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

Fred D Rachman, MD frachman@alliancechicago.org

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