Lay healthcare worker financial toxicity intervention: a pilot financial toxicity screening and referral program.

PURPOSE: Financial toxicity is a source of significant distress for patients with urologic cancers, yet few studies have addressed financial burden in this patient population. METHODS: We developed a financial toxicity screening program using a lay health worker (LHW) and social worker (SW) to assess and mitigate financial toxicity in a single academic medical clinic. As part of a quality improvement project, the LHW screened all newly diagnosed patients with advanced stages of prostate, kidney, or urothelial cancer for financial burden using three COST tool questions and referred patients who had significant financial burden to an SW who provided personalized recommendations. The primary outcome was feasibility defined as 80% of patients with financial burden completing the SW consult. Secondary outcomes were patient satisfaction, change in COST Tool responses, and qualitative assessment of financial resources utilized. RESULTS: The LHW screened a total of 185 patients for financial toxicity; 82% (n = 152) were male, 65% (n = 120) White, and 75% (n = 139) reported annual household income >$100,000 US Dollars; 60% (n = 114) had prostate cancer. A total of 18 (9.7%) participants screened positive for significant financial burden and were referred to the SW for consultation. All participants (100%) completed and reported satisfaction with the SW consultation and had 0.83 mean lower scores on the COST Tool post-intervention assessment compared to pre-intervention (95% confidence interval [0.26, 1.41]). CONCLUSION: This multidisciplinary financial toxicity intervention using an LHW and SW was feasible, acceptable, and associated with reduced financial burden among patients with advanced stages of urologic cancers. Future work should evaluate the effect of this intervention among cancer patients in diverse settings.

Automated patient selection and care coaches to increase advance care planning for cancer patients.

BACKGROUND: Advance care planning/serious illness conversations can help clinicians understand patients' values and preferences. There are limited data on how to increase these conversations, and their effect on care patterns. We hypothesized that using a machine learning survival model to select patients for serious illness conversations, along with trained care coaches to conduct the conversations, would increase uptake in cancer patients at high risk of short-term mortality. METHODS: We conducted a cluster-randomized stepped wedge study on the physician level. Oncologists entered the intervention condition in a random order over six months. Adult patients with metastatic cancer were included. Patients with <2 year computer-predicted survival and no prognosis documentation were classified as high-priority for serious illness conversations. In the intervention condition, providers received automated weekly emails highlighting high-priority patients and were asked to document prognosis for them. Care coaches reached out to these patients to conduct the remainder of the conversation. The primary endpoint was proportion of visits with prognosis documentation within 14 days. RESULTS: 6,372 visits in 1,825 patients were included in the primary analysis. The proportion of visits with prognosis documentation within 14 days was higher in the intervention condition than control condition: 2.9% vs 1.1% (adjusted odds ratio 4.3, p < .0001). The proportion of visits with advance care planning documentation was also higher in the intervention condition: 7.7% vs 1.8% (adjusted odds ratio 14.2, p < .0001). In high-priority visits, advance care planning documentation rate in intervention/control visits was 24.2% vs 4.0%. CONCLUSION: The intervention increased documented conversations, with contributions by both providers and care coaches.

Use of Machine Learning and Lay Care Coaches to Increase Advance Care Planning Conversations for Patients With Metastatic Cancer.

PURPOSE: Patients with metastatic cancer benefit from advance care planning (ACP) conversations. We aimed to improve ACP using a computer model to select high-risk patients, with shorter predicted survival, for conversations with providers and lay care coaches. Outcomes included ACP documentation frequency and end-of-life quality measures. METHODS: In this study of a quality improvement initiative, providers in four medical oncology clinics received Serious Illness Care Program training. Two clinics (thoracic/genitourinary) participated in an intervention, and two (cutaneous/sarcoma) served as controls. ACP conversations were documented in a centralized form in the electronic medical record. In the intervention, providers and care coaches received weekly e-mails highlighting upcoming clinic patients with < 2 year computer-predicted survival and no prior prognosis documentation. Care coaches contacted these patients for an ACP conversation (excluding prognosis). Providers were asked to discuss and document prognosis. RESULTS: In the four clinics, 4,968 clinic visits by 1,251 patients met inclusion criteria (metastatic cancer with no prognosis previously documented). In their first visit, 28% of patients were high-risk (< 2 year predicted survival). Preintervention, 3% of both intervention and control clinic patients had ACP documentation during a visit. By intervention end (February 2021), 35% of intervention clinic patients had ACP documentation compared with 3% of control clinic patients. Providers' prognosis documentation rate also increased in intervention clinics after the intervention (2%-27% in intervention clinics, P < .0001; 0%-1% in control clinics). End-of-life care intensity was similar in intervention versus control clinics, but patients with ≥ 1 provider ACP edit met fewer high-intensity care measures (P = .04). CONCLUSION: Combining a computer prognosis model with care coaches increased ACP documentation.