Exploring Factors That Impact Genetic Counseling Referral and Uptake Using Learning Health Approaches.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
추출되지 않음
I · Intervention 중재 / 시술
genetic counseling referrals, and of these, 73% completed a counseling visit
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
[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.
[INTRODUCTION] Germline testing and pretest genetic counseling are advised for many cancer patients, yet not all receive these services.
- OR 0.93
APA
Greenberg S, Wong B, et al. (2026). Exploring Factors That Impact Genetic Counseling Referral and Uptake Using Learning Health Approaches.. Learning health systems, 10(1), e70049. https://doi.org/10.1002/lrh2.70049
MLA
Greenberg S, et al.. "Exploring Factors That Impact Genetic Counseling Referral and Uptake Using Learning Health Approaches.." Learning health systems, vol. 10, no. 1, 2026, pp. e70049.
PMID
41560988 ↗
Abstract 한글 요약
[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%; = 0.01), while White (93% vs. 81%; = 0.047) and rural patients (42.7% vs. 6.1%; = 0.02) had higher uptake. Insurance status did not significantly affect referral or uptake.
[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.
[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%; = 0.01), while White (93% vs. 81%; = 0.047) and rural patients (42.7% vs. 6.1%; = 0.02) had higher uptake. Insurance status did not significantly affect referral or uptake.
[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.
🏷️ 키워드 / MeSH 📖 같은 키워드 OA만
🏷️ 같은 키워드 · 무료전문 — 이 논문 MeSH/keyword 기반
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