Prevalence and Access of Secondary Source Medication Data: Evaluation of the Southeastern Diabetes Initiative (SEDI).
Bottom Line: A paucity of retrievable data prevents researchers from effectively measuring, tracking and sharing outcomes on medication management.Electronic health records make medication data more numerous and accessible, yet the reliability and utility of electronically available data elements that reflect adherence has not been well established.The purpose was to evaluate data generated through routine healthcare delivery that are repurposed (ie, "secondary use") for research/QI/population health.
Affiliation: Duke School of Nursing, Durham, NC.
Medication non-adherence is a major public health issue, and measuring non-adherence is a crucial step toward improving it. A paucity of retrievable data prevents researchers from effectively measuring, tracking and sharing outcomes on medication management. High quality data derived from prescribing patterns, including behavioral and technology-based interventions, is necessary to support meaningful use, improve publicly reported quality metrics, and develop strategies to improve medication management. Electronic health records make medication data more numerous and accessible, yet the reliability and utility of electronically available data elements that reflect adherence has not been well established. We sought to explore the types of medication-related data captured over time in a series of patient encounters (n=5500) in a population-based intervention in four U.S. counties in the SouthEastern Diabetes Initiative (SEDI). The purpose was to evaluate data generated through routine healthcare delivery that are repurposed (ie, "secondary use") for research/QI/population health.
No MeSH data available.
Related in: MedlinePlus
Mentions: The SouthEastern Diabetes Initiative (SEDI) houses a multi-dimensional datamart, which includes clinic and hospital electronic health record (EHR) data from 4 counties in the southeastern United States. The project provides opportunities for development of the proposed data framework because it allows us to: 1) Harvest data from electronic sources in each county to create a comprehensive, integrated data warehouse to accurately reflect clinical and social data elements that can be represented at the individual, neighborhood, and community level; 2) Use those data to risk stratify patients and neighborhoods, allowing implementation of an intense clinical intervention from a multidisciplinary team that provides care to the highest risk patients as well as additional individual and neighborhood interventions to moderate risk patients and neighborhoods; and 3) Implement interventions informed by spatially-enabled informatics systems to longitudinally monitor individuals and populations with T2DM, thereby serving as the basis for decision support and evaluation of chronic illness interventions. Cumulatively, these data sources constitute a broad-scoped, connected data framework, the axis of which revolves on patient-centric activities for medication use. We developed a workflow algorithm to depict activities and Next, we evaluated the conceptual data framework using our existing SEDI datamart. We conducted four activities to evaluate the framework: 1) Obtained patient perspectives on challenges of managing medicines through systematic literature review; interview and focus groups; review of EHR; and evaluation of medication-related patient reported outcome measures; 2) Matched data sources with the activity model for medication management (Figure 1); 3) Applied the activity model to our SEDI datamart; and 4) Performed a gap analysis to evaluate data collection, curation, preservation and linkages.
No MeSH data available.