The Role of Health IT in Measuring and Reducing Disparities

1. The Role of Health IT in Measuring and Reducing Disparities Fred D Rachman, MD

2. Goals of Meaningful Use • Improve quality, safety, efficiency andreduce health disparities • Engage patients and families • Improve care coordination • Improved population and public health • Ensure adequate privacy and security protections for personal health information

3. Presentation Overview • Description of collaboration of Safety Net Health Centers to adopt EMR • Reflections of impact of HIT on efforts to reduce health disparities based upon our experience in integrating quality measures into EMR implementation • AHRQ funded project “EQUIP” • Work through Health Research Education Trust to capture race ethnicity data funded by Commonwealth and RWJ • Integration and testing of PCPI performance measures in collaboration with AMA

4. Considerations Identifying: • the disparity groups • the disparities are we going to evaluate • the measures we will use • the data to be collected • the data capture methods Displaying data in a way that is actionable Taking action

5. Alliance Overview • HRSA funded Health Center Controlled Network founded by 4 Federally funded Health Centers located on the Near North Side of Chicago • Aim is to provide infrastructure through which Centers can share services at higher quality and lower cost. • Emphasis on shared Health information technology platform • Implementation and support of a common, centrally hosted EMR with integrated decision support and performance measures

6. Alliance Overview Collaboration has grown to encompass 22 Safety Net health care organizations in 8 states, covering wide range of populations: • Founding member Health Centers target Latino, African American, Gay and Lesbian, and multicultural Immigrant and Homeless populations • Additional Centers add other groups such asNative American, and are both rural and urban.

7. Alliance Overview • Services provided by the Centers include including Primary Care and limited other specialties. Dental, Podiatry, Nutrition, Ophthalmology,, X-ray and diagnostic, Complementary therapies, Mental Health and Social Services, Health Education, and 92 Clinical delivery sites >325 FTE Providers >260,000 Patients ~1,000,000 Patient visits

8. HIT impact on quality • Enhanced availability of Information – patient and knowledge based • Facilitation of multidisciplinary care • Improved efficiency/use of resources • Evidence based decision support (active and passive) at point of care • Expanded options for display of information • Performance measurement • Reporting (individual and population) • Support of clinical translational science and clinical effectiveness research

9. EQUIP project goals • Implement EHRS in a network of Community Health Centers in a manner that ensures consistency and accuracy of health information across all practitioners, sites and populations. 2. Develop a data warehouse that will monitor, aggregate, and provide data to be used for clinical and system quality improvement. 3. Utilize the EHRS/data warehouse to facilitate and encourage the use of evidence-based practice measures at the point of care.

10. EQUIP project goals 4.Utilize the EHRS/data warehouse to facilitate continuous improvement of health care quality and safety and develop its function as a patient registry. 5. Promote and support the realization of the full potential of EHRS use in ambulatory care settings, particularly among safety net providers, to improve health care quality and safety.

11. AMA PCPI GE Healthcare Alliance EQUIP Project • Integration of Performance standards into a commercial EMR prior to implementation • Partnership between Measure Developer, Software Vendor and Clinician

12. Status of EHRS use at Alliance Live across delivery sites of 4 founding Health Centers Implementation includes specialized settings: school based, youth drop-in, dental Big Bang” - All staff, with full functionality of the system Productivity at pre-implementation levels or greater 265 concurrent users, more than 500 individual users.“ Regular quality reporting in dashboard format Formalized implementation approach and toolkit Expansion to other Health Centers Focus on post implementation optimization Pilot projects in Medical Device integration, Health Information Exchange and patient portal

13. Performance measure integration • Performance measures integrated into EHRS for Diabetes, cardiovascular disease, asthma, HIV and preventive care • Summary screens provide decision support related to the measures for selected conditions • Reports on AMA as well as other national measures specified in a clinical data warehouse • Dashboard reports on data extracted from the warehouse provided monthly to Health Centers • Clinic staff trained to perform drill down reports to target Health Center specific activities

