Lifescience technology is advancing at an unprecedented pace, and the clinical trials industry is no exception. The past few years have seen a steady rise in the adoption of decentralised clinical trials (DCTs) as an alternative to traditional clinical trials, and this trend is expected to continue in 2023 and beyond.
Decentralised clinical trials have gained popularity due to their ability to improve patient access, reduce costs, and accelerate study timelines. DCTs leverage digital technologies to enable patients to participate in clinical trials from the comfort of their own homes or nearby medical facilities, instead of having to travel to a centralised trial site. This has been particularly important during the COVID-19 pandemic, as traditional clinical trials were significantly impacted by lockdowns and social distancing measures.
However, as the adoption of DCTs increases, so do the challenges associated with implementing and managing them. One of the key challenges is the need for specialised software that can facilitate the remote collection, management, and analysis of clinical trial data.
In this article, we will explore the current state of decentralised clinical trial software and what we can expect to see in 2023.
The current state of decentralised clinical trials software
The software landscape for decentralised clinical trials is still relatively new and fragmented, with a variety of different vendors and platforms available.
These platforms typically offer a range of features, such as patient recruitment and retention tools, remote data capture, telemedicine capabilities, and data analytics. Some platforms also integrate with electronic health records (EHRs) and other systems to streamline data exchange and improve interoperability.
However, there are still some challenges associated with the adoption of DCT software, including regulatory compliance, data security and privacy, and patient engagement and retention.
In 2023, we can expect to see continued growth in the DCT software market, with new entrants and innovations in the space. Here are some of the trends we expect to see:
Increased adoption of artificial intelligence (AI) and machine learning (ML): Using AI and ML in clinical trials has the potential to significantly improve the efficiency and accuracy of clinical trials by automating tasks such as data cleaning and analysis, identifying patient cohorts, and predicting trial outcomes. We can expect to see more DCT software vendors incorporating AI and ML capabilities into their platforms in 2023.
Greater emphasis on patient-centred design: Patient engagement and retention are critical factors in the success of DCTs, and software vendors are starting to recognise the importance of patient-centred design. In 2023, we can expect to see more DCT software platforms that prioritise patient experience and usability, with features such as patient portals, mobile apps, and real-time feedback mechanisms.
Integration with wearable devices and Internet of things (IoT) sensors: Wearable devices and IoT sensors have the potential to transform clinical trials by enabling remote monitoring of patient health and activity data. In 2023, we can expect to see more DCT software platforms that integrate with these devices and sensors to enable real-time data capture and analysis.
Improved interoperability and data sharing: Interoperability and data sharing are critical components of DCTs, and we can expect to see more software vendors focusing on these areas in 2023. This may include the development of standardised data models and APIs, as well as partnerships with EHR and other systems to improve data exchange and reduce duplication.
Decentralised clinical trials are becoming increasingly important in the development of new drugs and medical treatments. These trials offer several advantages over traditional clinical trials, including increased flexibility, reduced costs, and improved patient access and engagement.
In recent years, the use of AI has emerged as a powerful tool for optimising decentralised clinical trials. AI algorithms can help improve patient recruitment and selection, automate remote monitoring and data collection, and build predictive models that can optimise trial design and dosing regimens.
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