Cecil Pollard, Director West Virginia University Office of Health Services Research

1. Applying electronic health record data to quality of care improvement and practice based research initiatives Cecil Pollard, Director West Virginia University Office of Health Services Research 2014 KBPRN Collaborative Conference

2. "The project described was supported by the National Institute of General Medical Science, U54GM104942.  The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH." 2014 KBPRN Collaborative Conference

3. Overview • Office of Health Services Research • Era of Big Data • Introduction of EHR’s • Concerns with Big Data • Repurposing of EHR’s • Practical applications using EHR’s • Where are we and where might we be going

4. West Virginia University Office of Health Services Research • 30 years collaborating with primary care and public health • Past 15 years focusing on quality improvement in chronic disease • Provider and patient education • Collaborating with about 50 community based primary care sites • Focus on underserved and rural populations • Also working with Caribbean and Latin American nations and U.S. Territories in the So. Pacific

5. 2014 KBPRN Collaborative Conference

6. By 1985 it had evolved into this 2014 KBPRN Collaborative Conference

7. Concerns over Data Accuracy • 1985-Devin and Murphy of IBM • Development of architecture for data warehousing • Focusing on high quality, historically complete data, and accurate data 2014 KBPRN Collaborative Conference

8. “Big Data” • July 1997 • The Problem of Big Data • The term "big data" was used for the first time in an article by NASA researchers Michael Cox and David Ellsworth. • The computer processing cold not keep up with the increase in the large amount of data being generated. 2014 KBPRN Collaborative Conference

9. Big data in health care • Knowledge translation between health analytics and the realities of patient care • The statement ‘There are right ways to analytics’ implies we may not be doing analytics correctly • Health care seems to think that big data will improve patient care and population health management • It isn’t about the data and how much you have, but about data management • We are creating data landfills • Turning data into useful information

10. The beginning of Electronic Health Records-1964 http://www.youtube.com/watch?v=t-aiKlIc6uk

11. So what were the promises from this 1964 experiment • Relieve doctors and nurses of some of their paperwork • Better management of diseases • Eliminate errors in medication and tests

12. What is current status • The promise of EHR’s • Have reduced paperwork • Reduced errors in patient medications and testing • Are we making best use of the data • Do we have good tools-software and skilled analyst 2014 KBPRN Collaborative Conference

13. Some examples of using EHR data • Example 1 – Patients with last HbA1c >=9 • Example 2 – Losing QI incentive pay • Example 3 – Identifying patients with hypertension 2014 KBPRN Collaborative Conference

14. Example 1 – Patients with last HbA1c >=9 (HRSA report) • Report showed 85% • Nurse responsible for QI at site questioned data • We found that only the hand-entered results from their in-house labs were picked-up (HRSA treats patients with missing HbA1c as >=9; missing data treated as non-compliant) • Lab reportsfrom outside vendor were missed • True statistic = 7% 2014 KBPRN Collaborative Conference

15. Example 2 – Excess prescription of antibiotics among children without proof of bacterial infection • Automated report on children receiving antibiotics showed excess prescribing among providers • Prescribing antibiotics for viral infections • Report was missing the diagnoses that should have been tied to the prescription • Automated report did not match the appropriate diagnoses with the prescriptions • Loss of $20,000 in incentive pay 2014 KBPRN Collaborative Conference

16. Example 3 – Identifying patients with hypertension • Worked with 11 primary care centers on under-diagnosis of hypertension • Identified patients based on ICD-9 coding • Noticed significant use of free text coding (the EHR allowed providers to use free text) • Found significant amount of patients with consistently high blood pressure readings but no diagnosis of hypertension (EHR missed this biomarker) • Found nearly 2000 patients missed across all sites 2014 KBPRN Collaborative Conference

17. Increase in HTN patients

18. John Snow and the Broad Street pump • John Snow’s chemical and microscopic examination was not able to conclusively prove the danger of the Broad Street pump. • Snow created a map to show how the cholera cases were clustered around the pump. • Pump handled removed upon new conclusion 2014 KBPRN Collaborative Conference

19. John Snow Revisited • How couldelectronic health records have help…? • EHR identifies all cases of cholera • Look at location indicators (addresses) • Create thematic map • Removed pump handle 2014 KBPRN Collaborative Conference

20. Identifying patients at-risk for diabetes • Previously, reliedon provider intervention at point of care to identify diabetes risk and think/make effort to refer the patient • One patient at a time • Inefficient • Identify at-risk patients using existing data • Clinic-wide • More efficient 2014 KBPRN Collaborative Conference

22. Using de-identified data from 14 WV primary care centers, we did the following: • Standardized the data in a common format (CDEMS) • Identified established patients by site (those receiving care for 12 months of more) • Excluded patients with a diagnosis of diabetes or pre-diabetes • Identified persons at risk for pre-diabetes based on CDC’s Group Lifestyle Balance criteria: • Age > 45 with last recorded BMI >25 • Age < 45 with last recorded BMI >25, with HTN, hyperlipidemia, gestational diabetes, family history of diabetes, or cardiovascular disease • Last fasting blood glucose in the range of 100-125 2014 KBPRN Collaborative Conference

23. Identifying patients Identified persons at risk for pre-diabetes based on CDC’s Group Lifestyle Balance criteria: • Age > 45 with last recorded BMI >25 • Age < 45 with last recorded BMI >25, with HTN, hyperlipidemia, gestational diabetes, family history of diabetes, or cardiovascular disease • Last fasting blood glucose in the range of 100-125

24. 94,283 no dx of DM • or pre-DM • 130,021 active Results • 106,367 established • 14 primary care centers: • 130,021 active patients • 106,367(81.8%) are established (receiving care for 12 months or more) • 94,283(88.6%) do not have a diagnosis of diabetes or pre-diabetes • Those patients are the focus of the analysis

25. Results-Identifies 10,673 (11.3%)

26. Discussion • Patients at-risk for pre-diabetes and in need of targeted screening can be identified using EHR data • Streamlines opportunity for patient identification, screening, and referral • No need for additional data collection at the sites • Making meaningful use of existing data 2014 KBPRN Collaborative Conference

27. Discussion • Early identification and intervention  opportunity for prevention • Improving outcomes and quality of life, and reducing long-term costs of care • Implementation highlights • Algorithms built using de-identified data • Identified data used to create lists of at-risk patients at individual sites • Each site contacted patient in an effort to recruit them for intervention 2014 KBPRN Collaborative Conference

28. Questions or comments

29. Some closing comments • At one time there were 450 different EHR’s in country • EHR’s need better Import/Export functions • Common Import/Export data formats • Should EHR’s be permitted to charge extra for analytics • EHR’s charge for each site to be connected to state Information Exchanges ($10,00)