Using Oncology Treatment Pathway Data to Evaluate Serious Illness Communication, Care Utilization, and End-of-Life Care for Patients With Cancer.

PURPOSE: Oncology treatment pathways provide decision support and encourage guideline adherence. Pathway data combined with electronic health record (EHR) data can identify patient populations with poor prognoses, low serious illness conversation (SIC) rates, and high acute care utilization that may benefit from targeted interventions. PATIENTS AND METHODS: We conducted a retrospective cohort analysis among adults with cancer treated at seven affiliated sites of the Dana-Farber Cancer Institute (DFCI) who had navigations within 21 treatment pathways between July 29, 2019, and March 8, 2023. DFCI clinicians previously identified pathway nodes with an estimated survival less than 1 year, termed poor prognosis (PP) nodes. We combined pathway data with EHR data to calculate the median overall survival (OS) and proportion of patients with SICs, acute care utilization (hospitalizations and emergency department visits), and outpatient palliative care 6 months after treatment node navigation for all, PP, and nonpoor prognosis (nPP) nodes. SICs were identified using the EHR advanced care planning (ACP) tab. RESULTS: There were 15,261 navigations for 10,203 patients (median age 66 years, 55% female, 85% White). The median OS was 13.8 months for all nodes, 7.8 months for PP nodes, and 21.0 months for nPP nodes. The ACP section of the EHR rate 6 months after navigation was 19.6% for PP nodes versus 11.0% for nPP nodes. There was substantial intragroup variability in OS and SIC rates among all nodes. SICs were recorded in the ACP tab for only 34.3% of decedents. Patients who navigated to PP nodes had higher levels of acute care utilization and palliative care encounters. CONCLUSION: Treatment pathway data enabled identification of patient populations with poor prognoses, low SIC rates, and high acute care utilization.

County-level jail and state-level prison incarceration and cancer mortality in the United States.

This study examined the association of county-level jail and state-level prison incarceration rates and cancer mortality rates in the United States. Incarceration rates (1995-2018) were sourced from national data and categorized into quartiles. County- and state-level mortality rates (2000-2019) with invasive cancer as the underlying cause of death were obtained from the National Vital Statistics System. Compared with the first quartile (lowest incarceration rate), the second, third, and fourth quartiles (highest incarceration rate) of county-level jail incarceration rate were associated with 1.3%, 2.3%, and 3.9% higher county-level cancer mortality rates, respectively, in adjusted analyses. Compared with the first quartile, the second, third, and fourth quartiles of state-level prison incarceration rate were associated with 1.7%, 2.5%, and 3.9% higher state-level cancer mortality rates, respectively. Associations were more pronounced for liver and lung cancers. Addressing adverse effects of mass incarceration may potentially improve cancer outcomes in affected communities.

Spending Analysis of Machine Learning-Based Communication Nudges in Oncology.

BACKGROUND: Serious illness conversations (SICs) in the outpatient setting may improve mood and quality of life among patients with cancer and decrease aggressive end-of-life care. Interventions informed by behavioral economics may increase rates of SICs between oncology clinicians and patients, but the impact of these interventions on end-of-life spending is unknown. METHODS: This study is a secondary analysis of a stepped-wedge cluster randomized, controlled trial that involved nine medical oncology practices and their high-risk patients at a large academic institution between June 2019 and April 2020. The study included 1187 patients who were identified by a machine-learning algorithm as high risk of 180-day mortality and who died by December 2020. The patients were randomly assigned to standard of care (controls) or to a behavioral intervention designed to increase clinician-initiated SICs. We abstracted spending - defined as inflation-adjusted costs for acute care (inpatient plus emergency room), office/outpatient care, intravenous systemic therapy, other therapy (e.g., radiation), long-term care, and hospice - from the institution's accounting system, and we captured spending at inpatient, outpatient, and pharmacy settings. To evaluate intervention impacts on spending, we used a two-part model, first using logistic regression to model zero versus nonzero spending and second using generalized linear mixed models with gamma distribution and log-link function to model daily mean spending in the last 180days of life. Models were adjusted for clinic and wedge fixed effects, and they were clustered at the oncologist level. For all patients with at least one SIC within 6 months of death, we also calculated their mean daily spending before and after SIC. RESULTS: Median age at death was 68years (interquartile range, 15.5), 317 patients (27%) were Black or of ethnicities other than white, and 448 patients (38%) had an SIC. The intervention was associated with lower unadjusted mean daily spending in the last 6 months of life for the intervention group versus controls ($377.96 vs. $449.92; adjusted mean difference, -$75.33; 95% confidence interval, -$136.42 to -$14.23; P=0.02), translating to $13,747 total adjusted savings per decedent and $13 million in cumulative savings across all decedents in the intervention group. Compared with controls, patients in the intervention group incurred lower mean daily spending for systemic therapy (adjusted difference, -$44.59; P=0.001), office/outpatient care (-$9.62; P=0.001), and other therapy (-$8.65; P=0.04). The intervention was not associated with differences in end-of-life spending for acute care, long-term care, or hospice. Results were consistent for spending in the last 1 and 3 months of life and after adjusting for age, race, and ethnicity. For patients with SICs, mean daily spending decreased by $37.92 following the first SIC ($329.87 vs. $291.95). CONCLUSIONS: A machine learning-based, behaviorally informed intervention to prompt SICs led to end-of-life savings among patients with cancer, driven by decreased systemic therapy and outpatient spending. (Funded by the Penn Center for Precision Medicine and the National Institutes of Health; ClinicalTrials.gov number, NCT03984773.).

