How Data Interoperability Can Be an Elixir for Expediting Clinical Trial Results

  • August 25, 2022
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The COVID-19 pandemic transformed the way industries all over the world do business. From supply chain management to digital workplace initiatives, organizations were forced to accelerate their digital transformations almost overnight.

It was no different for life sciences and biopharmaceutical companies. The pandemic turned out to be a key catalyst in pushing research-driven biopharma companies to adopt newer ways of bringing real-world data (RWD) and clinical data into continuing drug trials. After all, having source data without confounding bias for analyses is pivotal for optimal research.

In recent years, we have seen sources of clinical data multiply through the incentivization of RWD in clinical trials by global regulators. On the one hand, this has improved the richness of data collected through remote channels such as telemedicine, wearables, and IoT devices. On the other hand, it increases the complexities of streamlining metadata and harmonizing data within multiple clinical systems. The arduous process of cleaning and validating data to adhere to different healthcare and pharma standards adds precious time and increases the complexities of integrating RWD into clinical research.

21st Century Cures Act as a guidepost for newer streams of clinical data

The 21st Century Cures Act was signed into law to enable clinical research companies to invest in real-world evidence and data strategies by incentivizing them to create adaptive clinical trial designs incorporating new-age e-clinical sources.

In today’s world, sponsors, clinical research organizations, eClinical partners, and sites struggle with the data lifecycle (capture, clean, curate, ingest, use) given the nascency of interoperability standards. With hybrid or decentralized clinical trials becoming commonplace, it is imperative to have a robust approach to capture, record, ingest and report data for rapid study start-up.

As RWD sources come into the mainstream, the challenge here is how to make the data uniform so that — when aggregated — organizations can meaningfully search it, curate it, and use it for patient recruitment and trial design planning. Organizations can also perform research on behaviors, market access and outcomes.

Pharma 4.0 – the new driver of data interoperability

Electronic Data Capture (EDC) systems have been the preferred mode of data capture for use in studies for the past twenty years. With Pharma 4.0 setting in, there is a greater emphasis on bringing in outcomes-based study models through mobile health technologies, wellness apps, IoMT devices, and a host of data streaming technologies.

As clinical data lifecycle management becomes more complex, the Center for Devices and Radiological Health at the FDA has established a Digital Health CoE to bring in appropriate digital health data capture and usage method recommendations for organizations to promote cross-data collaboration amongst non-compatible clinical data streams. Gleaning data from non-standard formats — from patients, devices, SaMD, and social media streams (apart from FHR and CDISC) — and making it available for downstream processing is essential for the success of any new research.

Having prebuilt API-based software data adaptors for managing the lifecycle of clinical data steams makes it easy for organizations to configure study start-ups in record time. With study managers looking for ‘one-device’ or ‘super-app’ processes led by the integration of EDC, CTMS, eSource, ePRO, eCOA, analytics, site solutions, and more, the current need is to have a more flexible or open API-led data architecture and allowing the data to become actionable faster.

DCT or hybrid trials will be the preferred mode of clinical research

The pandemic has forced companies to examine their operating ecosystem to bring in remote clinical data collection, processing strategies, and toolkits. Decentralized Clinical Trials (DCTs) or more tenable hybrid clinical trials have benefitted companies by working through a tightly integrated mesh of data platforms and service models.

With Cloud-based PaaS enabling rapid clinical compute power for enterprise-wide TA studies, the ‘one-app’ or ‘one-device’ paradigm allows for clinical data transformation with more intuitive patient-focused engagement that results in a marked improvement in quality, time and cost.

Data interoperability maximizes clinical trial results

Due to ground rules established by organizations such as ICH, the clinical research world has come closer to operating on a model of data sharing, harmonization, and standardization. However, while there have been experiments to bring EHR and EDC systems closer to practical uses, there needs to be a system/process/platform driving plug-and-play based orchestration of clinical data streams.

Gradually, companies of all shapes and sizes will have to be invested in these new-age data instruments not to be left out of the digital clinical data evolution for predictable study end-point measurements. As a result, data interoperability will be critical for any life sciences organization if they want to maximize ROI and results from their clinical trials.

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Niteshkumar Dixit

Niteshkumar Dixit is a Life Sciences solutions senior director and pursuit leader with more than a decade of experience spanning IT/ITES Solutioning, Business Consulting, and Accounts Management for all aspects of the Life Sciences value chain. He has strong Life Sciences R&D domain expertise and has an affinity toward patient-centric solutions ecosystems, including digital therapeutics. Nitesh has worked with leading global IT/ITES suppliers to build out their industry GTM strategy, develop software products, partner with niche vendors, and provide sales funnel build support. He is a registered pharmacist and holds a master’s degree in Health Administration and post-graduate diplomas in Clinical Research and Financial Management.

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