Clinical research nurses (CRNs) play a key part in clinical trials, such as caring for trial patients, preparing trial protocols and other documentation, dealing with data collection, and improving patient recruitment and retention. Due to the broad nature of the role, there are significant improvements to be made in several aspects of the clinical trial process, in order to reduce the heavy burden on nurses.
While CRNs are crucial in the running of clinical trials, equipping them with tools to increase the efficiency and reduce their workload has been less than successful, partly due to too few tools available and due to nurses hesitancy to try new technology (1) . Neucruit aims to change that.
Following Neucruit’s interview with a Kelly, a CRN working for Imperial College London and Cancer Research UK, some surprising pain-points and promising technological advancements were discussed.
Firstly, nurses often have highly emotional conversations discussing clinical trial eligibility in patients with difficult-to-treat cancers, which leads to patient excitement at the prospect of novel drug trial, only to be told by nurses that they are no longer eligible for a study.
Explaining this further, Kelly noted that there is a “small window of opportunity” when patients are at a certain stage of their disease, with metastatic cancer, for example, where they may become eligible for different lines of treatment depending on the location(s) and rate of the metastasis. However, Kelly noted that clinical trial treatment options are not being offered fast enough to patients, largely due to clerical delays, in her opinion. This means that patients become no longer eligible for a specific trial treatment, resulting in these disappointing conversations with CRNs.
Most alarmingly, Kelly had suggested that the aforementioned issues could be largely avoided through advances in technology and automating data input for nurses. When using Neucruit, these patients could be flagged much earlier based on their pre-screening information, to better time the specific trial treatments they could be offered and potentially improve patient outcomes and contribute to further research (2).
In suggesting automation in the interview, this was met with positivity, as a means to stratify patients more efficiently by disease state and provide potentially life-saving treatment in some cases.
Another pain point for CRN nurses that Neucruit aims to solve, is the recruitment of patients. For some studies there are little patients available and nurses are aware of this from the beginning, so recruitment is a significant struggle. Often, this recruitment relies on “having relationships with hospitals or shared multidisciplinary teams in order to suggest eligible patients”, where sometimes there is “competition” for patients between hospitals. Obviously, this presents a problem not only in the bias in selecting patients, but as a bigger pool of potential patients beyond hospitals is often not considered. Thus, relying on such relationships and niche pools of people to recruit patients from leads to poorly diverse clinical trial populations. Similarly, Neucruit’s interview with Kelly found that clinicians often forget to suggest eligible patients for trials and are prone to clinician bias when they do choose which participants to take forward for a trial. Often this means that people of certain races, ages or genders aren’t chosen, simply due to this unconscious clinician bias, which further contributes to small and poorly diverse clinical trial populations (3).
When explaining Neucruit’s technology and aims to Kelly, the response was overwhelmingly positive, showing great eagerness to utilise Neucruit’s API.
Through automating some of the recruitment such as matching large patient databases to researchers and pre-screening large volumes of patients much faster and more efficiently, there would be more time for nurses to focus on patients care and on the research itself. This could provide a more positive outcome for nurses and patients in trials too.
Using Neucruit’s future natural language processing tools too, patients would be
approached using terms they understand (rather than using clinical language), to inform patients much better about the nature and involvement required in studies. In doing this, patient interest can be gauged and increased through patient-facing material, to ultimately improve patient retention while easing nurses’ workloads.
With this, Neucruit aims to introduce this technology to the CRN field to not only recruit patients more efficiently, but to also improve patient outcomes significantly, all while saving time for nurses who are critical in running clinical trials.
Healthcare A. 2019 2019-06-05T13:56:19-04:00. Available from: https://avanthealthcare.com/blog/articles/how-technology-is-impacting-nursing-practice-in-2019.stml.
Kelly Imperial College London and Cancer Research UK. A Clinical Research Nurses’ Perspective on Recruitment Issues. 2021.
Sethi N. ALS Patient Advocate interview. In: Neucruit, editor. 2021.
Optimising clinical trials with AI whitepaper
Download our "Optimising Clinical trials with AI" whitepaper to see how our data-driven patient recruitment strategies can optimise the clinical research process