Make no mistake, successful clinical drug development relies on accurate, reliable data from various sources to meet the demands of regulators, payers, sites, sponsors, CROs and patients.
Fortunately, technological innovations offer clinical researchers the ability to collect and aggregate data throughout the development process.
These innovations create opportunities to address complex research questions and accelerate access to new drugs and treatments.
While many of these technologies expedite timelines, reduce development costs and offer increased access to data, they also introduce new challenges.
To fully leverage technology-enabled access to patient, clinical, operational and outcome data for market authorisation applications, a clinical data strategy must be an integral part of every life science organisation’s infrastructure and the clinical trial plan from the early stages of development.
This strategy should encompass methods for collecting, exchanging and analysing data from various technologies used in the study and capturing real-world evidence and patient-centred outcome data to meet regulatory and payer requirements.
One of the most significant challenges in clinical trials is patient recruitment, which can be time-consuming and expensive. Various data sources, such as electronic health records, claims data and social media, however, can help identify eligible patients.
Additionally, there is a growing focus on diversifying the patient population in trials. A data strategy leveraging these sources can streamline recruitment and improve trial timelines.
Story of our times
The abundance of data from different sources and modalities poses a challenge due to the lack of industry-wide standards for data connectivity.
Interoperability issues persist, hindering seamless connectivity between electronic health records (EHRs), electronic medical records (EMRs), electronic data capture (EDC), electronic clinical outcome assessments (eCOA), electronic patient-reported outcomes (ePROs), labs and other operational data.
Innovative technology vendors, sponsors and CROs are collaboratively working to address these issues.
Groups like the Decentralised Trial Alliance (DTRA), TransCelerate, the Society for Clinical Data Management (SCDM) and the Avoca Quality Consortium are advancing standardisation concepts for technology-enabled clinical studies.
While regulators support patient-centric methods for clinical research, they have not provided clear guidance on data connectivity in larger healthcare ecosystems.
In the past year, regulatory agencies like the FDA and EMA have released multiple guidance documents in this domain. These guidelines have paved the way for the integration of novel technologies in clinical research, catering to patient preferences, convenience, and engagement.
Decentralised and virtual technologies have played a significant role in alleviating the burden on patients by bringing trials to their doorstep, but they also introduce a level of complexity into the process.
Technologies such as smartwatches, sleep trackers, blood glucose monitors and other wearables generate overwhelming amounts of data.
Hybrid design studies involving pharmacies, labs and community health providers add complexity to data aggregation for regulatory submissions.
Handling the truth
Multiple data entries and inconsistencies in data capture hinder efficient development by lacking a single source of truth.
For site staff working on trials from different sponsors or CROs, this increases the burden, risk and cost. Source data verification, a costly and time-consuming element, must be done manually to ensure data accuracy.
Harmonising independent systems can provide a single source of truth, reduce errors and benefit sponsors and site staff.
Incorporating a clinical data strategy in the study’s protocol can identify areas where multiple data entries obstruct obtaining a single source of truth.
Clinical monitors visiting research sites to audit data accuracy from EHRs or other sources into EDC can be eliminated by capturing and validating data directly from the source.
Some companies are using artificial intelligence and machine learning to enable seamless data flow between systems, reducing the human effort associated with trial activities.
The COVID-19 pandemic accelerated the use of decentralised clinical trial technologies and patient-centered approaches, improving the patient experience.
Complex clinical trials now involve e-consent, in-home nursing, telemedicine visits and electronic patient-reported outcomes, enhancing patient experiences but adding new complexities to an already complicated process.
To manage this complexity, a clinical data strategy must be developed in the early stages and included in the trial’s data management plan.
It should detail how each data point will be captured, collected and analysed, while remaining flexible to accommodate unforeseen situations or new technologies introduced during the study.
A robust clinical data strategy is no longer optional. It represents a critical instrument for evolving the clinical trial process into one that is patient-friendly, minimises risks and primed for success.
Moulik Shah is Vice President, Digital Health at Advanced Clinical
