Real world evidence is playing an increasingly important role in the pharmaceuticals industry, Health Economics and Outcomes Research (HEOR) and healthcare decision-making. However, the diverse nature and sheer volume of real world data presents a number of challenges to healthcare and life-sciences organizations that aspire to leverage real world evidence to, respectively, improve patient clinical outcomes and demonstrate the safety and value of pharmaceutical products. Whether it’s structured, multi-structured or unstructured, today’s real world data presents a barrier to insight because it is derived from a wide variety of sources—including electronic health records, genomics data, sensors, wearable devices, etc.—and stored across disconnected silos that make it difficult to integrate and transform this disparate information for analysis and insights.
We recently hosted a popular ISPOR educational webinar titled, Leveraging Data Science and Innovative Digital Technologies to Optimize Real World Evidence Generation. Moderated by Bill Fox, MarkLogic’s chief strategist for global healthcare and life sciences, and featuring Jyotsna Mehta of KEVA Health, Dr. Javier Jimenez of Sanofi and Dr. Ashish Atreja of Mount Sinai Health System, this webinar was centered around how data science and digital data technologies are empowering healthcare and life-sciences organizations to overcome their data challenges while transforming the way real world evidence is generated and used.
Evolutionary Applications of Real World Data
To realize their goals of fully and efficiently extracting value from healthcare data for real world evidence development and HEOR, leaders throughout the healthcare ecosystem are tackling obstructive data challenges and seeking innovative solutions via data science, digital health, multi-structured data integration, security and governance. Here are a few key highlights from the discussion:
The evolution of data science
In her segment on data science, Jyotsna Mehta, founder of KEVA Health, explores how data scientists extract knowledge and insight from data using scientific methods such as natural language processing (NLP), machine learning and AI. Complementing the work of analysts, data science has brought machine learning to Massachusetts General, the National Institute of Health and the FDA. More and more health organizations are saying goodbye to traditional methods of analysis.
A platform like MarkLogic enables data scientists to run thousands of variables on millions of data points. This means you can more easily target the right population for clinical trials, automate clustering of subgroups by demographic or treatment protocol and define populations for whom effective treatments don’t exist.
RWE matters because outcomes matter
In his presentation on real world evidence and patient outcomes, Dr. Javier Jimenez, vice president and global head of RWE and clinical outcomes at Sanofi, examines how RWE uses real world data to improve outcomes. Dr. Jimenez discusses methods to discover, develop and deliver new insights about healthcare interventions. He envisions a holistic approach to treatment, one that acknowledges the multi-factorial nature of health outcomes and respects the challenge presented by so much critical data existing outside of medical systems.
RWE offers the tools that help providers improve accuracy, integrate disparate data, collaborate with other caregivers more easily and correlate biomarkers with outcomes. This creates the ability to triangulate causal factors related to patient biology, healthcare practices and patient behavior.
In his remarks, Dr. Jimenez also explores the implications of more data in the hands of patients and always-evolving regulatory requirements. He concludes by introducing Sanofi’s big data platform launch that expands its RWE capabilities and helps improve patient outcomes.
Realizing the promise of value-based care
In his discussion on the role of digital health in value-based care, Dr. Ashish Atreja, chief innovation officer, medicine at Mount Sinai Health System, citing Mount Sinai’s guiding principle of “if our beds are filled, it means we have failed,” explores the role of technology in establishing value-based healthcare. To build a system based on outcomes, he advocates moving beyond reliance on electronic health records.
Working with data-savvy customers, Mount Sinai launched a groundbreaking action plan for digital health innovation. This sprawling, complex initiative leverages data from patients, providers, researchers and payers, among others.
Dr. Atreja and the team at Mount Sinai are transforming the patient care model, proving that digital medicine is indeed medicine.
Real World Evidence in the Spotlight
Why is real world evidence taking on greater urgency for healthcare and life sciences organizations? “The clinical trials system is broken,” notes the Food and Drug Administration (FDA), identifying real world evidence as the best solution for fixing its dysfunctional and clinical trials processes.
A rapidly changing regulatory approach to clinical trials is driving renewed and redoubled interest in expanding methods for collecting and utilizing patient data. Moving to the forefront of the life sciences and healthcare industries, RWE now presents a crucial means for pharmaceutical companies to demonstrate and prove the clinical and economic value of their products at less time and cost.
The average time from FDA application to approval of drugs
is 12 years and over $1 billion.”
Closing the evidence gap, however, will not be easy. There’s a lot of data out there.
We recognize the data challenges pharmaceutical organizations face in their quests for more efficient real world evidence generation. The datasets are diverse, technologically inharmonious and in some cases, owned by stakeholders that operate outside of the health and data science community.
Harmonizing previously siloed in-house data is difficult enough, and integrating data pools from out in the real world requires a fresh approach. There is a massive amount of data coming from outside the organization sourced by patients, payers, providers and even social media. It can be structured, unstructured, semi-structured and all different schema, making the process of data integration exponentially more complex.
This doesn’t have to be an unsolvable problem.
Behold the Great & Powerful Enablement of the ODH
We’ve built an agile, real world evidence platform that enables organizations to more efficiently integrate and transform every type of healthcare data into a single operational data hub (ODH). Unlike the murky data lake, MarkLogic’s multi-model database platform is a secure, properly governed foundation upon which you can actually run a digital health business.
This architecture was not conjured up in a conference room; it grew organically over 15 years. Early MarkLogic adopters in the healthcare, life sciences and financial sectors helped us create today’s enterprise architecture—the model for building a future-proof digital enterprise.
The ODH eliminates the distance between observing your business data and operationalizing it, allowing organizations to turn any type of data into the curated, usable data needed to enable AI, machine learning and business intelligence. This is how you turn data science insights into action.
Proven at the Fortune 50 level in pharma, MarkLogic enables life sciences and healthcare organizations to more quickly integrate disparate real world data, ensure its governance, find hidden connections across data and metadata with semantic search and quickly build searches for specific data views.
Learn how MarkLogic’s database platform enables organizations to realize faster time to insight in their real world evidence development efforts: