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. What types of signals are most valuable to these predictive models, and how do you determine whether the model has identified a meaningful clinical pattern rather than a misleading correlation?
The value of a signal comes down to timing and practical medical relevance, while accuracy is maintained through clear clinical logic and continuous outcome tracking. We focus on straightforward indicators like when a patient needs to switch medications, key lab results, and upcoming doctor appointments. Combining a patient’s health history with the right timing during a patient visit is what makes outreach effective.
To keep the model from chasing misleading patterns, like confusing a busy general doctor with a specialist who actually manages the condition, we guide the technology using standard medical guidelines and realistic treatment timelines. We then double-check our results against real-world prescription data and comparison groups to ensure we are only focusing on moments that lead to genuine changes in patient care.
OptimizeRx recently launched its Natural Language Audience Builder, which allows marketers to describe an audience using natural language and turn that request into brand-specific, coordinated HCP and consumer audiences. What happens technically between the initial prompt and the final audience, and how do you prevent ambiguity or hallucinations from influencing something that ultimately drives real-world decisions?
The process comes down to using language AI for interpretation, while relying on real-world clinical and EHR network data for the final NPI list output, and OptimizeRx’s Micro-Neighborhood® Targeting for consumer audiences. When a marketer inputs a prompt, the system breaks the request down into target parameters, such as medical specialties, patient volumes, and prescribing behaviors, without relying on static, pre-built audience segments.
Because real-world commercial decisions are on the line, hallucinations are prevented by ensuring the AI model acts as an interpreter that queries authenticated provider registries and real-world clinical datasets. The platform translates prompt details into verified NPI lists and Micro-Neighborhood audiences, allowing marketers to view, rank, and refine the exact matching doctor profiles and/or consumer segments before finalizing the target audience.
Traditional pharmaceutical marketing has often relied on relatively static segmentation based on historical prescribing behavior. How does AI change that model when audiences can instead be continuously reprioritized based on changing patient journeys and predicted care windows?
AI gives marketers the ability to respond to changes in the care journey as they happen. By bringing together signals such as prescribing behavior, patient populations, HCP engagement and other contextual data, models can identify when an HCP and their patient are approaching a meaningful decision point and continuously reprioritize the audience based on that context.
That means audience activation can evolve alongside the care journey. As patients enter a particular stage of care, the brand becomes more relevant to them and their treating HCP, and the timing, channel, and message should adjust as that window changes. Over time, the system can learn from those interactions and improve how it identifies the patients, HCPs, and timeframes most likely to influence prescribing behavior.
For pharmaceutical marketers, this creates a more dynamic, synchronized approach to HCP and consumer marketing, one that combines historical behavior with real-time signals to determine who to reach, when to reach them, and what will be most relevant in that moment.
Foundation models are extremely powerful at working with language, while specialized predictive models may be better suited to structured healthcare data and narrowly defined outcomes. How do you think about combining large language models with more traditional machine learning systems when building healthcare AI products?
Great question! As I alluded to earlier in the conversation, the goal isn’t to arbitrarily combine these AI techniques simply because it’s fun. It’s always to get to a more accurate prediction which can better solve a specific customer problem. So much healthcare data is structured (even though it can also be dirty, partial, and lagged), which makes it perfect for prediction using numerical and categorical features. That said, there are some aspects of the raw data pipeline (e.g., clinical notes, creative content/text) that could use foundational LLMs as a pre-processor to encode the feature before it’s applied to a structured prediction problem – like “is this patient ready to switch brands?”
OptimizeRx is increasingly connecting HCP and direct-to-consumer intelligence so that engagement with patients and providers can be better synchronized. How can AI connect those two sides of the healthcare journey while maintaining the privacy boundaries required when working with sensitive health data?
AI bridges patient and provider engagement by evaluating real-world clinical signals to identify shared moments of care, rather than tracking individual people. The technology connects the two sides by analyzing de-identified patient population trends alongside doctor prescribing behaviors. This allows marketing teams to align consumer education, like TV, digital, or social ads, with point-of-care information in the doctor’s electronic health record system, ensuring both patient and physician receive relevant information around the same treatment decision point.
