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Background: A lack of onsite clinical trials is the largest barrier to participation of cancer patients in trials. Development of an automated process for regional trial eligibility screening first requires identification of patient electronic health record data that allows effective trial screening, and evidence that searching for trials regionally has a positive impact compared with site‐specific searching. Methods: To assess a screening framework that would support an automated regional search tool, a set of patient clinical variables was analyzed for prescreening clinical trials. The variables were used to assess regional compared with site‐specific screening throughout the United States. Results: Eight core variables from patient electronic health records were identified that yielded likely matches in a prescreen process. Assessment of the screening framework was performed using these variables to search for trials locally and regionally for an 84‐patient cohort. The likelihood that a trial returned in this prescreen was a provisional trial match was 45.7%. Expanding the search radius to 20 miles led to a net 91% increase in matches across cancers within the tested cohort. In a U.S. regional analysis, for sparsely populated areas, searching a 100‐mile radius using the prescreening framework was needed, whereas for urban areas a 20‐mile radius was sufficient. Conclusion: A clinical trial screening framework was assessed that uses limited patient data to efficiently and effectively identify prescreen matches for clinical trials. This framework improves trial matching rates when searching regionally compared with locally, although the applicability of this framework may vary geographically depending on oncology practice density. Plain Language Summary: Clinical trials provide cancer patients the opportunity to participate in research and development of new drugs and treatment approaches. It can be difficult to find available clinical trials for which a patient is eligible.This article describes an approach to clinical trial matching using limited patient data to search for trials regionally, beyond just the patient's local care site.Feasibility testing shows that this process can lead to a net 91% increase in the number of potential clinical trial matches available within 20 miles of a patient.Based on these findings, a software tool based on this model is being developed that will automatically send limited, deidentified information from patient medical records to services that can identify possible clinical trials within a given region. To increase patient access to clinical trials, a prescreening framework was developed using deidentified data elements extracted from patient electronic health records to identify likely clinical trials in a regional manner. Feasibility testing shows that this framework effectively performs trial eligibility screening using limited patient data and improves trial matching rates because of regional search capability. [ABSTRACT FROM AUTHOR]

Background: Provider and institutional practices have been shown to have a large impact on cancer clinical trial enrollment. Understanding provider perspectives on screening for trial eligibility is necessary to improve enrollment. Methods: A questionnaire about incentives, barriers, process tools, and infrastructure related to opening trials and referring patients to onsite and offsite trials was administered to diverse stakeholders, including professional societies, advocacy organizations, and industry networks. Descriptive statistics were used to summarize findings. Results: Overall, 693 responses were received, primarily from physicians (42.7%) and nurses (35.6%) employed at hospital health systems (43.7%) and academic centers (36.5%). Approximately half (49.2%) screened all patients for onsite clinical trials with screening typically done by manual chart review (81.9%). The greatest incentive reported for offering trials was providing the best treatment options for patients (67.7%). Contracting and paperwork (48.5%) were the greatest barriers to opening more onsite trials. Offsite referrals were rare. Conclusions: Screening for trial eligibility is a largely manual and ad hoc process, with screening and referral to offsite trials occurring infrequently. Administrative and infrastructure barriers commonly prevent sites from opening more onsite trials. These findings suggest that automated trial screening tools built into workflows that screen in a site‐agnostic manner could result in more frequent trial eligibility screening, especially for offsite trials. With recent momentum, in part in response to the COVID‐19 pandemic, to improve clinical trial efficiencies and broaden access and participant diversity, implementing tools to improve screening and referral processes is timely and essential. Plain Language Summary: There are many factors that contribute to low adult enrollment in cancer clinical trials, but previous research has indicated that provider and institutional barriers are the largest contributors to low cancer clinical trial enrollment.In this survey, we sought to gain insight into cancer clinical trial enrollment practices from the perspective of health care providers such as physicians and nurses. We found that only approximately half of respondents indicated their institution systematically screens their patients for clinical trials and this process is manual and time consuming. Furthermore, we found that providers infrequently search for and refer patients to clinical trials at other sites.Creating better screening methods could improve enrollment in clinical trials. A questionnaire was administered to understand provider perspectives and needs related to screening for cancer clinical trial eligibility, including assessment of incentives, barriers, and infrastructure related to opening and referring to trials. Findings from the survey suggest that automated screening tools built into clinical workflows could result in more frequent trial eligibility screening, especially for offsite trials. [ABSTRACT FROM AUTHOR]

