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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. - PowerPoint PPT Presentation
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Applying electronic health record data to quality of care improvement and practice based research initiatives
Cecil Pollard, DirectorWest 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."
5/9/20142014 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
5/9/20142014 KBPRN Collaborative Conference
By 1985 it had evolved into this
5/9/20142014 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
5/9/20142014 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.
5/9/20142014 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-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
5/9/20142014 KBPRN Collaborative Conference
Some examples of using EHR data
Example 1 – Patients with last HbA1c >=9
Example 2 – Losing QI incentive payExample 3 – Identifying patients with
hypertension
5/9/20142014 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%
5/9/20142014 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
5/9/20142014 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
5/9/20142014 KBPRN Collaborative Conference
Increase in HTN patientsSearch Criteria Number Number added Cumulative
PercentICD-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
5/9/20142014 KBPRN Collaborative Conference
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
5/9/20142014 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
5/9/20142014 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 < 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
5/9/20142014 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 < 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
5/9/20142014 KBPRN Collaborative Conference
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
5/9/20142014 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)