Mike Rousselle, Chief AI Officer at OptimizeRx – Interview Series

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Mike Rousselle, Chief AI Officer at OptimizeRx, is an artificial intelligence and healthcare technology executive with nearly 15 years of experience applying AI to create business value. In his current role, he leads the development and application of AI-driven technologies designed to make communications between life sciences companies, healthcare professionals (HCPs), and patients more clinically relevant and actionable.

His work spans predictive intelligence, behavioral and clinical signals, audience targeting, and AI-powered tools that help identify important treatment and prescribing moments. Before joining OptimizeRx, Rousselle held roles at Athenahealth, Clarivate, and HubSpot, bringing experience across healthcare, data, and enterprise technology. He holds bachelor’s degrees in Economics and Finance from the Rochester Institute of Technology.

OptimizeRx is a healthcare technology company that connects life sciences organizations with healthcare providers and patients using data, artificial intelligence, and digital engagement technologies. Founded in 2006, the company initially focused on delivering coupons and co-pay assistance through electronic health record (EHR) and e-prescribing platforms, but has since expanded into a broader healthcare marketing and patient-engagement platform.

Its technology combines real-world health signals, predictive intelligence, point-of-care integrations, and omnichannel activation to help pharmaceutical companies identify relevant HCP and patient audiences and engage them during key moments in the treatment journey. More recently, OptimizeRx has expanded its AI capabilities with tools such as its Natural Language Audience Builder, which allows healthcare marketers to create HCP and privacy-safe consumer audiences using plain-language prompts

You’ve spent nearly 15 years applying AI to business problems across organizations including Athenahealth, Clarivate, and HubSpot, and at OptimizeRx you progressed from leading data products to serving as Chief AI Officer. How has that journey shaped your view of where AI can create meaningful value in healthcare, versus applications that may be technologically impressive but ultimately superfic

One of my key learnings over my career is that as “cool” as I find AI to be, and as fun as it is to utilize, it doesn’t matter AT ALL if the AI isn’t used in service of a customer’s problem. It’s not about using AI as a starting point; it’s about focusing on the concrete challenges and inefficiencies in healthcare that we can improve.

At OptimizeRx, we seek to solve a single, fundamental challenge: aligning brand information and communications more closely to the patient journey, so they can better support care decisions. That helps physicians who are inundated with advertising that may or may not be pertinent to them and their patients. It helps patients who are exposed to irrelevant or inaccurate health information on social media and other consumer channels.

And it helps life sciences brands who are constantly under pressure to meet their commercial goals. Our lodestone to solve these challenges is the brand-eligible patient.

If we use AI to find them earlier, more precisely, and just at the point where they become brand ready, we can deliver higher-utility information to their provider, serve more relevant DTC advertising, and help the brand spend its media dollars more efficiently and impactfully.

Much of the recent enterprise AI conversation has focused on generative AI and content creation. You’ve argued for moving toward AI that helps organizations make decisions. What distinguishes genuine “decision intelligence” from simply putting a generative AI interface on top of existing analytics?

The distinction comes down to whether AI is simply helping you access information, or if it’s actually helping you make better decisions. Putting a generative AI interface on top of analytics can make data easier to query or summarize, but the underlying intelligence hasn’t fundamentally changed. You’re still asking a person to interpret the information, determine what matters, and decide what to do next.

Genuine decision intelligence closes that gap by bringing together multiple signals, recognizing patterns and context, predicting likely outcomes, and translating those insights into recommended actions. Just as importantly, decision intelligence also learns from the results of those actions and uses that data to continuously improve future recommendations. In life sciences marketing, for example, that means moving beyond simply reporting on campaign performance to predicting which audiences are most likely to respond (and on what channels), anticipating which message is relevant, and measuring the accuracy of those predictions.

The value is in the closed-loop intelligence, meaning the ability to turn data into decisions and continuously improve those decisions over time. That’s the real trick to helping marketers better allocate resources appropriately and efficiently.

OptimizeRx’s Dynamic Audience Activation Platform uses AI and real-world data to identify healthcare providers treating potentially eligible patients and predict when engagement may be most relevant.