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American Society of Clinical Oncology, Journal of Clinical Oncology, 28(35), p. 3207-3214, 2017

DOI: 10.1200/jco.2016.70.7950

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Setting Quality Improvement Priorities for Women Receiving Systemic Therapy for Early-Stage Breast Cancer by Using Population-Level Administrative Data

This paper was not found in any repository, but could be made available legally by the author.
This paper was not found in any repository, but could be made available legally by the author.

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Abstract

Purpose Routine evaluation of quality measures (QMs) can drive improvement in cancer systems by highlighting gaps in care. Targeting quality improvement at QMs that demonstrate substantial variation has the potential to make the largest impact at the population level. We developed an approach that uses both variation in performance and number of patients affected by the QM to set priorities for improving the quality of systemic therapy for women with early-stage breast cancer (EBC). Patients and Methods Patients with EBC diagnosed from 2006 to 2010 in Ontario, Canada, were identified in the Ontario Cancer Registry and linked deterministically to multiple health care databases. Individual QMs within a panel of 15 QMs previously developed to assess the quality of systemic therapy across four domains (access, treatment delivery, toxicity, and safety) were ranked on interinstitutional variation in performance (using interquartile range) and the number of patients who were affected; then the two rankings were averaged for a summative priority ranking. Results We identified 28,427 patients with EBC who were treated at 84 institutions. The use of computerized physician electronic order entry for chemotherapy, emergency room visits or hospitalizations during chemotherapy, and timely receipt of chemotherapy were identified as the QMs that had the largest potential to improve quality of care at a system level within this cohort. Conclusion A simple ranking system based on interinstitutional variation in performance and patient volume can be used to identify high-priority areas for quality improvement from a population perspective. This approach is generalizable to other health care systems that use QMs to drive improvement.