Methods for improving efficiency in quality measurement: the example of pain screening

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Collecting unnecessary data when assessing quality of care wastes valuable resources. We evaluated three approaches for estimating quality-measure adherence and determined minimum visit data required to achieve accurate estimates.


We abstracted medical records for calculating physician-level pain screening rates as: visit-specific, using single-visit data for each patient; visit-level average, using data for all patients and visits; and patient-level average, using data from a subset of patients and visits.


VA Greater Los Angeles Health-care System, 2006.


One hundred and six patients with Stage IV solid tumors.


Pain screening at every medical encounter, measured by a 0–10 numeric rating scale and reported to the national Medicare insurance program under a ‘pay-for-reporting’ program.

Main Outcome Measures

Amount of visit data needed to reach the smallest 95% confidence interval (CI) and stable pain screening estimates.


Pain screening occurred at 22% (23/106; 95% CI: 14–30%) of initial visits and 50% (8/16; 95% CI: 25–75%) of single visits. Across all visits, screening adherence averaged 34% when estimated at the visit-level precision and 30% at the patient level. Maximum patient-level precision was reached at visit 4 (95% CI: ±8%) and visit level at visit 14 (95% CI: ±6%). Using patient-level and visit-level approaches, estimates stabilized at visits 8 and 11, respectively, and reached within 1 percentage point of the steady-state value at visits 4 and 9.


To address low-pain screening among cancer patients, an oncology pain screening measure may be most efficiently evaluated with data from a sample of patients and visits. This approach may be valid for visit-level quality measures in other settings.

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