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Purpose: The Merit-Based Incentive Payment System (MIPS) is currently the only federally mandated value-based payment model for oncologists. The weight of cost measures in MIPS has increased from 0% in 2017 to 30% in 2022. Given that cost measures are specialty-agnostic, specialties with greater costs of care such as oncology may be unfairly affected. We investigated the implications of incorporating cost measures into MIPS on physician reimbursements for oncologists and other physicians., Methods: We evaluated physicians scored on cost and quality in the 2018 MIPS using the Doctors and Clinicians database. We used multivariable Tobit regression to identify physician-level factors associated with cost and quality scores. We simulated composite MIPS scores and payment adjustments by applying the 2022 cost-quality weights to the 2018 category scores and compared changes across specialties., Results: Of 168,098 identified MIPS-participating physicians, 5,942 (3.5%) were oncologists. Oncologists had the lowest cost scores compared with other specialties (adjusted mean score, 58.4 for oncologists v 71.0 for nononcologists; difference, -12.66 [95% CI, -13.34 to -11.99]), while quality scores were similar (82.9 v 84.2; difference, -1.31 [95% CI, -2.65 to 0.03]). After the 2022 cost-quality reweighting, oncologists would receive a 4.3-point (95% CI, 4.58 to 4.04) reduction in composite MIPS scores, corresponding to a four-fold increase in magnitude of physician penalties ($4,233.41 US dollars [USD] in 2018 v $18,531.06 USD in 2022) and greater reduction in exceptional payment bonuses compared with physicians in other specialties (-42.8% [95% CI, -44.1 to -41.5] for oncologists v -23.6% [95% CI, -23.8 to -23.4] for others)., Conclusion: Oncologists will likely be disproportionally penalized after the incorporation of cost measures into MIPS. Specialty-specific recalibration of cost measures is needed to ensure that policy efforts to promote value-based care do not compromise health care quality and outcomes.

The merit-based incentive payment system (MIPS) is a value-based payment model created by the Centers for Medicare & Medicaid Services (CMS) to promote high-value care through performance-based adjustments of Medicare reimbursements. In this cross-sectional study, we examined the participation and performance of oncologists in the 2019 MIPS. Oncologist participation was low (86%) compared to all-specialty participation (97%). After adjusting for practice characteristics, higher MIPS scores were observed among oncologists with alternative payment models (APMs) as their filing source (mean score, 91 for APMs vs. 77.6 for individuals; difference, 13.41 [95% CI, 12.21, 14.6]), indicating the importance of greater organizational resources for participants. Lower scores were associated with greater patient complexity (mean score, 83.4 for highest quintile vs. 84.9 for lowest quintile, difference, -1.43 [95% CI, -2.48, -0.37]), suggesting the need for better risk-adjustment by CMS. Our findings may guide future efforts to improve oncologist engagement in MIPS., (© The Author(s) 2023. Published by Oxford University Press.)

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).

Importance: Medicare's Oncology Care Model (OCM) was an alternative payment model that tied performance-based payments to cost and quality goals for participating oncology practices. A major concern about the OCM regarded inclusion of high-cost cancer therapies, which could potentially disincentivize oncologists from prescribing novel therapies., Objective: To examine whether oncologist participation in the OCM changed the likelihood that patients received novel therapies vs alternative treatments., Design, Setting, and Participants: This cohort study of Surveillance, Epidemiology, and End Results (SEER) Program data and Medicare claims compared patient receipt of novel therapies for patients treated by oncologists participating vs not participating in the OCM in the period before (January 2015-June 2016) and after (July 2016-December 2018) OCM initiation. Participants included Medicare fee-for-service beneficiaries in SEER registries who were eligible to receive 1 of 10 novel cancer therapies that received US Food and Drug Administration approval in the 18 months before implementation of the OCM. The study excluded the Hawaii registry because complete data were not available at the time of the data request. Patients in the OCM vs non-OCM groups were matched on novel therapy cohort, outcome time period, and oncologist specialist status. Analysis was conducted between July 2021 and April 2022., Exposures: Oncologist participation in the OCM., Main Outcomes and Measures: Preplanned analyses evaluated patient receipt of 1 of 10 novel therapies vs alternative therapies specific to the patient's cancer for the overall study sample and for racial subgroups., Results: The study included 2839 matched patients (760 in the OCM group and 2079 in the non-OCM group; median [IQR] age, 72.7 [68.3-77.6] years; 1591 women [56.0%]). Among patients in the non-OCM group, 33.2% received novel therapies before and 40.1% received novel therapies after the start of the OCM vs 39.9% and 50.3% of patients in the OCM group (adjusted difference-in-differences, 3.5 percentage points; 95% CI, -3.7 to 10.7 percentage points; P = .34). In subgroup analyses, second-line immunotherapy use in lung cancer was greater among patients in the OCM group vs non-OCM group (adjusted difference-in-differences, 17.4 percentage points; 95% CI, 4.8-30.0 percentage points; P = .007), but no differences were seen in other subgroups. Over the entire study period, patients with oncologists participating in the OCM were more likely to receive novel therapies than those with oncologists who were not participating (odds ratio, 1.47; 95% CI, 1.09-1.97; P = .01)., Conclusions and Relevance: This study found that participation in the OCM was not associated with oncologists' prescribing novel therapies to Medicare beneficiaries with cancer. These findings suggest that OCM financial incentives did not decrease patient access to novel therapies.

