Clinical Trial Recruitment Research Articles

Reasons for ineligibility for clinical trials of patients with medication-resistant epilepsy.

Selection criteria for clinical trials for medication-resistant epilepsy are used to limit variability and to ensure safety. However, it has become more challenging to recruit subjects for trials. This study investigated the impact of each inclusion and exclusion criterion on medication-resistant epilepsy clinical trial recruitment at a large academic epilepsy center. We retrospectively identified all patients with medication-resistant focal or generalized onset epilepsy who attended an outpatient clinic over a consecutive 3-month period. We assessed each patient's eligibility for trials with commonly required inclusion and exclusion criteria to evaluate the proportion of eligible patients and the most common reasons for exclusion. Among 212 patients with medication-resistant epilepsy, 144 and 28 patients met the criteria for focal or generalized onset epilepsy, respectively. Overall, 9.4% (n = 20) patients were eligible for trials (19 focal onset and one generalized onset). Most patients were excluded from the study due to insufficient seizure frequency (58% of focal onset, 55% of generalized onset). A small proportion of patients with medication-resistant epilepsy were eligible for trials based on common selection criteria. These eligible patients may not be representative of the general population of patients with medication-resistant epilepsy. Insufficient seizure frequency was the most common reason for exclusion.

The role of artificial intelligence in hastening time to recruitment in clinical trials

Abstract Novel and developing artificial intelligence (AI) systems can be integrated into healthcare settings in numerous ways. For example, in the case of automated image classification and natural language processing, AI systems are beginning to demonstrate near expert level performance in detecting abnormalities such as seizure activity. This paper, however, focuses on AI integration into clinical trials. During the clinical trial recruitment process, considerable labor and time is spent sifting through electronic health record and interviewing patients. With the advancement of deep learning techniques such as natural language processing, intricate electronic health record data can be efficiently processed. This provides utility to workflows such as recruitment for clinical trials. Studies are starting to show promise in shortening the time to recruitment and reducing workload for those involved in clinical trial design. Additionally, numerous guidelines are being constructed to encourage integration of AI into the healthcare setting with meaningful impact. The goal would be to improve the clinical trial process by reducing bias in patient composition, improving retention of participants, and lowering costs and labor.

Site-specific factors associated with clinical trial recruitment efficiency in general practice settings: a comparative descriptive analysis

BackgroundRecruitment of participants is crucial to the success of randomised control trials (RCTs) but can be challenging and expensive. Current research on trial efficiency is often focused at the patient-level with an emphasis on effective recruitment strategies. Less is known about selection of study sites to optimise recruitment. We examine site-level factors that are associated with patient recruitment and cost efficiency using data from an RCT conducted across 25 general practices (GP) in Victoria, Australia.MethodsData on number of participants screened, excluded, eligible, recruited, and randomised from each study site were extracted from a clinical trial. Details regarding site characteristics, recruitment practices, and staff time commitment were collected using a three-part survey. The key outcomes assessed were recruitment efficiency (ratio of screened to randomised), average time, and cost for each participant recruited and randomised. To identify practice-level factors associated with efficient recruitment and lower cost, outcomes were dichotomised (25th percentile vs others) and each practice-level factor assessed against the outcomes to determine its association.ResultsAcross 25 GP study sites, 1968 participants were screened of which 299 (15.2%) were recruited and randomised. The mean recruitment efficiency was 7.2, varying from 1.4 to 19.8 across sites. The strongest factor associated with efficiency was assigning clinical staff to identify potential participants (57.14% vs. 22.2%). The more efficient sites were smaller practices and were more likely to be rural locations and in areas of lower socioeconomic status. The average time used for recruitment was 3.7 h (SD2.4) per patient randomised. The mean cost per patient randomised was $277 (SD161), and this varied from $74 to $797 across sites. The sites identified with the 25% lowest recruitment cost (n = 7) were more experienced in research participation and had high levels of nurse and/or administrative support.ConclusionDespite the small sample size, this study quantified the time and cost used to recruit patients and provides helpful indications of site-level characteristics that can help improve feasibility and efficiency of conducting RCT in GP settings. Characteristics indicative of high levels of support for research and rural practices, which often tends to be overlooked, were observed to be more efficient in recruiting.

Travel distance and time to dermatology clinical trial sites: a cross-sectional geospatial analysis

Despite increasing calls for diversity in clinical trial recruitment, data are lacking regarding disparities in access to dermatologic clinical trials. The objective of this study was to characterize travel distance and time to reach a dermatology clinical trial site considering patient demographic and location characteristics. We determined travel distance and time from every census tract population center in the United States to the nearest dermatologic clinical trial site using ArcGIS and linked travel estimates to demographic characteristics in each census tract based on 2020 American Community Survey. Nationally, patients travel an average of 14.3 miles and 19.7min to reach a dermatologic clinical trial site. Significantly shorter travel distance and time were observed for urban and Northeast residence, White and Asian race and private insurance relative to rural and Southern residence, Native American and Black race and public insurance (p < 0.001). These findings reveal disparate access regarding geographic region, rurality, race and insurance type, which may encourage investigators to allocate funding for travel assistance for underrepresented and disadvantaged groups to promote access and diversity in dermatologic clinical trials.

The potential impact of ChatGPT in clinical and translational medicine.

The potential impact of ChatGPT in clinical and translational medicine.

Factors, influencing to the recruitment of international multicenter clinical trials of II–III phases

Recruiting patients to a clinical trial is a process with many limitations. First of all, the time-limited and lengthening of the patient recruitment process in time increases the risk of failure of the entire study. In addition to the time factor, many other factors affect the recruitment of patients. The influence of factors is multidirectional and one factor can affect both positively and negatively the recruitment of patients. We have considered some of the factors within the framework of our proposed classification. Materials and methods . A retrospective analysis of 4 international multicenter clinical trials (MSCI) of phases II–III on the recruitment of patients, depending on the influence of factors, was carried out. The purpose of the study . To consider the influence of various factors on the set of patients by changing the set of patients and some other parameters and indicators. Statistical analysis. Descriptive statistics with typing. Results . The factors are classified into two groups and their influence on the recruitment of patients is considered. Discussion . According to the literature, the authors have developed a large number of classifications of groups, and we have proposed our own classification.

Abstract P570: The Association of Ideal Cardiovascular Health With Self-Reported Health in Black American Women

Introduction: Black American women suffer disproportionately from an increased burden of cardiovascular health (CVH) risk factors, earlier onset of cardiovascular disease (CVD), and higher CVD mortality when compared to their White women. The Partnering in Negating Statistics (P.I.N.S.) for Black Women Initiative is an academic-community-government partnership aimed at uplifting Black women to achieve total health and wellness through the provision of education, access to resources, and partnership in science. Women’s community wellness events have operationalized the P.I.N.S. Initiative mission since 2021, drawing Black women and their families to take part in resources like cardiovascular disease screenings (AHA’s Life’s Simple 7 [LS7] and Life’s Essential 8 [LE8]) and education. The study presented herein examined the association of ideal cardiovascular health (ICH) with self-reported health among Black American women who attended the community wellness events. Methods: Community wellness events focusing on Black women occurred in 2021 and 2022. At these events, a battery of measurements including CVH metrics (blood pressure, glucose, cholesterol, body mass index [BMI], physical activity, smoking, diet and sleep duration) and self-reported health(poor, fair, good, excellent) were collected from participants who chose to participate in biometric screening. The associations between LS7 (range 0-14)/LE8 (range 0-100) with self-reported health were tested using multinomial logistic regression controlling for age, income, and education. Results: One-hundred and twenty-three Black American women with complete LS7 and self-reported health data were included in the analysis 2021 and 2022, of those 63 of participants in 2022 had LE8 data. The mean age of participants was 50 (SD 14), 66%, 23%, 7% and 2% were employed, retired, unemployed and students, 76% and 15%, were enrolled in private insurance, Medicaid/Medicare and 15% had no insurance. Sixty-nine percent had educational attainment ≥ college. In fully adjusted analyses, a 1-unit increase (range 4-13) in LS7 was associated with a 34% lower odds of fair vs. excellent self-reported health (OR 0.66, 95%CI: 0.46, 0.96,p=0.0281). A 1-unit increase in LE8 (range 38-91) was associated with a 13% lower odds of fair vs. excellent self-reported health (OR 0.87, 95%CI: 0.78, 0.98, p=0.0258). Conclusion: Attainment of higher ICH was associated with lower odds of fair vs. excellent self-reported health among Black American women. This finding is important in community-based health interventions and clinical trial recruitment in historically underrepresented populations including Black women, as individuals who appropriately recognize their health status may be more likely to engage in clinical and community-based research to improve health.

