National survey of patient perspectives on cost discussions among recipients of copay assistance.

BACKGROUND: Individuals with cancer and other medical conditions often experience financial concerns from high costs-of-care and may utilize copay assistance programs (CAP). We sought to describe CAP recipients' experiences/preferences for cost discussions with clinicians. METHODS: We conducted a national, cross-sectional electronic-survey from 10/2022 to 11/2022 of CAP recipients with cancer or autoimmune conditions to assess patient perspectives on cost discussions. We used multivariable logistic regression models to explore associations of patient perspectives on cost discussions with patient characteristics and patient-reported outcomes (eg, financial toxicity, depression/anxiety, and health literacy). RESULTS: Among 1,566 participants, 71% had cancer and 29% had autoimmune conditions. Although 62% of respondents desired cost discussions, only 32% reported discussions took place. Additionally, 52% of respondents wanted their doctor to consider out-of-pocket costs when deciding the best treatment, and 61% of respondents felt doctors should ensure patients can afford treatment prescribed. Participants with depression symptoms were more likely to want doctors to consider out-of-pocket costs (OR = 1.54, P = .005) and to believe doctors should ensure patients can afford treatment (OR = 1.60, P = .005). Those with severe financial toxicity were more likely to desire cost discussions (OR = 1.65, P < .001) and want doctors to consider out-of-pocket costs (OR = 1.52, P = .001). Participants with marginal/inadequate health literacy were more likely to desire cost discussions (OR = 1.37, P = .01) and believe doctors should ensure patients can afford treatment (OR = 1.30, P = .036). CONCLUSIONS: In this large sample of CAP recipients with cancer and autoimmune conditions, most reported a desire for cost discussions, but under one-third reported such discussions took place.

Development of Machine Learning Algorithms Incorporating Electronic Health Record Data, Patient-Reported Outcomes, or Both to Predict Mortality for Outpatients With Cancer.

PURPOSE: Machine learning (ML) algorithms that incorporate routinely collected patient-reported outcomes (PROs) alongside electronic health record (EHR) variables may improve prediction of short-term mortality and facilitate earlier supportive and palliative care for patients with cancer. METHODS: We trained and validated two-phase ML algorithms that incorporated standard PRO assessments alongside approximately 200 routinely collected EHR variables, among patients with medical oncology encounters at a tertiary academic oncology and a community oncology practice. RESULTS: Among 12,350 patients, 5,870 (47.5%) completed PRO assessments. Compared with EHR- and PRO-only algorithms, the EHR + PRO model improved predictive performance in both tertiary oncology (EHR + PRO v EHR v PRO: area under the curve [AUC] 0.86 [0.85-0.87] v 0.82 [0.81-0.83] v 0.74 [0.74-0.74]) and community oncology (area under the curve 0.89 [0.88-0.90] v 0.86 [0.85-0.88] v 0.77 [0.76-0.79]) practices. CONCLUSION: Routinely collected PROs contain added prognostic information not captured by an EHR-based ML mortality risk algorithm. Augmenting an EHR-based algorithm with PROs resulted in a more accurate and clinically relevant model, which can facilitate earlier and targeted supportive care for patients with cancer.