Tuesday, August 9, 2022

Realizing the potential of AI and machine learning in clinical research

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Shreya Christinahttps://cafe-madrid.com
Shreya has been with cafe-madrid.com for 3 years, writing copy for client websites, blog posts, EDMs and other mediums to engage readers and encourage action. By collaborating with clients, our SEO manager and the wider cafe-madrid.com team, Shreya seeks to understand an audience before creating memorable, persuasive copy.

John Reites, Co-Founder and CEO, WIRE.

In recent years, talking about artificial intelligence (AI) and machine learning (ML) could spark customer interest, make it sound more innovative, or even seem to boost a company’s valuation. Unfortunately, many of these AI/ML discussion points were general, possibly premature, and not yet ready to deliver results. The clinical research industry has not been immune to these discussions and still sees many AI and ML approaches largely as something on the horizon rather than something available today.

In my time as CEO in the clinical trial platform arena, I’ve seen clinical research studies become more complex, target more targeted areas of disease, and cost more than ever before. Parallel to this evolution, the clinical research industry has a goal to do more: conduct more targeted studies, enroll more global participants more inclusive, collect more data, etc. Achieving these goals in the face of more complexity with less money and fewer people requires that industry eliminates as many inefficiencies as possible.

AI and ML now have an opportunity to provide positive value and impact in our industry. Using both, we can automate common tasks, expand research teams to support actionable work, and provide advanced notifications about potential emerging risks. Automating these tasks can also reduce the time investment, giving clinicians more time to support and interact with their patients.

The life sciences industry has certainly not scaled or realized the full potential of AI/ML, but several use cases are available today to start the journey. Here are four case studies that can encourage researchers to unlock new efficiencies and insights in their research.

1. Collect, analyze and implement participant feedback

A powerful example of effective use of AI and machine learning is to consistently bring the patient’s voice into clinical research by listening to their feedback and analyzing responses during the clinical trial process. For example, AI is already helping to automate surveys and capture voice data, minimizing the time it takes for people to collect and analyze this data. Once the data is captured, AI is used to: measure inflection, pitch and emotional signals that human observers often fail to recognize.

The power of the AI ​​and analytics processing is then extended with human language experts, who apply the insights gathered by AI to fully and accurately assess patient language, word choice, and more. The level of feedback available through AI and combined with expert analysis helps remove barriers to patient study success, design studies that respond to patient insights, and reduce costly participant dropout in long-term studies – ultimately addressing the industry’s two biggest challenges: recruitment and retention of clinical trials.

2. Expand research teams through Predictive Analytics

The more data available, the more useful automation will be. In decentralized clinical trials (DCTs), research study sponsors use a wide variety of tools to collect data from participants remotely. Not only does this help reduce the need for participants to visit clinical trial sites — an important key to making research accessible to more people — but they also help collect more data than would be possible in traditionally designed research studies.

Many tools, such as: eCOA, eDiaries, wearable sensors and mobile medical devices (e.g. spirometers, pulse oximeters and even mobile EKGs) allow you to collect data more frequently and in some cases continuously. This vast amount of data benefits from using advanced AI and ML to quickly gain insights that can help study teams. For example, machine learning algorithms can be used to detect specific markers in the data that can predict the risk of study dropout. By informing research teams about specific patients who are expected to drop out, early steps can be taken to provide patients with the support they need to remain in the study.

3. Performance Benchmarking

The use of AI and machine learning to identify trends and insights that make studies more efficient and effective is steadily increasing. Using real data from hundreds of clinical trials, researchers identify the specific factors that characterize highly successful trials, as well as factors indicative of trial challenges.

While still in the early stages of this process, clinicians are using AI and ML to learn from data insights, rather than relying solely on ad hoc feedback from research engagement. The ability to give ratings, features, and users a “Trip Advisor-like score” helps researchers compare data and develop new data baselines to benchmark performance. (Full disclosure: My company offers these types of solutions, as do others.) By using machine learning algorithms to update these scores and give clinicians insight, they can easily monitor how their research is progressing and react quickly if the score begins to decline.

4. Shifting manual data entry to automated data entry

Entering data is time consuming and for a team of highly skilled and talented professionals it is not the most productive of time. AI and/or ML can help study teams focus on higher value tasks. In clinical research, this means that more time is spent interacting with participants and less time using technological tools.

One such benefit is entering data into one system once, which then updates all other systems and fields through automation with an audit trail. Pulling data from one system and populating other systems to drive the highest quality data is happening today, but there is a lot of room for improvement using new technological tools. Our industry is now in the midst of this shift and has not yet reached our desired destination, but researchers can be encouraged with the early progress to date.

With so much data available and so much at stake – the successful development of new therapies – it is time to start using these AI/ML use cases to better listen to and gain insights from more patients and to advance clinical research. to make it more efficient. Automation through AI and ML can help, and these and other use cases should encourage the entire industry to embark on this journey today.

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