White Paper — The Clinical Development Digitalization Guide: Strategies to Drive Success
Introduction 2
Digitize Your Trial 3
Define a Clear Data Strategy 4
Empower Your Team 5
Focus on User Experiences to Make White Box Data-driven Decisions 7
Preparing for Digitization with the Right Technology Platform 9
The Next Phase in Clinical Development 10
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. This clinical development digitization guide includes strategies to overcome digital transformation challenges and accelerate innovation in clinical development.
Related Resource: 2022 Gartner® Market Guide for Life Science
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You will learn the necessary steps that organizations must take to embrace the transformation of processes that will help usher in a new way forward for clinical trials.
Digital transformation or ‘Digitization’ is a trend that is fundamentally changing the life sciences industry, where the rate of scientific discovery demands more advanced technology and strategies to solve challenges and support change and innovation. Consider the fact that patient participation still remains one of the biggest challenges in clinical research, and that a decentralized trial approach has been implemented among many sponsors to solve issues with meeting enrollment goals. And while decentralized studies are not novel concepts, the disruption of nearly 68 percent1 of clinical trials during the height of the pandemic last year has caused the widespread adoption of hybrid or virtual trial models.
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 capture 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
These areas establish a foundation from which the biopharmaceutical industry can begin to evolve trials to improve outcomes dramatically. By paving the way for digital transformation goals to be met, clinical research can focus on moving forward new treatments and experiences to patients.
1 Adams, B. (2020, July 1). More than two-thirds of trials hit by Covid-19 enrollment halts, with midstage tests the worst affected. FierceBiotech.
‘Continuous change is the status quo’.
Pooja Singh, Principal Analyst, The Gartner Top Predictions for Healthcare & Life Sciences in 2022, Presented 12.09.2021
Data allows clinical teams to understand the safety and efficacy of important new therapies. Yet in 25 years, data review, analysis and submission preparation have not changed. In fact, these processes are even more complex, as decentralized trials become more widely adopted and as the volume of disparate data that is collected continues to grow.
How to prepare for a successful clinical data strategy
By championing a data-centric technology approach, biopharmaceutical organizations can simplify data acquisition and analyses. This approach begins with a clear data strategy that defines data requirements and how that information will be presented. Central to an effective data strategy is the right clinical data platform. Importantly, you should look for automated solutions that are interoperable and provide full data lineage and governance. Once implemented, the platform can streamline all data into a single source of truth, which directly impacts these critical trial metrics and areas in trial management:
• Cycle-time reduction: Clinical data technologies have helped combat an upward trend in the last two years, in which trial cycle times have increased by 40 percent.
• Improved internal resource utilization: An automated data pipeline reduces the time spent on data cleaning and implementing standardizations.
• Data governance, quality and compliance assurances: Defined data management strategies enable quality assessments and ensure data governance and compliance.
The implementation of a data strategy and data-focused technology that centralizes all insights sets the stage for new opportunities to apply AI and machine learning to enhance data review processes. From enabling data managers and medical reviewers to work 4050 percent faster, to highlighting data trends and flagging potential risks, AI models that are supported by a clinical data cloud will help the biopharmaceutical industry take the next leap forward in optimizing clinical trials.
With so much attention being paid to the processes and technology needed to push digital transformation initiatives forward, the people who ground the important work behind clinical development should not be forgotten. These teams celebrate wins and inspire change; empowering them to work smarter is just as vital to meeting research goals as investments in technology are.
Hiring team members who share a passion for collaboration and represent a broad range of perspectives is one way to ensure that problems will be tackled from many points of view. Diversity in experience unlocks innovation and when new ideas are fostered by resources, teams are given room to thrive and discover. Here are ways to tap into critical insights to achieve research objectives:
• Frame the problem: Instead of focusing on the technology, consider the problem you are trying to solve. What happens when the known assumptions of that problem are tested? Crucial information is gleaned from looking first at the challenge. A solution may arise when a problem is approached in a new way.
• Set the goals: To achieve success, look to the results that you want to exceed in a specific model. If those results become the standard, experiments and efforts can be targeted to surpass those goals in clinical research.
• Commit to networking: Create an ecosystem that promotes entrepreneurship. By connecting with professionals who can extend your knowledge, you’re establishing a group of trusted professionals who can be relied on for consistent support or advice. This raises awareness of your research and increases the potential to discover new approaches and methods that can lead to breakthroughs in clinical development.
Data proliferation will continue to rise and demand for talent in key data management roles will only increase along with it. The 2019 Tufts-eClinical Solutions Data Strategies and Transformation study looked at how organizations are handling the influx of clinical trial data and showed the manual work required by data managers and other team members is both time-consuming and tedious. The implementation of a clinical data platform can support teams and help them avoid burnout.
