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 5/9/ KBPRN Collaborative Conference

"The project described was supported by the National Institute of General Medical Science, U54GM The content is solely the responsibility of the authors and does not necessarily represent the official views of the NIH." 5/9/ KBPRN Collaborative Conference

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

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

5/9/ KBPRN Collaborative Conference

By 1985 it had evolved into this 5/9/ KBPRN Collaborative Conference

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 5/9/ KBPRN Collaborative Conference

“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. 5/9/ KBPRN Collaborative Conference

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

The beginning of Electronic Health Records

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

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 5/9/ KBPRN Collaborative Conference

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 5/9/ KBPRN Collaborative Conference

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 reports from outside vendor were missed  True statistic = 7% 5/9/ KBPRN Collaborative Conference

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 5/9/ KBPRN Collaborative Conference

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 5/9/ KBPRN Collaborative Conference

Increase in HTN patients Search CriteriaNumberNumber addedCumulative Percent ICD-9 code12, ICD-9 code plus free text 13, ICD-9 code plus free text plus BP measures 14,8931, Total1,974

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 5/9/ KBPRN Collaborative Conference

John Snow Revisited  How could electronic health records have help…?  EHR identifies all cases of cholera  Look at location indicators (addresses)  Create thematic map  Removed pump handle 5/9/ KBPRN Collaborative Conference

Identifying patients at-risk for diabetes  Previously, relied on 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 5/9/ KBPRN Collaborative Conference

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 25, with HTN, hyperlipidemia, gestational diabetes, family history of diabetes, or cardiovascular disease  Last fasting blood glucose in the range of /9/ KBPRN Collaborative Conference

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 25, with HTN, hyperlipidemia, gestational diabetes, family history of diabetes, or cardiovascular disease  Last fasting blood glucose in the range of

Results  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 130,021 active 106,367 established 94,283 no dx of DM or pre-DM

Results-Identifies 10,673 (11.3%)

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 5/9/ KBPRN Collaborative Conference

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 5/9/ KBPRN Collaborative Conference

Questions or comments

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)