Importance Among patients with advanced solid malignant tumors, early specialty palliative care (PC) is guideline recommended, but strategies to increase PC access and effectiveness in community oncology are lacking. Objective To test whether algorithm-based defaults with opting out and accountable justification embedded in the electronic health record (EHR) increase completed PC visits. Design, Setting, and Participants This 2-arm cluster randomized clinical trial was conducted from November 1, 2022, to December 31, 2023. Eligible patients from 15 urban or rural clinics within a large community oncology network in Tennessee had advanced lung or noncolorectal gastrointestinal cancer and were identified by an automated EHR algorithm adapted from national guidelines. Data were analyzed between November 1, 2023, and March 4, 2024. Intervention At sites randomized to control, clinicians received weekly reports detailing PC referral rates compared with peer clinicians (peer comparison) and referred patients to PC at their discretion. At sites randomized to intervention, clinicians also received default PC orders using the EHR. Clinicians who opted out of PC consultation were asked to provide justification (accountable justification). If clinicians did not opt out, a study coordinator contacted patients to introduce and schedule PC visits using a standardized, predefined script. Main Outcomes and Measures The primary outcome was a completed PC consultation within 12 weeks of enrollment. Exploratory outcomes included quality of life, feeling heard and understood, and intensive end-of-life care. Outcomes were analyzed using clustered generalized linear and logistic regression models. Results The trial enrolled 562 patients (mean [SD] age, 68.5 [10.1] years; 288 male [51.2%]), of whom 433 (77.0%) had lung cancer. There were 130 of 296 patients (43.9%) randomized to the intervention group and 22 of 266 (8.3%) randomized to the control group who completed PC visits (adjusted odds ratio, 8.9 [95% CI, 5.5-14.6]; P < .001). Among 179 patients who died at the 24-week follow-up, 6 of 92 (6.5%) in the intervention group compared with 14 of 87 (16.1%) in the control group received systemic therapy within 14 days of death (adjusted odds ratio, 0.3 [95% CI, 0.1-0.7]; P = .05). There were no differences in quality of life, feeling heard and understood, or late hospice referral. Conclusions and Relevance In this randomized clinical trial of algorithm-based EHR defaults, the intervention increased PC consultations and decreased end-of-life systemic therapy. The intervention provides a scalable implementation strategy to increase specialty PC referrals in the community oncology setting. Trial Registration ClinicalTrials.gov Identifier: NCT05590962

Outcomes1. Understand and describe the role of qualitative analysis for evaluating a pragmatic randomized controlled trial implementing a risk-algorithm based default palliative care referrals with opt-out nudge for advanced lung and non-colorectal GI cancer patients.2. Understand and describe the results of our qualitative study exploring community oncology clinician perspectives about this novel intervention and evaluate how it might apply to their own practice setting specifically surrounding the goal of increasing palliative care access to advanced cancer patients.Key MessageIn a qualitative study of community-based oncologists, participants found risk algorithm-based default palliative care referrals within the electronic health record to be a helpful reminder and non-intrusive to their workflow. Contrary to existing findings on oncologist perspectives, this study shows a favorable view of the process of an opt-out nudge and goal of increasing earlier palliative care access.Introduction/ContextThis qualitative study is part of a pragmatic randomized control trial evaluating risk algorithm-based default referral nudges to palliative care (PC) in the oncologists’ electronic medical record (EMR) task flow across a large community oncology practice spanning multiple sites. This qualitative study is the first of its kind in the community oncology setting.MethodsWe conducted semi-structured interviews with oncology providers, including with physicians and nurse practitioners, in a large community oncology setting in the South. Purposive sampling was used to select clinicians involved in our intervention group. We asked providers for their perspectives on the risk algorithm, the opt out nudge for PC referrals, impact on their clinical workflow, facilitators and barriers to scaling this intervention, and overall perspectives on PC. Interviews lasted approximately 30 minutes and were conducted over Zoom, transcribed, and analyzed using Dedoose software.ResultsOf the twelve providers interviewed, the majority were physicians (11). Risk algorithm criteria were considered appropriate, with some confusion around age guidelines and suggestions to add more symptoms, screening for social support, and comorbidities. The nudge was seen as seamless and beneficial. The EMR nudge alert was viewed as a helpful reminder to busy clinicians, with the option to opt out on individual patients. Other facilitators included access to an internal outpatient PC program seen as a welcomed service, and a nurse coordinator as a resource to introduce PC to patients. Barriers included perceived staffing limitations; concerns about patient inconvenience around cost, transportation, and time; and individual patient factors, such as emotional fragility and appropriateness for PC due to low symptom burden or stable disease status.ConclusionThis intervention marks a novel way to improve access to PC. Contrary to existing findings on oncologist perspectives, this study shows a general acceptance of this process, which has minimal impact on workflow.KeywordsScientific Research / Models of Palliative Care Delivery