Artificial Intelligence in Inflammatory Bowel Disease Endoscopy: Implications for Clinical Trials.

Artificial intelligence shows promise for clinical research in inflammatory bowel disease endoscopy. Accurate assessment of endoscopic activity is important in clinical practice and inflammatory bowel disease clinical trials. Emerging artificial intelligence technologies can increase efficiency and accuracy of assessing the baseline endoscopic appearance in patients with inflammatory bowel disease and the impact that therapeutic interventions may have on mucosal healing in both of these contexts. In this review, state-of-the-art endoscopic assessment of mucosal disease activity in inflammatory bowel disease clinical trials is described, covering the potential for artificial intelligence to transform the current paradigm, its limitations, and suggested next steps. Site-based artificial intelligence quality evaluation and inclusion of patients in clinical trials without the need for a central reader is proposed; for following patient progress, a second reading using AI alongside a central reader with expedited reading is proposed. Artificial intelligence will support precision endoscopy in inflammatory bowel disease and is on the threshold of advancing inflammatory bowel disease clinical trial recruitment.

Challenges to Recruiting Men on Active Surveillance for Prostate Cancer in Clinical Chemoprevention Trials

Clinical trials play a critical role in evidence-based medicine, when rigorous scientific methodology is utilized to discover and test the effectiveness and safety of new drugs to prevent or cure diseases, including cancer. Participation in clinical trials thus becomes key to successful completion of these trials. Although it is estimated that >70% of Americans are inclined to participate in clinical trials, less than 5% of adult cancer patients participate in clinical trials. There is thus a large gap between those inclined to participate in clinical trials and actual participation in clinical trials. As with trials targeting men with prostate cancer (PCa) on active surveillance (AS), where the target population is mostly over 50 years of age, others have observed several challenges with recruitment and accrual in clinical trials. The participation rate is currently unavailable for men on primary and secondary chemoprevention trials. Additionally, with unanticipated environmental factors such as a pandemic or other natural emergencies that may severely impact the economy, personal property, travel and person-to person contact for study-related procedures, there is a need to continuously identify these challenges and determine solutions to recruitment barriers in chemoprevention trials to ensure timely completion of early phase trials. Recent studies regarding the impact of the pandemic on clinical trial recruitment have shown that cancer prevention trials were relatively more negatively impacted compared to cancer treatment trials. The goal of this manuscript is to review our experience in continuously evaluating the protocol and patient level challenges to recruiting subjects on AS for PCa in this cancer chemoprevention trial conducted at the Comprehensive Cancer Center (CCC) and report the contemporary strategies that we are utilizing to continue to recruit subjects in this trial. We provide data from our current trial as an example while discussing future strategies to improve overall clinical trial recruitment. These strategies can inform future design of contemporary cancer chemoprevention trials and, additionally, better select, focus and invest in strategies that are the most productive and efficient for recruiting target populations.

