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Purpose: The integration of pharmacies with oncology practices-known as medically integrated dispensing or in-office dispensing-could improve care coordination but may incentivize overprescribing or inappropriate prescribing. Because little is known about this emerging phenomenon, we analyzed historical trends in medically integrated dispensing., Methods: Annual IQVIA data on oncologists were linked to 2010-2019 National Council for Prescription Drug Programs pharmacy data; data on commercially insured patients diagnosed with any of six common cancer types; and summary data on providers' Medicare billing. We calculated the national prevalence of medically integrated dispensing among community and hospital-based oncologists. We also analyzed the characteristics of the oncologists and patients affected by this care model., Results: Between 2010 and 2019, the percentage of oncologists in practices with medically integrated dispensing increased from 12.8% to 32.1%. The share of community oncologists in dispensing practices increased from 7.6% to 28.3%, whereas the share of hospital-based oncologists in dispensing practices increased from 18.3% to 33.4%. Rates of medically integrated dispensing varied considerably across states. Oncologists who dispensed had higher patient volumes ( P < .001) and a smaller share of Medicare beneficiaries ( P < .001) than physicians who did not dispense. Patients treated by dispensing oncologists had higher risk and comorbidity scores ( P < .001) and lived in areas with a higher % Black population ( P < .001) than patients treated by nondispensing oncologists., Conclusion: Medically integrated dispensing has increased significantly among oncology practices over the past 10 years. The reach, clinical impact, and economic implications of medically integrated dispensing should be evaluated on an ongoing basis., Competing Interests: Ravi B. ParikhStock and Other Ownership Interests: Merck, Google, GNS Healthcare, Onc.AIConsulting or Advisory Role: GNS Healthcare, Cancer Study Group, Onc.AI, Thyme Care, Humana, NanOlogy, MerckResearch Funding: HumanaPatents, Royalties, Other Intellectual Property: Technology to integrate patient-reported outcomes into electronic health record algorithmsTravel, Accommodations, Expenses: The Oncology Institute of Hope and Innovation Michael J. FischEmployment: AIM Specialty HealthStock and Other Ownership Interests: AnthemPatents, Royalties, Other Intellectual Property: Healthcore, Inc, A subsidiary of Anthem, IncOpen Payments Link: https://openpaymentsdata.cms.gov/physician/767578https://openpaymentsdata.cms.gov/physician/767578 David DebonoEmployment: AnthemStock and Other Ownership Interests: Lilly Justin BekelmanStock and Other Ownership Interests: Reimagine CareHonoraria: National Comprehensive Cancer NetworkConsulting or Advisory Role: UnitedHealthcare, Reimagine Care Stephanie SchauderEmployment: Anthem, Inc, Piedmont Plastic Surgery & Dermatology (I)Research Funding: Anthem, IncTravel, Accommodations, Expenses: Anthem, Inc Gosia SylwestrzakOther Relationship: Anthem, Inc John J. BarronResearch Funding: HealthCore (Inst)Other Relationship: Anthem, Inc Rebecca CobbEmployment: AnthemStock and Other Ownership Interests: UnknownResearch Funding: AnthemTravel, Accommodations, Expenses: Anthem Dima M. QatoConsulting or Advisory Role: AbbVie Mireille JacobsonOther Relationship: Opioid Litigation on Behalf of Plaintiffs, UCI HealthNo other potential conflicts of interest were reported.

Purpose: Oncologists may overestimate prognosis for patients with cancer, leading to delayed or missed conversations about patients' goals and subsequent low-quality end-of-life care. Machine learning algorithms may accurately predict mortality risk in cancer, but it is unclear how oncology clinicians would use such algorithms in practice., Methods: The purpose of this qualitative study was to assess oncology clinicians' perceptions on the utility and barriers of machine learning prognostic algorithms to prompt advance care planning. Participants included medical oncology physicians and advanced practice providers (APPs) practicing in tertiary and community practices within a large academic healthcare system. Transcripts were coded and analyzed inductively using NVivo software., Results: The study included 29 oncology clinicians (19 physicians, 10 APPs) across 6 practice sites (1 tertiary, 5 community) in the USA. Fourteen participants had previously had exposure to an automated machine learning-based prognostic algorithm as part of a pragmatic randomized trial. Clinicians believed that there was utility for algorithms in validating their own intuition about prognosis and prompting conversations about patient goals and preferences. However, this enthusiasm was tempered by concerns about algorithm accuracy, over-reliance on algorithm predictions, and the ethical implications around disclosure of an algorithm prediction. There was significant variation in tolerance for false positive vs. false negative predictions., Conclusion: While oncologists believe there are applications for advanced prognostic algorithms in routine care of patients with cancer, they are concerned about algorithm accuracy, confirmation and automation biases, and ethical issues of prognostic disclosure., (© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.)

