2014 APHA: Advancing Meaningful Use through Disparities Analysis

Advancing Meaningful Use through Disparities Analysis of Clinical Data:

Lessons from the REAL Data Project at UTMB

Wei-Chen Lee, John Prochaska, Karl Eschbach, Christen Miller, Alexandra Nolen

Center to Eliminate Health DisparitiesUniversity of Texas Medical Branch, Galveston, TX

November 18, 2014

Center to Eliminate Health Disparities (CEHD)

APHA Disclosure Statement

There is no financial interest or relationship with a for-profit company to declare.

We thank the University of Texas Medical Branch Office of Waiver Operations, Clinical Information Services, the Oliver Center for Patient Quality and Safety, and Office of Health Policy and Legislative Affairs for their support.

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Outline

1. Describe the EMR system

2. Define the meaningful use of EMR

3. Evaluate the challenges for the meaningful use of EMR

4. Discuss the solutions to conquer challenges

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1. What is an EMR?

• Electronic Medical Record (EMR)

• Work through a computer software system (e.g. Epic) administered by Clinical Information Services

• Contains a patient’s medical (e.g. diagnoses) and non-medical (e.g. address) information

• Includes registry, analysis, and retrieval functions• Epic Software System used at UTMB

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Outline





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2.1 What is meaningful use?

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• Center for Medicare & Medicaid Services (CMS)

• Financial incentives for the meaningful use of certified EMR technology to improve patient care

• Three stages: data capture and sharing, advance clinical processes, and improved outcomes

• 15 Core Objectives + 5 Menu Measures + 6 Clinical Quality Measures = Meaningful Use (MU)• More than 50% of all unique patients should have

demographics recorded as structured data (preferred language, gender, race, ethnicity, and date of birth)


2.2 Using EMR to identify health disparities and achieve health equity

• Health Disparities: differences in the incidence and prevalence of health status/conditions between groups as a result of the systematic and unjust distribution of health services

• Health Equity: everyone has the opportunity to attain their full health potential and no one is disadvantaged from achieving this potential because of race/ethnicity, gender, education, geographic location, insurance, employment, or other social determinants

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2.3 Racial/Ethnic disparities in….

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2.4 The milestones of REAL DataOne of the DSRIP (Delivery System Reform Incentive Pool) projects approved by CMS as part of the Texas Medicaid 1115 Waiver: Strengthening the UTMB Health Information System to Reduce Health Disparities

1.Increase the number of unique patients registered with valid REAL data fields in three years (7,80013,20018,000)

2.Complete 48 tests every year

3.Identify top three health care disparities

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2.5 The process to get EMR data…• File the data query Health IT retrieves the data

Send the data in Excel format Data analysis Feedback to clinical/executive teams

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Outline





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3. What are challenges?

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3.1 Scope: No REAL…

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Center to Eliminate Health Disparities (CEHD) 14

Question U.S. OMB Existing FieldsEthnicity Do you consider

yourself Hispanic/Latino? (1)Yes(2)No

(1) Caucasian(2) Hispanic(3) Non-Hispanic(4) Patient refused(5) Unknown


Q. U.S. OMB Existing FieldsRace (1) American Indian

or Alaska Native(2) Asian(3) Black or African

American(4) Native Hawaiian

or Other Pacific Islander

(5) White

(1) American Indian or Alaska Native

(2) Asian(3) Black or African American(4) Caucasian (5) Hispanic or Latino(6) Native Hawaiian or Other

Pacific Islander(7) Unknown


3.2 Schedule: No time….

• Milestones

1. Semi-annual report in April and final report in October for all 1115 Waiver projects

2. One test a week• Not the priority for IT

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3.3 Resources: Workforce• Poor understanding of each other’s language

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Outline





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4. Suggestions: A-B-CA. Adjust the Epic system

1) Race: Multiracial groups2) Race/Ethnicity: Replace “unknown” with “others”3) Alternate approach: Two-question format One-

question

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B. Build awareness among all professionals

1) Stratify the quality performance measures by race and ethnicity

2) Develop new disparities specific measures• In health status and health services• Both favorable and adverse events

3) Track progress toward providing equitable care over time and create a timely feedback to clinical and administrative leaders

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C. Collaborate with more people to achieve our milestones and reduce racial/ethnic disparities

1) Existing data sources: share others’ data2) Provider: staff training, sustainable funding…etc.3) Community/Patient Population: be aware of the

benefits to collect accurate race and ethnicity information from them

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Thank you for your listening!

