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.

Lessons Learned About System-Level Improvement in Serious Illness Communication: A Qualitative Study of Serious Illness Care Program Implementation in Five Health Systems.

BACKGROUND: Serious illness communication is a key element of high-quality care, but it is difficult to implement in practice. The Serious Illness Care Program (SICP) is a multifaceted intervention that contributes to more, earlier, and better serious illness conversations and improved patient outcomes. This qualitative study examined the organizational and implementation factors that influenced improvement in real-world contexts. METHODS: The authors performed semistructured interviews of 30 health professionals at five health systems that adopted SICP as quality improvement initiatives to investigate the organizational and implementation factors that appeared to influence improvement. RESULTS: After SICP implementation across the organizations studied, approximately 4,661 clinicians have been trained in serious illness communication and 56,712 patients had had an electronic health record (EHR)-documented serious illness conversation. Facilitators included (1) visible support from leaders, who financially invested in an implementation team and champions, expressed the importance of serious illness communication as an institutional priority, and created incentives for training and documenting serious illness conversations; (2) EHR and data infrastructure to foster performance improvement and accountability, including an accessible documentation template, a reporting system, and customized data feedback for clinicians; and (3) communication skills training and sustained support for clinicians to problem-solve communication challenges, reflect on communication experiences, and adapt the intervention. Inhibitors included leadership inaction, competing priorities and incentives, variable clinician acceptance of EHR and data tools, and inadequate support for clinicians after training. CONCLUSION: Successful implementation appeared to rely on multilevel organizational strategies to prioritize, reward, and reinforce serious illness communication. The insights derived from this research may function as an organizational road map to guide implementation of SICP or related quality initiatives.

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.

Embedded Specialist Palliative Care in Cystic Fibrosis: Results of a Randomized Feasibility Clinical Trial.

Background: Cystic fibrosis (CF) is a progressive genetic disease characterized by multisystem symptom burden. Specialist palliative care (PC), as a model of care, has been shown to be effective in improving quality of life and reducing symptom burden in other conditions, but has not been tested in CF. Objectives: To develop and test the feasibility and acceptability of a specialist PC intervention embedded within an outpatient CF clinic. Design: Single-site, equal-allocation randomized pilot study comparing usual care with addition of four protocolized quarterly visits with a PC nurse practitioner. Participants: Adults with CF age ≥18 years with any of the following: FEV1% predicted ≤50, ≥2 CF-related hospitalizations in the past 12 months, supplemental oxygen use, or noninvasive mechanical ventilation use, and moderate-or-greater severity of any symptoms on the Edmonton Symptom Assessment Scale. Measurements: Randomization rate, intervention visit completion, data completements, participant ratings of intervention acceptability and benefit, and intervention delivery fidelity. Results: We randomized 50 adults with CF of 65 approached (77% randomization rate) to intervention (n = 25) or usual care (n = 25), mean age 38, baseline mean FEV1% predicted 41.8 (usual care), and 41.2 (intervention). No participants withdrew, five were lost to follow-up, and two died (88% retention). In the intervention group, 23 of 25 completed all study visits; 94% stated the intervention was not burdensome, and 97.6% would recommend the intervention to others with CF. More than 90% of study visits addressed topics prescribed by intervention manual. Conclusions: Adding specialist PC to standard clinic visits for adults with CF is feasible and acceptable.

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.

Earlier identification of seriously ill patients: an implementation case series.

OBJECTIVE: To describe the strategies used by a collection of healthcare systems to apply different methods of identifying seriously ill patients for a targeted palliative care intervention to improve communication around goals and values. METHODS: We present an implementation case series describing the experiences, challenges and best practices in applying patient selection strategies across multiple healthcare systems implementing the Serious Illness Care Program (SICP). RESULTS: Five sites across the USA and England described their individual experiences implementing patient selection as part of the SICP. They employed a combination of clinician screens (such as the 'Surprise Question'), disease-specific criteria, existing registries or algorithms as a starting point. Notably, each describes adaptation and evolution of their patient selection methodology over time, with several sites moving towards using more advanced machine learning-based analytical approaches. CONCLUSIONS: Involving clinical and programme staff to choose a simple initial method for patient identification is the ideal starting place for selecting patients for palliative care interventions. However, improving and refining methods over time is important and we need ongoing research into better patient selection methodologies that move beyond mortality prediction and instead focus on identifying seriously ill patients-those with poor quality of life, worsening functional status and medical care that is negatively impacting their families.

