In the final part of this video interview, Jim Murphy, CEO of Greenphire discusses the role of artificial intelligence, using trusted data, and simplifying protocols.
ACT: Is there any potential for the use of artificial intelligence (AI) in budgeting and forecasting? If so, how?
Murphy: Everybody's asking themselves what are the boundaries and what's the timeline for AI to touch their universe? I would say maybe as a starting point, the entire process of budgeting and forecasting is based on data patterns, structured data patterns that are applied through tools to extrapolate outcomes. That analytic use of data, it's not AI, obviously, but it is really thoughtful use of structured data, and I think about the idea of a ChatGPT or some sort of accessible AI tool that's using an open internet-based approach, you're not really going to be able to do a ton in terms of budgeting-type activity for clinical trials, because where those solutions do their best work is where there's just a ton of information out on the internet or in a closed system, where maybe the algorithm can be a little bit more targeted towards trusted data, but there really aren't places where that much trusted data exists to drive budgeting and nuance is tough for AI, and really nuance is that therapeutic area and geography basis, there's regulatory considerations that have to be in, so I think the short answer is, certainly there is a role, but I think good data analytics is really what is still the emerging need, which ultimately is just now being addressed. Fair market value has been around for some time, and is constantly getting better as we apply more real-time information into that equation and algorithm, patient-neutral spend and travel-related spend that's never existed. We're creating that now and really taking this amazing amount of insight and putting into hands of people.
I do think there are some interesting areas, though, that are adjacent to the budgeting process that there is some benefit that I can imagine more immediately from AI. Protocol simplification is absolutely a quest, make it easier for greater inclusion/exclusion criteria and enrollment and whatnot. I think there's broader health system data across different countries that's more accessible that might be used to do some of that chore of trying to figure out where things can be bent and flexed to simplify where possible, and then synthetic treatment arms, that's another angle off similar real-life, real-world data that maybe it's analytic, maybe it's an artificial intelligence opportunity, I'm not sure, it's sort of right there at the at the edge of the two. That would be my best insight for the most immediate-term in the budgeting and forecasting process, as well as maybe some adjacent areas.
