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Life Sciences Industry is Leveraging Real-World Evidence

Key takeaways:

  • Actionable real-world data (RWD) can help organizations conduct outcomes-based analyses to better understand the burden of disease states and more specifically the clinical and financial impact of certain drugs, devices, and treatments.
  • Innovations in data analytics and real-world evidence (RWE) are helping life sciences and medical device organizations support research efforts, solution development and regulatory compliance.
  • Evolving regulations are opening new opportunities for RWD/RWE use.

As medical solution development methods evolve, the power of real-world data (RWD) and real-world evidence (RWE) continues to play a key role in helping to increase research efficiency and accelerate the introduction of new treatments for patients.

Standardized and connected RWD is needed to generate actionable RWE that could help life sciences and medical device organizations discover new therapy targets, generate patient-centered clinical trials, expedite time to market for new products and indications, expand indications for existing products and support continued clinical innovation.

During a recent webinar, Myla Maloney, Chief Commercial Officer, PINC AIApplied Sciences and Soumi Saha, PharmD, JD, Senior Vice President of Government Affairs, Premier discussed RWD/RWE, their significance in medical solution development and provided real-world examples you won’t want to miss.

RWE is Important for the Future of Life Sciences

Drugs and devices can take 10 or more years to be developed with a median estimated cost of $1 billion to bring a new drug to market. Adding to the high cost is the fact that more than 80 percent of clinical trials in the U.S. fail to meet their patient recruitment timelines.

RWE is increasingly becoming an essential tool to help reduce these costs and ensure that innovative medical solutions are available in market, covered by payers and utilized for the right patient at the right time.

To generate the RWE necessary to prove a product does what it is intended to do, life sciences and medical device organizations need different types of data. These different sources of data are like parts of a puzzle and, when brought together, generate actionable information or create a complete puzzle, RWE. It is through this RWE that clinicians can overcome these challenges; enhance clinical trial design, feasibility and execution; and can accurately assess the benefits and risks of drugs and devices.

RWE Use in an Evolving Regulatory Environment

The regulatory landscape continues to evolve, and organizations must align their research and medical solution development with the latest guidance. In 2016, the 21st Century Cures Act was passed and has caused more regulatory agencies to recognize the promise of RWD/RWE. When combined with clinical trial research, RWD/RWE can help shorten the regulatory approval process of U.S. Food and Drug Administration (FDA) and European Union Medical Device Regulation approvals.

  • FDA’s Focus on Relevance and Reliability: The FDA has clarified that they will be evaluating RWD sources based on relevance and reliability in order to generate sufficiently robust RWE for regulatory purposes.
  • FDA Guidance for Drugs and Biologics: The FDA is working to issue guidance specific for drugs and biologics in a “three-part approach.”
  • Future Guidance for RWE: In December 2022, Congress passed the Consolidated Appropriations Act of 2023 regarding the use of RWD/RWE.

How can healthcare, life sciences and medical device organizations leverage RWD/RWE?

Healthcare organizations can utilize RWD to generate RWE to optimize research studies and clinical trials, discover new treatments, provide proof of therapy efficacy, demonstrate total cost of care and accelerate product development with the goal to improve the future of patient outcomes.

Here are four ways RWD/RWE can be leveraged.

1. Early Disease Identification and Intervention: Life sciences organizations and clinicians are continuously looking for ways to use data to address the gap between disease diagnosis and treatment. This requires much earlier identification of specific disease states by looking for subtle signs that are often found in the unstructured narrative or clinician notes in patient charts.

PINC AI data combined with natural language processing (NLP) technology is well suited for uncovering these details helping to identify which risk factors and clinical signs and symptoms are most predictive of subsequent disease development.

2. Enhance Clinical Trials: RWD from the PINC AI™ Healthcare Database (PHD) and tech-enabled solutions such as machine learning (ML) and NLP can help clinical trial staff rapidly and accurately screen for potential patients and help assess the suitability of trial sites.

This can help compress clinical research timelines and allow solutions to be potentially delivered at reduced costs.

3. Support Regulatory Submissions: Life sciences and medical device organizations need to align their research and product development with the latest regulatory guidance. Payers, regulatory bodies and health systems are increasingly demanding proof that drugs and devices deliver real value by doing what they are intended to do.

4. Expand Device Indications: Life sciences and medical device organizations consistently seek to streamline their processes. By using RWD/RWE to support expanding their indications for use, they can potentially avoid unnecessary randomized clinical trials, reduce their launch research spend and get their innovations to market faster.

The PINC AI™ Applied Sciences (PAS) Team is the Right RWD/RWE Partner

The PINC AI Applied Sciences (PAS) team is helping healthcare, life sciences and medical device organizations access the right RWD/RWE.

PINC AI™ data is the perfect solution for organizations’ RWD/RWE needs.

  • More than 20 years’ worth of data from 45 percent of U.S. hospital discharges, 812 million hospital outpatient and clinic encounters and 131 million office visits.
  • Data tokenization that allows linkage with very specialized data sources for a full longitudinal patient journey and the ability to incorporate social determinants of health and patient-reported outcomes (PRO) data to better understand patient populations.
  • Data spans the continuum of care, medication/device SKU-level utilization and different types of coverage from cash and commercial payer to Medicare and Medicaid.

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