Many payers and clinicians are committed to advancing value-based care through the establishment of alternative payment models (APMs) that incentivize practices and clinicians to improve quality and reduce cost. A multistakeholder working group has observed that in specialty fields such as oncology, despite many attempts to design and implement APM pilots for commercial and Medicare Advantage populations, practical challenges and small numbers of episodes and patients present headwinds to viability and scalability. Despite this, some payers report emerging good practices and are optimistic about APMs. Careful and realistic consideration of the specific goals of a proposed model is warranted, as is close examination of the feasibility of transferring risk.

Competing Interests: Dr Narayan has received research support from Bristol-Myers Squibb, Merck, TMunity Therapeutics, Pfizer, and Janssen; and consulting fees from Janssen, Pfizer, Myovant, Amgen, Regeneron, and Merck. Dr Ross has received consulting/speaker fees from Astellas, Bayer, Blue Earth, GenomeDx Biosciences, Janssen, Myovant, and Tempus. Dr Parikh has received grant support from Humana, the National Institutes of Health, the Prostate Cancer Foundation, the National Palliative Care Research Center, the Conquer Cancer Foundation, and the Veterans Administration; received personal fees and equity from GNS Healthcare and Onc.AI; received personal fees from the Cancer Study Group and Nanology; and honorarium from Flatiron and Medscape; and served on the board (unpaid) of the Coalition to Transform Advanced Care. Dr Nohria has received research support from Amgen; and served as a consultant for AstraZeneca, Bantam Pharmaceuticals, Boehringer Ingelheim, and Takeda Oncology. Dr Morgans has received research support from Astellas, Seattle Genetics, Bayer, Sanofi, and Myovant; and consulting fees from Astellas, AstraZeneca, AAA, Bayer, Clovis, Janssen, Dendreon, Myovant, Merck, Exelixis, Novartis, Pfizer, Blue Earth, Myriad, Sanofi, and Lantheus.

Purpose: Patients with cancer are at greater risk of developing severe symptoms from COVID-19 than the general population. We developed and tested an automated text-based remote symptom-monitoring program to facilitate early detection of worsening symptoms and rapid assessment for patients with cancer and suspected or confirmed COVID-19., Methods: We conducted a feasibility study of Cancer COVID Watch, an automated COVID-19 symptom-monitoring program with oncology nurse practitioner (NP)-led triage among patients with cancer between April 23 and June 30, 2020. Twenty-six patients with cancer and suspected or confirmed COVID-19 were enrolled. Enrolled patients received twice daily automated text messages over 14 days that asked "How are you feeling compared to 12 hours ago? Better, worse, or the same?" and, if worse, "Is it harder than usual for you to breathe?" Patients who responded worse and yes were contacted within 1 hour by an oncology NP., Results: Mean age of patients was 62.5 years. Seventeen (65%) were female, 10 (38%) Black, and 15 (58%) White. Twenty-five (96%) patients responded to ≥ 1 symptom check-in, and overall response rate was 78%. Four (15%) patients were escalated to the triage line: one was advised to present to the emergency department (ED), and three were managed in the outpatient setting. Median time from escalation to triage call was 11.5 minutes. Four (15%) patients presented to the ED without first escalating their care via our program. Participant satisfaction was high (Net Promoter Score: 100, n = 4)., Conclusion: Implementation of an intensive remote symptom monitoring and rapid NP triage program for outpatients with cancer and suspected or confirmed COVID-19 infection is possible. Similar tools may facilitate more rapid triage for patients with cancer in future pandemics., Competing Interests: Cody E. CotnerStock and Other Ownership Interests: JNJ, Novartis, Clover Health, TeladocPatents, Royalties, Other Intellectual Property: Patent pending for SelfCerve, a novel medical device designed to facilitate cervical cancer screening in low-to-middle income countries. David DoStock and Other Ownership Interests: Inovio Pharmaceuticals Will FerrellResearch Funding: Humana Suzanne McGettiganSpeakers' Bureau: Merck, Novartis, Bristol Myers Squibb, Pfizer Lynn M. SchuchterConsulting or Advisory Role: IncyteResearch Funding: GlaxoSmithKline (Inst), Merck (Inst), Bristol Myers Squibb (Inst)Expert Testimony: PfizerTravel, Accommodations, Expenses: Stand U 2 Cancer Lawrence N. ShulmanResearch Funding: Celgene (Inst), Independence Blue Cross (Inst) David A. AschEmployment: VAL HealthLeadership: VAL HealthStock and Other Ownership Interests: VAL HealthHonoraria: Deloitte, NACCME, LLCResearch Funding: United Health Group Christopher R. ManzResearch Funding: Genentech Ravi B. ParikhStock and Other Ownership Interests: Merck, Google, GNS HealthcareConsulting or Advisory Role: GNS Healthcare, Cancer Study Group, Onc.AITravel, Accommodations, Expenses: Conquer Cancer Foundation, Flatiron HealthNo other potential conflicts of interest were reported.

