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Patient-Centric Clinical Trials: Turning Opportunities into Standard Procedures

Over the past year, the industry has heard often and at length, about patient-focused decentralised trials (DCTs), as vendors and some early adopters report their early experiences in an area that is still evolving. It’s important to remember that the industry is still at a very early stage in digital clinical trial development.

Truly patient-focused trials are a goal that we have yet to attain. We still use ePRO and eCOA, as we have for 20 years, only now we talk about these tools as if they are brand new and operate under a decentralised banner. They don’t — at least, not yet. We may be using eConsent, but we still, have patients come to sites to initiate the process. That is not decentralised practice.

At the same time, most trial protocols have not reached a point where patients can participate flexibly based on their own preferences, which change from day to day. If we use multiple technologies to capture data — in essence, the same data from different patients — we must have simple methods to integrate and review that data in one step. That does not happen today.  The industry is just beginning to think about common standards for sharing clinical data.

Now that more clinical leaders have experienced, firsthand, the limitations of new tools designed to make trials more patient-centric, many have adjusted their expectations from new technology and are experimenting with different approaches. They’ve also seen that simply deploying more technology won’t make the patient journey any easier if it burdens research sites. In fact, we heard repeatedly at the recent DIA meeting (June 2022, Chicago) how technology overload was slowing down sites and their ability to work with patients. Today, some sites are reporting capacity as low as 30% of pre-COVID levels.

The Changing Role of Standards

One of the fundamental challenges facing the industry is developing standards for sharing clinical data that will enable digital decentralised trials. In the 30 years that Clinical Data Interchange Standards Consortium (CDISC) standards have been in place, industry thinking has changed considerably. Today, standards are no longer seen as intellectual property to be protected, but as approaches that must be open and shared, so that the industry can move to one set of clinical data standards. Results will improve efficiency, not only for trial stakeholders but for regulators, who will be able to inspect data more quickly.

Standards for data transfer will ensure that data is exchanged in the right format. Work will need to focus on two very different areas, designed for two very different purposes: clinical data (under the CDISC)1 and healthcare data (under Health Level Seven  HL 7).2 Connecting these different data sources, types, and aggregations will require ongoing effort. At this point, as the industry moves into patient-driven data collection, neither, on its own, is a fit. Patients are going to own more and more of their data, and this will likely afford a whole new category, that will lead to yet more complications if new approaches aren’t developed. The opportunity, therefore, is to find a way to generate, collate, and distribute data between point solutions that result in seamless connections between researchers and patients.  Only once those connections exist can the industry apply scientific rigour to clinical data, and only then can AI, ML, and NLP be applied to the data in an efficient and scalable manner.

Modern standards development for clinical trial data sharing will require tremendous effort. Even though this work has only begun, interesting ethical questions are already coming up that the industry must address. For example, some healthcare providers have proposed giving every patient a unique record ID. This tokenisation approach would facilitate faster data exchange but could also lead to concerns about data security and privacy and could potentially be vulnerable to misuse. This idea requires open and frank discussion, but it does highlight the raw need to be able to share data quickly and effectively.