Background: Among patients with serious illness, palliative care before hospice enrollment is associated with improved quality of life, reduced symptom burden, and earlier transitions to hospice. However, fewer than half of eligible patients receive specialty palliative care referrals. As most hospice clinicians and administrators have experience in specialty palliative care, several emerging programs propose engaging hospice clinicians to provide early palliative care.Objective: We sought to identify barriers and facilitators to upstream palliative care.Design: We conducted a key informant qualitative study among hospice administrators and clinicians.Setting/Subjects: We conducted semi-structured interviews with 23 hospice administrators and clinicians in eight states from March to August 2022. We identified participants using snowball and purposive sampling using states that participate in Medicare Advantage's value-based insurance design Model.Results: Respondents indicated that barriers to early palliative care included inadequate staffing and reimbursement. Hospice clinicians providing community-based palliative care can address access barriers and improve transitions to hospice. Respondents expressed desire for payer guidance in identifying eligible patients but were cautious about payers acting as direct palliative care providers. However, payers could facilitate uptake by broadening and specifying coverage of services to include goals of care conversations and symptom management. Routine referrals initiated by objective measures could potentially increase access.Conclusions: Utilizing hospice providers to provide upstream palliative care can increase access, improve outcomes, and ease the transition to hospice.

11028 Background: For oncology drugs receiving accelerated approval (AA), the Food and Drug Administration (FDA) enforces Postmarketing Requirements (PMRs) to verify clinical benefit prior to regular approval. Although previous work has characterized the rigor and timeliness of confirmatory studies, the association between specificity and scope of PMR statements and the timeliness of PMR submission and ultimate approval status is not well understood. Methods: We identified 161 oncology indications granted AA between January 2011 and July 2023 using FDA’s Cancer AA database. We linked indications to 181 corresponding PMR statements using the Drugs@FDA database. We analyzed PMR statement scope and specificity based on the presence or requirement of eight characteristics: target population, comparator (active or placebo-controlled), randomization, multicenter trial, blinding (double-blind or open-label), enrollment target, follow-up duration, and endpoints. We used chi-square and t-tests to analyze associations between PMR characteristics and two primary outcomes: indication approval status (regular vs. withdrawn) and PMR timeliness (on time vs. late). We defined “late” submissions as PMRs submitted or withdrawals occurring after an expected PMR Final Report submission date. Ongoing PMRs were excluded from the timeliness analysis unless expected dates occurred before August 2023. Results: Among 181PMR statements, 98% percent specified target population, 81% endpoint (44% overall survival), 63% use of randomization, 54% comparator (40% active; 14% placebo-controlled), 30% follow-up duration 26% enrollment targets, 24% multicenter trial, and 13% double-blinding (and 8% open-label). Among AAs converted to regular approval, 82% of PMRs were submitted on time vs. 24% on-time for withdrawn AAs (p=0.001). Regular approval PMRs were submitted 10.2 months ahead of expected dates, whereas withdrawn AAs were submitted 14.5 months behind expected dates (p<0.001). Compared late PMR submissions, on-time PMR submissions had lower enrollment (400 vs. 665, p=0.01), less blinding (19% vs. 40%, p=0.03), more frequent use of a single trial for both AA and PMR (38% vs. 8%, p=0.001), and primary endpoints other than overall or progression-free survival (36% vs. 5%, p<0.001). Compared to late PMR submissions, on-time PMR submissions more often followed PMR statements that specified follow-up duration (39% vs. 13%, p=0.006) and non-survival primary endpoints (30% vs. 8%, p=0.01). Conclusions: There is marked variability in the contents of PMR statements for oncology AA drugs. Several characteristics of PMRs – including smaller enrollment size, surrogate endpoints, and single trials for AA and PMR – are associated with timely submission. These findings inform the specificity and scope of future FDA PMRs for oncology AA indications.

