Fourth LEAPS Design Lab at MIT focuses on challenges and potential value of Real World Data and Evidence in rheumatoid arthritis learning ecosystem

Thursday, December 19, 2019

The LEAPS Project in the MIT Center for Biomedical Innovation’s NEWDIGS Initiative held its fourth Design Lab on December 11 and 12 at MIT. Innovators and leaders across the rheumatoid arthritis (RA) research and patient care stakeholder spectrum worked toward the project’s first goal, to design a Massachusetts-wide pilot project for a “learning engine” that can produce better evidence-driven outcomes for patients in ways that are more efficient for the system. 

 

RELATED: LEAPS Design Lab at MIT advances work on Massachusetts-wide rheumatoid arthritis “learning engine”

 

This Design Lab placed special focus on the roles of Real World Data (RWD) and Real World Evidence (RWE) in the RA learning engine. Interactive presentations explored the potential value to LEAPS, to the healthcare system, and to each stakeholder segment. Discussion leaders noted that a challenge to effectively collecting, sharing, and using RWD is the potential misalignment of parties’ interests or expectations of value. 

Participants noted movement in the healthcare system toward aligned interests but cautioned that practical complexities and economic imperatives currently limit development and application.

The discussion also examined a potential framework for making predictions and estimations of real world data value, and with what certainty, over given periods of time. There was consensus that the LEAPS project structure, by inviting open input from all stakeholder segments in an environment of mutual transparency and trust, shows potential to improve the relevance and accuracy of value estimates. 

The core LEAPS evidence generators—the Real World Discovery Platform (RWDP) and the Adaptative Point of Care (APoC) trial platform—directly address the replication and validation banes of leveraging real world data in today’s fragmented research and healthcare system. In addition, every research effort on either platform must promise fit-for-purpose evidence for a relevant regimen optimization issue as confirmed by patient, physician, and payer organizations. These critical design elements help ensure that LEAPS will catalyze broad impact and not repeat the disappointments of prior attempts to learn from RWD.

Sessions mixing participants with diverse perspectives and backgrounds identified transparency, accessibility, and credibility as key issues for RWE’s application in a learning ecosystem. They asked, Who’s demanding information from whom? Are expectations reciprocal or unilateral? Are parties’ privacy and rights—especially patients’—honored? Are conclusions drawn from one data source confirmed by others? Are they sound and replicable?

Participants also agreed that active stakeholder involvement depended on expectations of adequate value, that no system design features should threaten any party’s viability, and that the scope of a RWD initiative or platform needed specific definition and purpose—that the design principle should be “decision pull” not “evidence push.”

“There are many efforts ongoing to consolidate data from multiple sources across the health care system,” noted a participant expert in public health and RWE. (The Design Lab was conducted under the Chatham House Rule, assuring privacy of participants’ identities to encourage openness in discussion.) “The real value of LEAPS is that we are starting with the important questions and all stakeholders in mind, so that we can create a future system and process that will solve real problems. We do not assume that ‘if we build it, they will come.’”

 

LEAPS anticipates launching pilot modules in 2020, accompanied by a “playbook” of generalized principles that guide the project and which could be applied to other disease areas and in other geographies.