Data management is crucial for the success of clinical trials. Involving the collection, organization and analysis of data generated during the testing process, it ensures accuracy, integrity and reliability, enabling informed decisions and valid conclusions from the testing results. It also helps identify trends, patterns and potential risks associated with the trial.
Accurate and reliable data is also essential for regulatory submissions and safety monitoring and ensures that the trial is conducted in accordance with regulatory requirements and Good Clinical Practice (GCP) guidelines.
After the trial, data management continues to play a crucial role. Collecting real-world data on routine medical care can provide insight into the long-term risks and benefits of treatments in real-world settings.
However, collecting, collating and analyzing such a large amount of data is costly and time-consuming. This is where artificial intelligence (AI) comes into play.
How can AI help in clinical trials?
AI has permeated virtually every industry since its introduction just a few years ago, and the clinical trials industry is no exception.
AI tools can potentially improve the success and efficiency of clinical research by analyzing large amounts of patient data and medical records quickly and accurately. In particular, AI can contribute to clinical trials in several ways:
Patient Recruitment
According to a study by analytics firm GlobalData, approximately 80% of global clinical trials fail to recruit and retain enough patients to enroll on time. AI can effectively identify clinical trial candidates by analyzing patient data and medical records, narrowing down the search for optimal cohorts, and accelerating trial recruitment. It can also simplify entry criteria by analyzing the patient’s genetic makeup, physiological data and lifestyle factors.
Patient Adherence and Retention
Traditional clinical trials have an average dropout rate of 30% due to inconvenience, complex protocols, and lack of support. AI can improve patient experience, and therefore retention, by enabling remote health assessments and real-time medication tracking, thereby reducing non-adherence. AI-assisted apps can also provide reminders and allow patients to track their progress, ensuring appropriate engagement in the study.
Data analysis
Clinical trials generate large amounts of data that researchers manually review to uncover meaningful insights. AI can effectively analyze this data, finding patterns that human analysis might miss. AI-based models can predict drug toxicity, help select appropriate compounds, and even find data for new trials.
However, caution is required. Just like in more traditional analysis, biases inherent in the data must be corrected. AI-based systems also raise data security and privacy concerns, so maintaining the confidentiality of medical records is crucial.
Use AI to manage your data
In clinical trials, data is often collected as free text, submitted by patients or staff, but these texts can be unclear or contain errors. When this is not corrected, data quality and patient safety are compromised.
However, manually reviewing these texts from the electronic case report form (eCRF) presents a challenge that requires experts to understand and correct documentation errors, resulting in tedious but avoidable tasks.
Alcedis (a Huma company) has developed an AI solution to help you. Meteor is an AI application that streamlines the free text review process and automatically checks all text for adverse events, medications, treatments, and appropriate documentation language using AI models.
This increases patient safety, while also improving the quality of data collected in the trial. This solution is GAMP-5 validated and complies with relevant EMA guidelines.
Dr. Daniel-Timon Spanka, product manager for data analytics at Alcedis, says patient safety is the top priority in clinical research.
“Meteor helps identify potential problems, such as hidden adverse events, in a timely manner,” he says. “Problems that patients may develop during trials are recognized more reliably, significantly improving the safety and integrity of clinical trials. »
Improving the quality of the data collected is another important benefit of Meteor, says Dr. Spanka:
“With Meteor, we provide the user with an AI toolbox. We use it to help experts review texts and make the best possible decisions. This allows incorrect documents to be found quickly and efficiently.
Additionally, Meteor displays the entire patient history at a glance, eliminating the need for tedious eCRF searches for specific patient data. Dr. Spanka summarizes the benefits of the Meteor system as follows:
- Increased efficiency: By optimizing the data review process and providing fast, accurate insights, experts can focus on strategic decision-making and keep their minds free for essential aspects of the process.
- Superior data quality: AI reduces error rates in documentation, thereby increasing data quality and improving patient safety.
- Standardization: An intuitive user interface allows standardization of the revision of open clinical texts – beyond departmental borders.
- Compliance: Every user action is tracked in an audit trail to comply with the latest guidelines for computerized systems and electronic data in clinical trials.
Additionally, Meteor is browser-based software and therefore accessible from anywhere.
“Our Meteor software shows that the synergy between man and machine is shaping the future of clinical research,” says Dr. Spanka. “This AI-supported process not only saves time and money, but also significantly increases the quality of clinical trial data and contributes to patient safety. »
For more information on how the Meteor AI system can help you manage your clinical trial data, download the free document below.
