Navigation-Based Telehealth Informed Decision-Making for Prostate Cancer Screening in Black Men.
Djibril M BaChrispin KayembeJoe LittlejohnLauren J Van ScoyErika VanDykeJames WilliamsAvnish KatochNeil C ShookYue ZhangCraig LivelsbergerAlicia C McDonaldJoshua E MuscatPublished in: Current oncology (Toronto, Ont.) (2024)
The rapid increase in telehealth has the potential to bring informed decision-making for prostate cancer screening (PCS) at the population level to high-risk individuals. We utilized a global technology platform of electronic health records data repositories (TriNetX) to determine its utility for Navigator-guided decision-making aid for PCS in Black men ages 45-79 years with no history of prostate cancer and PSA testing. Patients from Pennsylvania were invited to participate in a telehealth-delivered informed decision-making session for PCS. Focus groups, social learning theory, visual diagrams, and quantitative data on PCS risks and benefits were used to develop the content of the sessions, which included numerical discussions of risks vs. benefits in Black men. Participants completed several surveys, including baseline demographic and numeracy questionnaires, a one-on-one telehealth session with a trained Navigator, post-Navigation surveys, and an optional follow-up session with a urologist. Eighty-seven participants were consented and recruited. Although the mean numeracy score was only 1.9 out of 6, more than 90% rated as good or excellent that the sessions aided their PCS decision-making skills. This study indicates that Navigation by telehealth offers the ability to assist in informed decision-making for PCS at the population level.
Keyphrases
- decision making
- prostate cancer
- electronic health record
- radical prostatectomy
- end stage renal disease
- high intensity
- healthcare
- ejection fraction
- human health
- mental health
- middle aged
- peritoneal dialysis
- high resolution
- high throughput
- machine learning
- climate change
- working memory
- patient reported outcomes
- sensitive detection
- deep learning
- loop mediated isothermal amplification