optimising the use of real world data to drive effective outcomes
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).
how can the abundance of real world data (rwd) from electronic health records (ehrs) enhance your late phase research studies while decreasing study costs?
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.
Database mapping provides a tool to systematically identify and evaluate RWD sources. Database mapping is a flexible methodology that can be customized to meet various needs, whether to locate data sources to answer a specific study question or to get a larger overview of RWD sources for an indication of interest. Watch our webinar乐和彩开户 to learn more.
as more biotech companies use rwe to support approvals, broader real-world outcomes for their products will emerge.
While randomised clinical trials (RCTs) remain the standard for evidence generation, RWE serves as a disrupter, bringing greater efficiency to the development life cycle and novel ways to improve regulatory decision-making - ultimately maximising revenue. Read our blog乐和彩开户 to learn more about the receptiveness of regulatory agencies for RWE, and the use of advancing technology to maximse RWE output.
Devising a comprehensive RWE strategy can ensure that smart decisions are made in how to best choose, synthesise and analyse available real world data (RWD) assets. By identifying what evidence will support regulators’ and payers’ decision making alike, sponsors can develop an evidence generation plan across the product life cycle to leverage outputs and identify data gaps. For more details read our latest blog.
The increasing availability of big data is creating a shift in the clinical research landscape, allowing researchers to make intelligent, strategic decisions based on real world evidence that is derived from the aggregation and analysis of real world data. Read our blog乐和彩开户 to learn how leveraging real world data can improve patient enrollment.
cross-sectional surveys (css) and medical chart reviews (mcrs) are both common study designs, and each has its strengths and limitations. however, many of those limitations can be overcome through a hybrid design approach that uses both types of study designs to enhance each another. whereas at one time, hybrid studies were difficult to do, they can now be performed efficiently, thanks to the availability of comprehensive, electronic medical records (emr) databases.
Read our blog to learn more on the benefits and of using this approach and how ICON's epidemiology experts can advise on the best study design.
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:
real world data from electronic health records can enhance your late phase research studies while decreasing study costs.
乐和彩开户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.
乐和彩开户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
乐和彩开户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.
乐和彩开户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:
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.Read blog
乐和彩开户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:
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:
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.
乐和彩开户understanding what evidence will best support a product’s value story is paramount for sponsors.
Watch this on-demand webinar乐和彩开户 to learn more:
Download our white paper, Real World Evidence Generation: The Value of Cross-Sectional Studies and Medical Chart Reviews乐和彩开户 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.
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.
See also our blog Smart Ways to Collect Real-World Data for Device Trials
乐和彩开户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 effectivenessRead blog
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 乐和彩开户to provide objective measures of sleep and activity patterns that are not subject to patient recall bias.