Hantel, Andrew

ABSTRACT Cancer mortality is inequitably distributed across the U.S. and globally, with historically marginalized groups, particularly racial and ethnic minorities, being disproportionately affected. Disproportionate representation in clinical cancer research is a consequence of inequitable access as well as a central factor contributing to outcome disparities; it limits efficacy and safety data for underrepresented groups while directing drug development to be most beneficial for those enrolled. To tackle this problem and improve the efficacy and safety of commercially available therapies, a consensus conference on strategies to promote Diversity, Equity, Inclusion, and Accessibility (DEIA) in cancer research was empaneled in September 2022. The resulting “Indianapolis Black Paper” presented here is an international collaborative white paper with authors from industry, academia, and the government, as well as patient advocacy and community health groups. First, it outlines the roles and responsibilities of key groups involved in clinical cancer research, as they relate to representation and equity. The Black Paper then delineates five steps to promote diversity in clinical research. This actionable strategy is called “DRIVE,” and it involves: incorporating a Diversity officer into clinical research studies, Ranking clinical studies for diversity, developing an Individual diversity, equity, inclusion, and access plan, a process for Verifying study diversity, and Elevating and Enhancing training of minority investigators and research team members. Finally, we outline the timeline and milestones of implementation. Purposefully ambitious and crosscutting industry, academia, and governmental policy, the Black Paper aims to address “injustices in health” with enhanced safety, purpose, and humanity.

Importance Artificial intelligence (AI) tools are rapidly integrating into cancer care. Understanding stakeholder views on ethical issues associated with the implementation of AI in oncology is critical to optimal deployment. Objective To evaluate oncologists’ views on the ethical domains of the use of AI in clinical care, including familiarity, predictions, explainability (the ability to explain how a result was determined), bias, deference, and responsibilities. Design, Setting, and Participants This cross-sectional, population-based survey study was conducted from November 15, 2022, to July 31, 2023, among 204 US-based oncologists identified using the National Plan & Provider Enumeration System. Main Outcomes and Measures The primary outcome was response to a question asking whether participants agreed or disagreed that patients need to provide informed consent for AI model use during cancer treatment decisions. Results Of 387 surveys, 204 were completed (response rate, 52.7%). Participants represented 37 states, 120 (63.7%) identified as male, 128 (62.7%) as non-Hispanic White, and 60 (29.4%) were from academic practices; 95 (46.6%) had received some education on AI use in health care, and 45.3% (92 of 203) reported familiarity with clinical decision models. Most participants (84.8% [173 of 204]) reported that AI-based clinical decision models needed to be explainable by oncologists to be used in the clinic; 23.0% (47 of 204) stated they also needed to be explainable by patients. Patient consent for AI model use during treatment decisions was supported by 81.4% of participants (166 of 204). When presented with a scenario in which an AI decision model selected a different treatment regimen than the oncologist planned to recommend, the most common response was to present both options and let the patient decide (36.8% [75 of 204]); respondents from academic settings were more likely than those from other settings to let the patient decide (OR, 2.56; 95% CI, 1.19-5.51). Most respondents (90.7% [185 of 204]) reported that AI developers were responsible for the medico-legal problems associated with AI use. Some agreed that this responsibility was shared by physicians (47.1% [96 of 204]) or hospitals (43.1% [88 of 204]). Finally, most respondents (76.5% [156 of 204]) agreed that oncologists should protect patients from biased AI tools, but only 27.9% (57 of 204) were confident in their ability to identify poorly representative AI models. Conclusions and Relevance In this cross-sectional survey study, few oncologists reported that patients needed to understand AI models, but most agreed that patients should consent to their use, and many tasked patients with choosing between physician- and AI-recommended treatment regimens. These findings suggest that the implementation of AI in oncology must include rigorous assessments of its effect on care decisions as well as decisional responsibility when problems related to AI use arise.

Introduction Artificial Intelligence (AI) is an emerging technology that promises advancements across blood cancer discovery and care delivery. There are already FDA-approved uses of AI in hematology and oncology but few data available about its use or utility. Concerns have been raised over AI's potential biases, “explainability,” and who is responsible for its use. Oncologists' familiarity with AI and perspectives on these issues are not well known and are critical to understanding the ethical deployment of AI in blood cancer care. Methods From January to July 2023, we performed a national cross-sectional survey of practicing oncologists. A draft survey instrument was developed by a team with content and methodological expertise. Iterative refinement occurred through cognitive debriefing with 5 practicing hematologist/oncologists. The final instrument included 24 questions covering domains of AI familiarity, predictions, explainability, and responsibilities. A random sample of US oncologists was identified using the National Plan & Provider Enumeration System. Paper surveys were mailed with $25 incentivizing gift cards. Reminder letters and phone calls were used for non-responders with an electronic survey option provided. Responses were analyzed descriptively. X2 testing assessed bivariate associations between responses and participant practice setting, with odds ratios (OR) reported. Results Of 399 mailed surveys, 12 were undeliverable and 201 were completed for a response rate of 51.9%. Question missingness was <1%. Participants were 63.2% White, 16.9% Indian Asian, 10.0% Eastern Asian or Pacific Islander, and 4.5% Black; 6.0% were Hispanic. They represented 37 states. The majority were male (63.7%), 40-59 years old (54.2%), and had no specific training in AI (52.2%); 68.2% had at least 11 years in practice and 29.9% practiced at a primary academic hospital. Questions assessing overall familiarity with and predictions related to AI are shown in Table 1. Those at primary academic hospitals were more likely to be familiar with radiology models (OR 2.82, p<0.001) but not pathology, prognostic, or clinical decision models (all p>0.05). They were more likely to predict that AI would improve side effect management (OR 2.01, p=0.03) and end-of-life decision making (OR 2.07, p=0.02). High proportions of respondents thought that AI prognostic (67.1%) and clinical decision models (62.1%) needed to be explainable by clinicians and researchers to be used in clinic. When posed with a scenario where an FDA-approved AI model selects a different regimen than the oncologist planned to propose (Figure 1), the largest proportion reported they would present both options and let the patient decide (37.3%). Of those recommending a regimen, respondents from primary academic hospitals more frequently recommend the AI-generated regimen (56.1%), while respondents from elsewhere recommended their original regimen (69.0%; OR 2.85, p=0.007). Acceptability of some direct-to-patient AI model applications is shown in Table 1. A majority of respondents (90.5%) thought that AI developers should be responsible for medico-legal problems caused by AI use; 47.8% thought that physicians using the tool should be responsible. Though most (76.1%) agreed that it was oncologists' responsibility to protect patients from biased AI tools, few (27.3%) were confident in their ability to identify how representative the data used in an AI model was relative to the population with that cancer (including 66.7% of those who agreed it was oncologists' responsibility). More respondents thought that patients should need to consent to the use of AI tools in cancer treatment decisions (81.1%) than cancer diagnosis (55.7%); those from primary academic hospitals were less likely to respond that patients should consent for treatment or diagnosis (OR 0.43, p=0.02 and OR 0.44, p=0.009). Conclusions In this national survey of oncologists' views on AI, there was optimism about its impact on care with notable caveats related to end-of-life care, patient trust, and care disparities. Despite many approving use of AI models too complex for patients to explain, a substantial proportion deferred to patient choice when AI and physician recommendations differed. There was also dissonance between accepting responsibility to protect patients from bias and respondents' self-perceived ability to do so.

