Real World Data
Optimising the use of real world data to drive effective outcomes
Harnessing Technology to Maximise RWE Value
Late phase research is undergoing rapid transformation due to the impact of healthcare digitalisation and the greater availability of and access to Real World Data (RWD).
Real world evidence (RWE) continues to drive healthcare and research discussions and decisions. ICON delivers Real World Intelligence™, bringing together innovative thinking and technology solutions to identify, generate, and communicate the clinical, safety and cost-effectiveness evidence that regulators, payers and providers demand.
Harnessing Technology to Maximise RWE Value
With the right technology infrastructure and support, sponsors can more completely leverage RWE across the enterprise for maximum value.
Read our white paper, “On a Technology-Enabled Collision Course: Clinical Research Meets Clinical Practice through Real World Evidence” and learn how to:
- Apply Real World Intelligence™ to drive RWE generation
- Enhance trial planning and recruitment using EHRs
- Identify the emerging applications of RWD that can increase research efficiency
- Implement a technological infrastructure to maximise the value of RWD.
Meeting Evidentiary Needs with EHRs
Real World Data from Electronic Health Records can enhance your late phase research studies while decreasing study costs.
RWD-powered, post-marketing studies require fewer resources and EHRs are an efficient data source to support observational studies. Retrospective and prospective analyses, as well as case-control cross-sectional studies, can be more cost-effectively performed using EHR data.
An EHR system can also be used as the information backbone of Pragmatic clinical trials by supporting recruitment efforts and automatically capturing outcomes data.
Read our whitepaper: Meeting Evidentiary Needs with Electronic Health Records white paper
Effective Ingestion and Normalization of Real World Data Sources
As more sources of anonymized Real World Data (RWD) become available, the ability to ingest, standardize and then link the collected disparate data sets is critical to creating insightful, analytical output.
Powerful data handling, combined with functionalities such as natural language processing (NLP), used to “read” unstructured RWD, and machine learning, expedite advanced analytics and predictions within a RWD platform
Watch this on-demand webinar to learn more on:
- How electronic medical records (EMRs), claims, and wearable device data can be ingested in one platform to create a single data repository
- How an AWS-powered RWE platform can normalize these disparate data sources to create meaningful output
- A case study showcasing a holistic analysis to uncover a full spectrum view of therapeutic area treatment patterns
Five Common Barriers to Harmonised Real World Data (RWD)
Duplicate data environments, redundant data subscriptions, and siloed data access are not providing a good enough return on investment for Real World Evidence (RWE) generation.
Within the last few years, the exploration and practical use of secondary data has grown exponentially and is now fuelling a new wave of digital disruption. The challenge presented with this new era of data use sees unprepared organisations spending massive amounts on secondary data for specific one-off use cases, without thought to how those data may be harmonised across the organization, which results in large scale data silos throughout the enterprise. Data silos bring an inherent inefficiency and create roadblocks to achieving the desired success.
Webinar: Overcoming 5 Common Barriers to Harmonised Real World Data
Read blogOptimise Your RWE Investments
Increasing drug development cost, the shift from volume to value-based pricing, and competition from generics and biosimilars, are forcing pharma and life science organizations are looking towards Real World Evidence (RWE) to prove the value – cost, safety, and effectiveness - of their products.
However, Real World Data comes in structured and unstructured format, in various data standards, and is laden with varied data quality issues.
Watch this on-demand webinar to learn more:
- Understand the key business use cases and stakeholders of RWE
- Gather an industry perspective on RWE and its impact on entire pharma value chain
- Learn the current challenges and the need for a platform based approach
- Understand the current state of RWE analytics implementation and associated complexity
- Understand RWE business problem with respect to observational research
- Get an overview of a platform-based approach to harness real-world datasets for observational research
How RWE Continues to Shape the Late Phase Research Landscape
Creating a comprehensive RWE strategy demands a focus on organizing and synthesizing the many real world data (RWD) asset options that are available to life sciences companies. With big data solutions advancing to the forefront of the healthcare ecosystem, having access to a fit-for-purpose RWE technology platform capable of aggregating multiple RWD sources and generating a continuum of insights is paramount.
Watch this on-demand webinar to learn more:
- The increase in availability of big data from primary and secondary sources and how it is shifting the research landscape
- The strategic initiatives needed to identify which datasets can answer your research objectives and how to bring them together into one platform
- The development of a fit-for-purpose RWE technology platform to maximize value from your data assets
- Applying these tactics to an innovative use case
Real World Evidence Across the Product Lifecycle
Payment models and federal reforms are increasingly focused on the real world impact of treatments and devices. More regulatory guidance is being released in both the US and EU around the use of RWE to support and enhance submissions and product uptake.
RWE can also make a large impact on how payers may cover a product based on the real world value it brings to the patient and the market.
Understanding what evidence will best support a product’s value story is paramount for sponsors.
Watch this on-demand webinar to learn more:
- The broader role RWE is playing in decision making across the product life cycle due to technology advancements and RWD availability
- The current EU and US regulatory landscape and guidance around use of RWE for enhanced regulatory submissions
- How RWE can be used to support a product’s value story and uptake of RWE as a payer decision making tool
Generate RWE Using Cross Sectional Studies and Medical Chart Reviews
Download our white paper, Real World Evidence Generation: The Value of Cross-Sectional Studies and Medical Chart Reviews to learn how this hybrid approach to study design can be advantageous for generating real world evidence. Get insights on the issues you need to consider to ensure that your study is planned to produce robust scientific data that can be extrapolated beyond the study population.
Reduce the Cost of Post-Market Surveillance with Real-World Data
Real-World Evidence (RWE) is derived from Real World Data (RWD), and early use of Real World Evidence can cut post-marketing study costs and Medical Device time-to-market.
Devices are especially good candidates for early RWE use since evidence collected in the context of actual patient care from previously approved versions or similar devices often can be used to supplement findings from clinical trials of the latest version in development.
Read our blog on how RWE can improve efficiency across the development continuum or download our two case studies
See also our blog Smart Ways to Collect Real-World Data for Device Trials
Blog: On Formulary Fast - Accelerating the Access and Reimbursement Process
Innovation in Real World Data Collection
Across the healthcare industry, intelligent data, meaning data that can be leveraged to make more informed decisions, and early planning for payer evidence generation, are integral to the solution of healthcare challenges including growing deficits, elderly populations, longer life spans and rising healthcare costs.
Wearables such as Fitbits can be incorporated into trials to lower costs and to improve the ease of gathering Real World Data. Other devices such as ingestible sensors, provide real time information on compliance as well as medication effectiveness
Read blogRWD and Alzheimer's
RWD such as sleep quality and quantity have clinical relevance in Alzheimer's disease, providing objective measures of sleep and activity patterns that are not subject to patient recall bias. Review the use of wearables in Alzheimer’s disease to provide objective measures of sleep and activity patterns that are not subject to patient recall bias.