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Original Article
June 1998

Diagnostic Delay in Breast Disease: A System Analysis of a Public Urban Hospital

Author Affiliations

From the Department of Surgery, Kings County Hospital Center and the State University of New York Health Science Center at Brooklyn. Dr Wall is now with the Department of Surgery, State University of New York, Buffalo.

Arch Surg. 1998;133(6):662-666. doi:10.1001/archsurg.133.6.662

Objective  To analyze the diagnostic process in 146 women referred to a breast clinic in an urban setting between January 1, 1994, and December 31, 1996.

Design  We devised the "diagnostic delay index (DDI)," defined as the time between the medical system's awareness of a diagnostic need and the completion of the diagnostic process. The time awaiting breast clinic consultation and the diagnostic events experienced—including clinic visits, imaging studies, and biopsies—were recorded. We stratified patients in 2 pathways (palpable masses and mammogram-identified lesions) and by benign or malignant outcome.

Results  Patients in pathways 1 (n = 85) and 2 (n=61) had a mean (±SD) DDI of 68.4 (±46.9) days and 71.9 (±35.2) days, respectively. Patients in both pathways who had a malignant outcome had a significantly lower DDI than those who had a benign outcome (47.5±30.9 days vs 78.6±42.6) (P<.001); this advantage was most pronounced in patients with palpable lumps. The average patient waited more than 3 weeks for both an initial clinic consultation and operating room access. Quartile analysis of the DDI revealed statistically significant differences in clinic access time, number of visits, diagnostic events per visit, and operating room access time. Regression analysis demonstrates the relationship between DDI and measured process variables: DDI= −21.11+0.09 age+1.86 pathway − 12.18 outcome+1.08 clinic access+11.91 visits+0.94 operating room access (R2=61.5%).

Conclusions  In a public hospital, diagnostic delay is related to inadequate access to surgical consultation and a delay in operating room access. Regression analysis demonstrates the relationships between these components of system diagnostic delay and suggests strategies for reducing the DDI.