The modernisation of clinical research is an important part of expediting the drug approval process, and this in turn is dependent on the success of clinical trials with a sufficient number of appropriate participants. A number of recent innovations promise to bring about this modernisation: for example, using artificial intelligence (AI) software to research medical databases for eligible participants reduces the time scale for recruitment. Telemedicine will encourage more patients to take part in clinical trials from the comfort of their own home. But as considerable a solution as they are, these new technologies do have their own challenges to face.

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Telemedicine and virtual clinical trials

Telemedicine is the use of remote technology, typically for clinical consultations and the delivery of healthcare. It enables communication between patients and healthcare providers outside the clinical environment. One example of telemedicine is the recording of vital signs during the sleep cycle using actigraphy bracelets, for example Fitbit. 

Telemedicine has facilitated clinical researchers in capturing and analysing data remotely. This method has been desirable for both patients and clinical organisations. Firstly, limited travelling to clinics is more likely to increase patient retention in clinical trials, allowing them to continue day-to-day life. Secondly, the data is more likely to be representative of real-word evidence, in comparison to clinics which do not always show a patient’s true behaviour in an unknown, clinical environment. 

Telemedicine has shown obvious potential in clinical trials for rare diseases. Rare diseases typically have small populations across the entire globe, hence a central site for clinical trials may not be accessible. In March 2017, the European Reference Networks (ERNs) were launched “as virtual networks enabling healthcare providers across Europe to access and share expertise for the care of patients with complex or rare disorders.” 

According to a 2017 publication, the ERN is composed of “at least ten healthcare providers from at least eight different Member States”. In addition to addressing the small number of patients, it enables the liaison of medical experts across the world. This offers an advantage for rare diseases, a therapeutic area which typically lacks knowledge of disease pathology and therapeutic options. 

The increasing number of virtual trials that emerged during the pandemic utilised telemedicine to continue clinical trials, reducing patient-clinician contact in COVID-19 risk assessments. In comparison to traditional clinical trials, patient recruitment, consent and data collection all occur virtually. Face-to-face clinical appointments are often eliminated altogether, along with physical sites. 

The development of virtual trials, also known as decentralised clinical trials, demonstrates the industry evolving rapidly to “deliver approaches that reduce patient burden, increase patient engagement, and promote trial continuity”. The success of virtual clinical trials throughout the pandemic demonstrated the benefits of a patient-centric approach to clinical research. 

One of the challenges to overcome with the digitalisation of clinical trials is the skepticism surrounding data security. The majority of clinical data is highly confidential, with data sources like electronic health records containing a patient’s entire medical history. 

Blockchain is the latest advancement in data security within clinical research. Blockchain technology is a shared system for recording transactions, tracking assets and building trust in a network. The benefit of this is that stored data is less vulnerable to hacking or infringement. Furthermore, the system involves verification steps which ensures the data is protected against unauthorised intervention. 

Artificial intelligence in patient recruitment

Patient recruitment is one of the many areas in clinical trial design streamlined by AI. Natural language processing (NLP) is a branch of AI that enables computers to analyse the written and spoken word. In the context of medicine, it has been used to “allow algorithms to search doctors’ notes and pathology reports for people who would be eligible to participate in a given clinical trial.” In a report by Nature, it is suggested that refinement of NLP could be used in clinical trials to search patient databases for eligible participants. The inclusion and exclusion criteria of clinical trials is typically written in plain text, so shouldn’t require complex algorithms like those required to analyse doctors’ notes.   

In addition to patient recruitment, AI models can also be used to enhance cohort selection. Electronic phenotyping is a well-established discipline which focuses on “reducing population heterogeneity, namely the process of identifying patients with specific characteristics of interest.” Using electronic medical records, “individuals with an explicit observable trait from large quantities of imperfect clinical patient data” can be identified, also known as phenotyping. 

This method is primarily used to reduce patient population heterogeneity rather than enhancing the quality of prognoses. Despite this, phenotyping with EMR data presents a number of challenges including “variation in the accuracy of codes, as well as the high level of manual input required to identify features for the algorithm and to obtain gold standard labels”.

There are a number of other challenges that exist at present. Firstly, a challenge for the pharmaceutical industry is the lack of personnel to operate AI/ML-based platforms. Furthermore, there is often skepticism about the quality of data generated by AI. Small organisations are often limited in their budget so cannot afford to invest in AI/ML technology. 

Cloud-based software

An optimal clinical trial management system (CTMS) is an important project management tool that supports pharmaceutical and clinical operations. A CTMS maintains contract and payment systems, document management, study milestones and contact management for sites and teams. Legacy CTMS’ are essentially outdated systems within clinical trial management. Recent events highlight the benefits of virtual clinical trials, but current CTMS’ will struggle to manage increasing amounts of data from many different platforms. 

Because of this, the industry is moving towards Cloud-based platforms. Cloud-based applications offer a unified clinical platform to collate documents and data, direct sharing of information with external partners and being able to configure a CTMS system to any type of clinical trial.

Innovations in AI and modern technology are going a long way to address some of the issues of traditional clinical research, including poor patient recruitment. While these innovations do come with their own hurdles to overcome, it is without doubt that in the future they will be refined to improve further.

To discuss these topics further with sector experts, and to ensure you remain up-to-date on the latest in clinical development, sign up for Proventa International’s Clinical Operations Strategy Meeting, set for 15 June 2021.

Charlotte Di Salvo, Junior Medical Writer
Proventa International