Enrollment in cancer clinical trials cannot occur without first successfully identifying trials for which patients are a match based on their clinical characteristics. A lack of onsite matching trials has been identified as the single largest barrier preventing patients from participating in clinical trials. The site‐agnostic cancer clinical trial matching environment is a mix of public and private tools and infrastructure that are not designed to work together to facilitate trial matching in an efficient manner. To identify policy and infrastructure solutions that could enable more effective and more frequent use of third‐party site‐agnostic matching, the American Cancer Society Cancer Action Network held a summit to examine challenges and propose consensus recommendations that could address those challenges. At this 2019 summit, stakeholders in this field examined these barriers and challenges and made a number of policy and infrastructure recommendations to improve the ability of this environment to work in a more coordinated and efficient manner. Third‐party, site‐agnostic cancer clinical trial matching services hold the potential to help identify clinical trial opportunities for significantly more patients than are offered clinical trial participation via current onsite‐only trial eligibility screening. Fully leveraging the benefits of such services, however, will only be possible with improvements to this sector, and here a set of consensus recommendations are provided. [ABSTRACT FROM AUTHOR]

Background: Measuring function with valid and responsive tools in patients with cancer is essential for driving clinical decision‐making and for the end points of clinical trials. Current patient‐reported outcome measurements of function fall short for many reasons. This study evaluates the responsiveness of the Patient‐Reported Outcomes Measurement Information System (PROMIS) Cancer Function Brief 3D Profile, a novel measure of function across multiple domains. Methods: Two hundred nine participants across five geographically distinct tertiary care centers completed the assessment and pain rating at two outpatient cancer rehabilitation clinic visits. Patients and providers completed a global rating of change measure at the second visit to indicate whether the patient was improving or worsening in function. Multiple response indices and linear models measured whether the measure was responsive to self‐reported and clinician‐rated changes over time. Correlations between changes in function and changes in anchors (pain rating and performance status) were also calculated. Results: Function as measured by the PROMIS Cancer Function Brief 3D Profile changed appropriately as both patients and clinicians rated change. Small to moderate effect sizes supported the tool's responsiveness. Function was moderately correlated with pain and more strongly correlated with performance status, and changes in function corresponded with changes in anchor variables. No floor/ceiling effect was found. Conclusions: The PROMIS Cancer Function Brief 3D Profile is sensitive to changes over time in patients with cancer. The measure may be useful in clinical practice and as an end point in clinical trials. Lay summary: We gave patients a questionnaire by which they told their physicians how well they were functioning, including how fatigued they were.This study tested that questionnaire to see whether the scores would change if patients got better or worse. Lay summary: We gave patients a questionnaire by which they told their physicians how well they were functioning, including how fatigued they were.This study tested that questionnaire to see whether the scores would change if patients got better or worse. Short Abstract The PROMIS Cancer Function Brief 3D Profile was found to be responsive to measuring change in function in patients with cancer. The changes were correlated with changes in performance status, pain, and both patient‐ and physician‐reported global ratings of change. [ABSTRACT FROM AUTHOR]

Clinical trials are critical components of modern health care and infrastructure. Trials benefit society through scientific advancement and individual patients through trial participation. In fact, billions of dollars are spent annually in support of these benefits. Despite the massive investments, clinical trials often fail to accomplish their primary aims and trial enrollment rates remain low. Prior efforts to improve trial conduct and enrollment have had limited success, perhaps due to oversimplification of the complex, multilevel nature of trials. For these reasons, the authors propose applying implementation science to the clinical trials context. In this commentary, the authors posit clinical trials as complex, multilevel evidence-based interventions with significant societal and individual benefits yet with persistent gaps in implementation. An application of implementation science concepts to the clinical trials context as means to build common vocabulary and establish a platform for applying implementation science and practice to improve clinical trial conduct is introduced. Applying implementation science to the clinical trials context can augment improvement efforts and build capacity for better and more efficient evidence-based care for all patients and trial stakeholders throughout the clinical trials enterprise. [ABSTRACT FROM AUTHOR]