Background: While health systems have implemented multifaceted interventions to improve physician and patient communication in serious illnesses such as cancer, clinicians vary in their response to these initiatives. In this secondary analysis of a randomized trial, we identified phenotypes of oncology clinicians based on practice pattern and demographic data, then evaluated associations between such phenotypes and response to a machine learning (ML)-based intervention to prompt earlier advance care planning (ACP) for patients with cancer., Methods and Findings: Between June and November 2019, we conducted a pragmatic randomized controlled trial testing the impact of text message prompts to 78 oncology clinicians at 9 oncology practices to perform ACP conversations among patients with cancer at high risk of 180-day mortality, identified using a ML prognostic algorithm. All practices began in the pre-intervention group, which received weekly emails about ACP performance only; practices were sequentially randomized to receive the intervention at 4-week intervals in a stepped-wedge design. We used latent profile analysis (LPA) to identify oncologist phenotypes based on 11 baseline demographic and practice pattern variables identified using EHR and internal administrative sources. Difference-in-differences analyses assessed associations between oncologist phenotype and the outcome of change in ACP conversation rate, before and during the intervention period. Primary analyses were adjusted for patients' sex, age, race, insurance status, marital status, and Charlson comorbidity index. The sample consisted of 2695 patients with a mean age of 64.9 years, of whom 72% were White, 20% were Black, and 52% were male. 78 oncology clinicians (42 oncologists, 36 advanced practice providers) were included. Three oncologist phenotypes were identified: Class 1 (n = 9) composed primarily of high-volume generalist oncologists, Class 2 (n = 5) comprised primarily of low-volume specialist oncologists; and 3) Class 3 (n = 28), composed primarily of high-volume specialist oncologists. Compared with class 1 and class 3, class 2 had lower mean clinic days per week (1.6 vs 2.5 [class 3] vs 4.4 [class 1]) a higher percentage of new patients per week (35% vs 21% vs 18%), higher baseline ACP rates (3.9% vs 1.6% vs 0.8%), and lower baseline rates of chemotherapy within 14 days of death (1.4% vs 6.5% vs 7.1%). Overall, ACP rates were 3.6% in the pre-intervention wedges and 15.2% in intervention wedges (11.6 percentage-point difference). Compared to class 3, oncologists in class 1 (adjusted percentage-point difference-in-differences 3.6, 95% CI 1.0 to 6.1, p = 0.006) and class 2 (adjusted percentage-point difference-in-differences 12.3, 95% confidence interval [CI] 4.3 to 20.3, p = 0.003) had greater response to the intervention., Conclusions: Patient volume and time availability may be associated with oncologists' response to interventions to increase ACP. Future interventions to prompt ACP should prioritize making time available for such conversations between oncologists and their patients., Competing Interests: This study was funded by a grant from the National Palliative Care Research Center. The authors have declared that no competing interests exist.

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.)

Introduction: Patients with cancer often receive care that is not aligned with their personal values and goals. Serious illness conversations (SICs) between clinicians and patients can help increase a patient's understanding of their prognosis, goals and values., Methods and Analysis: In this study, we describe the design of a stepped-wedge cluster randomized trial to evaluate the impact of an intervention that employs machine learning-based prognostic algorithms and behavioral nudges to prompt oncologists to have SICs with patients at high risk of short-term mortality. Data are collected on documented SICs, documented advance care planning discussions, and end-of-life care utilization (emergency room and inpatient admissions, chemotherapy and hospice utilization) for patients of all enrolled clinicians., Conclusion: This trial represents a novel application of machine-generated mortality predictions combined with behavioral nudges in the routine care of outpatients with cancer. Findings from the trial may inform strategies to encourage early serious illness conversations and the application of mortality risk predictions in clinical settings., Trial Registration: Clinicaltrials.gov Identifier: NCT03984773., Competing Interests: Declaration of Competing Interest The authors have no conflict of interest disclosures relevant to this project. Outside of the scope of this work, Dr. Parikh serves as a consultant for GNS Healthcare. Dr. Patel is supported by career development awards from the Department of Veterans Affairs HSR&D, and is a founder of Catalyst Health, a technology and behavior change consulting firm., (Copyright © 2020 Elsevier Inc. All rights reserved.)