14. Considerations in implementing higher level functionality: Vision Acceptance of common vision of quality by clinicians is required as well as understanding and agreement on the relationship between evidence based recommendations, decision support and quality measures Willingness and ability to capture and process relevant data by clinical staff is also required 11/15/2014

15. Considerations in implementing higher level functionality: Technical • Underlying functionality of software must allow data to be defined and captured in uniform ways mapped to practice recommendations and performance measures • Population level analysis, and algorithms for measures may require more complex analysis or queries than are native to an EMR. • System must be modifiable as measures and recommendations change over time

16. Considerations in implementing higher level functionality: Implementation • Full use of system • Workflow analysis to optimize use • Data capture for has to simple and integrated into the workflow • Training both initial and ongoing to support adherence to data capture methods and intended workflows • Integration with other electronic databases (eg, laboratory) to increase accuracy and efficiency • Infrastructure for using data to make improvements.

17. Practice Guideline Structured Data Entry Decision Support Patient Status

18. Key aspects of performance measurement through EHRS Define data elements and incorporate into end user screens Work with measure developers to specify the measures for collection through the EMR Develop reporting algorithms that incorporate appropriate inclusion and exclusion criteria Export to an environment (data warehouse) for more sophisticated data uses Dedicated resources and an approach to introducing systems changes to produce improvement 11/15/2014

19. Measure Specifications Measure Developers need to provide Measure Definitions Numerator Denominator Exclusions Coding Specifications Code sets (LOINC, ICD-9, CPT Codes) Location in EHRS (problem list, diabetes template) Algorithms

20. Population level report

21. Provider Level Drill Down

22. Patient Level Drill Down

23. Export to Excel Dashboard Report CDW Turning Data into Information

25. Health Outcomes by Provider Reporting at individual provider level encourages local accountability for improvements

28. Socioeconomic Data Standardization Project • Convene health Centers to educate them on models of race/ethnicity/socioeconomic status indicators • Develop concensus on definitions • Granular data which respects individual Community/Health Center needs mapped to standardized concepts (CDC/OMB) • Develop technical methodology and workflows for data collection • Train staff for implementation • Use reporting to evaluate value

30. Health Outcomes

31. Using the Data • Refining clinical tools within the EMR • Sharing interventions/best practices among the Centers • Testing interventions: education, more intensive case management • Evaluating community factors: mapping, community level assessment.

32. Challenges for Performance Measurement Competing/Multiple Performance Measurement Sets with unaligned performance measures. Lack of Clinical Data Standards for many important medical concepts (such as Foot Exam, Pt. Education, etc) Inconsistent data definitions across different EHR Vendors Inconsistent collection of socioeconomic data 11/15/2014

33. What are we truly measuring? Capture of data element from data source outside the EHRS – no formal arrangement (e.g. colonoscopy) Capture of data element from data source outside the EHRS - formal arrangement for resulting (e.g. eye exam from formal referral resource) Capture of data element requiring entry of observation in standardized way by practitioner (e.g. foot exam) Capture of data element as easily objective defined observation captured by EHRS (e,g. blood pressure) Direct electronic of data element and/or result through order entry or interface (e.g. Hgb A1C measure and result)

34. At what level do we need to measure disparity? Health Care Institution Health System EHRS HIE PHR Patient 11/15/2014

35. How might HIT create/increase disparity? • Current funding incentives leave out safety net settings such as free clinics, nurse managee clinics, outreach programs, and other organizations serving uninsured or underinsured populations. • Increasing role on consumer use of technology to manage health may leave out many disparity groups, as access may be limited by factors such as language and economics.

36. Connecting the pieces Community/patient experience data to inform national initiatives Data Warehouse Reporting: Pt- and population-level National Data/ Research Functionality to Perform Detailed Queries of EHRS Evidence-based Guidelines Quality Data Patient Care Point-of-Care Decision Support

37. Fred D Rachman, MD frachman@alliancechicago.org ??????