Identification of the optimal pathway to reach an accurate diagnosis in the absence of an early detection strategy for ovarian cancer.

Identification of the optimal pathway to reach an accurate diagnosis in the absence of an early detection strategy for ovarian cancer.

Identification of the optimal pathway to reach an accurate diagnosis in the absence of an early detection strategy for ovarian cancer.

Gynecol Oncol. 2012 Aug 29;

Authors: Hess LM, Stehman FB, Method MW, Weathers TD, Gupta P, Schilder JM

Abstract

OBJECTIVES: There is a lack of knowledge about the health care events experienced by individual patients that lead to a definitive diagnosis of ovarian cancer (OC). The goal of this study was to describe the various pathways and to identify an optimal path to accurate diagnosis. METHODS: Women who were referred to gynecologic oncology for a suspected OC were enrolled to this study. Medical records (MRs) from all health care providers were obtained from the time the patient recalled first suspecting a health issue through the time of diagnosis to build a decision tree model. A Monte Carlo simulation was conducted of 83,000 patients to identify the optimal pathway to reach diagnosis. RESULTS: In the Monte Carlo simulation, gynecologic oncologists and gynecologists accounted for the most efficient diagnosis in over 37.9% and 29.2% of suspected OC cases, respectively, in terms of the least amount of time to reach diagnosis. Gynecologic oncologists were further associated with the fewest health care visits needed to reach diagnosis in 37% of the simulation cases; however, 23% of trials were indifferent to any specific provider. CONCLUSIONS: The decision tree provides a more comprehensive view of the complexity in reaching an accurate diagnosis of OC. This analysis was able to identify the health care utilization patterns that underlie the events that occur to reach an accurate diagnosis in the setting of a suspected OC, and was able to identify the most efficient pathways that utilize the fewest health care resources in the least amount of time.

PMID: 22940492 [PubMed - as supplied by publisher]

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