The clinical trials industry is changing rapidly to move with today's technological advancements and at the front of this exciting movement is the adoption of AI (Artificial Intelligence) and ML (Machine Learning).
Our industry has successfully incorporated both innovations to help push the boundaries of medical research and increase its effectiveness and efficiency through using these tools to help streamline and automate critical processes, which previously would have taken far longer to complete.
The volume of data that clinical trials need to ingest, and compute is larger than ever, yet the need for efficiency and the ever-critical integrity of the data is greater than ever to enable successful submissions.
Thankfully with the adoption of AI & ML in clinical trials, Clinical Data Management have the tools which will facilitate efficient and quality work throughout the journey of the study, which may also help aid the ever-growing demand for virtual or decentralised trials.
The Main Ways to Optimise with Automation in Clinical Trials
Below are the some of the main options to help streamline your processes through automation:
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Machine Learning (ML)
This is something that we are beginning to focus on within Quanticate, it is the use of machine learning algorithms to help create powerful analytical outputs which will greatly enable our ability to identify trends in the subject's data which will lead to our ability to make critical trial decisions in real time, something which the ICH E6(R3) guidelines reference. This is made possible by ML algorithms analysing the vast amount of data that is contained in clinical trials to identify patterns and correlations present within the data.
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Artificial Intelligence (AI)
AI can extract information from multiple data sources, which works well if you have a unified data platform such as a data workbench. The AI extraction will ingest all of the data and assist in analysing, reporting and structuring of the analysis, this provides a huge benefit and can reduce the number of resources to manage large volumes of data by automating the steps which would normally require a number of FTE’s (Full Time Employees). There is also the advantage that AI can assist with every day validation tasks such as data review and query generation with the technologies ability to learn and adapt to the study environment.
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Virtual Trials
As referenced earlier, the ability to streamline and automate your processes through AI and ML will only benefit and increase the options for virtual trials. This can be achieved by enhanced technologies which will allow for increased data collection at the subject's home, which in turn should create greater satisfaction and happiness for the subject which reduces the risk of them being lost to follow up. AI can also be utilised to help guide and assist subjects, which once again will increase efficiency via streamlining the process and communication channels.
The Benefits of Utilising Automation
Here are some essential skills for Data Managers to ensure the successful capture, clean and lock of clinical trial data, contributing to the efficacy and effectiveness of clinical research outcomes:
- Cost Reduction – AI and ML should lead to a reduction in processes that relied on manual work, the very nature of the streamlining and automation of AI and ML will result in operational efficiencies thus reducing time spent
- Greater Efficiency – Automation of processes will help reduce the time and effort required to manage clinical trials and the at times cumbersome processes required to successfully run your trial.
- Enhanced Data Quality – Due to the nature of automation and the high level of validation attributed to AI and ML the volume of potential errors should be reduced which would then lead to cleaner data.
- Establishment of Virtual Trials – Patient care is critical and one way to assist patients is to make their life easier and the roll out of virtual trials will aid this. With the help of AI and ML driving technological advancements, virtual trials will become more accessible, making the ability to recruit easier.
- Patient Care – The introduction of AI and ML analysing subject data in real time will not only aid decisions in the design of the clinical trial but will also help identify any potential safety issues within the trial earlier.
- Speed of Data – AI and ML will help facilitate the management of large data which in turn will speed up the clinical trial process as tasks which could have taken days will be dramatically reduced.
Key Considerations when Adopting AI and ML
While the adoption of Ai and ML may seem to be an obvious step, there factors that need to be considered:
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Investment
Adoption or implementation of any new technology can be a costly enterprise as there would need to be time and money invested to ensure that you get the best out the system. As with all technology, you must ensure that robust procedures and working instructions are created along with the rigorous regulatory requirements for technology validation. There may also be the additional cost of supporting hardware that would need to be put in place.
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Resource
While AI and ML are now commonplace within industry discussions and conferences, most companies do not have AI and ML designated departments. There would need to be an investment in either bringing in new employees or retraining existing staff to ensure that you have the infrastructure necessary to make the use of AI and ML a success.
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Process and Security
There are numerous AI applications available, the most common being Generative AI (Gen AI) tools such as ChatGPT, Claude and Gemini, which utilise the search engine, internet and open access resources to learn and provide responses. However, such systems need to be treated with care as you would have company information and confidential study data that needs to be protected while also ensuring that you are meeting all privacy and security measures while handling clinical data.
The Future of Automation in Clinical Data Management
The adoption of AI and ML will play a big part in the ability for companies in the clinical trials world to automate and streamline their processes and the advantages and forward steps that this could provide a company are vast, ranging from basic efficiency of tasks to revolutionising the processes that currently exist in the company to facilitate greater cost and quality gains. AI and ML have already shown that it has the capability to greatly reduce the risk of errors from manual based tasks and manage large amounts of data with minimal fuss.
AI and ML will play a major role in Clinical Data Management moving forward and it is important for companies to start looking into investment or creating strong partnerships with established AI/ML experts to ensure that they can continue to progress and grasp the latest technological offerings when available.
Improve the accuracy and efficiency of your clinical trial data
Quanticate’s Clinical Data Management Team are dedicated to ensuring high quality clinical data and have a wealth of experience in data capture, processing and collection tools. Our team offer flexible and customised solutions across various unified platforms, including EDC's. If you would like more information on how we can assist your clinical trial submit an RFI.