Background: Serious illness conversations (SICs) are an evidence-based approach to eliciting patients' values, goals, and care preferences that improve patient outcomes. However, most patients with cancer die without a documented SIC. Clinician-directed implementation strategies informed by behavioral economics ("nudges") that identify high-risk patients have shown promise in increasing SIC documentation among clinicians. It is unknown whether patient-directed nudges that normalize and prime patients towards SIC completion-either alone or in combination with clinician nudges that additionally compare performance relative to peers-may improve on this approach. Our objective is to test the effect of clinician- and patient-directed nudges as implementation strategies for increasing SIC completion among patients with cancer., Methods: We will conduct a 2 × 2 factorial, cluster randomized pragmatic trial to test the effect of nudges to clinicians, patients, or both, compared to usual care, on SIC completion. Participants will include 166 medical and gynecologic oncology clinicians practicing at ten sites within a large academic health system and their approximately 5500 patients at high risk of predicted 6-month mortality based on a validated machine-learning prognostic algorithm. Data will be obtained via the electronic medical record, clinician survey, and semi-structured interviews with clinicians and patients. The primary outcome will be time to SIC documentation among high-risk patients. Secondary outcomes will include time to SIC documentation among all patients (assessing spillover effects), palliative care referral among high-risk patients, and aggressive end-of-life care utilization (composite of chemotherapy within 14 days before death, hospitalization within 30 days before death, or admission to hospice within 3 days before death) among high-risk decedents. We will assess moderators of the effect of implementation strategies and conduct semi-structured interviews with a subset of clinicians and patients to assess contextual factors that shape the effectiveness of nudges with an eye towards health equity., Discussion: This will be the first pragmatic trial to evaluate clinician- and patient-directed nudges to promote SIC completion for patients with cancer. We expect the study to yield insights into the effectiveness of clinician and patient nudges as implementation strategies to improve SIC rates, and to uncover multilevel contextual factors that drive response to these strategies., Trial Registration: ClinicalTrials.gov , NCT04867850 . Registered on April 30, 2021., Funding: National Cancer Institute P50CA244690., (© 2021. The Author(s).)

Purpose: Machine learning models developed from electronic health records data have been increasingly used to predict risk of mortality for general oncology patients. But these models may have suboptimal performance because of patient heterogeneity. The objective of this work is to develop a new modeling approach to predicting short-term mortality that accounts for heterogeneity across multiple subgroups in the presence of a large number of electronic health record predictors., Methods: We proposed a two-stage approach to addressing heterogeneity among oncology patients of different cancer types for predicting their risk of mortality. Structured data were extracted from the University of Pennsylvania Health System for 20,723 patients of 11 cancer types, where 1,340 (6.5%) patients were deceased. We first modeled the overall risk for all patients without differentiating cancer types, as is done in the current practice. We then developed cancer type-specific models using the overall risk score as a predictor along with preselected type-specific predictors. The overall and type-specific models were compared with respect to discrimination using the area under the precision-recall curve (AUPRC) and calibration using the calibration slope. We also proposed metrics that characterize the degree of risk heterogeneity by comparing risk predictors in the overall and type-specific models., Results: The two-stage modeling resulted in improved calibration and discrimination across all 11 cancer types. The improvement in AUPRC was significant for hematologic malignancies including leukemia, lymphoma, and myeloma. For instance, the AUPRC increased from 0.358 to 0.519 (∆ = 0.161; 95% CI, 0.102 to 0.224) and from 0.299 to 0.354 (∆ = 0.055; 95% CI, 0.009 to 0.107) for leukemia and lymphoma, respectively. For all 11 cancer types, the two-stage approach generated well-calibrated risks. A high degree of heterogeneity between type-specific and overall risk predictors was observed for most cancer types., Conclusion: Our two-stage modeling approach that accounts for cancer type-specific risk heterogeneity has improved calibration and discrimination than a model agnostic to cancer types., Competing Interests: Ravi B. ParikhStock and Other Ownership Interests: Merck, Google, GNS HealthcareConsulting or Advisory Role: GNS Healthcare, Cancer Study Group, Onc.AITravel, Accommodations, Expenses: Conquer Cancer Foundation, Flatiron HealthNo other potential conflicts of interest were reported.

