Ethos: 4 Considerations for a
Principled MLR in the AI Era

AI agents are accelerating content review by identifying the tasks that demand human expertise while automating the rest. In these videos, Ethos experts Dr. Sheuli Porkess and Dr. Nick Broughton outline a 4-step methodology for a principled MLR in the AI era. Their approach leverages agentic AI to keep MLR reviewers laser-focused on critical decisions, moving toward a more efficient, exception-based MLR review model.

Consideration 1: Reimagine MLR for the AI Era

Consideration 2: Integrate the Human Element within Agentic MLR

“AI is very good at augmenting human decision making within the MLR process, such as checking references, but it can’t make decisions in areas where humans can reasonably disagree”.

^{– Dr. Nick Broughton, Founder & Managing Director, Ethos}

Consideration 3: Define Clear Goals with AI in MLR

“It’s really important that people work out what their goals are when introducing AI. Are we just going to do the same as we’ve always done—but maybe a bit faster—or are we actually trying to improve the quality of materials and reduce risk?”

^{– Dr. Nick Broughton, Founder & Managing Director, Ethos}

Consideration 4: The Future for Medical Leaders with Agentic MLR

“AI provides an amazing opportunity to do things differently, but it won’t happen if we don’t lean into it. We must ensure it works for our teams, our organizations, and ultimately, our patients.”

^{– Dr. Sheuli Porkess, Co-author, Ethos Onyx AI in Pharma, Ethos}

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