We sought to evaluate an electronic referral form to increase referral for genetic risk assessment of women with newly diagnosed epithelial ovarian cancer.
A form summarizing referral for genetic counseling for women with ovarian cancer was introduced into the electronic medical record allowing gynecologic oncologists to electronically submit a request for genetic services. Analysis compared patient and provider characteristics for women newly diagnosed with ovarian, fallopian tube, and primary peritoneal cancer referred 1 year before and after introducing the form. All patients were seen in a single fee-for-service university-based cancer center clinic.
There were 86 newly diagnosed ovarian cancer patients seen before and 83 seen after the introduction of the electronic referral form. Most lived in the metropolitan area and had stage III to IV disease, serous histology, a documented family history, and a treating oncologist who was less than 10 years from completion of fellowship. Postintervention referral rates increased from 17% to 30% (P = 0.053). Factors best predicting referral were whether the patient was seen after the intervention (P = 0.009), resided in the metropolitan area (P = 0.006), and had been identified as at high hereditary risk (P < 0.0001). Sixty percent of the referred patients participated in counseling. There were no differences in baseline characteristics of the referred patients before and after the intervention.
Referral rates increased with the introduction of an electronic medical record referral form suggesting that streamlining the physician referral process might be effective at increasing referrals for cancer genetic risk assessment.
*Department of Obstetrics, Gynecology and Women’s Health, Division of Gynecologic Oncology; †Masonic Cancer Center; and ‡School of Public Health, University of Minnesota, Minneapolis, MN.
Address correspondence and reprint requests to Melissa A. Geller, MD, MS, University of Minnesota, MMC 395, 420 Delaware St. SE, Minneapolis, MN 55445. E-mail: firstname.lastname@example.org.
Supported in part by NIH Grant P30 CA77598 using the Biostatistics and Bioinformatics Core shared resource of the Masonic Cancer Center, University of Minnesota, and by the National Center for Advancing Translational Sciences of the National Institutes of Health Award Number UL1TR000114.
The authors declare no conflicts of interest.
Received January 22, 2014
Accepted March 13, 2014