Abstract WP47: Telemedicine And Remote Research Practice Use In Clinical Trials Among StrokeNet Sites

Background: A hybrid of remote and in-person conduct of research may improve recruitment and retention rates in clinical trials. However, little is known about the current state of remote research practices (RRPs) in stroke trials. Our objective was to characterize the use of telemedicine and RRPs among sites, including Regional Coordinating Centers (RCCs), within NIH StrokeNet. Methods: All NIH StrokeNet clinical sites were sent a standardized questionnaire surveying their voluntary adoption of telemedicine and RRPs in stroke clinical trial recruitment both locally and remotely at affiliated stroke hospitals. The survey was distributed to attendees of the spring StrokeNet conference. The survey results are described using descriptive statistics. Results: Overall, 39 StrokeNet sites (16 RCCs [64.0%]) responded to the survey between April 2022 and July 2022. Thirty-one sites (79.5%) used telemedicine in clinical trials in the following settings: outpatient (66.7%), Emergency Department (46.2%), and inpatient (41.0%). Telemedicine was most commonly utilized for follow-up assessment (51.3%), trial screening (43.6%), obtaining informed consent (43.6%), and guiding the study intervention (33.3%). Reported rates of electronic informed consent use was as follows: 30 sites (76.9%) for acute stroke trials, 22 (56.4%) for secondary stroke prevention trials, and 15 (38.4%) for stroke recovery trials. Only 9 sites (23.1%) reported remotely enrolling patients at affiliated telestroke hospitals and 8 sites (20.5%) initiated study interventions remotely. Conclusions: Telemedicine and e-consent were used at many responding StrokeNet clinical trial sites. Future research is needed to understand the added value of telemedicine and RRPs in clinical research, barriers to adoption, and ideal future state.

Abstract WP83: An Artificial Intelligence Based Approach To Clinical Trial Recruitment: The Impact Of Viz Recruit On Enrollment In The Embolise Trial

Background: EMBOLISE (NCT 04402632) is an ongoing randomized controlled trial investigating the safety and efficacy of middle meningeal artery embolization for the treatment of subacute or chronic subdural hematoma (SDH). We hypothesized that the implementation Viz RECRUIT SDH, an artificial-intelligence (AI)-based mobile recruitment platform, would aid enrollment by automatically pre-screening suspected candidates for SDH and coordinating communication and image sharing among the entire research team. Methods: Patient enrollment in EMBOLISE prior to and after implementation of Viz RECRUIT SDH at a primary (PSC) and comprehensive stroke center (CSC) was compared. Viz RECRUIT SDH automatically detects SDH in head non-contrast computed tomography (NCCT) images and reports the volume, maximum thickness, and midline shift. The EMBOLISE trial was activated on May 5, 2021, and Viz RECRUIT SDH was activated on October 6, 2021; therefore, the pre-Viz cohort consisted of all patients from EMBOLISE to Viz activation (131 days), and the post-Viz cohort consisted of all patients from Viz activation until August 18, 2022 (316 days). For the post-Viz cohort, additional data is presented on AI-based screening metrics. Results: Prior to implementation of the AI-based software, there were 5 patients enrolled (0.99 patients/month). After implementation of the software for trial screening, enrollment increased by 36% to 1.35 patients/month (14 total enrolled), and there were no screen failures. Over the entire post-Viz period, a total of 6804 NCCTs were prescreened by Viz with 208 total SDH detections (3% prevalence). A total of 50 detections were made at the PSC and 158 came from the CSC. 79% of all alerts for suspected SDH were viewed, and 48% of all viewed alerts occurred within 10 minutes. Conclusions: The implementation of an AI-based software for the automatic screening of SDH patients increased the enrollment rate in the EMBOLISE trial. Moreover, in a busy research institution with 13 active studies in 2022, the automatic alerts of all potential patients keep our attention on EMBOLISE with coordinated screening and communication in a single mobile application.