PURPOSE Representativeness in acute leukemia clinical research is essential for achieving health equity. The National Cancer Institute's mandate for Comprehensive Cancer Centers (CCCs) to define and assume responsibility for cancer control and treatment across a geographic catchment area provides an enforceable mechanism to target and potentially remediate participatory inequities. METHODS We examined enrollee characteristics across 15 Cancer and Leukemia Group B/Alliance cooperative group adult acute leukemia clinical trials (N = 3,734) from 1998 to 2013, including participation in optional companion biobanks. We determined enrollment odds by race-ethnicity for all participants adjusted for national incidence, and for those enrolled at CCCs adjusted for catchment area incidence. We modeled biobank participation by sociodemographics using logistic regression. RESULTS Non-Hispanic (NH)-White patients were more likely to be enrolled than NH-Black, NH-Asian, or Hispanic patients (odds ratio [OR], 0.75, 0.48, and 0.44, respectively; all P < .001), but less likely than NH-Native American patients (OR, 1.91; P < .001), adjusted for national incidence. Enrollment odds were lower for NH-Black, NH-Asian, and Hispanic patients at CCCs adjusted for catchment area incidence (OR, 0.57, 0.26, and 0.32, respectively; P < .001); differences were driven by overenrollment of NH-White patients from outside self-defined catchment areas (18.1% v 12.3%; χ2 P = .01) and by CCCs with less absolute enrollee diversity (rank sum P = .03). Among all enrollees, NH-White race-ethnicity and lower neighborhood deprivation correlated with biobank participation (OR, 1.81 and 1.45, respectively; P = .01 and .03). For CCC enrollees, the correlation of race-ethnicity with biobank participation was attenuated by a measure accounting for their site's degree of enrollment disparity but not neighborhood deprivation. CONCLUSION Acute leukemia clinical research disparities are substantial and driven by structural trial enrollment barriers at CCCs. Real-time CCC access and enrollment monitoring is needed to better align research participation with local populations.

Abstract Introduction Race-ethnic disparities in clinical trial enrollment have the potential to bias findings, limit generalizability, misdirect drug development, and reduce equitable access to novel therapy. The degree to which such disparities exist within acute myeloid leukemia (AML) North American cooperative group trials, biobanks, and correlative studies remain unclear, as are the factors that influence biobank and correlative study participation among trial enrollees. In addition, the National Cancer Institute's (NCI) mandate for Comprehensive Cancer Centers (CCC) to designate catchment areas has not been explored as a mechanism through which AML enrollment disparities can be addressed. Methods We analyzed enrollment data from the 9 Alliance/CALGB AML treatment trials, 2 biobank protocols, and 2 correlative studies active from 1998-2013 and with published results. Trial enrollees could consent to biobank and/or correlative study participation. We compared participation rates of United States (US) enrollees for the mutually exclusive racial-ethnic groups of non-Hispanic (NH)-white, NH-Black, NH-Asian, NH-Native American, and Hispanic using X 2 testing, with NH-white as the comparator and reporting odds ratios (OR) and 95% confidence intervals (CI). Rates were adjusted by national incidence according to the Surveillance, Epidemiology, and End Results program and the US Census. Testing was repeated for the 55% of participants enrolled at 15 NCI CCCs recruiting ≥10 patients, where incidence was adjusted for catchment area size and demographics. Logistic regression models, clustered by trial, were performed to assess the following predictors for biobank and correlative study participation among trial enrollees: race-ethnicity (NH-white vs non-white), site type (CCC status), age (10-year increments), sex, neighborhood urbanity (urban vs rural) and poverty (&lt;20% vs ≥20% below poverty line) by zip code, and distance from site (10-mile increments). Results There were 3041 trial enrollees at US sites; participant characteristics and demographics by race-ethnicity are shown in Table 1. 