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The Role of Health IT in Measuring and Reducing Disparities. Fred D Rachman, MD. Goals of Meaningful Use. Improve quality, safety, efficiency and reduce health disparities Engage patients and families Improve care coordination Improved population and public health - PowerPoint PPT Presentation
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The Role of Health IT in Measuring and Reducing Disparities
Fred D Rachman, MD
Goals of Meaningful Use
• Improve quality, safety, efficiency and reduce health disparities
• Engage patients and families• Improve care coordination• Improved population and public health• Ensure adequate privacy and security protections
for personal health information
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
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 methodsDisplaying data in a way that is actionableTaking action
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
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 as Native American, and are both rural and urban.
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
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
EQUIP project goals
1. 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.
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.
EQUIP Project
• Integration of Performance standards into a commercial EMR prior to implementation
• Partnership between Measure Developer, Software Vendor and Clinician
AMA PCPIGE Healthcare
Alliance
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
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
04/19/23
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
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
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.
Structured Data Entry
Practice Guideline
Patient Status
Decision Support
04/19/23
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
Measure Specifications
Measure Developers need to provide Measure Definitions
Numerator DenominatorExclusions
Coding SpecificationsCode sets (LOINC, ICD-9, CPT Codes)Location in EHRS (problem list, diabetes
template) Algorithms
Population level report
Provider Level Drill Down
Patient Level Drill Down
Turning Data into Information
CDW Export to Excel Dashboard Report
Average A1c Value
8.1 7.9 7.8 7.9 8.0
7.0
0.02.04.06.08.0
10.0
Good
Health Outcomes by Provider
Reporting at individual provider level encourages local accountability for improvements
Race Definition/ Comments Center 1 Center Center 3 Center 4 EHRS standard
African Amer./Black Center 1: Africans born in
America Yes Yes Yes) Black
American Indian/ Alaskan Native Native American Yes Yes No Yes Native American
Native American No No Yes Native American
Asian Yes Yes Yes Yes Oriental/ Asian
White HHO: White (not Hispanic) Yes White
Caucasian Yes Yes Yes White
Colombian Yes
Cuban Yes
Hispanic/Latino Yes No No Hispanic
Hispanic HHO: Hispanic other all races No No Yes Yes Hispanic
Mexican HHO: Mexican Hispanic Yes
Indian/Not American NNHSC: East Indian Yes No No Oriental/ Asian
Polish No No Yes Other
Pacific Islander
Centers 1 ,2, and 3 use this for its Native Hawaiian also. HHO: Other Pacific Islander Yes Yes Yes Yes Pacific Islander
Native Hawaiian No No No Yes Native Hawaiian
Puerto Rican Yes
Middle Eastern No No Yes Other
Multiracial No Yes No Multiracial
OtherBlacks from Africa come under
this for Center 1 Yes Yes Yes Other
Unknown HHO: Unknown, un reported Yes Yes Yes Yes Unknown
Economic Indicators Center 1 Center 2 Center 3 Center 4 EHRS
Income Yes Yes
Annual/Biweekly Income Yes Yes
Monthly Income Yes
Family size Yes Yes Yes Yes
Verification date Yes
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
Patient Distribution by RaceN=16,160 Patients
African American, 38%
Hispanic, 38%
Asian, 2%Caucasian, 17%
Other, 2%Native
American, 0%
Undetermined, 2%
% Patients Aged >= 50 With Influenza Vaccination
11%
25%
16%
34%
20%
CDC Benchmark = 65%
0%
25%
50%
75%
100%
AfricanAmerican
Asian Caucasian Hispanic NativeAmerican
% Women Aged 50-69 With A Mammogram
50%
72%
49%
61%
36%
NCQA Benchmark = 69%
0%
25%
50%
75%
100%
African American Asian Caucasian Hispanic Native American
% Adult Diabetics w/HbA1c > 9% (poor control)
11%
25%
16%
34%
20%
NCQA Benchmark = 29%
0%
25%
50%
AfricanAmerican
Asian Caucasian Hispanic NativeAmerican
Health OutcomesHealth OutcomesDOQ-IT Diabetes Measures
Alliance Centers vs. MQIC National
93%
25%
71%69%
78%
71%
18%
45%
88%
12%
72%
79%
86%
63%
24%21%
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
% With HgbA1cTest
% with HgbA1cwith Last >9.0
% w/BP and<140/90
% with LDL % with LDL and<130 mg/dl
% with UrineProtien Test
% with EyeExam
% with FootExam
ALLIANCE (N=3.3KDiabetics)MQIC Primary Care(N=196.1K Diabetics)ALLIANCE CTR HIGH
ALLIANCE CTR LOW
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.
04/19/23
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
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)
What are we truly measuring?
04/19/23
EHRS
PHR
HIE
At what level do we need to measure disparity?
Health Care Institution Health System
Patient
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.
National Data/Research
Data Warehouse Reporting:
Pt- and population-level
Patient Care
Evidence-based Guidelines
Point-of-Care Decision Support
Quality Data
Functionality to Perform Detailed Queries of EHRS
Community/patient experience data to
inform national initiatives
Connecting the Connecting the piecespieces