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Using HIT to (Help) Measure Quality

Using HIT to (Help) Measure Quality. James M. Walker, MD, FACP CMIO Geisinger Health System. Agenda. Geisinger Overview Challenges Processes used to select metrics Lessons Learned. Geisinger Overview. 40 counties (mostly poor, elderly, and under-served) 4 hospitals; 30,000 discharges

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Using HIT to (Help) Measure Quality

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  1. Using HIT to (Help) Measure Quality James M. Walker, MD, FACP CMIO Geisinger Health System

  2. Agenda • Geisinger Overview • Challenges • Processes used to select metrics • Lessons Learned

  3. Geisinger Overview • 40 counties (mostly poor, elderly, and under-served) • 4 hospitals; 30,000 discharges • 41 clinic sites, 1.5 M visits • 2.4 million patients in EHR • HMO

  4. Clinical Information Systems • Outpatient EHR – all docs, all activities • Inpatient EHR – complete 2007 • Patient EHR – 87,000 users • Outreach EHR – 250,000 encounters/year • RHIE – 4 M patients, record locator, results transmission • Research –EHR benefits, information-optimized care processes

  5. Challenges • A welter of (evolving) requirements • Defining quality internally at least as much as externally • Achieving the minimum number of data elements (and definitions) required to • manage quality internally and • report to various recipients.

  6. Challenges • A welter of (evolving) requirements • Un-organized recipients • Lack of standards

  7. Lack of Standards • Lead time • Topics • Data elements (with definitions) • Communications standards • Operating systems and applications

  8. Challenges • A welter of (evolving) requirements • Disorganized recipients • Lack of standards • Organizational silos • Fragmented data repositories • Data capture • Clinician acceptance

  9. Metric-Selection Processes • Legacy metrics • Clinical-process optimization

  10. Clinical-Process Optimization • Chronic and Acute Care • Create internal consensus on performance standards and metrics. • Re-design processes to capture structured data for metrics. • Create internal consensus on reports (internal and external).

  11. Metric-Selection Processes • Legacy metrics • Clinical-process optimization • Literature Review (inpatient) • Data Standards and Applications Committee

  12. Data Standards and Applications Committee • Define unified reporting needs (internal and external). • Commission necessary database (and data-warehouse) changes. • Coordinate development of information-capture prompts and tools. • Feed needs forward to policymakers.

  13. Lessons Learned (internal) • Prioritize internal and external needs. • Focus organizational attention: • Multiple voices over time • The tipping point on the business case • One organizational set of quality metrics • Integrate the report, the databases, and the capture tools.

  14. Lessons Learned (external) • One national set of quality metrics • One place to send the one data set • EHRs designed to prompt for and report the right data

  15. jmwalker@geisinger.edu

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