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Streamlining Data Consolidation in DCTs for More Accurate Research Outcomes

The increasing need to bring effective and safe therapeutics to market faster is pushing clinical research organisations (CROs) toward decentralised clinical trials (DCTs). As a result, the incorporation of decentralised components in clinical trials is predicted to soar by 17% by the end of 2023, outnumbering the peak activity recorded in 2021. The advancement of DCTs has unveiled the potential for researchers to gather more real-world data, allowing them to collect direct-from-patient reporting. However, with the increase in real-world data and the adoption of decentralised trial models, the need for consolidating and integrating disparate data sets and technology suites has spiralled.

MedCity News, in a recent post, has shed light on the approaches of managing the massive troves of data in DTCs for efficiently measuring trial outcomes. Addressing this crucial need to consolidate complex data sets into a single actionable source of truth to operationalise successful DCTs requires organisations to prioritise three key areas of focus, according to the medical news platform.

In the article, researchers are firstly urged to adopt high-end digital tools and infrastructure to capitalise on the opportunities presented by direct-from-patient data. It is because high-end digital health tools, capable of accumulating and analysing data and deriving actionable insights from it, can significantly increase trial efficacy, thus boosting the success rate.

Next, the article underscores the data capture and remote enrollment tools in DCTs as an enabler of more patient-centric trials. Besides streamlining trial processes, the digital components in DCTs, hybrid or remote study models can significantly enhance trial logistics. However, providing adequate training for staff while keeping all involved stakeholders well-informed is pivotal for harnessing the full potential of the decentralised components used in DCT study models. “The site needs to know how to conduct proper assessments with and through these technologies and how they augment the trial workflow. On the patient side, often patients and caregivers (who must share the data) are not initially comfortable with using various digital technologies, so it is imperative they are provided adequate training to ensure their data is being collected and shared properly,” according to MedCity News.

Lastly, health technologies must be leveraged to maximise participant diversity and augment patient experience throughout the process. Enhanced real-time communication and feedback enabled by DHTs provide flexibility, resulting in improved patient engagement and retention. For example, DCT systems that can schedule trial activities, sync data input in digital health tools, and manage electronic case report forms (eCRFs) and electronic informed consent (eConsent) significantly reduce patient burden and increase patient enrollment rate by allowing them to participate in trials from the comfort of their homes. According to research, less than 5% of the US population participates in clinical trials, and up to 50% of started trials are not completed due to inadequate enrollment. However, DCT and remote study models can shift the paradigm to a much higher patient participation rate and lower screening failures.

In addition, unlike site-based clinical visits that capture a brief snapshot of a patient’s health, DCT platforms such as ObvioHealth allow trial teams to get a longitudinal view of how their patients’ health may be trending. Continued monitoring of the captured data on health outcomes, risk factors, or both, based on the symptoms logged by participants with ePRO in DCT apps, helps sponsors make data-driven decisions about patient care.

According to the article, as the industry strives for a more inclusive clinical trial experience, focusing on these key areas can help bridge the gap between sites, patients, and trial teams, ultimately accelerating the development and commercialisation of life-improving therapeutics.

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