ArticleLearning health systems2026
Exploring Factors That Impact Genetic Counseling Referral and Uptake Using Learning Health Approaches.
Article in Learning health systems, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
- Exploring Factors That Impact Genetic Counseling Referral and Uptake Using Learning Health Approaches.Learning health systems · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Introduction: Germline testing and pretest genetic counseling are advised for many cancer patients, yet not all receive these services. Electronic Health Records (EHRs) offer a valuable resource to measure referral to genetic counseling (referral receipt) and uptake (completion of counseling). This study uses EHR data to assess demographic factors influencing genetic counseling referral and uptake among prostate cancer patients, serving as a learning health system model. Methods: We included prostate cancer patients who met germline testing and counseling criteria at an NCI-designated cancer center from January 1, 2018, to June 30, 2022. Demographic factors-age at diagnosis, race, employment, insurance, and geographic region-were assessed for associations with genetic counseling referral and uptake. Analyses involved descriptive statistics, two-group comparisons, and regression models. Results: Among 356 prostate cancer patients, only 34.2% received genetic counseling referrals, and of these, 73% completed a counseling visit. Older patients were less likely to receive referrals (OR = 0.93, 95% CI [0.89-0.97]) and complete visits (OR = 0.92, 95% CI [0.87-0.96]). Patients employed full-time were more likely to receive referrals (39.2% vs. 23.1%; Conclusion: This study demonstrates the potential of EHRs to identify demographic disparities in genetic counseling services. Using a learning health system approach, healthcare institutions can leverage EHR data to design targeted interventions aimed at improving access and reducing disparities in genetic services, ultimately enhancing patient outcomes.
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