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. 2020 Feb 25;8(2):e2630.
doi: 10.1097/GOX.0000000000002630. eCollection 2020 Feb.

Quality Measures in Postmastectomy Breast Reconstruction: Identifying Metrics to Improve Care

Jacob S Nasser  1   2 , William H J Chung  2 , Ryan A Gudal  2 , Sandra V Kotsis  2 , Adeyiza O Momoh  2 , Kevin C Chung  2
Affiliations

Quality Measures in Postmastectomy Breast Reconstruction: Identifying Metrics to Improve Care

Jacob S Nasser et al. Plast Reconstr Surg Glob Open. .

Abstract

Background: Specific measures tailored to the properties of individual procedures will ensure the appropriate evaluation of quality. Because postmastectomy breast reconstruction (PMBR) is becoming increasingly common, a review of the literature is timely to identify potential breast reconstruction-specific measures that can be applied by institutions and national healthcare organizations to improve quality.

Methods: We searched PubMed and Embase for studies examining the quality of care for patients undergoing PMBR. Data extracted from the articles include basic study characteristics, the number of quality metrics, type of quality metric (defined by Donabedian model), and the domain of quality (defined by the National Academy of Medicine).

Results: A total of 2,158 articles were identified in the initial search, and 440 studies were included for data extraction. The most common type of quality measure was outcome measures (91%), and the least common measure was structure measures (1%). The most common metrics were operative time (41%), hospital type (28%), and aspects of the patient-provider interactions (20%). Additionally, we found that timeliness and equity were least common among the 6 National Academy of Medicine domains.

Conclusions: We identified metrics utilized in the PMBR, some of which can be further investigated through high-level evidence studies and incorporated into policy. Because many factors influence surgical outcomes and breast reconstruction is driven by patient preferences, an inclusion of structure, process, and outcome metrics will help improve care for this patient population. Moreover, nonpunitive initiatives, specifically quality collaboratives, may provide an avenue to improve care quality without compromising patient safety.

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Figures

Fig. 1.
Fig. 1.
Quality assessment. A, Donabedian classification of quality metric type. B, National Academy of Medicine Classification of Quality Metric Domain.
Fig. 2.
Fig. 2.
Flowchart of study cohort.
Fig. 3.
Fig. 3.
Quality assessment of included studies. A, Types of quality metrics in included studies. B, Domain of quality measures in included studies. Some studies examined >1 domain of quality.
Fig. 4.
Fig. 4.
Frequency of structure metrics.
Fig. 5.
Fig. 5.
Frequency of process metrics.
Fig. 6.
Fig. 6.
Frequency of outcome metrics.
See this image and copyright information in PMC

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