Content Analysis of Serious Illness Conversation Documentation: Structured vs. Free-Text Information.

CONTEXT: Clear, accessible, and thorough documentation of serious illness conversations helps ensure that critical information patients share with clinicians is reflected in their future care. OBJECTIVES: We sought to characterize and compare serious illness conversations recorded in two different ways in the electronic health record to better understand patterns of serious illness conversation documentation. METHODS: We performed content analysis of serious illness conversations documented in the electronic health record, whether documented via structured tab or free-text clinical notes, for patients (n = 150) with advanced cancer who started a treatment associated with a poor prognosis between October 2020 and June 2022. A multidisciplinary team iteratively developed a codebook to classify serious illness conversation content (e.g., goals/hopes) on a preliminary sample (n = 30), and two researchers performed mixed deductive-inductive coding on the remaining data (n = 120). We reviewed documentation from 34 patients with serious illness conversations documentation in the structured tab only, 43 with documentation in only free-text clinical notes, and 44 with documentation of both types. We then compared content between documentation types. RESULTS: Information documented more frequently in structured tabs included fears/worries and illness understanding; clinical notes more often included treatment preferences, deliberations surrounding advance directives, function, and trade-offs. Qualitative insights highlight a range of length and detail across documentation types, and suggest notable authorship by palliative and social work clinicians. CONCLUSION: How serious illness conversations are documented in the electronic health record may impact the content captured. Future quality improvement efforts should seek to consolidate documentation sources to improve care and information retention.

Association of Remote Patient-Reported Outcomes and Step Counts With Hospitalization or Death Among Patients With Advanced Cancer Undergoing Chemotherapy: Secondary Analysis of the PROStep Randomized Trial.

BACKGROUND: Patients with advanced cancer undergoing chemotherapy experience significant symptoms and declines in functional status, which are associated with poor outcomes. Remote monitoring of patient-reported outcomes (PROs; symptoms) and step counts (functional status) may proactively identify patients at risk of hospitalization or death. OBJECTIVE: The aim of this study is to evaluate the association of (1) longitudinal PROs with step counts and (2) PROs and step counts with hospitalization or death. METHODS: The PROStep randomized trial enrolled 108 patients with advanced gastrointestinal or lung cancers undergoing cytotoxic chemotherapy at a large academic cancer center. Patients were randomized to weekly text-based monitoring of 8 PROs plus continuous step count monitoring via Fitbit (Google) versus usual care. This preplanned secondary analysis included 57 of 75 patients randomized to the intervention who had PRO and step count data. We analyzed the associations between PROs and mean daily step counts and the associations of PROs and step counts with the composite outcome of hospitalization or death using bootstrapped generalized linear models to account for longitudinal data. RESULTS: Among 57 patients, the mean age was 57 (SD 10.9) years, 24 (42%) were female, 43 (75%) had advanced gastrointestinal cancer, 14 (25%) had advanced lung cancer, and 25 (44%) were hospitalized or died during follow-up. A 1-point weekly increase (on a 32-point scale) in aggregate PRO score was associated with 247 fewer mean daily steps (95% CI -277 to -213; P<.001). PROs most strongly associated with step count decline were patient-reported activity (daily step change -892), nausea score (-677), and constipation score (524). A 1-point weekly increase in aggregate PRO score was associated with 20% greater odds of hospitalization or death (adjusted odds ratio [aOR] 1.2, 95% CI 1.1-1.4; P=.01). PROs most strongly associated with hospitalization or death were pain (aOR 3.2, 95% CI 1.6-6.5; P<.001), decreased activity (aOR 3.2, 95% CI 1.4-7.1; P=.01), dyspnea (aOR 2.6, 95% CI 1.2-5.5; P=.02), and sadness (aOR 2.1, 95% CI 1.1-4.3; P=.03). A decrease in 1000 steps was associated with 16% greater odds of hospitalization or death (aOR 1.2, 95% CI 1.0-1.3; P=.03). Compared with baseline, mean daily step count decreased 7% (n=274 steps), 9% (n=351 steps), and 16% (n=667 steps) in the 3, 2, and 1 weeks before hospitalization or death, respectively. CONCLUSIONS: In this secondary analysis of a randomized trial among patients with advanced cancer, higher symptom burden and decreased step count were independently associated with and predictably worsened close to hospitalization or death. Future interventions should leverage longitudinal PRO and step count data to target interventions toward patients at risk for poor outcomes. TRIAL REGISTRATION: ClinicalTrials.gov NCT04616768; https://clinicaltrials.gov/study/NCT04616768. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): RR2-10.1136/bmjopen-2021-054675.