12075 Background: Among patients with cancer, early outpatient specialty palliative care (PC) concurrent with cancer-directed treatment improves quality of life and symptom burden, decreases aggressive end-of-life care, and is endorsed by national guidelines. However, nearly half of patients with advanced cancer do not receive specialty PC prior to dying. The objective of this study was to test the impact of oncologist-directed default PC referral orders on early PC utilization and quality of life. Methods: This 2-arm pragmatic randomized trial was conducted in a large, rural community oncology practice. Eligible patients met one of 5 NCCN guideline-based indications (uncontrolled symptoms, recent hospitalization or ED visit, ECOG PS≥3; active stage IV malignancy; CNS metastasis) for specialty PC referral. Four teams, consisting of unique physicians, advance practice providers, and social workers, were randomized in a 1:1 fashion to intervention vs. control. Clinicians and care team members in the intervention arm received an electronic health record (EHR) message with a default pended PC referral order for eligible patients. Clinicians could opt out. Clinicians in the control arm received no EHR message. An adjusted cox proportional hazards model with clustered standard errors was used to assess the primary outcome of completed PC visits within 24 weeks of enrollment. Adjusted logistic regression models with inverse probability censoring weighting to account for differential mortality risk were used to assess secondary outcomes of absolute and change in quality of life per FACT-G score at 9 weeks, among intervention patients who received PC, compared to a random subset of controls. Results: Among 266 eligible patients, 252 (94.7%) were White, 147 (55.3%) were female, and 204 (78%) had stage IV disease. The most common cancers were gastrointestinal (26.3%), breast (19%), and lung (17%). In the intervention arm, physicians opted-out of 62% of referrals. Rates of completed palliative care visits were 14.6% in the intervention arm vs. 8.1% in the control arm (adjusted hazard ratio 1.34 [95% CI 1.25-1.54], p<0.001). Patient-reported quality of life was greater in the intervention arm than the control arm (mean change in FACT-G score at 9 weeks: 6.56 [SD 8.9] intervention vs. -4.48 [SD 13.5] control; adjusted difference 11.4, p=0.05). Rates of intensive end-of-life care were similar in both control and intervention groups. Conclusions: Compared with controls, default referrals to specialty PC among patients who met guideline-based criteria led to increases in completed palliative care visits and improved quality of life. Default PC referrals may be an effective strategy to improve access to early specialty palliative care in community oncology, although effect sizes were tempered by high opt-out rates. Clinical trial information: NCT05365997 .

