AI Strategies for Accelerating Clinical Trial Timelines and ROI

Clinical trials are crucial processes that ensure the advancement of medical science and healthcare. In the process of research and trials, huge amounts of data are generated and to effectively manage and decipher them it takes a lot of time and manual labor. The demanding nature of clinical trials makes them tedious. However, with the advent of Artificial Intelligence (AI), these challenges are being addressed. AI comes with the promise of innovation that is transforming trials, accelerating timelines, and increasing return on investment (ROI).

Also Read: AiThority Interview with Asaf Somekh, Co-Founder & CEO of Iguazio (acquired by McKinsey)

How AI Can Speed Up Clinical Trials and Deliver More Value

The incorporation of AI technology represents a significant shift that’s shaping the future of both preclinical and clinical trials, as well as the healthcare industry overall. AI empowers clinical trials with cutting-edge tools to manage the growing volume of data. With its advanced algorithms, AI can analyze vast amounts of data, enable precise and data-driven decision-making, and optimize trial outcomes.

Let’s explore how AI works to accelerate clinical trials and return on investment (ROI).

Study Design

AI has incredible potential to analyze previous clinical data, scientific research works, and patient study reports and develop study designs to improve the outcome of trials. This can be done by choosing the right endpoints, establishing eligibility criteria, and planning randomization procedures. With the data processed by AI, standard protocols can be created that can shorten the trial timeline and enhance the chances of completing trials successfully. Moreover, AI enables the digitization of manual or traditional protocol formats by selecting suitable endpoints and inclusion/exclusion criteria based on the analyzed information, which helps streamline the trial process.

Trial Site Identification and Selection

Identifying and selecting the right site is crucial when beginning a clinical trial. Choosing the wrong site can lead to many challenges, such as delays in trial timelines and difficulties in recruiting suitable participants due to mismatched population demographics. Importantly, the misallocation of resources can have serious implications for both time and finances.

AI can be used to analyze metrics such as demographics and geographic considerations and evaluate the outcomes of prior trials conducted at potential sites. By generating robust site profiles through this analysis, AI can help in identifying and selecting the most suitable sites for conducting trials. This approach not only facilitates faster trial initiation but also enhances the probability of achieving desired trial outcomes efficiently.

Data Management

Data from clinical trials is accumulating at a staggering rate. The data needs to be refined, and accurate information has to be extracted from it to get correct reports. This is beyond the capability of humans, and other traditional methods of analysis are not preferred because they require a lot of manual processing, quality control, and edit checks.

Also Read: The Risks Threatening Employee Data in an AI-Driven World

• Data Cleaning:

Any inconsistencies, anomalies, or missing values can be identified, and these erroneous entries can be cleaned using AI technology.

– ML can fill in missing values by predicting them using patterns observed in the rest of the dataset.

– Natural language processing (NLP) techniques can extract structured information from unstructured data, facilitating better data organization and cleaning. It can also help researchers find patterns and relationships within the data that might otherwise be missed or take too long to identify.

-AI algorithms can detect and remove duplicate entries within datasets, improving data quality and reducing redundancy.

• AI code audit:

AI and ML offer assistance in code surveys by identifying issues like potential bugs, security vulnerabilities, code quality concerns, and other basic issues. These issues are often overlooked during the human code review process. This ensures improved efficiency (coding within minutes), compliance with the regulatory standards, and accuracy such that other aspects of the clinical trial can be focused on.

• Remote Source Data Verification (rSDV):

AI and ML can be used in clinical trials to verify the integrity of the collected data. Using AI/ML, the information collected from the participants for the clinical trials can be cross-checked against the original sources. This process identifies any irregularities or discrepancies in the data and greatly reduces the burden of manual processing.

Using AI for data management significantly minimizes the effort involved in sifting through vast amounts of data and filtering out non-essential information. This enables one to concentrate exclusively on essential data points and anomalies, thereby reducing time and effort and accelerating clinical trials.

Patient Processing

AI can improve patient recruitment and retention by identifying and screening potential participants based on inclusion and exclusion criteria. This feature is beneficial in lowering the time and expenses typically involved in patient recruitment, which is a common clinical research issue. Here are a few aspects of patient processing that are being actively addressed by AI/ML:

• Patient Recruitment:

Recruiting patients is the most time-consuming aspect of clinical trials, often taking up to a third of the study duration. AI and ML offer solutions by automating and optimizing eligibility criteria, significantly reducing recruitment times. AI/ML streamlines patient identification in EMR/EHR records based on study protocols, accelerating recruitment processes. Additionally, they analyze patient data from public databases to identify regions with higher disease prevalence which helps in shaping recruitment strategies.

• Patient Retention:

In clinical trials, challenges continue after patient enrollment, with about 30% dropping out due to factors such as frequent visits, inconvenience, or insufficient support. It has the capability to forecast the likelihood of participants deviating from protocols or withdrawing from the study, allowing for focused interventions from the beginning to the end of the research.This approach enhances engagement, improves patient experience, and increases ROI by boosting protocol adherence and retention rates.

 Regulatory Document Authoring

The traditional creation of Case Report Forms (CRFs) is a time-consuming and error-prone process. AI and ML offer a compelling solution through CSR Automation. This approach leverages ML algorithms to automatically generate the CRF based on the Study Protocol and the Study Analysis Report (SAR). This automation offers significant benefits such as faster trial completion time, enhanced consistency, and minimized errors.

Simulating Drug Effects for Safer Development

AI in clinical research has introduced a revolutionary concept known as digital twins for patients. By leveraging medical history, genetics, and real-time health data, AI can generate virtual replicas of individuals, all while adhering to strict data privacy and security protocols. These dynamic models allow researchers to forecast treatment effectiveness and simulate safety outcomes in a virtual environment. Through virtual trials conducted with digital twins, researchers can evaluate how different interventions may affect individual patients, refining treatment approaches and reducing potential risks. This technology contributes to a significant ROI by reducing development costs, accelerating trials, and increasing success rates by stimulating potential outcomes.

Also Read: Essential Steps for Intelligent Document Processing in Clinical Trials

Shaping the Future of Clinical Trials with AI

AI is transforming clinical trials by accelerating timelines and enhancing ROI through faster drug development, improved patient care, and enhanced regulatory oversight. It efficiently identifies promising drug candidates early, minimizing late-stage failures and reducing costs. AI optimizes patient selection for trials, leading to quicker recruitment and more diverse participant groups. Automation of tedious tasks like data entry and scheduling enhances efficiency and cuts operational expenses. Furthermore, AI explores new applications for existing drugs, extending their utility and avoiding the need for complete drug discovery processes. The future holds promising advancements in AI’s role in clinical trials, revolutionizing research and patient outcomes.

[To share your insights with us as part of editorial or sponsored content, please write to psen@itechseries.com]