Our successful transition to value-driven healthcare, where biomedical innovation is both patient-centered and sustainable, depends largely upon our ability to generate better evidence, more efficiently, to enable better decision-making for all stakeholders across the value chain of research and development (R&D) and healthcare delivery (patients, manufacturers, regulators, payers, providers, and academic researchers).
The goal of the LEAPS Project is to design and pilot an innovation ecosystem for one disease that drives more value, faster, to patients, in ways that work for all stakeholders. Ultimately, patients will receive timely access to the most appropriate therapeutics for their needs, and stakeholders will have the evidence they need, when they need it, to improve their decisions related to the development, access, and use of therapeutics for the target disease. Decision-making will be improved through the collaborative design of a distributed, purpose-driven evidence generation infrastructure – the LEAPS “Learning Engine”- that stakeholders both contribute to and benefit from, in order for it to be efficient, scalable, and sustainable over time.
The LEAPS Project provides a unique opportunity for stakeholders to collaborate on the design of a next-generation innovation ecosystem for a target disease. This showcase prototype will be piloted using Massachusetts as a statewide, testbed environment. LEAPS will leverage proven NEWDIGS methods and tools in designing and catalyzing pilot activities.
Participants in the first LEAPS Design Lab in July 2018 were tasked with considering disease areas offering the prospect of significant healthcare impact and identifying early practical and technical challenges to the pilot project’s success. Four possible diseases were considered and discussed: Asthma, Type 1 Diabetes, Type 2 Diabetes, and Rheumatoid Arthritis.
After careful deliberation with the MIT CBI Leadership Team, the LEAPS Strategic Advisory Network, and the LEAPS Steering Committee, the program will advance Rheumatoid Arthritis (RA) as the primary target for the MA Pilot project. In addition, building on input from the Design Lab, Asthma will become a secondary disease target, helping the teams to make sure that the toolkits and learnings are scalable in different disease areas. Though RA will move forward as the first pilot, LEAPS hopes to catalyze other pilots through dissemination of its generalizable principles for application in other disease ecosystems.
LEAPS will drive impact in three critical domains: product innovation, regimen optimization, and disease modification/prevention. Its work streams will evolve over time but are guided initially by the following aims:
- Enhance evidence planning and production across the drug development life span to fuel sustainable, patient-centered innovation
- Apply systems engineering methods and tools to enable seamless, continuous learning and improvement across the innovation value chain (from R&D to care delivery) for a target disease
- Assess potential applications of transformative technologies and methods, such as blockchain and artificial intelligence/machine learning
Elements of the strategic vision for LEAPS were explored at the Next Wave Forum hosted by MIT NEWDIGS on December 12-13, 2017 in Cambridge, Massachusetts. The 2-year pilot design phase of LEAPS was launched in January 2018.
|Research and publications
Get original LEAPS Project research and learn about the complex challenges and potential solutions for a patient- and disease-oriented learning ecosystem
|LEAPS Project Description
Get an overview of the project, see who's involved, and learn how to get involved
|LEAPS video and interviews
Gigi Hirsch discusses LEAPS and the NEWDIGS initiative for SSRC
Gigi Hirsch interviewed for The Evidence Forum
Dynamic Dossier in the Cloud (first LEAPS Incubator project)
LEAPS December 2019 Design Lab
LEAPS June 2019 Design Lab
LEAPS December 2018 Design Lab
LEAPS July 2018 Design Lab
PPD Evidera joins LEAPS
MIT News of the LEAPS the launch announcement
The Next Wave in Adaptive Biomedical Innovation: Advancing Platform Trials into End-to-End Rapid Learning Systems