Development and Validation of a Novel Literature-Based Method to Identify Disparity-Sensitive Surgical Quality Metrics

Elzerie de Jager, Adele A. Levine, Chris J. Sidey-Gibbons, N. Rhea Udyavar, Charles Liu, Margherita Lamaina, Melinda A. Maggard Gibbons, Clifford Y. Ko, Helen R. Burstin, Adil H. Haider, David B. Hoyt, Andrew J. Schoenfeld, Joel S. Weissman, L. D. Britt

Research output: Contribution to journalArticlepeer-review


BACKGROUND: Disparity in surgical care impedes the delivery of uniformly high-quality care. Metrics that quantify disparity in care can help identify areas for needed intervention. A literature-based Disparity-Sensitive Score (DSS) system for surgical care was adapted by the Metrics for Equitable Access and Care in Surgery (MEASUR) group. The alignment between the MEASUR DSS and Delphi ratings of an expert advisory panel (EAP) regarding the disparity sensitivity of surgical quality metrics was assessed. STUDY DESIGN: Using DSS criteria MEASUR co-investigators scored 534 surgical metrics which were subsequently rated by the EAP. All scores were converted to a 9-point scale. Agreement between the new measurement technique (ie DSS) and an established subjective technique (ie importance and validity ratings) were assessed using the Bland-Altman method, adjusting for the linear relationship between the paired difference and the paired average. The limit of agreement (LOA) was set at 1.96 SD (95%). RESULTS: The percentage of DSS scores inside the LOA was 96.8% (LOA, 0.02 points) for the importance rating and 94.6% (LOA, 1.5 points) for the validity rating. In comparison, 94.4% of the 2 subjective EAP ratings were inside the LOA (0.7 points). CONCLUSIONS: Applying the MEASUR DSS criteria using available literature allowed for identification of disparity-sensitive surgical metrics. The results suggest that this literature-based method of selecting quality metrics may be comparable to more complex consensus-based Delphi methods. In fields with robust literature, literature-based composite scores may be used to select quality metrics rather than assembling consensus panels.

Original languageEnglish
Pages (from-to)856-861
Number of pages6
JournalJournal of the American College of Surgeons
Issue number6
Publication statusPublished - 1 Dec 2023
Externally publishedYes


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