223 Background: Serious illness conversations (SICs) about patients' values and care preferences are tied to improved outcomes and quality of life. However, SICs can be hard to implement early in the cancer care journey, and questions remain about how and when to start. Following a pragmatic trial testing behavioral nudges to promote SICs in oncology (NCT04867850), we used an explanatory-sequential mixed methods design to gauge patient and clinician perspectives on the best communication strategy for SICs. Methods: Thirty patients with cancer at high mortality risk and 16 oncology clinicians were recruited from our academic cancer center. Guided by the Consolidated Framework for Implementation Research, our interviews systematically assessed multi-level factors shaping SIC implementation. In addition to interviews, patients completed a structured survey with validated measures on patient-focused communication and care planning. Survey data were studied descriptively, interviews were analyzed thematically, and both were integrated to triangulate qualitative and quantitative results. Results: Participants described two SIC types, which we classified as event-triggered and phased. Clinicians identified event-triggered SICs as prompted by acute incidents (e.g., disease stage transition) and more likely to lead to EHR documentation. However, they were seen as prohibitively time-consuming, and as such, did not become a top priority until a crisis arose. Patients were also uncomfortable with extensive early SICs, wanting to keep hope and avoid information overload soon after a diagnosis. They preferred not to discuss "turns for the worse" until they faced an acute challenge, at which point they valued thorough SICs. In surveys, almost all patients reported asking about benefits (96%), risks (93%), and quality of life (81%) tied to treatment, but few raised sensitive topics like planning for if things got worse (44%). By contrast, phased SICs were described as ongoing, progressive conversations not prompted by specific events and that arose early but deepened over time as a patient and care team nurtured their relationship. Patients and clinicians described phased SICs as better at promoting person-centered care and an essential foundation for SICs prompted by potential transitions. However, since these discussions occur gradually, clinicians reported challenges documenting them, reducing their value at informing future decisions and the ability to know whether conversations occurred at all. Conclusions: Interventions to increase SICs often focus on acute moments in the care trajectory. However, our study suggests that ongoing, staged approaches to SICs are vital for goal-concordant care as well and may be more easily integrated into busy clinical workflows. Further work should investigate ways to support clinicians in both SIC types, such as flexible documentation tools aligned with each approach. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

PURPOSE: The Oncology Care Model (OCM) is Medicare's first bundled payment program for patients with cancer. We examined baseline characteristics of OCM physician participants and markets with high OCM physician participation to inform generalizability and complement the ongoing practice-level evaluation of the OCM. METHODS: In this cross-sectional study, we identified characteristics of US medical oncologists practicing in 2016, using a national telephone-verified physician database. We linked these data with Dartmouth Atlas and Medicare claims data from 2011 through 2016 to identify characteristics of markets with high OCM participation. We used logistic regression to examine relationships between market characteristics and OCM participation. RESULTS: Of 10,428 US medical oncologists, 2,605 (24.9%) were listed in an OCM practice. There were no differences in sex or medical training between OCM participants and nonparticipants, although OCM participants were slightly younger. OCM participants practiced in larger (median daily patient volume, 80 v 55 patients) and urban practices (95.2% v 90.7%) and were less likely to be part of a health system (41.0% v 60.4%) or solo practice (45.5% v 67.4%; all P <.001). Participation was higher in southern and mid-Atlantic markets. Markets with high OCM physician participation had higher specialist density, hospital care intensity, and acute care use at the end of life (all P <.001). Market-level penetration of Accountable Care Organizations (adjusted odds ratio, 4.65; 95% CI 3.31 to 6.56; P <.001) and Medicare Advantage (adjusted odds ratio 2.82; 95% CI, 1.97 to 4.06; P <.001) were associated with higher OCM participation. CONCLUSION: In the first description of oncologists participating in the OCM, we found differences in practice demographics, care intensity, and exposure to nontraditional payment models between OCM-participating and nonparticipating physicians. Such provider-level differences may not be captured in Medicare's practice-level analysis. [ABSTRACT FROM AUTHOR]

1. 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. In 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. This 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. We 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. Of 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. This 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. Scientific Research / Models of Palliative Care Delivery [ABSTRACT FROM AUTHOR]