Predictive Analytics and AI Are Reshaping Medication Adherence: Moving Beyond Patient Education to Actionable Intervention
Key Insights
Non-adherence to treatment plans costs the U.S. healthcare system an estimated $105 billion annually, with 70% of prescriptions going unfilled despite investments in patient education and digital tools.
AI-driven predictive models using real-world data from EHRs and claims can identify patients at risk of non-adherence due to access barriers, side effects, or cost concerns, enabling timely interventions.
The FDA's 2025 guidance on patient-focused drug development (PFDD) underscores the regulatory push to integrate patient perspectives into therapeutic development and adherence strategies.
Despite significant advances in diagnostics, therapeutics, and digital innovation, a persistent gap remains between clinical intent and patient action. A diagnosis is made, a treatment plan is outlined, yet momentum is too often lost somewhere in between. Non-adherence carries massive consequences: by some estimates, $105 billion annually in additional healthcare costs, along with poor health outcomes for patients. For years, the healthcare system has approached this problem by increasing patient engagement through education, adherence programmes, and digital tools. But information alone rarely drives action, and healthcare providers, many managing high patient caseloads, frequently lack the insight needed to identify patients at risk of stopping their medication.
The limits of awareness-driven engagement
Historically, life sciences marketing has engaged patients through broad-based omnichannel advertising about therapies — their efficacy, potential side effects, and long-term outcomes. Marketers have also educated healthcare providers through educational materials, office visits, and conferences. These approaches are effective in helping identify potential therapies and initiating treatment, but they are not designed to keep patients on their treatment plan or address barriers to adherence. Most programmes are measured simply by generating a first script — a significant problem given that 70% of prescriptions written in the United States go unfilled.
Even when patients understand why a treatment plan is being recommended, the path forward can introduce friction. Scheduling follow-ups, navigating referrals, or experiencing "sticker shock" from unexpected out-of-pocket costs can all become roadblocks. As Steve Silvestro, CEO of OptimizeRx (search), explains: "Treatment adherence depends on how well the next step is defined, how easily it can be acted on, and whether it's reinforced across the care journey. When these elements aren't tightly coordinated, even the most engaged patients can stall."
Predictive analytics as an enabler of action
The opportunity lies in moving toward more predictive, responsive engagement. AI-driven models applied to real-world data — such as clinical data from patients' electronic health records (EHR) or claims data — can help identify when a patient is at risk of non-adherence, whether due to access barriers, confusion around treatment, concerns about side effects, or a lack of symptom relief, and trigger timely, tailored interventions.
Predictive models can analyse EHR and lab data to flag when values indicate a problem, such as a diabetes (search) patient prescribed metformin who is experiencing uncontrolled blood sugar. Similarly, these models can draw on claims data to anticipate when a patient may be approaching the Medicare "donut hole" and could experience a significant increase in medication costs. Life sciences marketers can use these signals to engage healthcare providers at the point of care with highly targeted, relevant communications — prompting conversations about adherence barriers while educating them about available support resources.
Behavioural science and the patient voice
Amy Bucher, PhD, chief behavioural officer at Lirio (search), argues that connecting with a patient's motivation — their desire to engage with a behaviour and achieve a goal — can help empower them to overcome barriers to change. "Behavioural scientists design experiences that align with what motivates people, such as values and identities that remain fairly stable over the life span," Bucher notes. "People are more likely to sustain behaviours when they can see the alignment with what matters to them."
This perspective is reinforced by regulatory developments. In late 2025, the US Food and Drug Administration (FDA) unveiled guidance on patient-focused drug development (PFDD), emphasising the importance of gathering patient input into drug development, focusing on what matters to patients, and measuring outcomes that are fit-for-purpose. The intention is to bring patient and caregiver perspectives into product development and regulatory processes, ultimately resulting in more suitable and successful therapies. Front-loading the patient voice into therapeutic development can help proactively address needs before they become barriers.
AI-driven personalisation at scale
While people's values and identities shift slowly, their barriers to action may change quickly. Someone new to GLP-1 therapy may grapple with self-injection, while a more seasoned patient may experience barriers to ongoing access or disappointment in the pace of results. AI is adaptive by design: techniques such as reinforcement learning, trained on data indicating patient engagement — interactions with outreach, obtaining and filling prescriptions, and using connected devices — can quickly identify signals to select the right content, timing, and channel to drive action for each individual. This allows an approach that is both scalable and highly personalised.
Aligning commercial strategy with care delivery
As patients' care needs become more complex and healthcare professionals manage increasingly comorbid patient populations, there is a growing need to proactively address barriers that could lead to non-adherence. This is where commercial strategy, patient support, and care delivery must become more tightly aligned. The focus should be on enabling action at the moments that matter most — right at the point of prescribing — connecting insights, interventions, and engagement directly within clinical workflows.
Those life sciences organisations that can operationalise these proactive strategies will be better positioned to deliver on the promise of innovation, translating advances in treatment into meaningful impact for patients. As Bucher concludes: "The most successful therapeutic products, ultimately, are the ones that people adhere to over time. That's why combining AI and behavioural science with a healthy dose of the patient voice is a winning strategy."