Dr Steve Pickette: How AI Can Revolutionize Formulary Management

Steve Pickette, PharmD, BCPS, chief clinical pharmacist at InpharmD, explains that artificial intelligence (AI)–driven formulary analysis can significantly enhance the efficiency and accuracy of drug selection processes by rapidly compiling and analyzing evidence, although it requires careful oversight to mitigate risks related to data bias, privacy, and AI inaccuracies.

At The American Journal of Managed Care^®’s Institute for Value-Based Medicine (IVBM) event in Seattle, Washington, cohosted with Providence, Pickette presented on the use of AI in formulary management. The IVBM event highlighted integrative approaches in neuroscience population health, with additional presentations on the Alzheimer disease landscape, nonopioid pain management, and the impact of biosimilar market expansion.

This interview was edited for clarity.

Transcript

How can AI-driven formulary analysis improve the efficiency and accuracy of drug selection processes in health care systems?

In many ways, it's really exciting. The great thing about AI is that it saves time on things. It doesn't do anything that we can't do, but it can do it much more quickly, and now can match closely to the work done by people. So if you think about the formulary process, all the steps involved in it, you have to accumulate all the evidence, put it all together summarized in a document. In my experience, there was about a 6-week cycle using residents to draft something, edit it, get it right, get all the information, get it in the tables, etc. AI can do that in 20 seconds. It's not perfect, but it's really close. And then the drug information specialist takes it from there so it gains efficiency in many fold of current.

So [AI] saves the time that you would spend on developing that analysis to actually engaging with the providers and working on assessing the information that you have. What I have been working on and developing that I'm not aware that anyone else has done is to use the AI to go the next step; it's done the analysis. It knows the dosing, the equivalence based on the evidence, and the alternatives. Then, to extend the use of AI to do that financial analysis, looking at at drug A vs drug B: what if you converted, how much would it cost, could you convert it for all indications? There's analytics that does that out there, but it doesn't know the evidence. So, combining the evidence and the analytics is pretty exciting for me.

What are the potential risks and challenges of implementing AI in formulary decision-making, particularly regarding bias and data privacy?

There are risks involved that need to be addressed. The first one that comes to mind is that you need to be able to trust in the data that's being used. You can't necessarily trust that studies are not biased. You can't just take at face value the information that you get out there. There are misleading representations. So it's pretty nuanced.

And so the philosophy at InpharmD is that we don't [just] trust the AI. We trust, but verify. We don't ever provide information that AI has generated without analysis by a human specialist that assesses it, and so that's the first concern. And then AI has hallucinations, as they say, and sometimes just gets the data wrong and you can't really even understand why, because it doesn't really track back to just how it made the decision. But we also look for those things and use it to train the AI to reduce the incidence of that. So that, I think, addresses most of the most of the concerns.