The power and potential of artificial intelligence (AI) and machine learning (ML) in the healthcare industry are unparalleled. The use of automated technology systems like digital workflows, orchestration and storing of data digitally is helping to shape their application, enabling far greater personalization of treatment for patients and greater efficiency and speed of clinical trials.
As the amount of health data increases, the possibility of establishing a self-driving clinical trial is closer to reality – offering an incredible opportunity to drastically improve the capabilities of pharmacology, medical organizations and patient outcomes.
But where do we begin? What does this reality look like? And how can we ensure unparalleled accuracy? Let me walk you through it.
Step 1: Crafting Optimal Study Design
The design of clinical research is fundamental to guarantee its success. Having well-developed and concise protocols is vital for successful trials, as inadequate designs can greatly increase costs, reduce efficacy, and threaten success. By utilizing AI and ML technology, research teams can easily create efficient study plans, doing away with manual design and leading to more rapid and precise settings and less potential for mistakes. In addition, AI can help determine the most suitable countries and research sites for a study while suggesting strategies to increase recruitment numbers and expedite study launches based on previously gathered data.
The integration of cutting-edge technologies helps stakeholders, such as regulatory agencies, insurers, and participants, identify the most optimal and beneficial research paths. While comprehensive validation processes allow designers to evaluate and, if necessary, modify the plan before launching the study.
Step 2: Streamlining Key Processes with Automation
Similarly to the role played in study design, AI and ML heavily assist in making site identification, patient recruitment, pharmacovigilance, clinical monitoring and early signal detection more beneficial and efficient for study organizers.
When it comes to site identification and patient recruitment, one main step in clinical trial execution is finding the correct site that will yield the appropriate patients for the study at hand – a process that has become increasingly difficult as research becomes more specialized. Failure to conquer this step leads to longer timelines, increased expenses and a higher possibility of failure. But the integration of AI and ML can help reduce the risks associated with unsuccessful recruitment. Through mapping of patient populations and identifying the most likely sites to enroll the right people for the study, CROs/sponsors can have greater assurance that their recruitment efforts are having the greatest impact. Furthermore, added analytics investigate factors such as enrollment, safety, compliance and data quality, all of which must be tailored to the specific type of trial.
Not only that, but models can be trained with data from previous studies to predict which sites would be the most successful for a new project. This enables sponsors to open fewer sites, quicken the enrollment process, and lessen the risk of not having enough participants.
Step 3: Optimizing Quality Assurance Through Clinical Monitoring
Once a study has kicked off, multiple pressures follow. To guarantee accurate research is conducted, clinical trials must be continually monitored to identify and remove any potential risks related to patient safety, data accuracy, and adherence to protocol. Usually, this requires a lot of tedious manual work to evaluate risks at individual sites and create strategies to address them. The deployment of AI and ML; however, helps to lighten the workload by providing a way to analyze potential risk factors and develop predictive analytics that generate more meaningful clinical monitoring insights. Technologies can also be used to proactively spot sites that may struggle with performance and to predict which patients may be more likely to experience adverse events.
Step 4: Improving Safety and Overall Patient Care
Apart from active patient and site monitoring, in pharmacovigilance (the process and science of monitoring the safety of medicines), large numbers of structured and unstructured data need processing to guarantee quality control and oversight. Technologies like optical character recognition (OCR), natural language processing (NLP) and deep neural networks are used to help format data easily. AI/ML are applied to automate laborious, manual processes such as translating and converting safety case records and adverse drug reaction (ADR) reports assisting evaluation and review of the effects of pharmaceuticals. These tools perform data analysis activities to identify potential adverse events, for example, by scanning conversations on social media and other websites, allowing research directors to improve the safety of patients while streamlining their workload.
In addition, by utilizing the latest technology, algorithms are being developed to analyze medical data such as symptoms and treatments. Technology can assess a patient’s data quickly and flag any irregularities, prompting clinicians to take further action in a timelier manner. As a result, diagnosis is more proactive and effective, which is particularly beneficial in diseases like Alzheimer’s, which is usually only detected after it has progressed.
Transforming Clinical Research One Step at a Time
All in all, the use of advanced technologies has revolutionized how we conduct research, expedite drug discoveries, diagnose, and treat patients – all while yielding more accurate and comprehensive decision making and more meaningful and precise outcomes. As we approach the reality of the self-driving clinical trial, we must focus on the elements that will make new technology integrations a success.
In my opinion, this starts with establishing a robust infrastructure that supports trustworthy and unbiased data aided by protocols that meet global reach, security, and regulatory requirements. Vital to this idea is the ability to enable the processing and storage of a variety of content types (e.g., video, documents, audio, devices, and data) to provide users with a seamless experience. Additionally, it is essential for users to ensure ongoing monitoring and maintenance of the technology for reliable and unbiased results—as advanced technology is only as smart as the data it’s fed.
About Gary Shorter
Gary Shorter is the Head of Artificial Intelligence at IQVIA Technologies, a global provider of advanced analytics, technology solutions, and clinical research services to the life sciences industry. Gary Shorter holds an MSc and has served as a global biostatistics lead for multiple compounds in clinical trials. His 25-plus years of experience allows him to bring the same level of quality and domain expertise to the realm of AI, to ensure that quality AI tools are built and validated to the rigor of regulatory agencies’ expectations. His recent products include Auto-eTMF and Auto-Translation specifically trained to clinical operations needs.