To maintain strict privacy compliance, the AI operates entirely on de-identified, aggregated data using our patented Micro-Neighborhood technology that I mentioned above. Instead of targeting individual patients or using personal health tracking, the system analyzes clinical trends at a localized, geographic level, and cross-references those trends with doctor activity in the area. This approach creates a fully synchronized campaign that respects consumer privacy, ensuring full compliance with HIPAA and state privacy laws while still delivering timely, coordinated care messaging.
As AI moves from generating recommendations to influencing who is contacted, when they are contacted, and what action should happen next, the consequences of a bad prediction become more significant. Where should human oversight remain mandatory, and which decisions do you believe AI can eventually make autonomously?
In life sciences, there’s a meaningful difference between an AI system determining which audience is most relevant for an educational message and one making a decision that could directly affect a patient’s care. The closer an AI-driven decision gets to clinical judgment, patient eligibility, or other high-impact outcomes, the more important human accountability becomes.
There is also a distinction between oversight and intervention. AI doesn’t necessarily need a person approving every low-risk action, but it does need clear boundaries, auditability, and mechanisms to detect when its behavior falls outside those boundaries. That allows humans to remain accountable without creating a bottleneck that undermines the value of automation.
I believe AI can increasingly make autonomous decisions around lower-risk, high-volume activities, such as audience prioritization, channel selection, timing and sequencing, when those decisions are grounded in governed data and continuously monitored. The goal should be to reserve human judgment for the decisions where context, accountability, and consequences require it, while allowing AI to handle the operational complexity humans simply cannot manage at scale. I also think it’s valuable to keep licensed clinicians as “humans in the loop” at the appropriate checkpoints. And vendors who have MDs on staff are the ones that will strike the balance of scale and compliance.
There is often a gap between an AI model performing well according to technical metrics and actually improving outcomes in the real world. What metrics matter most when evaluating AI in life sciences commercialization, and how do you avoid optimizing for proxies such as engagement when the ultimate objective should be better patient outcomes?
AI in commercialization should ultimately be judged by the value it creates across the care journey, not by how efficiently it can optimize a single interaction. That requires recognizing that engagement is a means to an end: a click, completed interaction, or even a highly accurate prediction only matters if it helps create a more relevant intervention that can influence what happens next.
For life sciences, I’d look at the progression of metrics – from the quality of the audience and timing of the intervention, to changes in HCP behavior such as prescribing, and ultimately the downstream impact on patients. The challenge is that the further downstream you go, the harder outcomes are to measure and attribute, which makes it tempting to optimize for the metrics that are easiest to capture.
That’s where AI strategy matters. We need to design measurement frameworks that connect model performance and engagement signals to meaningful behavioral and, where possible, clinical outcomes. At OptimizeRx, that means using real-world healthcare data to understand not just whether we reached someone, but whether we reached the right person at the right point in the care journey, and whether that intervention made a measurable difference.
Looking three to five years ahead, what does an AI-native life sciences commercialization organization look like? Do you expect autonomous agents to begin managing parts of the commercial decision loop themselves, or will the more important evolution be AI continuously providing intelligence to human teams?
I think the “agent” framework may not be as relevant in future years. Instead, I think about life sciences companies restructuring themselves as data ecosystems that any AI technology can arbitrarily use. Even in 5 years, I don’t expect many “business decisions” to be completely automated via agents. Instead, I expect the scale of experiments on the R&D side to increase rapidly, and I expect the intelligence going into a human’s commercial decision making to be much more powerful. Right now, life sciences companies are organized around org structures that then dictate the ways they operate, to reference Conway’s Law. Hence, we see HCP and DTC marketing still being done in silos, and analytics and marketing teams are not fully aligned. As the underlying data ecosystem changes, the human teams will change themselves, becoming more cross-functional, and therefore still in line with Conway’s law. Organizations will be quicker to implement the insights that AI can already deliver, as well as the new insights that AI will offer us in the future as it continues to become more sophisticated.
Thank you for the great interview. Readers who would like to learn more about the company and its healthcare technology solutions can visit OptimizeRx.