The Impact of Communication Skills Training on Cardiology Fellows' and Attending Physicians' Perceived Comfort with Difficult Conversations.

BACKGROUND: Cardiologists need to decide which treatments are appropriate for seriously ill patients and whether they align with patient goals. Reconciling medical options with patients' wishes requires skilled communication. Although there is evidence that communication is teachable, few cardiologists receive formal training. OBJECTIVE: To demonstrate that providing communication skills training to cardiologists is feasible and improves their perceived preparedness (PP) for leading difficult conversations. METHODS: CardioTalk is a workshop to improve communication through short didactic sessions followed by interactions with standardized patients. Competencies include giving bad news, defining goals of care, responding to emotion, supporting religious beliefs, and withdrawing therapies. Settings/Subjects: First year cardiology fellows, heart failure fellows, and cardiac intensive care unit attendings. MEASUREMENTS: Surveys evaluated the curriculum's efficacy and learners' PP before and after the workshop. RESULTS: Eight cardiology attendings and 20 cardiology fellows participated. Eighty-nine percent reported having any prior education in communication. Fellows reported more prior education than attendings (100% vs. 62.5%, p = 0.017). Level of PP improved in all competencies for all learners. Ninety-six percent of respondents would recommend the training to peers. All attendings felt that it should be required for cardiologists in the cardiac intensive care unit and reported improved preparedness to teach communication to learners. CONCLUSION: All learners improved in levels of PP in communication competencies. CardioTalk is the first described training program that prepares cardiologists for the challenges they face when having conversations with seriously ill patients.

Primary palliative care for heart failure: what is it? How do we implement it?

Heart failure (HF) is a chronic and progressive illness, which affects a growing number of adults, and is associated with a high morbidity and mortality, as well as significant physical and psychological symptom burden on both patients with HF and their families. Palliative care is the multidisciplinary specialty focused on optimizing quality of life and reducing suffering for patients and families facing serious illness, regardless of prognosis. Palliative care can be delivered as (1) specialist palliative care in which a palliative care specialist with subspecialty palliative care training consults or co-manages patients to address palliative needs alongside clinicians who manage the underlying illness or (2) as primary palliative care in which the primary clinician (such as the internist, cardiologist, cardiology nurse, or HF specialist) caring for the patient with HF provides the essential palliative domains. In this paper, we describe the key domains of primary palliative care for patients with HF and offer some specific ways in which primary palliative care and specialist palliative care can be offered in this population. Although there is little research on HF primary palliative care, primary palliative care in HF offers a key opportunity to ensure that this population receives high-quality palliative care in spite of the growing numbers of patients with HF as well as the limited number of specialist palliative care providers.

Benchmarking health-related quality-of-life data from a clinical setting.

OBJECTIVES: Health-related quality of life (HRQoL) is an important clinical outcome, yet there is little guidance for its interpretation in clinical settings. One approach would use benchmarking to contextualize HRQoL results. Our objective was to construct a nationally representative HRQoL benchmark for use with a clinical sample. STUDY DESIGN: Cross-sectional analysis of HRQoL scores from: 1) the 2011 Medical Expenditures Panel Survey (MEPS), a representative sample of the noninstitutionalized US population; and 2) outpatient academic and community cardiology clinics within a large health system in 2012 and 2013. METHODS: The 2011 MEPS includes 21,959 adults who completed the HRQoL measures; 414 reported visiting a cardiologist. Of 1945 outpatient index visits during the study period that were not for outpatient cardiac catheterization, 1434 patients completed the HRQoL measures. The primary outcome was the Short Form 6-Dimension questionnaire (SF-6D). The secondary outcomes were the Mental Component Summary score and the Physical Component Summary score. RESULTS: The local cardiology clinic sample was 42% female with a mean Charlson Comorbidity Index (CCI) score of 1.74. The MEPS subsample of cardiology patients more closely matched the local cardiology clinic sample (43% female; mean CCI score of 1.57) than the entire MEPS sample (52% female; mean CCI score of 0.62). SF-6D scores for the local cardiology clinic sample were significantly better, statistically and clinically, in 4 of 5 age strata than the MEPS subsample of cardiology patients. CONCLUSIONS: HRQoL benchmarks can be created from current public datasets. Subgroups in national samples may provide more valid benchmarks for clinical populations.

