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Establishing achievable benchmarks for quality improvement in systemic therapy for early‐stage breast cancer
Author(s) -
Powis Melanie,
Sutradhar Rinku,
Gonzalez Alejandro,
Enright Katherine A.,
Taback Nathan A.,
Booth Christopher M.,
Trudeau Maureen,
Krzyzanowska Monika K.
Publication year - 2017
Publication title -
cancer
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.052
H-Index - 304
eISSN - 1097-0142
pISSN - 0008-543X
DOI - 10.1002/cncr.30804
Subject(s) - benchmarking , medicine , breast cancer , benchmark (surveying) , funnel plot , stage (stratigraphy) , quality management , performance indicator , febrile neutropenia , cancer , medical physics , service (business) , neutropenia , meta analysis , chemotherapy , paleontology , management , geodesy , marketing , economics , business , publication bias , biology , geography , economy
BACKGROUND Setting realistic targets for performance is a consistent challenge in quality improvement. In the current study, the authors used administrative data to define achievable targets for a panel of 15 previously developed quality indicators (QIs) focusing on systemic therapy in patients with early‐stage breast cancer. METHODS Deterministically linked administrative databases were used to identify patients with TNM stage I to stage III breast cancer who were diagnosed between 2006 and 2010 in Ontario, Canada. For each individual indicator, data‐driven empirical benchmarks were calculated using the pared‐mean benchmark approach. Variation in institution‐level performance for each indicator was examined through the construction of funnel plots. RESULTS A total of 28,303 patients with early‐stage breast cancer were identified, 43% of whom received adjuvant chemotherapy. For the 9 QIs for which receiving the service or outcome was desirable (ie, consultation with a medical oncologist), the benchmark varied from 40.9% to 100%. For the 6 indicators for which not receiving the service or outcome was desirable (ie, incidence of febrile neutropenia), the benchmark varied from 0% to 49.0%. There was substantial variation noted with regard to the number of institutions meeting the target and the amount of interinstitution variation between the QIs. Top performing institutions varied by indicator, with no individual institution meeting the benchmark for all indicators. For the majority of indicators, institution size was not found to be correlated with performance. CONCLUSIONS Data‐derived benchmarking can be used to facilitate quality improvement by identifying areas of both good as well as suboptimal performance while defining an achievable target for which to strive. Cancer 2017;123:3772–3780. © 2017 American Cancer Society