Coaches Activating, Reaching, and Engaging Patients to Engage in Advance Care Planning: A Randomized Clinical Trial.

Importance: Advance care planning (ACP) remains low among patients with advanced cancer. Multilevel interventions compared with clinician-level interventions may be more effective in improving ACP. Objective: To evaluate whether a multilevel intervention could improve clinician-documented ACP compared with a clinician-level intervention alone. Design, Setting, and Participants: This randomized clinical trial, performed from September 12, 2019, through May 12, 2021, included adults with advanced genitourinary cancers at an academic, tertiary hospital. Data analysis was performed by intention to treat from May 1 to August 10, 2023. Intervention: Participants were randomized 1:1 to a 6-month patient-level lay health worker structured ACP education along with a clinician-level intervention composed of 3-hour ACP training and integration of a structured electronic health record documentation template (intervention group) or to the clinician-level intervention alone (control group). Main Outcome and Measures: The primary outcome was ACP documentation in the electronic health record by the oncology clinician within 12 months after randomization. Secondary, exploratory outcomes included shared decision-making, palliative care use, hospice use, emergency department visits, and hospitalizations within 12 months after randomization. Results: Among 402 participants enrolled in the study, median age was 71 years (range, 21-102 years); 361 (89.8%) identified as male. More intervention group participants had oncology clinician-documented ACP than control group participants (82 [37.8%] vs 40 [21.6%]; odds ratio [OR], 2.29; 95% CI, 1.44-3.64). At 12-month follow-up, more intervention than control group participants had palliative care (72 [33.2%] vs 25 [13.5%]; OR, 3.18; 95% CI, 1.91-5.28) and hospice use (49 [22.6%] vs 19 [10.3%]; OR, 2.54; 95% CI, 1.44-4.51). There were no differences in the proportion of participants between groups with an emergency department visit (65 [30.0%] vs 61 [33.0%]; OR, 0.87; 95% CI, 0.57-1.33) or hospitalization (89 [41.0%] vs 85 [46.0%]; OR, 0.82; 95% CI, 0.55-1.22). Intervention group participants had fewer hospitalizations than control group participants (mean [SD] number of hospitalizations per year, 0.87 [1.60] vs 1.04 [1.77]) and a lower risk of hospitalization (incidence rate ratio, 0.80; 95% CI, 0.65-0.98). Conclusions and Relevance: In this randomized clinical trial, a multilevel intervention improved oncology clinician-documented ACP compared with a clinician-level intervention alone for patients with genitourinary cancer. The intervention is one approach to effectively increase ACP among patients with cancer. Trial Registration: ClinicalTrials.gov Identifier: NCT03856463.

Lessons Learned from a Multi-Site, Team-Based Serious Illness Care Program Implementation at an Academic Medical Center.

Background: Patients with serious illness benefit from conversations to share prognosis and explore goals and values. To address this, we implemented Ariadne Labs' Serious Illness Care Program (SICP) at Stanford Health Care. Objective: Improve quantity, timing, and quality of serious illness conversations. Methods: Initial implementation followed Ariadne Labs' SICP framework. We later incorporated a team-based approach that included nonphysician care team members. Outcomes included number of patients with documented conversations according to clinician role and practice location. Machine learning algorithms were used in some settings to identify eligible patients. Results: Ambulatory oncology and hospital medicine were our largest implementation sites, engaging 4707 and 642 unique patients in conversations, respectively. Clinicians across eight disciplines engaged in these conversations. Identified barriers that included leadership engagement, complex workflows, and patient identification. Conclusion: Several factors contributed to successful SICP implementation across clinical sites: innovative clinical workflows, machine learning based predictive algorithms, and nonphysician care team member engagement.

The Team-Based Serious Illness Care Program, A Qualitative Evaluation of Implementation and Teaming.

CONTEXT: Earlier and more frequent serious illness conversations with patients allow clinical teams to better align care with patients' goals and values. Nonphysician clinicians often have unique perspectives and understanding of patients' wishes and are thus well-positioned to support conversations with seriously ill patients. The Team-based Serious Illness Care Program (SICP) at Stanford aimed to involve all care team members to support and conduct serious illness conversations with patients and their caregivers and families. OBJECTIVES: We conducted interviews with clinicians to understand how care teams implement team-based approaches to conduct serious illness conversations and navigate resulting team complexity. METHODS: We used a rapid qualitative approach to analyze semistructured interviews of clinicians and administrative stakeholders in two team-based SICP implementation groups (i.e., inpatient oncology and hospital medicine) (n = 25). Analysis was informed by frameworks/theory: cross-disciplinary role agreement, team formation and functioning, and organizational theory. RESULTS: Implementing team-based SICP was feasible. Theme 1 centered on how teams formed and managed to come to an agreement: teams with rapidly changing staffing/responsibilities prioritized communication, whereas teams with consistent staffing/responsibilities primarily relied on protocols. Theme 2 demonstrated that leaders and managers at multiple levels could support implementation. Theme 3 explored strengths and opportunities. Positively, team-based SICP distributed work burden, timed conversations in alignment with patient needs, and added unique value from nonphysician team members. Role ambiguity and conflict were attributed to miscommunication and ethical conflicts. CONCLUSION: Team-based serious illness communication is viable and valuable, with a range of successful workflow and leadership approaches.

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.

Considerations in the reliability and fairness audits of predictive models for advance care planning.

Multiple reporting guidelines for artificial intelligence (AI) models in healthcare recommend that models be audited for reliability and fairness. However, there is a gap of operational guidance for performing reliability and fairness audits in practice. Following guideline recommendations, we conducted a reliability audit of two models based on model performance and calibration as well as a fairness audit based on summary statistics, subgroup performance and subgroup calibration. We assessed the Epic End-of-Life (EOL) Index model and an internally developed Stanford Hospital Medicine (HM) Advance Care Planning (ACP) model in 3 practice settings: Primary Care, Inpatient Oncology and Hospital Medicine, using clinicians' answers to the surprise question ("Would you be surprised if [patient X] passed away in [Y years]?") as a surrogate outcome. For performance, the models had positive predictive value (PPV) at or above 0.76 in all settings. In Hospital Medicine and Inpatient Oncology, the Stanford HM ACP model had higher sensitivity (0.69, 0.89 respectively) than the EOL model (0.20, 0.27), and better calibration (O/E 1.5, 1.7) than the EOL model (O/E 2.5, 3.0). The Epic EOL model flagged fewer patients (11%, 21% respectively) than the Stanford HM ACP model (38%, 75%). There were no differences in performance and calibration by sex. Both models had lower sensitivity in Hispanic/Latino male patients with Race listed as "Other." 10 clinicians were surveyed after a presentation summarizing the audit. 10/10 reported that summary statistics, overall performance, and subgroup performance would affect their decision to use the model to guide care; 9/10 said the same for overall and subgroup calibration. The most commonly identified barriers for routinely conducting such reliability and fairness audits were poor demographic data quality and lack of data access. This audit required 115 person-hours across 8-10 months. Our recommendations for performing reliability and fairness audits include verifying data validity, analyzing model performance on intersectional subgroups, and collecting clinician-patient linkages as necessary for label generation by clinicians. Those responsible for AI models should require such audits before model deployment and mediate between model auditors and impacted stakeholders.