Applying electronic health record data to quality of care improvement and practice based research initiatives Cecil Pollard, Director West Virginia University

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

5/9/20142014 KBPRN Collaborative Conference

"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."


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


By 1985 it had evolved into this


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


“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.


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-1964

http://www.youtube.com/watch?v=t-aiKlIc6uk



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


Some examples of using EHR data

Example 1 – Patients with last HbA1c >=9

Example 2 – Losing QI incentive payExample 3 – Identifying patients with

hypertension


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%


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


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


Increase in HTN patients

Search Criteria Number Number added Cumulative Percent

ICD-9 code 12,919 --- 86.7

ICD-9 code plus free text

13,817 898 92.3

ICD-9 code plus free text plus BP measures

14,893 1,078 100

Total 1,974

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


John Snow Revisited

How could electronic health records have help…?

EHR identifies all cases of choleraLook at location indicators

(addresses)Create thematic mapRemoved pump handle


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


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


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

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 dataStreamlines opportunity for patient identification,

screening, and referralNo need for additional data collection at the sitesMaking meaningful use of existing data


Discussion

Early identification and intervention opportunity for preventionImproving outcomes and quality of life, and

reducing long-term costs of care Implementation highlights

Algorithms built using de-identified dataIdentified data used to create lists of at-risk patients at

individual sitesEach site contacted patient in an effort to recruit them for

intervention


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)

Documents

Applying electronic health record data to quality of care improvement and practice based research initiatives Cecil Pollard, Director West Virginia University