93.9% of patients participated in a biobanking study and 60.0% in a correlative study. National incidence adjusted enrollment odds by race-ethnicity are shown in the Figure (top); NH-Black, NH-Asian, and Hispanic persons were enrolled at significantly lower rates than NH-whites; NH-Native American enrollment was significantly higher. Enrollment odds were even lower for NH-Black, NH-Asian, and Hispanic enrollees at CCC sites when adjusted by catchment area incidence (Figure; bottom). Among trial enrollees, there were no univariable predictors of biobank participation, however, male sex (OR 1.12; 95% CI 1.01, 1.37; p=0.04) and NH-white race-ethnicity (OR 1.33; 95% CI 1.12,1.57; p&lt;0.001) were associated with correlative study participation. Multivariable models of correlative study participation, with predictors selected based on univariable significance, are shown in Table 2 for all trial enrollees and when restricted to biobank enrollees; in both cases, NH-white race predicted participation. Conclusions Across 15 years of AML cooperative group studies, there were several enrollment disparities by race-ethnicity, which were more pronounced at CCC sites. Over 90% of trial enrollees participated in biobanking, with no race-ethnic differences seen. However, correlative study participation among trial enrollees was higher for NH-whites. Taken together, these data suggest that efforts should focus on increasing trial and correlative study participant diversity-but not biobanking-within NCI-designated CCC catchment areas. Reasonable next steps include identifying key structural, provider, and patient-based barriers to trial and correlative study participation at CCC sites and developing inclusive, multilevel interventions to address them. Figure 1 Figure 1. Disclosures Eisfeld: Karyopharm (spouse): Current Employment. Stock: Pfizer: Consultancy, Honoraria, Research Funding; amgen: Honoraria; agios: Honoraria; jazz: Honoraria; kura: Honoraria; kite: Honoraria; morphosys: Honoraria; servier: Honoraria; syndax: Consultancy, Honoraria; Pluristeem: Consultancy, Honoraria. Stone: AbbVie Inc, Actinium Pharmaceuticals Inc, Aprea Therapeutics, BerGenBio ASA, ElevateBio, Foghorn Therapeutics, GEMoaB, GlaxoSmithKline, Innate Pharma, Syndax Pharmaceuticals Inc, Syros Pharmaceuticals Inc, Takeda Oncology: Other: Advisory Committee; Agios Pharmaceuticals Inc, Novartis;: Research Funding; ACI Clinical, Syntrix Pharmaceuticals, Takeda Oncology: Other: Data Safety & Monitoring. DeAngelo: Abbvie: Research Funding; Blueprint: Research Funding; Takeda: Consultancy; Autolus: Consultancy; Forty-Seven: Consultancy; Incyte: Consultancy; Jazz: Consultancy; Novartis: Consultancy, Research Funding; Pfizer: Consultancy; Servier: Consultancy; Amgen: Consultancy; Agios: Consultancy; Glycomimetrics: Research Funding. Byrd: Newave: Membership on an entity's Board of Directors or advisory committees; Vincerx Pharmaceuticals: Current equity holder in publicly-traded company, Membership on an entity's Board of Directors or advisory committees; Novartis, Trillium, Astellas, AstraZeneca, Pharmacyclics, Syndax: Consultancy, Honoraria.

ABSTRACT Background Medication shortages prevent patients from receiving optimal care. Despite the frequency with which medical trainees care for inpatients, no assessment of their experiences in medication shortage management has been performed. Objective We evaluated trainees' experiences managing medication shortages. Methods We performed a cross-sectional survey of trainees postgraduate year 2 (PGY-2) and above in medicine, anesthesiology, and emergency medicine departments at 2 academic centers in 2018–2019. Categorical and ordinal assessments evaluated shortage awareness, substitution availability, pharmacy and therapeutics committee-based restrictions, communication, and education. Regressions were performed to determine effect of PGY, department, and institution on responses. Results A total of 168 of 273 subjects completed the survey (62% response rate). Most (95%, 159 of 168) reported managing medication shortages during training; 51% (86 of 168) described managing clinically relevant shortages daily or weekly. Seventy-seven percent (129 of 168) noted equivalent alternatives were unavailable at least one-quarter of the time, and 43% (72 of 168) reported clinically necessary medications were restricted at least weekly. Fifty-four percent (89 of 168) and 64% (106 of 167) of respondents discussed clinically relevant shortages with supervising physicians or patients “some of the time” or less, respectively. Most respondents (90%, 151 of 168) reported they would benefit from shortage management training, but few (13%, 21 of 168) reported prior training. Conclusions Although trainees reported frequent involvement in clinically impactful shortage management, medication shortage communication between trainees and supervising physicians or patients appears sporadic. Medication shortage management training is uncommon but perceived as beneficial.