Policy Points With increasing integration of artificial intelligence and machine learning in medicine, there are concerns that algorithm inaccuracy could lead to patient injury and medical liability. While prior work has focused on medical malpractice, the artificial intelligence ecosystem consists of multiple stakeholders beyond clinicians. Current liability frameworks are inadequate to encourage both safe clinical implementation and disruptive innovation of artificial intelligence. Several policy options could ensure a more balanced liability system, including altering the standard of care, insurance, indemnification, special/no-fault adjudication systems, and regulation. Such liability frameworks could facilitate safe and expedient implementation of artificial intelligence and machine learning in clinical care., (© 2021 Milbank Memorial Fund.)

Pharmaceutical agents in oncology currently have high attrition rates from early to late phase clinical trials. Recent advances in computational methods, notably causal artificial intelligence, and availability of rich clinico-genomic databases have made it possible to simulate the efficacy of cancer drug protocols in diverse patient populations, which could inform and improve clinical trial design. Here, we review the current and potential use of in silico trials and causal AI to increase the efficacy and safety of traditional clinical trials. We conclude that in silico trials using causal AI approaches can simulate control and efficacy arms, inform patient recruitment and regimen titrations, and better enable subgroup analyses critical for precision medicine., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Machine learning algorithms may address prognostic inaccuracy among clinicians by identifying patients at risk of short-term mortality and facilitating earlier discussions about hospice enrollment, discontinuation of therapy, or other management decisions. In the present study, we used prospective predictions from a real-time machine learning prognostic algorithm to identify two trajectories of all-cause mortality risk for decedents with cancer. We show that patients with an unpredictable trajectory, where mortality risk rises only close to death, are significantly less likely to receive guideline-based end-of-life care and may not benefit from the integration of prognostic algorithms in practice.

Sociobehavioral phenotypes are actionable risk profiles based on empirically derived social, economic, and behavioral factors that, if applied appropriately, can help healthcare organizations address social determinants of health.

The purpose of this study was to evaluate whether home-delivered meals, and the frequency of delivery, reduces self-reported falls among homebound older adults. Data come from a randomized parallel three-arm study of 371 older adults on seven Meals on Wheels programs' waiting lists. Participants were randomly assigned to receive (a) daily meal delivery ( n = 139); (b) once weekly, frozen meal delivery ( n = 106); or (c) control, remain on the waiting list for meals ( n = 126). Participants were surveyed at baseline and 15 weeks post randomization. At follow-up, 36 (28.6%) in the control group, 29 (27.4%) receiving once weekly delivered meals, and 33 (23.7%) receiving daily delivered meals reported a fall (compared with control, daily meal risk ratio [RR] = 0.83, 95% confidence limits [CL] = [0.55, 1.25]; frozen meal RR = 0.96, 95%CL = [0.63, 1.45]). Our study suggests that daily delivered meals may reduce the risk of falls. Additional work is needed to understand the effect of meals on falls, particularly among previous fallers, a high-risk subgroup.

Recent US healthcare policy changes set the stage for integrating palliative and end-of-life care into routine medicine. However, these changes may not affect the daily practice of medical trainees-fellows, residents, and medical students-who as front-line care providers are not always equipped with the skills needed to treat terminally ill patients. We review evidence on trainees' discomfort with end-of-life care and highlight limitations of recent policy changes. Key barriers to proficiency include inadequate conversational training, prognostic uncertainty, and unfamiliarity with hospice and palliative care. Educator-, accreditor-, and policy-level interventions may improve trainees' experience caring for seriously ill patients., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Early, integrated palliative care has been shown to improve quality of life and reduce utilization in both inpatient and outpatient settings. As health systems shift to risk-based payment structures, palliative care will play an increasing role in improving value of care outside of the hospital. Based on successful models of community-based palliative care, we identify six principles - interdisciplinary team-based care; 24/7 access and responsiveness; concurrent palliative care with disease-directed treatment; targeting services to high-risk patients; integrated medical and social supports; and caregiver support - that are widely implemented because of their impact on improving value for seriously ill individuals., (Copyright © 2016 Elsevier Inc. All rights reserved.)