Background: Clinical trial patient accrual continues to be challenging despite the identification of multiple physician, patient, and system barriers. Expanded collection of demographic data, including socioeconomic status (employment, income, education) and comorbidities, can enhance our understanding of the identified barriers, inform the development of interventions to overcome these barriers, and recognize their impact on treatment outcomes. A clinical trials screening tool was developed to collect expanded demographic data and barriers to trial enrollment; it has been implemented in the National Cancer Institute Clinical Oncology Research Program. The purpose of this article is to describe the development and implementation of the tool and to share information obtained during the first 43 months of its use.Methods: There were 19,373 entries collected; 74% of those screened enrolled in a clinical trial. Demographic characteristics were compared between those screened and those enrolled. They varied significantly between the groups.Results: Reasons for nonenrollment included ineligibility (50%), eligible but declined (47%), eligible but physician declined to offer participation (2%), and eligible but the study was suspended (1%). The most common reasons for ineligibility were failure to meet the protocol-specific stage of cancer, the presence of comorbidities, and the symptom-eligibility score was not met. The most common reason for eligible patients declining participation was that they had no desire to participate in research.Conclusions: The tool provides valuable information about the characteristics of individuals who are screened and enrolled in National Cancer Institute-sponsored trials, as well as about barriers to enrollment in trials. The data also inform protocol development and interventions at the patient, provider, and institutional level. [ABSTRACT FROM AUTHOR]

Background: Provider implicit bias can negatively affect clinician‐patient communication. In the current study, the authors measured implicit bias training among pediatric oncology providers and exposure to implicit association tests (IATs). They then assessed associations between IATs for race and socioeconomic status (SES) and recommendations for clinical trial enrollment. Methods: A prospective multisite study was performed to measure implicit bias among oncology providers at St. Jude Children's Research Hospital and affiliate clinics. An IAT was used to assess bias in the domains of race and SES. Case vignettes were used to determine an association between bias and provider recommendation for trial enrollment. Data were analyzed using Student t tests or Wilcoxon tests for comparisons and Jonckheere‐Terpstra tests were used for association. Results: Of the 105 total participants, 95 (90%) had not taken an IAT and 97 (92%) had no prior implicit bias training. A large effect was found for (bias toward) high SES (Cohen d, 1.93) and European American race (Cohen d, 0.96). The majority of participants (90%) had a vignette score of 3 or 4, indicating recommendation for trial enrollment for most or all vignettes. IAT and vignette scores did not significantly differ between providers at St. Jude Children's Research Hospital or affiliate clinics. No association was found between IAT and vignette scores for race (P =.58) or SES (P =.82). Conclusions: The authors noted a paucity of prior exposure to implicit bias self‐assessments and training. Although these providers demonstrated preferences for high SES and European American race, this did not appear to affect recommendations for clinical trial enrollment as assessed by vignettes. The current multisite study measures implicit bias in the domains of race and socioeconomic status among pediatric oncology providers in an academic center and community‐based practices. The impact of implicit bias in clinical trial enrollment is assessed using case vignettes. [ABSTRACT FROM AUTHOR]

Clinical trials are fundamental for advances in cancer treatment. The traditional framework of phase 1 to 3 trials is designed for incremental advances between regimens. However, our ability to understand and treat cancer has evolved with the increase in drugs targeting an expanding array of therapeutic targets, the development of progressively comprehensive data sets, and emerging computational analytics, all of which are reshaping our treatment strategies. A more robust linkage between drugs and underlying cancer biology is blurring historical lines that define trials on the basis of cancer type. The complexity of the molecular basis of cancer, coupled with manifold variations in clinical status, is driving the individually tailored use of combinations of precision targeted drugs. This approach is spawning a new era of clinical trial types. Although most care is delivered in a community setting, large centers support real‐time multi‐omic analytics and their integrated interpretation by using machine learning in the context of real‐world data sets. Coupling the analytic capabilities of large centers to the tailored delivery of therapy in the community is forging a paradigm that is optimizing service for patients. Understanding the importance of these evolving trends across the health care spectrum will affect our treatment of cancer in the future and is the focus of this review. With advances in cancer biology, precision therapeutics, and big data, clinical trial designs are evolving. They are transforming cancer care and research across the biomedical enterprise. [ABSTRACT FROM AUTHOR]