Christopher McSpiritt is VP, Life Sciences Strategy at Domino Data Lab. He drives understanding of customer needs and works with product management and marketing teams to drive go-to-market approaches within the life sciences sector. Christopher began focusing on the Life Sciences industry when he joined a small eClinical startup in 2005. Since then, he has had the opportunity to work at both consulting firms and leading software companies as a project manager, business analyst, consultant, product manager, and strategist.
Every life sciences organization needs to consider how best to apply machine learning (ML) to RWE to support better patient outcomes. Here’s how they can ensure RWE is “ML-ready.”
As RWE continues to influence the future of drug development, pharma companies will inevitably encounter hurdles. But with the right approach, they can be overcome to unlock critical insights and bring better therapies to patients faster.
What if health plans could identify member decline before an avoidable hospitalization occurs? Real-time clinical visibility into long-stay SNF members uncovers risk earlier and drives better outcomes.
By treating model governance as a proactive, integral part of operations rather than a box to check after the fact, life sciences organizations can uphold compliance, better allocate their resources, and gain a competitive edge.