A Novel Big Data Screening Tool to Identify and Reduce CRN
PI: James Zhang and David Meltzer, Department of Medicine
Dates: 7/1/2016 – 6/30/2018
Description: Up to a third of older patients report cost-related medication non-adherence (CRN), adversely affecting patient outcomes and raising health care costs. Despite the institution of Medicare Part D, recent studies have suggested that the CRN rates in the sicker patients did not decease or even worsened. There is no known low-cost, automated tool to screen and identify patients with CRN. Neither copays nor income accurately predict non-adherence by themselves and these measures do not have the sensitivity or specificity to identify CRN. Common measures of non-adherence (e.g., medication possession ratio, gaps in filling of prescriptions) are not specific to non-adherence related to financial costs and thus suffer from low sensitivity and specificity because of other factors such as cultural preferences and side effects that are correlated with non-adherence. Universal approaches in which all patients are asked about the financial burdens of medications and CRN are costly and unlikely to be followed by providers.
The overall aim of this proposal is to use data from the Health and Retirement Study (HRS), a nationally representative survey linked with Medicare claims data, to construct and assess the validity of a novel big-data screening tool by connecting information on the delays in filling prescriptions until their monthly SS income becomes available. We hypothesize that this novel automated indicator may be a useful and low-cost predictor of potential cost-related non-adherence (CRN). We think that this measure may be especially useful among persons for whom social security is a large fraction of their income and whose insurance leaves them with significant out-of-pocket medication costs, and those with high disease burden. The novel automated indicator of potential delay in filling prescriptions until social security check availability will be measured by comparing prescription fill dates from Medicare Part D data to the date each month that the individual’s social security check is available when a gap in refilling is present. We think that the automated CRN measure could be implemented in pharmacy claims data at almost no cost in order to identify patients at increased risk of CRN and target them for potential interventions. Such interventions could include sending reminders to physicians or clinic staff to ask high-risk patients if CRN is occurring and discuss potential solutions, such as generic or preferred-tier formulary substitutions, or manufacturers discount programs. High risk patients could also be sent mailings or other notices to encourage them to discuss any CRN issues with their providers.
The proposed research is informed by our preliminary research on the prevalence of CRN, the patterns of CRN behaviors, and the association between ordering prescription with social security availability and self-reported CRN. This research will take place at the University of Chicago. We have a uniquely qualified research team with expertise in the Medicare program, clinical research, claims data analysis, and economic aspects of health and healthcare. Dissemination of the research results in the national professional conferences and professional journals are also planned. The research will be of value to clinician, health services researchers, health care organizations, and policy makers to target the patients at the high risk of CRN, reduce costs, and improve patient-centered outcomes.