The hidden costs of data management churn from burnout include financial fallout and added workload on others, while important initiatives get shelved. There is a better way forward with technology that enables data managers to automate and unify all data sources into a single source of truth. This allows for projects to be more transferable within teams for faster transitioning and ramp up for new team members while reducing manual data review activities by 35%1
White Paper — The Clinical Development Digitalization Guide: Strategies to Drive Success
Infographic: The High Cost of Data Management Churn
Infographic
The Problem is Compounding
70%
Current State
There are not enough highly qualified Data Managers (DMs) to work on your projects.
There are 4,000+ open DM jobs1
Trials are increasingly complex
Volume, velocity and variety overall – accelerated by decentralized data sources.
There is a high turnover rate in data management
Technology infrastructure lagging
75%
of companies are still using SAS or EDC/Excel as the core of their data integration and review strategy.
The Dead-End Cycle of Burnout
Losing a Team Member
You Lose:
Manual work takes a long time
Data review using SAS & Excel is difficult, manual and time consuming and suitable for greater automation!
67% Lose team member
Almost 1/2 of your time spent:4
. Transitioning work Recruiting
Training new hires for 4 months
Remaining team works 15 - 30% harder Team burns out
You are forced to...
Only focus on critical path work
De-prioritize important initiatives such as planning & standardization
of all workers believe burnout has worsened during the pandemic.3
What if I outsource everything?
Having your data spread across numerous vendors only compounds the problemimpacting data quality & timelines. The providers you work with are experiencing, on average, higher churn rates.
On the Insider Blog: The Top Three Hidden Costs of Data Management Churn
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.
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 realtime. 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.
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.
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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.
Gain self-service access to all clinical data for faster and easier medical review
Optimize your data management process with a clinical data workbench
White Paper — The Clinical Development Digitalization Guide: Strategies to Drive Success
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 reports 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.
How To: Data-driven clinical operations teams use a clinical data platform approach Watch now
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.
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 done in black box programs.
How To: Maximize the value of a clinical data platform approach for biostatistics & clinical programming Watch now
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.
White Paper — The Clinical Development Digitalization Guide: Strategies to Drive Success
Finding the right technology platform is a key step in driving digitization efforts. With a purpose-built clinical data cloud like elluminate®, life sciences organizations can meet today’s challenges in managing the rise in clinical data volume and variety. Selecting the right technology with robust clinical and operational data management capabilities is the next step in building your digital transformation strategy.
A Guide for Evaluating Clinical Data Platforms
This guide outlines key capabilities to consider when evaluating a clinical data platform and highlights how the elluminate Clinical Data Cloud stacks up.
Key Platform Capabilities Needed in a Modern Clinical Data Platform
Automated Data Ingestion and Integration
Data Storage and Processing
Data Provisioning and Exchange
Data and Object Unification
Low Code Data Mapping and Transformation
Data Governance and Data Quality
Data Privacy, Data Access and Control
Metadata Repository Capabilities and Integration
elluminate Clinical Data Cloud Capability
Out-of-the-box Data Connectors* *
Scalable System Performance and Security
Out-of-the-box Traceability
Platform Interoperability
Authentication and Authorization
Dynamic Data Mapping and Transformation
Data Management and Review
Full Enablement Program
Risk-based Quality Management (RBQM)
*elluminate comes with more than 20 out-of-the box connections with other eClinical Systems
AI-enabled
The pandemic has transformed clinical development and has accelerated digitization across the life sciences industry. In addition to redesigned patient and site experiences, the processes and tools used for clinical data management, review, synthesis, standardization, and analysis have also changed. As the volume, variety, and velocity of data in research has increased, so has the need for improving the experiences of data managers, clinical trial managers, clinical programmers and statisticians. Moving at ‘the speed of science’ means having clinical data available in near-real time to make decisions with confidence and accuracy while ensuring patient safety. Drug development teams are leveraging modern technology to reduce manual efforts, optimize resources, and enhance productivity throughout the clinical trial lifecycle.
The elluminate Clinical Data Cloud is an advanced platform designed to make clinical development easier, faster and more transparent. To speed up drug development and research, organizations must leverage innovative and modern technology such as clinical data platforms to streamline clinical data management and capture insights quickly. By combining data-driven strategies with modern technology adoption, life sciences organizations can begin to maximize the value of their clinical data, help their teams accelerate development processes, and serve patients more efficiently.
For more information or to request a demonstration, visit us at eclinicalsol.com or email us at info@eclinicalsol.com.
eClinical Solutions helps life sciences organizations around the world accelerate their digital clinical initiatives. Our intelligent clinical data platform and data services give our clients real-time, self-service access to all their data from one centralized location; plus advanced analytics that help them make smarter, faster business decisions.