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Article

Identification of Performance Indicators across a Network of Clinical Cancer Programs

1
McGill Univ, Dept Family Med, Montreal, PQ H3A 2T5, Canada
2
McGill Univ, Jewish Gen Hosp, Segal Canc Ctr, Montreal, PQ H3T 1E2, Canada
*
Author to whom correspondence should be addressed.
Curr. Oncol. 2016, 23(2), 81-90; https://doi.org/10.3747/co.23.2789
Submission received: 2 January 2016 / Revised: 3 February 2016 / Accepted: 4 March 2016 / Published: 1 April 2016

Abstract

Background: Cancer quality indicators have previously been described for a single tumour site or a single treatment modality, or according to distinct data sources. Our objective was to identify cancer quality indicators across all treatment modalities specific to breast, prostate, colorectal, and lung cancer. Methods: Candidate indicators for each tumour site were extracted from the relevant literature and rated in a modified Delphi approach by multidisciplinary groups of expert clinicians from 3 clinical cancer programs. All rating rounds were conducted by e-mail, except for one that was conducted as a face-to-face expert panel meeting, thus modifying the original Delphi technique. Four high-level indicators were chosen for immediate data collection. A list of confounding variables was also constructed in a separate literature review. Results: A total of 156 candidate indicators were identified for breast cancer, 68 for colorectal cancer, 40 for lung cancer, and 43 for prostate cancer. Iterative rounds of ratings led to a final list of 20 evidence- and consensus-based indicators each for colorectal and lung cancer, and 19 each for breast and prostate cancer. Approximately 30 clinicians participated in the selection of the breast, lung, and prostate indicators; approximately 50 clinicians participated in the selection of the colorectal indicators. Conclusions: The modified Delphi approach that incorporates an in-person meeting of expert clinicians is an effective and efficient method for performance indicator selection and offers the added benefit of optimal clinician engagement. The finalized indicator lists for each tumour site, together with salient confounding variables, can be directly adopted (or adapted) for deployment within a performance improvement program.
Keywords: performance measures; quality indicators; health care quality assessment; quality improvement; quality of cancer care performance measures; quality indicators; health care quality assessment; quality improvement; quality of cancer care

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MDPI and ACS Style

Khare, S.R.; Batist, G.; Bartlett, G. Identification of Performance Indicators across a Network of Clinical Cancer Programs. Curr. Oncol. 2016, 23, 81-90. https://doi.org/10.3747/co.23.2789

AMA Style

Khare SR, Batist G, Bartlett G. Identification of Performance Indicators across a Network of Clinical Cancer Programs. Current Oncology. 2016; 23(2):81-90. https://doi.org/10.3747/co.23.2789

Chicago/Turabian Style

Khare, S.R., G. Batist, and G. Bartlett. 2016. "Identification of Performance Indicators across a Network of Clinical Cancer Programs" Current Oncology 23, no. 2: 81-90. https://doi.org/10.3747/co.23.2789

APA Style

Khare, S. R., Batist, G., & Bartlett, G. (2016). Identification of Performance Indicators across a Network of Clinical Cancer Programs. Current Oncology, 23(2), 81-90. https://doi.org/10.3747/co.23.2789

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