Grounded Practical Theory Analysis of Patient-Provider Communication with Black Women Participating in Breast Cancer Clinical Trials

Background:Previous literature suggests breast cancer clinical trial participation among Black women has declined in recent years by as much as 35%. Though the literature identifies barriers to participation for this population, little has been studied about how researchers can address these barriers. This study investigates the communication between healthcare providers and Black women to reveal how providers and researchers can positively influence their perceptions of breast cancer clinical trial participation.Methods:Fourteen women (n=14) who self-identified as Black, Black American, or African American, were interviewed about their communication experiences with healthcare providers regarding breast cancer clinical trial participation. Each transcribed interview was coded using thematic analysis. Grounded Practical Theory was introduced to give insight into the patient-provider communication needs of Black breast cancer research participants.Findings:The findings fell into four categories: (1) impressions of participants toward their providers, (2) reflections on the clinical trial recruitment experience, (3) communication relationships with medical and research providers, and (4) cultural aspects of patient-provider communication. One major finding was that an important way women learn about clinical trials is through conversations with their oncologists. However, only 29% of Black women interviewed were informed of their clinical trial by a healthcare provider, suggesting that Black women may not be receiving the information they need to participate in clinical trials.Conclusion:By understanding existing patient-provider communication typologies, we can improve these methods of communication to increase the interest and participation of Black women in breast cancer clinical trials.Implications:Clinical trials provide data to healthcare providers about treatment options for breast cancer. If minoritized populations are continually underrepresented in clinical trials, these treatments might not prove to be efficacious in Black women. Researchers must make the necessary investment of resources and effort to better understand the needs of Black women in clinical trial recruitment.

Patient hesitancy in perioperative clinical trial enrollment during the COVID-19 pandemic.

The COVID-19 pandemic has caused tremendous disruptions to non-COVID-19 clinical research. However, there has been little investigation on how patients themselves have responded to clinical trial recruitment during the COVID-19 pandemic. To investigate the effect of the COVID-19 pandemic on rates of patient consent to enrollment into non-COVID-19 clinical trials, we carried out a cross-sectional study using data from the Nitric Oxide/Acute Kidney Injury (NO/AKI) and Minimizing ICU Neurological Dysfunction with Dexmedetomidine-Induced Sleep (MINDDS) trials. All patients eligible for the NO/AKI or MINDDS trials who came to the hospital for cardiac surgery and were approached to gain consent to enrollment were included in the current study. We defined "Before COVID-19" as the time between the start of the relevant clinical trial and the date when efforts toward that clinical trial were deescalated by the hospital due to COVID-19. We defined "During COVID-19" as the time between trial de-escalation and trial completion. 5,015 patients were screened for eligibility. 3,851 were excluded, and 1,434 were approached to gain consent to enrollment. The rate of consent to enrollment was 64% in the "Before COVID-19" group and 45% in the "During COVID-19" group (n = 1,334, P<0.001) (RR = 0.70, 95% CI 0.62 to 0.80, P<0.001). Thus, we found that rates of consent to enrollment into the NO/AKI and MINDDS trials dropped significantly with the onset of the COVID-19 pandemic. Patient demographic and socioeconomic status data collected from electronic medical records and patient survey data did not shed light on possible explanations for this observed drop, indicating that there were likely other factors at play that were not directly measured in the current study. Increased patient hesitancy to enroll in clinical trials can have detrimental effects on clinical science, patient health, and patient healthcare experience, so understanding and addressing this issue during the COVID-19 pandemic is crucial.

An international survey of current management practices for polymyalgia rheumatica by general practitioners and rheumatologists.

