developing a metric set for measuring and reporting ambulatory quality of care in the setting of...
TRANSCRIPT
Developing a Metric Set for Measuring and Reporting
Ambulatory Quality of Care in the Setting of Health IT with HIE
Lisa M. Kern, MD, MPHRina V. Dhopeshwarkar, MPH
Rainu Kaushal, MD, MPHHITEC
Cornell UniversityNew York-Presbyterian Hospital
HITEC
September 2008
Background
• ¾ of states are pursuing development and implementation of health information exchange (HIE)
• New York State is investing $250 million in infrastructure for health information technology (health IT) and HIE
–Largest state-based investment of taxpayer dollars
www.staterhio.org, www.health.state.ny.us/technology
HEAL NY Program
• HEAL NY: Healthcare Efficiency and Affordability Law for New Yorkers Capital Grants Program– Funding distributed in waves:• 1st wave in 2006• 2nd wave in 2008
– Projects include both health IT and HIE– Grantees were required to evaluate the
effects of their projects
HITEC: The Health Information Technology Evaluation Collaborative
• A formal collaborative of 4 universities in New York (Cornell, Columbia, SUNY Albany, University of Rochester)
• Established with the endorsement of the New York State Department of Health
• Established to conduct rigorous evaluations of HEAL NY projects in order to maximize learning and produce generalizable results
Limitations of Existing Metric Sets
• Existing metric sets developed to evaluate the quality of healthcare delivered in an ambulatory care setting:– Rely on manual chart review (expensive and
laborious)
– Claims data (lack clinical nuance)
– Do not presume communication between health care providers
– Not designed to take into account incremental effect of receiving clinical data from outside sources
Specific Aims
1. Develop a modified set of quality metrics that can be retrieved electronically and is sensitive to the types of improvements in quality that health IT with HIE may contribute to the ambulatory care setting
2. Validate the modified quality metrics set through review by a panel of national experts in quality measurement and national experts in HIE
3. To test the reliability of electronic retrieval of the modified quality metrics set, by comparing electronic retrieval to manual retrieval
4. To evaluate the long-term effects of using health IT with HIE on improving health care quality, using the modified metric set
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Conceptual Framework
Ele
ctro
nic
Rep
ortin
g
Clinical Data Residing
Elsewhere
Quality Report
Electronic Receipt of
Clinical Data by Health Care Provider with an EHR at or Before the
Point of Care
Medical Decision Making
HIE
“Sensitivity to EHR with HIE”“Suitability for
Electronic Reporting”
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Overall Methodology in Brief
1. Conduct a literature review for existing ambulatory care quality metric sets.
2. Determine if any of the metrics retrieved should be excluded.
3. Articulate the domains and assumptions upon which each metric would be rated.
4. Rate the existing metrics.
5. Develop novel metrics.
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Inclusion Criteria for Metric Sets
• Included metric sets had to be:– Endorsed by• A national quality organization,• A national professional organization, or• A national research organization,
OR– Specifically address quality of transitions
across health care settings
• Included metric sets could be general or disease-specific
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Exclusion Criteria
• Not in the ambulatory setting– Emergency department care was excluded.
• Not adult primary care– Obstetrics, pediatrics, cancer care and HIV
care were excluded.
• Provider, practice or health plan characteristics
• Satisfaction or experience of patients or providers
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Metric Selection (continued)
17 metric sets = 1064 metrics
925 metrics
502 metrics
139 Duplicates
423 Excluded
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Rating Process: Round One
• Each metric was reviewed by 2 raters on 2 dimensions, each on a scale from 0-6– Impact of HIE on medical decision making – Suitability for electronic reporting
• Ratings were summed across dimensions and averaged across raters
• 59 metrics scored high (≥9 out of 12)
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Rating Process: Round Two
• Each metric was reviewed by several raters on 5 dimensions, each on a scale from 0-6– Feasibility of delivering data electronically– Impact on medical decision making– Clinical importance– Feasibility of reporting data electronically– Global rating (4-7 raters for each metric)
• Ratings were averaged across raters for the global rating
• 18 scored high (≥4 out of 6)
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Diseases Represented by Top-Scoring Existing Metrics
• Asthma (1 metric)
• Cardiovascular Disease (3)
• Congestive Heart Failure (1)
• Diabetes (4)
• Medication/Allergy Management (2)
• Mental Health (1)
• Osteoporosis (1)
• Prevention (5)
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Novel Metrics
• Developed through an iterative process with national quality experts
• Cover topics related to efficiency and coordination of care– Test Ordering (3 metrics)–Medications (4)– Referrals (2)– Revisits (3)
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Next Step
• Test reliability of electronic reporting vs. manual chart review for selected existing metrics and for novel metrics
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Assumptions for Determining EHR+HIE Sensitivity
• We assumed the perspective of a primary care physician who has the following characteristics:– Is board-certified and competent– Has been in a community-based practice x 10 years– Has a relatively stable panel of patients– Has an electronic health record (EHR), which is
linked only to generalist partners– Is in a practice with the technical capacity to
participate in an HIE
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Factors Relevant for RatingEHR+HIE Sensitivity
1. Whether needed data elements would be missing in the absence of HIE (Relevance)
2. Ease of electronic transmission of data elements to the provider (Feasibility)
3. Impact of electronic transmission (Impact) on: Processes of care and/or patient outcomes Utilization
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Factors Relevant for RatingSuitability for eReporting
1. How commonly this metric appears in other quality metric sets (Importance)
2. How often the data needed for this metric are currently structured (Feasibility)
3. If data are not currently structured, how easy would it be technically to create a structured format (Feasibility)