NIH BD2K Think Tank. Session 2: Multiple Providers/EHRs for Single Participant; Multiple Other Data Sources

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1 NIH BD2K Think Tank Session 2: Multiple Providers/EHRs for Single Participant; Multiple Other Data Sources Jeffery Talbert, PhD University of Kentucky

2 Agenda KDOC Project Example Background Strategy Lessons Learned Conclusions

3 The Kentucky Diabetes and Obesity Collaborative (KDOC) Vision: Develop a healthcare data repository that will be used to improve the health of Kentuckians via QI activities, comparative effectiveness, and research. The KDOC data repository will bring together up-todate clinical data from multiple primary care safetynet providers, plus Medicaid claims data, all linked at patient level. Secure web-based will facilitate use while maintaining appropriate levels of privacy and security.

4 The Kentucky Diabetes and Obesity Collaborative (KDOC) KDOC partnering organizations Kentucky Primary Care Association (KPCA) Individual FQHCs Kentucky Medicaid: Medical & KHIE Directors University of Kentucky Academic Health Center Center for Clinical and Translational Science Center for Health Services Research Institute for Pharmaceutical Policy and Outcomes

5 Strategy: acquire data to support innovative research and education Acquire Data Identify collaborators with potential research data And, with a need for data services Provide Services Data collection, management, integration, analysis Design analytics and QI reporting New Research Use data model for research and training Cohort identification, registries, HSR, outcomes to improve population health

6 Activate new collaborations to improve quality and create opportunities for research Payers QI Cost-Effectiveness UK Develop Community Based Translational Research Network KPCA QI Support IPA Gain-sharing ACA Expansion

7 Participating Federally-Qualified Community Health Centers Eight Community Health Centers (FQHCs) serving 39 mostly rural Kentucky counties; diabetes prevalence as high as 17% and obesity prevalence as high as 51% 2 to 15 clinic sites / FQHC with 6 to 31 providers / FQHC 19,900 to 143,000 annual patient visits per FQHC Approx 124,000 patients served by the 8 FQHCs, total Five different EMR brands across the 8 FQHCs; time since EMR implementation: from < 1 year to several years vendors: eclinicalworks, Nexgen, Meditab, Greenway, Allscripts

8 Technical Workflow Complete regulatory documents Select data extraction process Load into ETL staging area ETL process to standardize data models, link to Medicaid Load into KDOC data martenable Tableau reporting tools

9 Lessons Learned Diverse site technical infrastructure Very vendor dependent (new EMR users) Limited site IT staff (contractors, part time, busy with day job) Limited site database capacity (required flexible after hours connectivity, multiple small reports) 3 Processes: 1)Special KDOC data extracts 2)Core database access 3)Core EMR reporting tool

10 Lessons Learned: Labs None of the systems use any standard for Labs We did the standardization ourselves using RELMA, but still had to manually verify that codes didn't get left out Issues with the linking data within the site There would be a Medical Record Number (MRN) specified in the Lab file that didn't exist in the Patient file

11 Lessons Learned: MRN, IDs, Encounters Some systems tied everything together with an Encounter Id, some did not In some multi-site set-ups, Providers were given multiple ID s across sites Quality of data related to technical knowledge of staff Vendor issues.

12 Conclusions Interfacing with EMRs for data transfer into a shared repository cannot yet be standardized. EMR vendor characteristics and practice-based concerns must be addressed one-by-one Expert technical assistance is required for practices to share clinical data for QI or research EMR Vendors should be at the table from the beginning

13 So how do we get clean data into the warehouse? Iterative process Load data Generate reports Review results with clinic director Drill down in a real time process to discover data issues

14 Hypertension Care Monitoring

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