Let Us Have the Conversation: Serious Illness Communication in Oncology: Definitions, Barriers, and Successful Approaches.

Serious illness communications are crucial elements of care delivery for patients with cancer. High-quality serious illness communications are composed of open, honest discussions between patients, caregivers, and clinicians regarding patient's communication preferences, expected illness trajectory, prognosis, and risks and benefits of any recommended care. High-quality communication ideally starts at the time of a patients' cancer diagnosis, allows space for and response to patient emotions, elicits patients' values and care preferences, and is iterative and longitudinal. When integrated into cancer care, such communication can result in improved patient experiences with their care, care that matches patients' goals, and reduced care intensity at the end of life. Despite national recommendations for routine integration of these communication into cancer care, a minority of patients with cancer receive such communication. In this chapter, we describe elements of high-quality serious illness communication, patient-, clinician-, institution-, and payer-level barriers, and successful strategies that can routinely integrate such communication into cancer care delivery.

Remote Patient-Reported Outcomes and Activity Monitoring to Improve Patient-Clinician Communication Regarding Symptoms and Functional Status: A Randomized Controlled Trial.

PURPOSE: Routine collection of patient-generated health data (PGHD) may promote earlier recognition of symptomatic and functional decline. This trial assessed the impact of an intervention integrating remote PGHD collection with patient nudges on symptom and functional status understanding between patients with advanced cancer and their oncology team. METHODS: This three-arm randomized controlled trial was conducted from November 19, 2020, to December 17, 2021, at a large tertiary oncology practice. We enrolled patients with stage IV GI and lung cancers undergoing chemotherapy. Over 6 months, patients in two intervention arms received PROStep-weekly text message-based symptom surveys and passive activity monitoring using a wearable accelerometer. PGHD were summarized in dashboards given to patients' oncology team before appointments. One intervention arm received an additional text-based active choice prompt to discuss worsening symptoms or functional status with their clinician. Control patients did not receive PROStep. The coprimary outcomes patient perceptions of oncology team symptom and functional understanding at 6 months were measured on a 1-5 Likert scale (5 = high understanding). RESULTS: One hundred eight patients enrolled: 55% male, 81% White, and 77% had GI cancers. Patient-reported clinician understanding did not differ between control and intervention arms for symptoms (4.5 v 4.5; P = .87) or functional status (4.5 v 4.3; P = .31). In the intervention arms, combined patient adherence to weekly symptom reports and daily activity monitoring was 64% and 53%, respectively. Intervention patients in the PROStep versus PROStep + active choice arms reported low burden from wearing the accelerometer (mean burden [standard deviation], 2.7 [1.3] v 2.1 [1.3]; P = .15) and completing surveys (2.1 [1.2] v 1.9 [1.3]; P = .44). CONCLUSION: Patients receiving PROStep reported high understanding of symptoms and functional status from their oncology team, although this did not differ from controls.

Creating a culture for change: Lessons from behavioral economics and complexity science to increase serious illness conversations for patients with cancer.

Patient-centered cancer care requires communication between patients and clinicians about patients' goals, values, and preferences. Serious illness communication improves patient and caregiver outcomes, the value and quality of cancer care, and the well-being of clinicians. Despite these benefits, there are competing factors including time, capacity, bandwidth, and resistance. Health systems and oncology practices have opportunities to invest in pathways that assist patients and clinicians to engage in serious illness conversations. We discuss how applying insights from behavioral economics and complexity science may help clinicians engage in serious illness conversation and improve patient-centered cancer care.

Leveraging Goals of Care Interventions to Deliver Personalized Care Near the End of Life.

This Viewpoint discusses barriers to and opportunities for incorporating goal of care communications into end-of-life care.