To explore current management practices for PMR by general practitioners (GPs) and rheumatologists including implications for clinical trial recruitment. An English language questionnaire was constructed by a working group of rheumatologists and GPs from six countries. The questionnaire focused on: 1: Respondent characteristics; 2: Referral practices; 3: Treatment with glucocorticoids; 4: Diagnostics; 5: Comorbidities; and 6: Barriers to research. The questionnaire was distributed to rheumatologists and GPs worldwide via members of the International PMR/Giant Cell Arteritis Study Group. In total, 394 GPs and 937 rheumatologists responded to the survey. GPs referred a median of 25% of their suspected PMR patients for diagnosis and 50% of these were returned to their GP for management. In general, 39% of rheumatologists evaluated patients with suspected PMR >2 weeks after referral, and a median of 50% of patients had started prednisolone before rheumatologist evaluation. Direct comparison of initial treatment showed that the percentage prescribing >25 mg prednisolone daily for patients was 30% for GPs and 12% for rheumatologists. Diagnostic imaging was rarely used. More than half (56%) of rheumatologists experienced difficulties recruiting people with PMR to clinical trials. This large international survey indicates that a large proportion of people with PMR are not referred for diagnosis, and that the proportion of treatment-naive patients declined with increasing time from referral to assessment. Strategies are needed to change referral and management of people with PMR, to improve clinical practice and facilitate recruitment to clinical trials.

A hybrid machine learning approach for prediction of conversion from mild cognitive impairment to dementia

Mild cognitive impairment (MCI) represents a precursor to dementia for many individuals; however, some forms of MCI tend to remain stable over time and do not progress to dementia. In fact, conversion rates vary substantially depending on the diagnostic criteria used and the nature of the analytic sample and clinical setting. To identify personalized strategies to prevent or slow the progression of dementia and to support the clinical development of novel treatments, we need to develop new approaches for modelling disease progression that can differentiate between progressive and non-progressive MCI subjects. The aim of this study was to develop a novel prognostic machine learning (ML) framework utilising longitudinal information encoded in efficient, cost-effective, and non-invasive markers to identify MCI subjects that are at risk for developing dementia. Our approach was developed using the dataset from the National Alzheimer’s Coordinating Center. We built two prognostic models based on the patient data from 3 (n = 768) (Model 1) and 4 (n = 409) (Model 2) assessment visits. A novel hybrid prognostic approach, using cognitive trajectory classes, generated through unsupervised learning (Stage 1), as input in supervised MLmodels (Stage 2), was developed and systematically tested. Our unsupervised learning approach (Stage 1) involved: (i) the implementation of the longitudinal data partitioning method allowing for clustering trajectories based on their shapes; (ii) validation of the optimal number of clusters using three different Clustering Validity Indices (CVIs), and (iii) application of the fusion-based methods for combining CVIs into the fused normalized CVI scores, averaged for each cluster partition to determine the final number of trajectory classes for each type of clinical scores. In Stage 2, we built four types of prognostic models based on random forest (RF), Support Vector Machines (SVM), logistic regression (LR), and kNN ensemble approaches. Classification models incorporating both clinical scores and cognitive trajectory classes input showed up to 6.5 % higher accuracy than models based only on clinical scores (p < 0.05 in all cases). Given the patient data from three time points (Model 1), the highest recorded prediction accuracy was achieved for the ensemble and RF model, i.e., 85.0 % (standard deviation: 3.1 %) and 84.6 % (4.1 %) respectively. Using the patient data from four time points (Model 2), the highest accuracy was reported for RF and ensemble models, i.e., 87.5 % (6.1 %) and 86.8 % (3.7 %) respectively. We showed that the incorporation of the output of unsupervised learning significantly improved the performance of supervised ML models. Our prognostic framework can be applied to improve recruitment in clinical trials and to select early interventions for individuals at high risk of developing dementia.

Demographic recruitment bias of adults in United States randomized clinical trials by disease categories between 2008 to 2019: a systematic review and meta-analysis

