From inefficiency to innovation—AI is helping C-suiters reshape the future of clinical trials.
Table of Contents
Why NOW is the time for AI-enabled clinical trial research?
Which transformation approach works best for C-suiters to revolutionize clinical trials?
How can healthcare leaders benefit across various aspects of the clinical research process?
Working in the fast-developing digital age, the life sciences industry experiences a radical shift rooted in the latest technologies. Artificial intelligence (AI) is one such technology that has gained strength as a powerful tool that has a lot of power to transform different facets of healthcare. The gold benchmark of evidence-based medicine, clinical trials, is also swept by this wave. The application of AI to clinical trials can positively affect the clinical trial efficiency, accuracy, and lead to patient improvement.
In the article, we are going to discuss the advantages of incorporating AI into the conventional clinical trial procedure that healthcare leaders can enjoy.
Having all the above advantages, it is essential to answer what the pain points are in the conventional clinical trial process.
The conventional clinical trial mechanisms come with a host of problems, as they are time-consuming, expensive, involve fewer patients, and the results are analyzed in a complicated manner. Deloitte released a report that pointed out that only 11 percent of the drugs that go through Phase I clinical trials actually prove to be successful in being able to be codified as such by regulatory authorities, implying that innovation is grossly needed in this area. Nonetheless, With the help of AI technologies, C-suite executives should be able to change the paradigm in clinical trials, resolving the described challenges and opening entirely new possibilities.
The use of AI-powered platforms enables offering intelligent insights and recommendations to the leaders in the healthcare industry by maximizing patient recruitment and retention efforts and ensuring patient safety due to the timely solving of adverse events. With the help of AI, clinical trial stakeholders and leaders can anticipate an acceleration of the process of enrolling different people and cut down on the rate of dropouts, and eventually, faster delivery of innovative treatments to patients in need.
C-suite leaders should use the chance to transform clinical trials with AI to proceed with meaningful change and address the challenges that need to be resolved with immediate effect in the industry. In this way, they will be able to introduce an age of quicker, more efficient, and patient-driven drug development that can change the face of healthcare all over the world.
But it brings us to address the question, “Why NOW is the time for AI-enabled clinical trial research”?
To answer this, let us check the two fronts in which technology is advancing:
1. Standardized approaches to industrialization and scaling of machine learning (ML)—for example, MLOps (ML operations) and DataOps (data operations) alongside customized services and platforms.
2. Development of deep-learning approaches for designing new molecules and computer vision, which are increasingly accessible through public code repositories and academic literature.
These two fronts i.e. advancements in technology and regulatory openness to innovation have combined to make AI-enabled clinical trial research a practicable proposition. So with this reasonable and realistic timeframe, healthcare leaders have the opportunity to leverage the advancements and help solve the biggest pain points by integrating AI and transforming clinical trials.
Bringing us to another question, which transformation approach works best for C-suiters to revolutionize clinical trials?
1. Asset-Centric Restructuring
Reorganization of R&D enables asset teams that are fast-paced, like small biotech companies. A sample of this strategy is the decentralization of authority and the development of an agile culture and a customized talent model.
2. Operating-Model Enhancement
Increasing efficiency and encouraging innovation through improving the R&D operating model. This may involve a reconsideration of the allocation of contract research organisations (CROs) and upstreaming required functionality, developing vehicles to make speedy development a reality, or transforming the global innovation base in terms of forming new alliances, trade affiliations, proliferating in Asia, or other routes.
3. Functional Optimization
Optimizing specific parts of the value chain within key R&D functions to gain a competitive edge. Target areas may involve innovative trial design, digital-enabled site selection, agile study start-up and conduct, submission excellence, and launch planning.
Even though these transformative approaches have yielded successful results, how can healthcare leaders benefit across various aspects of the clinical research process?
Study Design
Utilizing AI, ML, and natural language processing (NLP) tools, leaders can harness extensive healthcare data sets to evaluate and choose optimal primary and secondary endpoints in study design. This ensures the definition of the most relevant protocols for regulators, payers, and patients. In this way, the adverse effects of the poorly designed studies on cost- and efficiency-related parameters, as well as the possibility of the trial, can be addressed by healthcare leaders. This will support the best study design approach through informing the best country and sites approach, enrollment models, identification of patients, and start-up strategy.
Subsequently, good study design leads to a number of benefits. It causes more predicted outcomes, faster protocol development time, fewer rescripts of the protocol, and increased efficiency during the study overall. In addition to this, it also increases the rate of recruitment, decreases non-enrolling sites, and minimizes protocol amendments. All of these improvements make it much more likely to succeed, and allow to make much more realistic and precise planning.
Site Identification and Patient Recruitment
Finding the appropriate trial sites where there is an adequate number of patients available is a perennial problem in clinical research. The more specific the population is, the more challenging to achieve the recruitment targets, which leads to increased costs, longer duration, and an increased chance of failure. The Tufts Center for the Study of Drug Development (CSDD) was making reports; stating that about 50 percent of all locations are unsuccessful in achieving enrollments.
The common-sense way to mitigate such risks comes down to the use of AI and ML to determine the locations of the highest possible recruitment potential and prescribe the right course of recruitment. This can be done by studying the patient population and planning to focus on the areas that are likely to give the most patients who are eligible. Through this, the sponsor can cut expenses on the number of sites required, a faster recruitment process, and lower the likelihood of a site not enrolling enough participants during the phase.
With such on-site and off-site benefits, healthcare leaders can deliver value quickly in the present times, bringing us to address what lies ahead for the future of clinical trial development.
With improvements in artificial intelligence (AI) and data analytics, clinical trials will likely be made more efficient, more accurate, and patient-focused. As noted in industry reports, AI can transform numerous aspects of drug development, ranging from study design and patient recruitment to data analysis and regulatory submissions.
With the use of AI algorithms, predictive models, and machine learning, healthcare executives can better identify appropriate patient populations, create the best trial protocols, and process enormous amounts of data at record speeds with maximum accuracy. Not only does this hasten the drug development process, but it also maximizes patient safety and reduces clinical trial effectiveness issues.
In addition, AI platforms facilitate real-time tracking of patients’ health, early identification of adverse events, and individualized treatment suggestions, bringing about a more customized and efficient model of healthcare. Looking ahead, the future of clinical development is promising, with AI and emerging technologies to further transform how new drugs are discovered, developed, and brought to patients globally.
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