social Security (SS) payments and the dates of prescription orders for 559 Medicare beneficiaries who were primary SS claimants at high risk of hospitalization in an urban academic medical center, this study identified patients who ordered their outpatient prescription within 2 days of receipt of monthly SS payments in 2014. The predictive power of this information on CRN was assessed using multivariate logistic regression analysis.

Results: Among the 559 Medicare patients at high risk of hospitalization, 137 (25%) reported CRN. Among those with CRN, 96 (70%) had ordered prescriptions on receipt of SS payments one or more times in 2014. The area under the Receiver Operating Curve was 0.70 using the predictive model in multivariate logistic regression analysis.

Conclusion: With a new approach to combining the survey data and EHR data, patients’ behavior in delaying filling of prescription until funds from SS checks become available can be measured, providing some predictive value for cost-related medication non-adherence. The big-data approach is a valuable tool to identify patients at risk of CRN and can be further expanded to the general population and sub-populations, providing a meaningful risk-stratification for CRN and facilitating physician–patient communication to reduce CRN.

Keywords:

• Cost-related medication non-adherence
• big data
• screening

Transparency

Declaration of funding

This study is funded in part by a grant from Robert Wood Johnson (RWJ) Foundation 73487 (Meltzer), and a Pilot and Feasibility Grant from Chicago Center for Diabetes Translation Research (Zhang & Meltzer).

Declaration of financial/other relationships

JXZ and DOM have disclosed that they have no significant relationships with or financial interests in any commercial companies related to this study or article. JME peer reviewers on this manuscript have no relevant financial or other relationships to disclose.

Acknowledgments

Subjects for this study were originally recruited for a separate study funded by the Center for Medicare and Medicaid Innovation.