The Role of Health IT in Measuring and Reducing Disparities (Text Vers

On September 14, 2009, Fred D Rachman made this presentation at the 2009 Annual Conference. Select to access the PowerPoint® presentation (1.4 MB) (Plugin Software Help).

Slide 1

The Role of Health IT in Measuring and Reducing Disparities

Fred D Rachman, MD

Slide 2

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

Slide 3

Presentation Overview

• Description of collaboration of Safety Net Health Centers to adopt EMR [electronic medical records]
• Reflections of impact of HIT [health information technology] 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

Slide 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

Slide 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

Slide 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 as Native American, and are both rural and urban.

Slide 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

Slide 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

Slide 9

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.

Slide 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.

Slide 11

EQUIP Project

• Integration of Performance standards into a commercial EMR prior to implementation
• Partnership between Measure Developer, Software Vendor and Clinician

Slide 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

Slide 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

Slide 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

Slide 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

Slide 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.

Slide 17

Image demonstrates practice guidlines, patient status, structured data and decision support.

Slide 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

Slide 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

Slide 20

Image shows a population level report

Slide 21

Image shows a Provider Level Drill Down

Slide 22

Image shows a Patient Level Drill Down

Slide 23

Image shows Turning Data into Information

Slide 24

Image shows Health Outcomes Dashboard

Slide 25

Health Outcomes by Provider

Reporting at individual provider level encourages local accountability for improvements

Slide 26

Image shows Centers by Race

Slide 27

Image shows Centers by Economic Indicator

Slide 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

Slide 29

Image shows various graphs

Slide 30

Image shows Health Outcomes

Slide 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.

Slide 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

Slide 33

Image shows measurement criteria

Slide 34

Image shows measurement disparity

Slide 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.

Slide 36

Image shows Connecting the pieces

Slide 37

Image shows a row of question marks.

Fred D Rachman, MD
frachman@alliancechicago.org