12002 Background: Patients with advanced solid malignancies often experience poor quality of life and aggressive end-of-life care. Early specialist palliative care (PC) can improve these outcomes. However, most patients do not receive a PC referral before death, with clinician inertia and difficulty identifying high-risk patients being barriers to initiating PC referrals. Methods: This was a 2-arm pragmatic cluster-randomized clinical trial. Eligible patients had stage 3 or 4 lung or non-colorectal gastrointestinal cancer. An automated electronic health record (EHR) algorithm, adapted from NCCN Palliative Care prognostic or psychosocial risk factors, assigned each patient a score from 0-20; high-risk patients with scores ≥2 (if stage III disease) or ≥1 (stage IV) were eligible. We randomized 15 clinics in a large community oncology network, stratifying randomization based on patient volume. In the intervention arm, oncologists received weekly default EHR notifications prompting specialty PC referral for high-risk patients. If oncologists did not opt out, a coordinator introduced specialty PC to patients using a standard script and offered to schedule a PC visit. In the control arm, oncologists referred to PC at their discretion. Adjusted Cox proportional hazards models with clustered standard errors assessed the primary outcome of completed PC visit at 12 weeks. Clustered logistic regression models assessed intervention impacts on change in quality of life (measured using PAL-14) from baseline to 9 weeks and intensive end-of-life care (no hospice enrollment prior to death, chemotherapy receipt within 14 days of death). To address acceptability of the intervention among clinicians, we conducted semi-structured interviews with 12 clinicians post-trial. Results: Among 562 patients (296 intervention; 266 control), mean age was 68.5, 79.5% were White, 48.8% were female, and 77.0% had lung cancer. Mean risk score was similar for intervention and control patients (3.0 vs. 3.2). In the intervention arm, 89% of clinicians allowed PC referrals and 79% of patients agreed to PC visits. Compared to control, the intervention resulted in higher rates of completed PC visits (46.6% vs. 11.3%, adjusted odds ratio 5.4, 95% CI 3.2 to 9.2). Among 179 decedents, compared to control, the intervention decreased end-of-life chemotherapy (6.5% vs. 16.1%, p = 0.06). There was no difference in quality of life or hospice among decedents. In interviews, clinicians viewed algorithm criteria as appropriate and the nurse coordinator as a resource to introduce PC to patients. Perceived barriers included staffing limitations and inappropriateness for PC due to low symptom burden or stable disease. Conclusions: In a large community oncology network, algorithm-based default PC referrals were acceptable to clinicians and led to > 3-fold increase in specialty PC and decreased end-of-life chemotherapy. Clinical trial information: NCT05590962 .

Background: Patients with serious illnesses have unmet symptom and psychosocial needs. Specialty palliative care could address many of these needs; however, access varies by geography and health system. Virtual visits and automated referrals could increase access and lead to improved quality of life, health outcomes, and patient-centered care for patients with serious illness. Objectives: We sought to understand referring clinician perspectives on barriers and facilitators to utilizing virtual tools to increase upstream access to palliative care. Design: Participants in this multisite qualitative study included practicing clinicians who commonly place palliative care referrals across multiple specialties, including hematology/oncology, family medicine, cardiology, and geriatrics. All interviews were transcribed and subsequently coded and analyzed by trained research coordinators using Atlas.ti software. Settings/Subjects: This study included 23 clinicians (21 physicians, 2 nonphysicians) across 5 specialties, 4 practice settings, and 7 states in the United States. Results: Respondents felt that community-based specialty palliative services including symptom management, advance care planning, physical therapy, and mental health counseling would benefit their patients. However, they had mixed feelings about automated referrals, with some clinicians feeling hesitant about not being alerted to such referrals. Many respondents were supportive of virtual palliative care, particularly for those who may have difficulty accessing physician offices, but most respondents felt that such care should only be provided after an initial in-person consultation where clinicians can meet face-to-face with patients. Conclusion: Clinicians believe that automated referrals and virtual palliative care could increase access to the benefits of specialty palliative care. However, virtual palliative care models should give attention to iterative communication with primary clinicians and the perceived need for an initial in-person visit.