Improvement in symptom burden within one day after palliative care consultation in a cohort of gynecologic oncology inpatients.

OBJECTIVE: The aim of this study is to evaluate the magnitude and time course of change in symptom burden after palliative care (PC) consultation in a cohort of gynecologic oncology inpatients. METHODS: Women with a gynecologic malignancy and PC consultation for symptom management between 3/1/12 and 2/28/13 were identified. Charts were reviewed for demographic and disease characteristics. Symptom scores on a modified Edmonton Symptom Assessment System (ESAS) scale were abstracted for pain, anorexia, fatigue, depression, anxiety, nausea and dyspnea. Prevalence of moderate-to-severe symptom intensity was compared between the day of PC consultation (D1), the day after PC consultation (D2) and the last recorded symptoms before discharge (DLast). RESULTS: PC was consulted for symptom management during 129 admissions of 95 unique patients. Median age was 59, 84% were white and 67% had stage III/IV disease, with ovarian the most common site (52%). Symptom prevalence on D1 for at least mild intensity ranged 14% (dyspnea) to 80% (pain) and for at least moderate intensity from 3% (dyspnea) to 50% (pain). Statistically significant decreases in prevalence of moderate to severe symptom intensity between D1 and DLast occurred for pain, anorexia, fatigue and nausea (magnitude 58-66%) and between D1 and D2 for pain, fatigue and nausea (magnitude 50-55%). The majority of the improvement that occurred between D1 and DLast happened by D2. CONCLUSIONS: PC consultation is associated with improvement in symptom burden, the majority of which occurs within one day of consultation. PC may be an effective tool for symptom management in patients with moderate to severe symptom intensity even during short hospitalizations and should be considered early in the hospitalization to effect timely symptom relief.

Predictors of palliative care consultation on an inpatient gynecologic oncology service: are we following ASCO recommendations?

OBJECTIVE: Determine predictors of inpatient palliative care (PC) consultation and characterize PC referral patterns with respect to recommendations from the American Society of Clinical Oncology (ASCO). METHODS: Women with a gynecologic malignancy admitted to the gynecologic oncology service 3/2012-8/2012 were identified. Demographic information, disease and treatment details and date of death were abstracted from medical records. Student's t-test, Fischer's exact test or χ(2)-test was used for univariate analysis. Binomial logistic regression was used for multivariate analysis. RESULTS: Of 340 patients analyzed, 82 (24%) had PC consultation. Univariate predictors of PC consultation included race, cancer type and stage, recurrent disease, admission frequency, admission for symptom management or malignant bowel obstruction (MBO), discharge to skilled nursing facility (SNF) and number of lines of chemotherapy. On multivariate analysis, significant predictors of PC consultation were recurrent disease (OR 2.4, 95% CI 1.1-5.3), number of admissions (≥ 3, OR 10.9, 95% CI 3.4-34.9), admission for symptom management (OR 19.4, 95% CI 7.5-50.1), discharge to SNF (OR 5, 95% CI 1.9-13.5) and death within 6 months (OR 16.5, 95% CI 6.9-39.5). Of patients considered to meet ASCO guidelines, 53% (63/118) had PC referral. Of patients referred to PC, 51.2% (42/82) died within 6 months of last admission. CONCLUSIONS: Patients referred to inpatient PC have high disease and symptom burden and poor prognosis. High-risk patients, including those meeting ASCO recommendations, are not captured comprehensively. We continue to use PC referrals primarily for patients near the end of life, rather than utilizing early integration as recommended by ASCO.