The US Food & Drug Administration has stated that digital technology is driving a revolution in healthcare. The lines between healthcare delivery and clinical research are blurring, as the patient becomes a key partner and focus. We are seeing a rapid expansion in the use of mobile and patient-centric devices, exponential growth in the volume and diversity of life sciences data and acceleration in the use of data-dependent computation to gain insight and automate – loosely called artificial intelligence (AI). These digital health trends are naturally combining to transform the patient experience and the application of new scientific ideas and breakthroughs.
The Covid-19 pandemic taught us that rapid and innovative responses are not only possible but extremely effective. AI has the potential to permeate all aspects of clinical research and support the implementation of more patient-centric, decentralised clinical trials. Patient centricity has been driving innovation in clinical trials for some time, but ever-increasing levels of digitisation and the availability of advanced tools and expertise are accelerating the process. Against the backdrop of this increased use of AI in clinical trial design, patient identification, mobile technology, remote monitoring and clinical data management, we also see a corresponding rise in the need to regulate AI.
Enabling Greater Patient Centricity
Clinical trial protocols increasingly need to be more patient-centric, and this means incorporating more remote monitoring and virtual trial elements, as well as making healthcare a core component of a trial. Machine learning models can be trained to predict the impact of a particular trial design, and unsupervised methods can be used to provide deep insight into the possible outcomes of a particular approach. The application of AI to the design of a clinical trial can optimise the number and diversity of patients needed to reach the desired endpoints and give the patients who do participate a higher value experience.
Using real-world evidence such as health insurance claims data, AI can foster a ‘right patients right time’ philosophy for each trial. Where the human mind finds it impossible to make connections, AI algorithms can combine the meaning of a health insurance code with that of a medication code, a social media event and many other data points to establish a robust country/site mix that drives the overall strategy for the trial. Identifying the right patients ensures better diversity and inclusion and a better patient experience, as well as scientific success. Diversity objectives range from reaching all patients who can benefit from participation in a clinical trial to ensuring that the trial collects the maximum range of data within the scientific inclusion/exclusion criteria. Identifying the right patients also means you identify the right investigators and expedite must be carefully curated, cleaned, and standardised for statistical analysis, which are coming under the eye of data scientists. Machine learning and natural language processing techniques are transforming the way we look at clinical data, allowing us to take a more risk-based and patient-centric approach. Furthermore, the diversification of data sources, such as the data from remote monitoring devices, is driving the need for smarter, more efficient ways of conducting data management. This need is matched by the increased focus on AI as a means of optimising all aspects of the clinical research value chain.
