Why data management and decision-making go hand-in-hand in clinical research

The pharmaceutical industry is fuelled by data. Huge amounts of information are available from sources as diverse as patient health records, wearables, and social media content. If sponsors can harness it, this data can provide the key to saving time, labour and costs across the pharmaceutical value chain, ultimately bringing better drug products to patients sooner.

According to a new GlobalData report, 27% of healthcare professionals believe that data will trend as the second most disruptive emerging technology in the pharmaceutical sector in 2023, meaning the time to embrace the data revolution is now. And, as the pharmaceutical sector continues to generate more and more of it, GlobalData analysts suggest that companies need to embed data-driven decision-making into their everyday processes.

But key to the decision process is effective data management. Done right, data management can be leveraged to help optimise the entire pharmaceutical value chain, from target identification to end-user reach.

Data in clinical trials

For example, huge amounts of data can be mined from electronic health records (EHRs) and other sources to optimise clinical research, from drug identification to treatment plan design. Clinical trials themselves produce extremely large volumes of information from sources including claims data, patient registries, wearables, medical devices, previous outcomes, biomarkers and genomic sequencing. When it is properly utilised, this information can be used to improve patient outcomes.

For healthcare providers, effective management of these data sets offers the chance to better identify high-risk patients, offer at-home and customised care, and perform intelligent patient follow-up. Throughout a clinical trial, data analysis can enable better overall decision-making by researchers in areas including trial design and site selection. It reduces the chance of inefficiencies and medical errors, promotes more coordinated care and even offers the possibility to develop new treatments and provision pathways.

From patient enrolment to drug marketing

One of the most critical ways in which data management can serve clinical trials is in patient recruitment. Traditionally, a lot of time and money can be wasted at this stage – and if it isn’t done right, sponsors can face downstream issues including participant drop-out.

But by analysing and effectively storing large volumes of data from multiple sources, sponsors can pinpoint the participants most likely to benefit from a trial. And, as clinical research becomes more niche, more specific cohorts of patients are required, making intelligent target identification more important than ever.

With a robust data management system in place, an advanced analysis of electronic health and medical record data can be performed, allowing researchers to identify concentrations of relevant patients. It also creates the possibility to go directly to patients to explain the proposed treatment for their condition and even offer the option of managing their own data. With better patient engagement having been proven to result in better health outcomes and reduced dropout rates, sponsors are increasingly recognising the vital importance of enrolling the right patients and communicating with them effectively.

Once the clinical trial is successful and it’s time to take a new drug to market, further analysis of these datasets can power enhanced insights into manufacturing and supply chains, offering opportunities to increase efficiency and agility. Plus, market information can help predict consumer behaviours, identify target groups, and offer personalised advertising, boosting the chance of a successful market launch.

How it’s done

AI and advanced analytics can only work on high-quality data. Large and complex, managing these datasets requires powerful and innovative processing application software. The software must be able to handle capture, storage, analysis, search, sharing, visualisation, querying, updating and protection for enormous volumes of data.

Thankfully, better data management is made possible by Cloud-based data storage, which uses a Cloud computing provider that manages and stores data as a service. The Internet of Things, comprising a system of connected objects, can collect and transfer data wirelessly and automatically, enabling more information to be gathered.

Taimei, a leading innovator in the R&D and life science industry, offers a digital platform for clinical research that integrates advanced AI, data management, Cloud computing, and mobile internet technologies that enables pharmaceutical companies to get the most out of their data and reap the benefits that come with data-driven decision-making.

Taimei’s eCollect, for example, is a robust electronic data capture (EDC) system, specifically designed for complex clinical studies. Automated, robust and data-driven, it offers data management capabilities that can adapt to complexity and constant change, meaning sponsors can achieve real-time, actionable insights.

To find out more about how data management can improve your clinical studies, download the whitepaper