Healthcare has been shifting to a value-based, personalized paradigm,” says Brett Davis, general manager, ConvergeHEALTH by Deloitte. “Reimbursement pressures and the unsustainability of the cost trajectory we’ve been on has been driving everyone to get to more of an outcomes-based or value-based construct as opposed to a fee-for-service model.”
Personalization in medicine is driven by the convergence of our deepening understanding of genomics and our improving digital health technologies—the timing is right. Real-world data sources are combining with traditional data sources—clinical trials, electronic medical records, genomic data, insurance claims, streaming connected devices, social media, meteorological data, and patient reported data—to offer a wealth of possible applications. And now maturing technologies like machine learning and algorithmic methodologies can make sense of these big, noisy, complicated datasets. Together, these data sources and analytic technologies seek to answer what treatments work for whom, why, in what context, and at what cost.
And everyone wants in.
“Health payers are trying to use this data to decide what their reimbursement strategies should be; patients are going to increasingly have access to a lot of this data to make decisions and it will influence what they want to do,” Davis told me. “Obviously, health systems need to use this data to improve care pathways and clinical decisions. And then of course biopharmaceutical companies need to wrap their arms around, and figure out how to get access to and analyze this data.”
Regulatory Blessing
Real-world evidence brings with it a host of opportunities. Will more data help better design trials and find the right patient cohorts? Will more data better predict adverse events, and allow more nuanced safety and pharmacovigilance modeling? Will more data allow companies to better commercialize, price, and get reimbursed for their innovations?
“This is a tectonic shift for the pharmaceutical industry,” Davis said.
And in fact, biotech and pharma are being increasingly freed to use this data. In December 2016, the 21st Century Cures Act called for U.S. Food & Drug Administration to develop a framework and guidance for evaluating real-world evidence in the context of drug regulation to support approvals of new indications for previously approved drugs, and to support or fulfill post-approval study requirements.
FDA has been using real-world evidence for safety surveillance and development of drugs for rare diseases in which randomized controlled trials are impossible. And clinical trials commonly use real-world data to determine end-points, such as gathering deaths from the National Death Index or tumors from a tumor registry. But now the FDA is sending signals that broader use is appropriate and encouraged.
Using real-world evidence to answer questions about dosing regimens, long-term outcomes, and outcomes in various subpopulations “is preferable to having no evidence whatsoever,” wrote three FDA staff members including Janet Woodcock, director of FDA’s Center for Drug Evaluation and Research, in a Viewpoint article published in the Journal of the American Medical Association last month (doi:10.1001/jama.2017.9991).
Woodcock and her co-authors listed several ways real-world data can improve efficiency and cost-savings in clinical trials. “Real-world data may be used to aid in the design of a clinical trial by assisting in the selection of study sites that are more likely to enroll study participants, provide a basis for power calculations, provide a prior for a Bayesian statistical analysis, provide an external control group, and guide enrichment. Real-world data may also be used during the conduct of a trial to reduce duplication of data input such as baseline medical history, automated adverse event reporting, and end-point ascertainment.”
In remarks delivered to the National Academy of Sciences on September 19, Scott Gottlieb, Commissioner of Food and Drugs, further committed to expanding FDA’s use of real-world evidence.
“The FDA’s interest in advancing the adoption of RWE in support of its programs remains a top priority. And it’s a high priority of mine,” Gottlieb said. “We need to close the evidence gap between the information we use to make FDA’s decisions, and the evidence increasingly used by the medical community, by payers, and by others charged with making healthcare decisions.”
Drug discovery, development, clinical testing, and post-market use should be considered a continuum, Gottleib said. “No product is all risky and uncertain one day, and completely safe and effective the next.” So it makes sense to apply real-world evidence earlier in the drug development process. Gottleib also committed to publishing consensus definitions that relate to how different parts of FDA apply real-world data to regulatory considerations.
Paradigm Shift
With FDA’s blessing, companies can now pursue the application of real-world evidence with even greater energy. We are at a tipping point, Davis said, a shift from real-world data 1.0 to real-world data 2.0.
“What we’re really trying to do is have the real-world evidence and the clinical trial converge and have a much closer connection,” said Bill Fox, global CTO for healthcare and life sciences at MarkLogic. “That presents two kinds of opportunities for collaboration. One is internal, so you start to have parts of a business that weren’t tied together before, weren’t on the same page, weren’t necessarily working together start to work together, that it creates really important opportunities for collaboration in the market… [And] now a big pharma might be collaborating with a hospital system or a provider system, or patients directly in order to gather this real-world evidence from them.”
But new data types and data streams will do nothing but bog down the system without the next generation technologies to navigate them.
“Real world evidence is really sort of a lightning rod for digital transformation and for the way that these pharmas are doing business and the way that they think about their IT,” said Fox.
At Celgene, for instance, a company-wide information initiative has led to the creation of a new big data platform to manage real-world evidence. Celgene’s Synapse platform lets the company ask big picture questions that have the potential to drive the business in new ways. “There are certain types of studies that inform decisions at multiple stages of the pipeline,” said Patrick Loerch, senior director of data science at Celgene. For example, “building out treatment flows based on real world data. It’s getting an understanding of how a given disease is being treated in the real world, which is a question that informs decisions throughout the entire pipeline.” (Read more: Celgene’s Big Data IQ
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Celgene is in good company. Last year, the Deloitte Center for Health Solutions surveyed 15 leading life sciences companies to understand the current state of their RWE capabilities. The results of the Real-World Evidence Benchmark were released late in 2016. Among the findings, the company discovered that over half of survey respondents plan to invest in their RWE programs to significantly increase their capability in this space.
The investments are broad. About 60% of companies surveyed are using the cloud to maximize their ability to store and analyze data. Whether relying on off or on-premises storage, companies are making software and platform investments to ensure the best use of RWE. There are staffing investments as well; companies are paying closer attention to improving their data science skills, seeking experts in statistics, machine learning, and computer science to generate insights from disparate data sources.
Data Dearth
But the technology is only half the battle; many in pharma and biotech don’t feel they have as much real world evidence as they’d like, and gaining access to the data is challenging. Sixty percent of Deloitte’s survey respondents reported their biggest challenge was gaining access to the real-world evidence they need.
“Pharma as an industry has spent billions of dollars collectively setting up the infrastructure for running clinical trials. But that infrastructure for clinical trials is somewhat useless to capture and manage real-world evidence, information, and data,” Davis said. In fact, the Deloitte survey respondents want real world data far before clinical trials. Nearly 50% of survey respondents believe that R&D is the biggest area of opportunity going forward to leverage real-world evidence.
So pharma and biotech are looking for partnerships with patient advocacy groups, health systems, and other parties capturing these data, Davis said, citing recent major partnerships like those between Amgen and Humana, Merck and Aetna.
Networks are also connecting more data by either disease area or geography. For example, the ORIEN Network—Oncology Research Information Exchange Network—started at Moffitt Cancer Center in 2006, but has now expanded to a network of over 15 cancer centers nationwide. ORIEN partners use a common protocol: Total Cancer Care, which provides a standard system for tracking patient molecular, clinical, and epidemiological data and follows the patient throughout his or her lifetime. Partners have access to one of the world’s largest clinically annotated cancer tissue repositories and data from more than 100,000 patients who have consented to the donation for research.
“For pharma, it is a little bit of a change in mindset. Historically pharma wants to own the data. The reality is in this new world of healthcare data being electronic… the concept of ownerships shouldn’t necessarily be focused on,” Davis said. “It’s access and use that are key.”