12012 Background: Performance status (PS) assessment is critical for clinical trial eligibility assessment and treatment selection. However, PS is based on the subjective impression of the clinician and is often inaccurate. Wearable accelerometers may allow clinicians to more objectively assess PS. In this analysis of 2 separate prospective studies, we define and externally validate an “Objective Performance Status” (OPS) by measuring the association between daily physical activity and overall survival among patients with metastatic cancer. Methods: We first measured daily physical activity (in step counts) using a wearable accelerometer over a prescreening period (median 14 d, range 3-28 d) in a Spanish observational prospective study (PIC123-18_FJD) embedded during the screening period for a phase 1 clinical trial in patients with locally advanced unresectable or metastatic solid and hematological malignancies. A multivariable Cox proportional hazards model was used to derive an OPS by measuring associations between mean daily steps with overall survival (OS), adjusting for patient demographics. We subsequently externally validated this OPS cutoff in a separate prospective cohort of patients with metastatic non-small cell lung and gastrointestinal cancers who wore a wearable accelerometer continuously for 6 months enrolled in a randomized trial of proactive symptom monitoring at a large academic center in the United States (NCT04616768) Results: Full data was available for 123 patients (70 in OPS derivation cohort; 53 external validation cohort). The cut-off selected to define the OPS was determined by univariate survival analysis to be mean daily step count = 1200 meterswith poor OPS (≤1200 m/day, of whom 46% had clinician-recorded ECOG ≥1) had greater mortality than patients with good OPS ( > 1200 m/day, 85% with ECOG 0) (3.2 vs. 11.2 mos median OS, adjusted hazard ratio 5.76, 95% CI 2.98-11.1, p≤0.001). In the external validation cohort, poor OPS strongly predicted mortality compared to good OPS (unadjusted HR 5.22, 95% CI 1.24-21.88, p = 0.02) and was a better predictor of mortality than clinician- (HR = 1.00, 95% CI 0.24-4.17, p = 1.0) or patient-reported ECOG (HR = 1.16, 95% CI 0.14-9.45, p = 0.89). Conclusions: The OPS is an independent, externally validated prognostic factor for survival and could serve as an objective surrogate for traditional methods of PS assessment in clinical trials and choice of therapy. Clinical trial information: NCT04616768 (external validation trial) . Concordance between OPS and clinician-reported ECOG. ECOG = 0 ECOG≥1 Kappa (95%CI) Spanish trial Distance (meters) ≥1200 36 (85.7%) 15 (53.6%) 0.34 (0.12-0.56) < 1200 6 (14.3%) 13 (46.4%) External validation trial Distance (meters) ≥1200 17 (85.0%) 22 (33.3%) 0.15 (-0.04-0.35) < 1200 3 (15.0%) 11 (66.7%)

1625 Background: Older adults aged ≥65 with advanced cancer are underrepresented in treatment trials. This gap limits informed decision-making regarding the dose and tolerability of proposed therapies. Standard chemotherapy is associated with >50% serious adverse event rate among older adults, with frequent dose reduction and early treatment discontinuation. A "start low, go slow" (SLGS) approach begins systemic therapy at lower-than-standard doses and increases the dose if well-tolerated. This alternative dosing strategy has shown value in minimizing adverse events and functional decline without compromising the overall effectiveness of treatments. We conducted the first systematic review and meta-analysis of SLGS effectiveness among older adults across advanced cancers. Methods: This review, was registered with PROSPERO and adhered to PRISMA criteria, covering PubMed, Journal of Geriatric Oncology and EMBASE from January 2000 until December 15, 2024. Eligible study designs were randomized controlled trials, retrospective trials, and non-randomized clinical trials with patients with advanced cancers who underwent systemic therapy. We reported all studies that studied the SLGS approach. Our studied outcomes were overall survival (OS), progression-free survival (PFS), treatment discontinuation, and toxicity. Data extraction included author, patient, cancer type, treatment, and survival outcomes. A meta-analysis using fixed effects and risk ratios (RR) was performed when sufficient data was available; significant outcomes had α<0.05. Results: Our search identified 13 studies testing SLGS strategies in oncology, including 3,508 patients, with a median age 63-78 years. The -represented cancers included colorectal (6 studies, 3059 patients total), lung (2 studies, 113 pts), chronic myeloid leukemia (2 studies, 127 pts), non-Hodgkin lymphoma (1 study, 45 pts), and prostate (2 studies, 164 pts) cancers. Ten (77%) studies assessed OS and PFS. Five studies (39%) compared SLGS against standard doses, finding no significant differences in PFS and OS across all trials. Dose escalation rates for SLGS ranged from 5% to 60%. The ability to complete planned cycles was higher with SLGS compared to standard dose (1 study, 43% vs 26%, p=0.04). Treatment discontinuation was not different for SLGL vs. standard dose (5 studies, meta-analysis RR 1.07, 95% CI 0.90-1.27, p=0.42). Toxicity ranged from 5% to 89% across studies; SLGS had lower grade ≥3 adverse events compared to standard dose (4 studies, meta-analysis RR 0.88, 95% CI 0.80-0.94, p < 0.001). Conclusions: This is the first systematic review and meta-analysis analyzing the SLGS approach to systemic therapy dosing in older adults with advanced cancer. Compared to standard-dose systemic therapy, older adults pursuing a SLGS strategy had greater completion of planned cycles, reduced toxicity, and similar survival.