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References

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1. Health Equity and Race and Ethnic Data: http://www.coloradotrust.org/attachments/0002/0973/CT_Race_EthnicityBrief_vFinal2.pdf

2. Assessing Racial and Ethnic Barriers to Effective Health Care: http://content.healthaffairs.org/content/21/3/91.full.pdf+html

3. Improving Data Collection Across the Health Care System: http://www.ahrq.gov/research/findings/final-reports/iomracereport/reldata5.html

4. HHS Action Plan to Reduce Racial and Ethnic Health Disparities: http://minorityhealth.hhs.gov/npa/files/Plans/HHS/HHS_Plan_complete.pdf

5. A Plan for Action: Key Perspectives from Racial/Ethnic Disparities Strategy Forum: http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2690363/

6. Who, When, and How: The Current State of Race, Ethnicity, and Primary Language Data Collection in Hospitals: http://www.commonwealthfund.org/usr_doc/hasnain-wynia_whowhenhow_726.pdf

7. Reducing Health Care Disparities: www.hpoe.org/Reports-HPOE/Equity_Care_Report_August2013.PDF

8. Federal Strategic Plan to Reduce Health IT Disparities: http://www.healthit.gov/buzz-blog/from-the-onc-desk/federal-strategic-plan-disparities/

9. Race and Ethnic Health Disparities: http://www.cahpf.org/GoDocUserFiles/449.disparities_highlights.pdf

10. Examples of instructions, phrasing, and terminology to capture race and ethnicity data: http://www.ahrq.gov/research/findings/final-reports/iomracereport/reldata3tab3-6.html

http://www.coloradotrust.org/attachments/0002/0973/CT_Race_EthnicityBrief_vFinal2.pdf

http://content.healthaffairs.org/content/21/3/91.full.pdf+html

http://www.ahrq.gov/research/findings/final-reports/iomracereport/reldata5.html

http://minorityhealth.hhs.gov/npa/files/Plans/HHS/HHS_Plan_complete.pdf

http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2690363/

http://www.commonwealthfund.org/usr_doc/hasnain-wynia_whowhenhow_726.pdf

http://www.hpoe.org/Reports-HPOE/Equity_Care_Report_August2013.PDF

http://www.healthit.gov/buzz-blog/from-the-onc-desk/federal-strategic-plan-disparities/

http://www.cahpf.org/GoDocUserFiles/449.disparities_highlights.pdf

http://www.ahrq.gov/research/findings/final-reports/iomracereport/reldata3tab3-6.html


References (contd.)11. Defining Categorization Needs for Race and Ethnicity Data:

http://www.ahrq.gov/research/findings/final-reports/iomracereport/reldata3b.html

12. Improving Quality and Achieving Equity: http://www2.massgeneral.org/disparitiessolutions/z_files/disparities%20leadership%20guide_final.pdf

13. Racial/Ethnic Disparities and Health Policy: A view from the field: http://www.slideshare.net/simon23/racialethnic-disparities-and-health-policy

14. Healthcare Disparities and the Role of Personal Health Records: http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_043826.hcsp?dDocName=bok1_043826

15. HER Intelligence: http://ehrintelligence.com/tag/health-it-and-patient-engagement/

16. Utah Health Disparities: http://www.health.utah.gov/disparities/data/disparitiessummary/HealthDisparitiesSummariesPresentation.pdf

17. Enhancing Public Hospital’s Reporting of Data on Racial and Ethnic Disparities in Care: http://www.commonwealthfund.org/publications/fund-reports/2007/jan/enhancing-public-hospitals-reporting-of-data-on-racial-and-ethnic-disparities-in-care

18. Standards for the Classification of Federal Data on Race and Ethnicity: http://www.whitehouse.gov/omb/fedreg_race-ethnicity

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http://www.ahrq.gov/research/findings/final-reports/iomracereport/reldata3b.html


Reporting of Race and Ethnic Data in UTMB EMR, by Patient Encounter, 10/1/2013 to 9/30/2014Race/Ethnicity White Hispanic Not

HispanicPatient Refused Unknown Total

American Indian or Alaska Native 0 458 1,675 0 1,896 4,029

Asian 0 204 9,094 13 7,797 17,108Black 0 1,422 87,973 15 64,403 153,813White 27 207,599 220,913 65 245,266 673,870Hispanic 0 5 0 0 0 5Native Hawaiian or Other Pacific Islander

0 310 613 0 378 1,301

Unknown 0 79,283 2,490 45 12,800 94,618Total 27 289,281 322,758 138 332,540 944,744

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Differences vs. Disparities

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1. Patient-level: Patient’s attitude, preferences for treatment, health literacy, genetic / biological risks, years in U.S. and so on

2. Encounter: Cultural barriers, linguistic barriers, timely follow-up …etc.

3. System-level: Fragmentation of healthcare systems, lack of research, and so on