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Digitize Your Trial | Focus on End-User Experiences to Make White Box Data-driven Decisions

Over the last two years, the life sciences industry has seen a dramatic acceleration in digitization and technology adoption, especially in clinical development. The industry has embraced numerous technical capabilities such as decentralized trial platforms and risk-based quality management solutions that had been “emerging” for many years and are now being rapidly implemented at scale.

Clinical developers, regulators, and patient advocates are energized not only by the rapid ten years pace of progress the industry has made, but also by the increased possibility of delivering on the promise of personalized medicine at a faster pace. As the rate of scientific discovery demands more advanced technology and strategies to solve challenges and support change and innovation, Digital Transformation or ‘Digitization’ is fundamentally changing how the life sciences industry approaches clinical trials.

This trend toward making studies more accessible to patient populations, particularly in remote areas — and the investments in technology infrastructure to promote decentralized study models — will continue.

What are the necessary steps that organizations must take to embrace the transformation of processes that will usher in a new way forward for clinical trials? We can start by thinking about the key elements to enable your digital transformation objectives:

  • Social and mobile technology that captures diverse data sources and connects patients and clinical researchers
  • Data and analytics that enable greater control over data quality and drive real-time insights
  • Cloud technology that can scale, process and store data efficiently
  • AI and machine learning models to reduce manual efforts
  • Agile practices that leverage managed service providers, cloud vendors and technology partners to remain lean and nimble

Benefits of Implementing a Clinical Data Platform for Key Stakeholders

With the adoption of more decentralized models and the incorporation of more data sources in clinical trials, it is critical that organizations modernize their data infrastructure. A clinical data cloud can be implemented to unify all data streams and serve as a single source of truth, enabling data visibility for all stakeholders and teams. By centralizing the data, issues can be identified sooner, and decision-making can be accelerated.

The following use cases summarize the pre/post data platform implementation states for key functions and stakeholders across the clinical enterprise. Working from one source of truth provides clinical teams with greater transparency and traceability while also optimizing collaboration across teams and reducing rework and duplication of efforts. Ultimately empowering clinical teams to make open box data driven decisions throughout all stages of a trial.

Medical Monitoring

Before: Assessing safety adequately requires downloading data regularly, reviewing numerous data sources that are out of date, and going back and forth with different data intermediaries to get the analyses that are needed based on changing conditions. Manual tracking sheets are used to keep current with data coming in from various sources and prior trials are cross-referenced for potential associations.

After: The centralized clinical data platform-based approach provides assurance with sophisticated visualizations and analytics techniques that can be used to review data across sources and trials in real- time. Self-service data exploration tools eliminate barriers and delays associated with having to ask a programmer or statistician to write a program and generate output for review.

Data Management 

Before: Data management has become heavily dependent on data cleaning tools provided by EDC systems. However, non-EDC and external data sources contribute significantly to overall data volume. EDC tools are designed to collect data from investigator sites and are incapable of managing data from numerous sources that are not represented by a case record form. To reconcile data and manage discrepancies, manual approaches – such as using excel spreadsheets and issuing manual queries – are performed.

After: An advanced clinical data workbench integrates data from various sources and provides a single location for all data components. Visualizations complement automated listings that track changed data in order to detect outliers and focus on the most critical data points. Shared access to the same data listings and visualizations enables real-time collaboration with medical monitoring and safety teams, reducing rework and improving decision making.

Clinical Operations

Before: Working in highly outsourced models, clinical project managers and operational teams are often inundated with excel trackers, enrollment trackers, visit completion project trackers, and clinical milestone trackers. To keep their organization updated, much of their time is spent downloading report from various systems, creating pivot tables of combined data to understand project status, and troubleshooting risk. They get data from many different sources – often monthly feeds from various CROs and specialty vendors – and manually pull it all together. Experienced clinical operations resources are scarce. Teams are already stretched with ensuring proper oversight and regulatory compliance and have little bandwidth to manage data chaos.

After: Clinical trial managers are spending far less time in excel or managing trackers and more time overseeing, managing and mitigating the risks identified across numerous data sources via visualizations and analytics available for their trial. Clinical operations teams are well aligned with their cross functional counterparts and are leveraging operational data to provide program updates to stakeholders and inform future plans.

Statisticians and Programmers

Before: It is very rare for two protocols to be identical in clinical development, creating a challenge for biometrics teams. Clinical programming teams spend considerable time defining specifications, debugging, and validating data in order to consolidate and standardize disparate data sets. Data lineage and traceability for programming and derivations requires manual work in order to retrace steps and reconstruct what was

After: An automated data ingestion and data mapping tool with a graphical interface allows mapping to be done without a highly skilled programmer, eliminating human error and minimizing debugging time. Required datasets and standards are defined upfront and made available to cross-functional teams and stakeholders. A fully connected advanced Statistical Programming Environment (SCE) allows programmers and statisticians to have more time for statistical analysis work, reducing downstream time for analysis deliverables and submission deliverables early-on. Programs are broadly available for reuse across studies along with data sets making data exploration and insights more accessible. Data lineage reporting and traceability for all mapping and programs are available speeding development time and optimizing resources and standards.

The complete Clinical Development Digitization Guide shares deeper insights on the necessary steps organizations must take to empower teams and embrace the transformation of processes that will help usher in a new way forward for clinical trials. Contents of the guide also include how to prepare for digitization with the right technology platform.

Read the White Paper

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2024 Industry Outlook: Driving Tomorrow’s Breakthroughs with Clinical Data Transformation