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

    loading  Checking for direct PDF access through Ovid

Abstract

Objective

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.

Design

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.

Setting

VA Greater Los Angeles Health-care System, 2006.

Participants

One hundred and six patients with Stage IV solid tumors.

Intervention

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.

Results

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.

Conclusion

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.

Related Topics

    loading  Loading Related Articles