Patients with cancer often experience physical and psychosocial distress and receive care that is unwanted or unwarranted, especially near the end of life. Serious illness conversations (SICs) - person-centered conversations about patients' prognosis, values, and goals to inform treatment and care planning - are a key element of high-quality oncology care and decrease patient distress. Penn Medicine, a large academic health center, and the Abramson Cancer Center sought to address low rates of documented conversations by implementing the Serious Illness Care Program, an evidence-based care delivery innovation that includes communication tools, clinician training, and system changes. SIC reminders were targeted toward patients at high risk for 180-day mortality. The intervention led to a sustained quadrupling of rates of conversations and no negative impact on the quality of those conversations. In addition, the program was associated with decreases in aggressive end-of-life care, including chemotherapy in the last days of life. Penn Medicine's experience describes how a structured program, along with senior leadership and a communication guide, can transform serious illness communication at a large academic health system.

Introduction Palliative care (PC) is a medical specialty focusing on providing relief from the symptoms and stress of serious illnesses such as cancer. Early outpatient specialty PC concurrent with cancer-directed treatment improves quality of life and symptom burden, decreases aggressive end-of-life care and is an evidence-based practice endorsed by national guidelines. However, nearly half of patients with advanced cancer do not receive specialty PC prior to dying. The objective of this study is to test the impact of an oncologist-directed default PC referral orders on rates of PC utilisation and patient quality of life.Methods and analysis This single-centre two-arm pragmatic randomised trial randomises four clinician-led pods, caring for approximately 250 patients who meet guideline-based criteria for PC referral, in a 1:1 fashion into a control or intervention arm. Intervention oncologists receive a nudge consisting of an electronic health record message indicating a patient has a default pended order for PC. Intervention oncologists are given an opportunity to opt out of referral to PC. Oncologists in pods randomised to the control arm will receive no intervention beyond usual practice. The primary outcome is completed PC visits within 12 weeks. Secondary outcomes are change in quality of life and absolute quality of life scores between the two arms.Ethics and dissemination This study has been approved by the Institutional Review Board at the University of Pennsylvania. Study results will be disseminated in peer-reviewed journals and scientific conferences using methods that describe the results in ways that key stakeholders can best understand and implement.Trial registration number NCT05365997.

268 Background: Patients with advanced solid malignancies often experience high symptom burden and aggressive end-of-life care. Early specialist palliative care (PC) can improve symptom management, mood, and quality of life. However, many patients do not receive PC before they die, with clinician inertia and difficulty identifying high-risk patients being barriers to initiating PC referrals. Methods: In a 2-arm pragmatic randomized clinical trial among patients with stage 3 or 4 lung or non-colorectal gastrointestinal malignancies, oncology clinics were randomly assigned to intervention or usual practice. In the intervention arm, oncologists received automated algorithm-based default notifications prompting specialty PC referral. The control arm received usual care, in which oncologists could refer to PC at their discretion. The algorithm was adapted from NCCN guidelines, identified high-risk patients based on prognosis or symptom/psychosocial burden, and was integrated into the EHR. Oncologists were alerted weekly about eligible patients and had the option to opt-out of PC referral. If there was no response or agreement, a PC coordinator introduced and offered a PC visit to the patient. The primary outcome was a completed PC visit within three months of identification among high-risk patients. We report descriptive results from an interim analysis of the trial. Results: 15 practices (7 clinics and 32 physicians in intervention; 8 clinics and 31 physicians in control) and 567 patients (299 intervention; 268 control) were randomized. Rates of completed early PC visits were 46.4% (139/299) the intervention arm and 11.2% (30/268) in the control arm. The opt-out rate in the intervention arm was 10.7%. Among clinicians who did not opt-out, 79% of their patients agreed to an initial PC visit. Adjusted analyses and demographic data will be completed and available by September 2023. Conclusions: Algorithm-based default PC referrals meaningfully increased utilization of specialty PC within a community oncology practice. Targeting high-risk patients using guideline-based risk stratification prevented overwhelming PC capacity. Further analyses will assess the impact of the intervention on end-of-life care, acute care utilization, patient-reported outcomes, and explore clinician perspectives. Clinical trial information: NCT05590962 .

