AI-driven text analytics improve clinical trial processes
New AI-driven text analytics solutions have the potential to transform the clinical trial process by enhancing data collection speeds and insights. This will help biopharma and medical device companies more quickly and accurately develop effective treatments that benefit patients worldwide.
Using these AI-driven tools, clinical trial teams and scientists – including investigators, clinicians, and researchers – will be able to extract and leverage clinical trial data from unstructured medical texts with improved speed, efficiency, and accuracy. As a result, these scientists will be able to better identify relevant keywords and phrases, infer meaningful connections, compile statistics, and streamline data analysis.
Accessing valuable information at scale
Traditionally, unstructured text – like physicians’ notes – sat in databases and was not terribly useful to people running clinical trials. Now, AI-driven text analytics are changing that. Data scientists will be able to gather vastly more information, learn from it, and make more informed clinical trial decisions and recommendations.
This matters greatly because an incredible 80% of healthcare data is unstructured, including doctors’ notes, radiology images, pathology slides, and patient-reported outcomes. It can, therefore, be extremely difficult for humans to store, search, analyse, and share with various teams across different organisations using manual methods.
Using AI-driven text analytics, researchers can access information at scale, collaborate more easily with teams across organisations, and collectively use these insights to minimise risk, meet regulatory obligation, and propel their clinical trial approvals – and patient outcomes – forwards.
Better information at researchers’ fingertips
Text analytics provide a more holistic view of each patient. Previously, clinical trial teams only had easy access to structured data, such as trial subjects’ age, gender, vital signs, and blood pressure. Adding unstructured data to the mix provides a more comprehensive picture of each patient which, in turn, means a more comprehensive trial.
Reviewing physicians’ notes, for example, gives researchers a clearer understanding of how patients were feeling at each appointment. They can learn more about their mental and physical status, see their medication dosage, side effects, and more.
One of the biggest focuses in clinical trials is trying to improve the quality of life for people with health issues. Often, during the clinical trial process, researchers assess each subject’s quality of life during and after treatment. With structured notes, researchers track patients by whether they feel good or bad, and how they’d rate their pain on a scale of 1 to 10. But with unstructured notes, researchers can get a better assessment of a patient’s quality of life, beyond just a 1-10 pain scale.
AI-driven text analytics offer a deeper dive into “sentiment analysis”, which reports whether a patient’s condition was favourable, neutral, or unfavourable on any particular day. It’s also helpful for patient-reported outcomes, where the subject self-reports how they’re feeling.
With the rise of virtual appointments, researchers can use AI and text analytics to evaluate patients’ facial and body language to accurately determine whether they’re happy, concerned, angry, in pain, and so on. Combined, this delivers a greater analysis of patients’ quality of life, which can be invaluable to success in the clinical trial process.
Benefits of text analytics in clinical trials
There are various, significant benefits to using text analytics in clinical trials, including:
- Speed and efficiency: Text analytics accelerates the data extraction process, significantly reducing the time required to derive insights from textual data. What once took months can now be accomplished in a matter of days or even hours, allowing for faster decision-making and the expeditious completion of clinical trials.
- Enhanced accuracy: Human error is inevitable when dealing with extensive textual data. Text analytics minimises these errors by providing consistent and precise results. This improves the quality of data used in decision-making, leading to more reliable outcomes.
- Predictive insights: Text analytics can uncover hidden patterns and trends within textual data, providing valuable predictive insights that can guide clinical trial protocols and patient recruitment strategies. This proactive approach can save both time and resources.
- Risk mitigation: By identifying adverse events or safety concerns in real-time, text analytics helps mitigate risks and ensure the safety of trial participants. Timely detection of potential issues enables quick response and resolution.
- Cost savings: Reducing the time and resources required for data extraction and analysis translates into significant cost savings for pharmaceutical companies, research organisations, and healthcare institutions.
Text analytics can also draw some conclusions and understand intent, such as interpreting common abbreviations and correcting misspellings, further accelerating the effort of clinical research teams and reducing frustration for all involved.
AI and the future of clinical trials
AI is impacting clinical trials at many different touchpoints, and text analytics is one small – but essential – way that the technology is improving the clinical trial process. Text analytics will provide a more holistic view, allowing clinical trial teams to review unstructured information more quickly. This will, ideally, amplify the clinical trial process, help speed up the clinical trial timeline and, ultimately, lead to better, faster treatments for patients.
The goal for all involved is to streamline the clinical trial process, which has historically been long, slow, and expensive. If the medical industry can, collectively, find ways to reduce the timeline and cost of clinical trials then, hopefully, we’ll be able to reduce the cost of medication and medical devices accordingly. AI’s impact on text analytics offers limitless opportunities for clinical trials, and it will be exciting to see how the medical industry, as a whole, will use these tools to develop new solutions to help people live longer, healthier lives.
