title: 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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: a Section of Hospital Medicine , Department of Medicine , The University of Chicago, Chicago, IL , USA ;
authors.forename: James X
authors.initials: JX
authors.lastname: Zhang
authors.affiliation: a Section of Hospital Medicine , Department of Medicine , The University of Chicago, Chicago, IL , USA ;|b Department of Economics , The University of Chicago, Chicago, IL , USA ;|c Irving B. Harris School of Public Policy Studies , The University of Chicago , Chicago , IL , USA.
authors.forename: David O
authors.initials: DO
authors.lastname: Meltzer
mesh_terms: D000368:Aged; D000369:Aged, 80 and over; D057286:Electronic Health Records / Q000706:statistics & numerical data; D005260:Female; D006304:Health Status; D006801:Humans; D008297:Male; D006278:Medicare / Q000706:statistics & numerical data*; D008875:Middle Aged; D011306:Prescription Fees / Q000706:statistics & numerical data*; D012372:ROC Curve; D012307:Risk Factors; D012943:Social Security / Q000706:statistics & numerical data*; D012959:Socioeconomic Factors; D014481:United States; D016903:Drug Monitoring
keywords: Cost-related medication non-adherence; big data; screening