BackgroundPatients 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. ObjectiveThe 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. MethodsThe 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. ResultsAmong 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. ConclusionsIn 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 RegistrationClinicalTrials.gov NCT04616768; https://clinicaltrials.gov/study/NCT04616768 International Registered Report Identifier (IRRID)RR2-10.1136/bmjopen-2021-054675

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.

Importance Federal and state policymakers continue to pursue work requirements and premiums as conditions of Medicaid participation. Opinion polling should distinguish between general policy preferences and specific views on quotas, penalties, and other elements. Objective To identify views of adults in Kentucky regarding the design of Medicaid work requirements and premiums. Design, Setting, and Participant A cross-sectional survey was conducted via telephone and the internet from June 27 through July 11, 2019, of 1203 Kentucky residents 9 months before the state intended to implement Medicaid work requirements and mandatory premiums. Statistical analysis was performed from October 2019 to August 2023. Main Outcomes and Measures Agreement, disagreement, or neutral views on policy components were the main outcomes. Recruitment for the survey used statewide random-digit dialing and an internet panel to recruit residents aged 18 years or older. Findings were weighted to reflect state demographics. Of 39 110 landlines called, 209 reached an eligible person (of whom 150 participated), 8654 were of unknown eligibility, and 30 247 were ineligible. Of 55 305 cell phone lines called, 617 reached an eligible person (of whom 451 participated), 29 951 were of unknown eligibility, and 24 737 were ineligible. Internet recruitment (602 participants) used a panel of adult Kentucky residents maintained by an external data collector. Results Percentages were weighted to resemble the adult population of Kentucky residents. Of the participants in the study, 52% (95% CI, 48%-55%) were women, 80% (95% CI, 77%-82%) were younger than 65 years, 41% (95% CI, 38%-45%) were enrolled in Medicaid, 36% (95% CI, 32%-39%) were Republican voters, 32% (95% CI, 29%-36%) were Democratic voters, 14% (95% CI, 11%-16%) were members of racial and ethnic minority groups (including but not limited to American Indian or Alaska Native, Asian, Black, Hispanic or Latinx, and Native Hawaiian or Pacific Islander), and 48% (95% CI, 44%-52%) were employed. Most participants supported work requirements generally (69% [95% CI, 66%-72%]) but did not support terminating benefits due to noncompliance (43% [95% CI, 39%-46%]) or requiring quotas of 20 or more hours per week (34% [95% CI, 31%-38%]). Support for monthly premiums (34% [95% CI, 31%-38%]) and exclusion penalties for premium nonpayment (22% [95% CI, 19%-25%]) was limited. Medicaid enrollees were significantly less supportive of these policies than nonenrollees. For instance, regarding work requirements, agreement was lower (64% [95% CI, 59%-69%] vs 72% [95% CI, 68%-77%]) and disagreement higher (26% [95% CI, 21%-31%] vs 20% [95% CI, 16%-24%]) among current Medicaid enrollees compared with nonenrollees ( P = .04). Among Medicaid enrollees, some beliefs about work requirements varied significantly by employment status but not by political affiliation. Among nonenrollees, beliefs about work requirements, premiums, and Medicaid varied significantly by political affiliation but not by employment. Conclusions and Relevance This study suggests that even when public constituencies express general support for Medicaid work requirements or premiums, they may oppose central design features, such as quotas and termination of benefits. Program participants may also hold significantly different beliefs than nonparticipants, which should be understood before policies are changed.