To promote health equity within the United States (US), randomized clinical trials should strive for unbiased representation. Thus, there is impetus to identify demographic disparities overall and by disease category in US clinical trial recruitment, by trial phase, level of masking, and multi-center status, relative to national demographics. A systematic review and meta-analysis were conducted using MEDLINE, Embase, CENTRAL, and ClinicalTrials.gov, between 01/01/2008 to 12/30/2019. Clinical trials (N = 5,388) were identified based on the following inclusion criteria: study type, location, phase, and participant age. Each clinical trial was independently screened by two researchers. Data was pooled using a random-effects model. Median proportions for gender, race, and ethnicity of each trial were compared to the 2010 US Census proportions, matched by age. A second analysis was performed comparing gender, race, and ethnicity proportions by trial phase, multi-institutional status, quality, masking, and study start year. 2977 trials met inclusion criteria (participants, n = 607,181) for data extraction. 36% of trials reported ethnicity and 53% reported race. Three trials (0.10%) included transgender participants (n = 5). Compared with 2010 US Census data, females (48.3%, 95% CI 47.2–49.3, p < 0.0001), Hispanics (11.6%, 95% CI 10.8–12.4, p < 0.0001), American Indians and Alaskan Natives (AIAN, 0.19%, 95% CI 0.15–0.23, p < 0.0001), Asians (1.27%, 95% CI 1.13–1.42, p < 0.0001), Whites (77.6%, 95% CI 76.4–78.8, p < 0.0001), and multiracial participants (0.25%, 95% CI 0.21–0.31, p < 0.0001) were under-represented, while Native Hawaiians and Pacific Islanders (0.76%, 95% CI 0.71–0.82, p < 0.0001) and Blacks (17.0%, 95% CI 15.9–18.1, p < 0.0001) were over-represented. Inequitable representation was mirrored in analysis by phase, institutional status, quality assessment, and level of masking. Between 2008 to 2019 representation improved for only females and Hispanics. Analysis stratified by 44 disease categories (i.e., psychiatric, obstetric, neurological, etc.) exhibited significant yet varied disparities, with Asians, AIAN, and multiracial individuals the most under-represented. These results demonstrate disparities in US randomized clinical trial recruitment between 2008 to 2019, with the reporting of demographic data and representation of most minorities not having improved over time.

Patient Recruitment in Clinical Trials: Areas of Challenges and Success, a Practical Aspect at the Private Research Site

Patient Recruitment in Clinical Trials: Areas of Challenges and Success, a Practical Aspect at the Private Research Site

Challenges and Opportunities in Modernizing Clinical Trial Recruitment.

Clinical trial recruitment is ripe for innovation. The current model is costly, often results in poor recruitment and offers inequitable access. To improve this system, we envision a peer-to-peer blockchain platform where patients control the depth and breadth of how their medical information is shared.

Racial disparities in pancreatic cancer clinical trials: Defining the problem and identifying solutions.

Underrepresented minority patients with pancreatic cancer have differential access to cancer treatments, including clinical trials. The successful conduct and completion of clinical trials is critical to improve outcomes for patients with pancreatic cancer. Therefore, it is essential to consider how to maximize eligibility of patients for both therapeutic and non-therapeutic clinical trials. It is important for clinicians and for the health system to understand individual-, clinician-, and system-level barriers to recruitment, enrollment, and completion of clinical trials to alleviate bias. Understanding strategies that lead to improved enrollment of underrepresented minorities, socioeconomically disadvantaged individuals, and underserved communities will improve generalizability of cancer clinical trials and advance health equity.

Crossing barriers: the burden of inflammatory bowel disease across Western Europe.

An estimated 2.5-3 million individuals (0.4%) in Europe are affected by inflammatory bowel disease (IBD). Whilst incidence rates for IBD are stabilising across Europe, the prevalence is rising and subsequently resulting in a significant cost to the healthcare system of an estimated 4.6-5.6 billion euros per year. Hospitalisation and surgical resection rates are generally on a downward trend, which is contrary to the rising cost of novel medication. This signifies a large part of healthcare cost and burden. Despite publicly funded healthcare systems in most European countries, there is still wide variation in how patients receive and/or pay for biologic medication. This review will provide an overview and discuss the different healthcare systems within Western Europe and the barriers that affect overall management of a changing IBD landscape, including differences to hospitalisation and surgical rates, access to medication and clinical trial participation and recruitment. This review will also discuss the importance of standardising IBD management to attain high-quality care for all patients with IBD.