The Role of Health IT in Measuring and Reducing Disparities

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

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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

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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

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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

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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.

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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
• gt325 FTE Providers
• gt260,000 Patients
• 1,000,000 Patient visits

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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

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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.

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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.

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EQUIP Project

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

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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

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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

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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

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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

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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.

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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

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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

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Population level report
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Provider Level Drill Down
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Patient Level Drill Down
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Turning Data into Information
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Health Outcomes by Provider

• Reporting at individual provider level encourages
  local accountability for improvements

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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

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Health Outcomes
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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.

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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

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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)
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At what level do we need to measure disparity?
Health Care Institution
Health System
EHRS
HIE
PHR
Patient
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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.

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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
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??????

• Fred D Rachman, MD
• frachman_at_alliancechicago.org