Background: Early serious illness conversations (SICs) about goals of care and prognosis improve mood, quality of life, and end-of-life care quality. Algorithm-based behavioral nudges to oncologists increase the frequency and timeliness of such conversations. However, clinicians' perspectives on such nudges are unknown. Design: Qualitative study consisting of semistructured interviews among medical oncology clinicians who participated in a stepped-wedge cluster randomized trial of Conversation Connect, an algorithm-based intervention consisting of behavioral nudges to promote early SICs in the outpatient oncology setting. Results: Of 79 eligible oncology clinicians, 56 (71%) were approached to participate in interviews and 25 (45%) accepted. Key facilitators to algorithm-based nudges included prompting documentation of conversations, peer comparisons, performance reports, and validating norms around early conversations. Barriers included cancer-specific heterogeneity in algorithm performance and the frequency and tone of text messages. Areas of improvement included utilizing different information channels, identifying patients earlier in the disease trajectory, and incorporating patient-targeted messaging that emphasizes the value of early conversations. Conclusions: Oncology clinicians identified key facilitators and barriers to Conversation Connect. These insights inform future algorithm-based supportive care interventions in oncology. Controlled trial (NCT03984773).

PURPOSEMachine 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.METHODSWe 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.RESULTSAmong 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.CONCLUSIONRoutinely 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.

INTRODUCTION:Patients with advanced cancers often face significant symptoms from their cancer and adverse effects from cancer-associated therapy. Patient-generated health data (PGHD) are routinely collected information about symptoms and activity levels that patients either directly report or passively record using devices such as wearable accelerometers. The objective of this study was to test the impact of an intervention integrating remote collection of PGHD with clinician and patient nudges to inform communication between patients with advanced cancer and their oncology team regarding symptom burden and functional status.METHODS AND ANALYSIS:This single-centre prospective randomised controlled trial randomises patients with metastatic gastrointestinal or lung cancers into one of three arms: (A) usual care, (B) an intervention that integrates PGHD (including weekly text-based symptom surveys and passively recorded step counts) into a dashboard delivered to oncology clinicians at each visit and (C) the same intervention as arm B but with an additional text-based active choice intervention to patients to encourage discussing their symptoms with their oncology team. The study will enrol approximately 125 participants. The coprimary outcomes are patient perceptions of their oncology team's understanding of their symptoms and their functional status. Secondary outcomes are intervention utility and adherence.ETHICS AND DISSEMINATION:This study has been approved by the institutional review board at the University of Pennsylvania. Study results will be disseminated using methods that describe the results in ways that key stakeholders can best understand and implement.TRIAL REGISTRATION NUMBERS:NCT04616768 and 843 616.

BACKGROUND:Google and Apple's Exposure Notifications System (ENS) was developed early in the COVID-19 pandemic to complement existing contact tracing efforts while protecting user privacy. An analysis by the Associated Press released in December 2020 estimated approximately 1 in 14 people had downloaded apps in states one was available. In this study, we assessed the motivation and experience of individuals who downloaded ENS apps from the Google Play and Apple App Stores.METHODS:We collected review text, star rating, and date of rating for all the reviews on ENS apps in the Google Play and Apple App stores. We extracted the relative frequency of single words and phrases from reviews and created an open vocabulary language, with themes categorized by the research team, to study the salient themes around reviews with high (3-5 stars), neutral (3 stars), and negative (1-2 stars) ratings using logistic regression.RESULTS:Of 7622 reviews obtained from 26 states between 04/07/2020 to 03/31/2021, 6364 were from Google Play Store, and 1258 were from Apple App Store. We obtained reviews for a total of 38 apps, with 25 apps from the Google Play Store and 13 apps from the Apple Play Store. 78% of the reviews are either 1 star or 5 stars. Positive reviews were driven by ease of use, support for the state government in creating the app, and encouragement for others to download, as well as engage in other COVID-19 precautions. Negative and neutral reviews focused on issues with app functionality (i.e., installation and tracking errors).CONCLUSIONS:Uptake was the largest barrier to success for ENS apps, but states can use insight from app store reviews to better position themselves if they choose to develop further public health apps.