The COVID-19 pandemic has disrupted industries across the globe, including the Life Sciences and Healthcare (LSH) sector. One area that has been significantly impacted is the field of clinical trials and the processes involved in collecting, managing and reporting trial data.

The far-reaching effects of the global pandemic

Clinical trials are crucial in driving drug development by collecting and analyzing real-world patient drug response data. However, the COVID-19 pandemic significantly impacted 2020 and beyond. During the height of the pandemic, outpatient visits and ambulatory care practices saw a drastic reduction of as much as 60%. This had inevitable consequences on healthcare delivery and the participation of patients in clinical trials. Furthermore, safety regulations put in place to contain the spread of COVID-19 had unintended consequences for clinical trials.

These regulations restricted access to in-person follow-ups for ongoing trials, hindering proper monitoring and evaluation. Additionally, the limitations on healthcare services and patient mobility prevented the enrollment of new patients in trials, effectively bringing them to a halt. The trials that were able to continue suffered from the following

1. A lack of on-site monitoring
2. Resource and digital non-compliance
3. Insufficient participation
4. Limited access to all strata of the participant population
5. Inconsistent and unreliable patient data

The absence of in-person patient participation undoubtedly caused disruptions to existing processes. However, it also accelerated the adoption of emerging technologies and the cultivation of innovative digital capabilities that enable remote trial participation while maintaining robust clinical data management protocols.

As a result, the way data is traditionally gathered and curated through in-person interactions and paper processes is now being replaced by alternative methods that include:

1. Universal remote consent: FDA-approved telephone, video and e-mail consent collection protocols
2. Telemedical monitoring and trial visits: Virtual visits using video calls instead of in-person visits
3. Standardizing remote event adjudication: E-mails, calls and messages are being used to collect regular patient updates with a central adjudicator to monitor the digital endpoints
4. Novel clinical trial design: Trials are being conducted more rapidly by comparing pre-specified and investigational therapies with placebos to identify the subgroups that respond best to the treatment

Trends in data collection

By ensuring trial continuity, these innovative new approaches solved one critical problem while simultaneously creating a new set of challenges: increased complexity all along the lifecycle of data collection, management and reporting because these processes require continuous oversight to ensure compliance. Of course, new challenges lead directly to innovative new solutions and clinical trials in recent years have benefited from emerging digital-powered solutions for seamless and non-disruptive data collection methods. For example:

1. Patient-centric trials are gaining momentum as advocacy groups facilitate increased patient involvement in the planning stages
2. Decentralized clinical trials are seeing improved data collection through electronic patient-reported outcomes (e-PROs), electronic data collection (EDC) for remote data access and verification through remote monitoring
3. Digital endpoints like wearables are playing a prominent role in collecting patient data that can be leveraged to devise more relatable and patient-centric strategies
4. Data analytics and visualization are witnessing an integrated approach for the clinical research associates (CRAs), investigators and medical monitors as well as the drug safety monitoring committees, with real-time access now enabling reduced trial times and better safety oversight
5. Data management is becoming more and more digitally empowered, with the likes of OCR-powered case report form (CRF) creation, automated CRF annotation, digital data preparation, integrated validation and submission for statistical analysis of the trials
6. Data strategies now encompass all the various sources of data, requiring robust digital capabilities to collect, reconcile and clean the data, as well as perform a data-driven risk management assessment to streamline oversight
7. Evolution of data management is more automated, along with the integration of the validation processes involved

These emerging technologies, solutions and methodologies illustrate that modern life sciences and healthcare enterprises must embrace digital transformation processes. But the pandemic disrupted not only the way the industry has historically approached transformation but the speed at which it has had to adapt to and adopt it, as well. With that in mind, HCLTech has been working with several biopharma companies to ensure seamless and disruption-free enablement of clinical data management for drug research and development. And we have learned a few important lessons from these engagements:

1. It is critical to enable clinical eSubmission automation - in accordance with CDISC SDTM standards - through a single platform for all data conversions.
2. Data traceability and security are requirements along with re-enabling transformation rules across all ongoing concurrent studies.
3. For an enterprise using Oracle and OBIEE (Oracle Business Intelligence Enterprise Edition) to consolidate clinical and safety data, all data load processes must be automated and the data warehouse re-architected to avoid redundant data processing. To that end, we hosted the data integration as a service enterprise in 21 Code of Federal Regulations (CFRs) Part 11-validated environments and also developed one RPD (Rapidfire Database) and one OBIEE to provide business users easy access.

Trends in data management, reporting and analysis

It’s important to note that clinical data is being treated differently now, especially in hybrid clinical trials, as digitally enabled remote endpoints generate large amounts of data. This has significantly increased LSH enterprise efforts in terms of data analysis. At the same time, several key challenges have surfaced, including:

1. High variation in participation
2. Increased monitoring for patient compliance
3. Increased expenses from onboarding 3rd-party care providers
4. Maximized effort of integrating home care with trial sites

To address these challenges and drive the right value using clinical data, life sciences and healthcare enterprises are beginning to leverage solutions based on analytics-powered electronic health record (EHR) insights, digital training for care providers, direct-to-patient engagement platforms and more. With the increase in the volume of clinical data, there are also new approaches to how this data is being managed. One of the more prominent emerging approaches is the knowledge graph.

Knowledge Graph databases use fully integrated e-clinical technologies to identify the relationship between various elements of clinical data, like medication, adverse events and patient responses to different batches of drugs vs. a placebo, drug performance mapping and more. These databases enable rapid analysis across individual and multiple clinical trials for meta-analysis.

Conclusion

Clinical trials generate massive amounts of data, but life sciences and healthcare enterprises have only sometimes been able to use it fully.

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Clinical trials generate massive amounts of data, but life sciences and healthcare enterprises have only sometimes been able to use it fully. However, with recent digital technology advancements, accelerated during and after the COVID-19 pandemic, the industry now has the capability and responsibility to use this data to deliver personalized care solutions that prioritize patients' needs.