PLEASE NOTE: The content on this page refers to last year’s sold out event.
We are in the planning stages for the 2019 event on April 8–10 and expect to release new information in early to late fall of 2018. Please contact us if you have specific questions.
Tuesday, April 23, 2019
Early career Harvard Medical School investigators kick-off the 2018 World Medical Innovation Forum with rapid fire presentations of their high potential new technologies. Nineteen rising stars from Brigham Health and Massachusetts General Hospital will give ten-minute presentations highlighting their discoveries and insights that will disrupt the field of artificial intelligence. This session is designed for investors, leaders, donors, entrepreneurs and investigators and others who share a passion for identifying emerging high-impact technologies. To view speakers and topics, click here.
3rd Floor and 7th Floor
Senior clinical leaders, current and past Forum Chairs, will share perspectives on the range of impact of AI on clinical practice. Discussion will highlight the rapid evolution of AI as a practical clinical tool and short and mid-term prospects for adoption in cancer, cardiovascular and neurological care.
Given the scarcity of late-stage assets, prolonged timelines and enormous costs of bringing drugs to market, AI-based approaches to target discovery, drug design and drug repurposing hold significant promise to positively disrupt the existing R&D paradigm.
The first wave of EHR adoption has focused primarily on digitizing the patient record – with a more recent focus on building interactive clinical decision support capabilities. Development and implementation of CDS applications currently requires clinical staff to observe trends in data, develop protocols to act on these trends and work with technical staff to codify the logic into executable form. As NLP and computer vision capabilities become more advanced, algorithms will identify and propose actions reflecting patterns in the data. The panel will discuss existing challenges and whether AI technology will ultimately support an unsupervised learning approach in the EHR to identify trends and possible responses at both the patient and population level?
AI based approaches to conduct faster and more efficient clinical trials are beginning to emerge. Current approaches include applying predictive tools to perform more targeted patient recruitment and more accurate eligibility assessment. Panelists will discuss timelines for AI technology to have a measurable effect on trial cost and time to conduct the trial. Bottlenecks to applying this technology at scale and whether there will be a measurable effect on the cost of bringing drugs to market over the next decade will also be examined.
Wednesday, April 24, 2019
Historical barriers have driven increased medical costs and decreasing access since the 1960s. The “Iron Triangle of Healthcare” continues to represent a tenuous balance of quality, cost and accessibility – economists have lamented attempts to optimize one characteristic at the expense of the others. The accumulation of innovations in care delivery (e.g. shift to lower cost providers and settings), population management, value based reimbursement and hospital administration are having a measurable effect. Can AI based technologies accelerate the pace of innovation and finally bend the cost and access curves in the US?
The drug development process is highly complex and has many drivers. The panel will discuss the strategic impact of AI on the entire process and the implications for healthcare overall. How will the combination of factors – research strategy, drug development, regulatory approvals, reimbursement and clinical effectiveness – be influenced by the implementation of AI. Panelists will discuss short and mid-term prospects and whether AI will ultimately lead to a restructuring of the pharma model to develop new therapies.
The promise of machine learning and big data in in healthcare seems boundless – but healthcare data is massive and complex, and organizing and managing this data is the first step to an AI-empowered healthcare system. Technology giants are investing in solutions to overcome these data engineering challenges, but with many visions of the future of healthcare data jockeying for dominance, what will the future of healthcare data really look like? Can we finally liberate the value of data for patient care? And how will it happen?