ArticleJournal of medical economics2016
Identifying patients with cost-related medication non-adherence: a big-data approach.
Article in Journal of medical economics, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
7 citing papers in PubMed, 1 synthesis or guideline pooled it, 16 citations in OpenAlex.
- Measurement Properties of Existing Patient-Reported Outcome Measures on Medication Adherence: Systematic Review.Journal of medical Internet research · 2020Pooled it
- Psychotropic medication non-adherence and its determinants among adult psychiatric patients in Ethiopia.Social psychiatry and psychiatric epidemiology · 2026Article
- A computational framework for longitudinal medication adherence prediction in breast cancer survivors: A social cognitive theory based approach.PLOS digital health · 2025Article
- Article
- Machine Learning and Medication Adherence: Scoping Review.JMIRx med · 2021Article
- Social capital and cost-related medication nonadherence (CRN): A retrospective longitudinal cohort study using the Health and Retirement Study data, 2006-2016.SSM - population health · 2020Article
- Cost-related delay in filling prescriptions and health care ratings among medicare advantage recipients.Medicine · 2019Observational
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
backgroundMillions 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).
objectiveBy 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.
methodsCRN 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.
resultsAmong 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.
conclusionWith 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.
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Registered trials
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