Lack of participation in clinical trials (CTs) is a major barrier for the evaluation of new pharmaceuticals and devices. Here we report the results of the analysis of a dataset from ResearchMatch, an online clinical registry, using supervised machine learning approaches and a deep learning approach to discover characteristics of individuals more likely to show an interest in participating in CTs.

We trained six supervised machine learning classifiers (Logistic Regression (LR), Decision Tree (DT), Gaussian Naïve Bayes (GNB), K-Nearest Neighbor Classifier (KNC), Adaboost Classifier (ABC) and a Random Forest Classifier (RFC)), as well as a deep learning method, Convolutional Neural Network (CNN), using a dataset of 841,377 instances and 20 features, including demographic data, geographic constraints, medical conditions and ResearchMatch visit history. Our outcome variable consisted of responses showing specific participant interest when presented with specific clinical trial opportunity invitations (‘yes’ or ‘no’). Furthermore, we created four subsets from this dataset based on top self-reported medical conditions and gender, which were separately analysed.

The deep learning model outperformed the machine learning classifiers, achieving an area under the curve (AUC) of 0.8105.

The results show sufficient evidence that there are meaningful correlations amongst predictor variables and outcome variable in the datasets analysed using the supervised machine learning classifiers. These approaches show promise in identifying individuals who may be more likely to participate when offered an opportunity for a clinical trial.

Poor clinical trial (CT) recruitment is a significant barrier to translating basic science discoveries into medical practice. Improving support for primary care provider (PCP) referral of patients to CTs may be an important part of the solution. However, implementing CT referral support in primary care is not only technically challenging, but also presents challenges at the person and organization levels.

The objectives of this study were (1) to characterize provider and clinical supervisor attitudes and perceptions regarding CT research, recruitment, and referrals in primary care and (2) to identify perceived workflow strategies and facilitators relevant to designing a technology-supported primary care CT referral program. Focus groups were conducted with PCPs, directors, and supervisors.

Analysis indicated widespread support for the intrinsic scientific value of CTs, while at the same time deep concerns regarding protecting patient well-being, perceived loss of control when patients participate in trials, concern about the impact of point-of-care referrals on clinic workflow, the need for standard processes, and the need for CT information that enables referring providers to quickly confirm that the burdens are justified by the benefits at both patient and provider levels. PCP suggestions pertinent to implementing a CT referral decision support system are reported.

The results from this work contribute to developing an implementation approach to support increased referral of patients to CTs.

Radiotherapy randomised controlled trials provide evidence to support the development of new techniques and dose/fractionation regimens. Some radiotherapy trials have previously had to close early or revise targets due to low recruitment rates. Many authors have recommended research into recruitment strategies for many areas of medicine, however little work has been carried out in the specific field of radiotherapy.

Using a survey of research radiographers followed by radiotherapy patient interviews, this project provides perspectives on motives for patient participation in radiotherapy clinical trials, and how to best support people through this decision-making process.

The main factors influencing participation identified by the radiographers were altruism, treatment fatigue and concerns about the trial arms, lack of resources and lack of commitment from some medical colleagues. For patients the main factors were mainly emotional; altruism, and fears for efficacy of different trial arms featured, with requests for timely communication of trial information.

We recommend that strategies should be offered proactively to support patients through the decision-making process when considering trial participation. Research radiographers are ideally qualified to offer support and expert knowledge to these patients.