A Clinical Decision Support Model for Early Ovarian Cancer Detection in Rural Practice: A Quality Assurance Project.

Abstract

Background: Ovarian cancer is the deadliest gynecological malignancy, often diagnosed at advanced stages due to vague symptoms and lack of standardized screening. Rural populations face limited access to specialty care, delayed referrals, and reduced availability of diagnostic resources. Objective: The purpose of this quality assurance project was to propose the implementation of a workflow informed by a clinical decision support system (CDSS) to support earlier identification, risk stratification, and referral for patients at increased risk for ovarian cancer. Methods: A retrospective chart review was conducted of 13 female patients aged 18-80 years diagnosed with ovarian cancer at a rural gynecology clinic between January 2022 and December 2023. Findings were analyzed using Avedis Donabedian's structure-process-outcome framework to identify gaps in care delivery. Results: Of the participants, 69.2% were aged 50 years or older. Obesity was present in close to 70%, and approximately 23% were positive for BRCA1/2 pathogenic variants. Additionally, 69.2% presented with symptoms, and 69.2% were diagnosed with advanced-stage disease (Stage III-IV), highlighting missed opportunities for early detection. Conclusions: The findings highlight the need for standardized diagnostic workflows to improve the timeliness of ovarian cancer identification and referral. Implications for Nursing: Based on retrospective chart review findings, a proposed CDSS workflow was developed to guide early identification and referral in a rural gynecology clinic.