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Health Services Research & Development

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2005 HSR&D National Meeting Abstract


3001 — Enhancing Performance Monitoring: What Can We Learn from Physician Explanations of Poor Care?

Author List:
Hogan MM (VA Ann Arbor Healthcare System)
Kerr EA (VA Ann Arbor Healthcare System)
Hofer TP (VA Ann Arbor Healthcare System)
Hayward RA (VA Ann Arbor Healthcare System)
Asch SM (Greater LA Healthcare System)

Objectives:
Standard performance monitoring in VA and managed care relies on explicit assessments using a guideline-based checklist approach for a limited number of conditions and measures. Using physician structured implicit review (SIR) we compared physician’s explanations for poor care with three explicit performance measurement systems to identify areas where explicit performance monitoring can be enhanced.

Methods:
We sampled 621 veterans with at least two visits in each of two years in two VHA regions, oversampling for diabetes and COPD. The sample was 97% male with a mean age of 62. Twelve physicians reviewed medical records used an SIR instrument and explained poor care ratings. Poor care explanations were matched to indicators in three explicit systems; QA Tools, with 30 conditions and over 400 indicators, VA External Peer Review Program (EPRP) and the Health Plan Data and Information Set (HEDIS), each with fewer conditions and indicators.

Results:
Overall, 54% of the 2480 explanations matched indicators in any of the explicit systems. Matches varied by explicit system (QA Tools, 49.9%, EPRP, 39%, HEDIS, 13.6%) and by condition (prevention, 82.8%; diabetes, 59.2%; hypertension, 41.9%; COPD, 37.3%; other chronic conditions 28.5%). We further examined matching and non-matching explanations. For example, for diabetes there were 239 explanations of poor care that matched physician explanations, and 75% (179) were accounted for by four commonly measured items: renal evaluation (N=46), foot exams (N=46), eye exams (N=45), and A1c monitoring (N=42). Among non-matching diabetes explanations (N=165), the most common were failure to change medications in response to the patient’s condition (e.g., elevated A1c) (24%, N=40), failure to work up abnormal test results (15%, N=24), and other ongoing assessment (13%, N=22).

Implications:
Explanations were more likely to match explicit indicators for conditions with good evidence and commonly used standards, e.g., prevention and diabetes. Explanations for poor care were frequently not covered in any of three explicit measurement systems.

Impacts:
Explicit performance monitoring has the advantages of being readily understood and informing health systems about specific areas for quality improvement. Implicit reviews offer opportunities to identify potential new indicators for inclusion in explicit performance monitoring.


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