How AI Is Rethinking Patient Engagement in Pharma  

A few years ago, the FDA set up the Patient Engagement Advisory Committee to ensure patients have a voice in decisions that affect them. The group includes patient scholars and influencers who help shape policies and recommendations. In the future, we may see health ministries and healthcare organizations worldwide adopting a similar approach.

What does this trend mean for life sciences? It signals patient activation. They are no longer passive recipients of care but active participants who expect to be heard.  

For companies, this means finding timely and respectful ways to engage with patients at every stage of their journey. It also means building experiences that reflect what patients value, not just what the brand wants to communicate. 

AI is making this easier. It can personalize interactions, anticipate patient needs, and help people stay on track with treatment. But before we dive into this, let’s pause to define what patient engagement is.

What is Patient Engagement in the Pharmaceutical Industry? 

Patient engagement in pharma is about shaping experiences that meet patients’ expectations, support their well-being, and foster a healthier mindset. And while pharma companies mostly interact with HCPs and payers and not patients directly, they can still deliver clinical trial experiences that speak to their needs and offer the relevant information to help them achieve their health goals. 

By prioritizing patient engagement, pharma companies can empower patients to take a more active role in their care, act sooner, and achieve better health outcomes. 

Unsolved Patient Engagement Problems 

Back in the early 2010s, it felt strange for a company to call itself patient-centric. Now it’s a common brand message, and many organizations have taken meaningful steps toward serving patients better. Yet, a few key problems are still left unaddressed. 

Compliance concerns limit creativity 

Regulatory requirements strictly shape what life sciences companies can communicate to patients. As a result, new topics or channels are often approached cautiously. This makes messaging feel generic and overly conservative, which makes it hard to connect with audiences, especially Gen Z, 63% of whom prefer casual, approachable interactions over formal, standoffish ones. 

To think outside the box, you first need to know where the box is. When teams see compliance not as a checklist of limits but as a set of helpful boundaries, it can fuel creativity instead of restricting it. 

Engagement is still about products, not patients 

Pharma has gotten much better at giving HCPs value instead of another promotional pitch. But on the patient side, the industry still defaults to product-first thinking. The emotional, behavioral, and day-to-day realities of living with a condition often sit outside the frame. 

IQVIA shows that fewer than half of patients stay on therapy after the first year, costing pharma companies around 37% of potential annual revenue. Patients with chronic illnesses miss doses, take their medications incorrectly, and abandon treatment in year one.  

If you zoom out, it becomes clear: patients disengage not because treatment doesn’t work. But because they feel unheard, unsupported, and unsure how to stay on track. 

Rewriting the Patient-Pharma Relationship 

A pharmaceutical brand can show it puts customers first through the following patient engagement strategies. 

Anticipate patients’ needs before they arise 

Listening to customers doesn’t always result in the best solutions. As Henry Ford said:

If I had asked people what they wanted, they would have said faster horses.  

Data, however, doesn’t lie. It can uncover patients’ true pain points and needs, even those they might not be aware of, enabling brands to make more effective decisions. 

Organizations that rely on data are 23 times more likely to acquire customers than those who don’t. AI-powered data analytics let life science brands do more than explain what happened or why it happened. It helps them forecast what is likely to happen next and choose the best action for each patient.  

It works a bit like Amazon. If you buy, say, the same pet food every month, Amazon recognizes the pattern and stocks it in a warehouse near you. So even before you remember you are running low, the item is already close by and ready to ship. Similarly, brands can use data to predict patterns that signal patients might drop out of treatment and provide timely support. 

Listen to what patients are saying 

The best patient engagement strategies also come directly from patients. While HCPs may stay silent due to professional boundaries, patients are more vocal when their experiences are exceptionally positive or negative. Social media becomes their platform to share these stories.  

When negative reviews appear, timely intervention is crucial not only to retain this particular patient, but also because they may share their experience with friends or, worse, the post could go viral, harming your brand’s reputation. 

AI social listening tools let you monitor mentions of your brand and automatically detect sentiments and emotions, such as frustration or excitement. You can also track competitors’ mentions and industry-related keywords to stay informed about what drives motivation and drop-off. 

Offer hyper-personalized experiences 

Most patients take four to six medications, each with their own schedule. And let’s not forget, they’re also managing their own doctor’s visits, plus appointments for their children or their parents. It’s a lot.  

AI lets brands create a space where adherence feels simple. The system can send the right reminders and motivating messages at the right moment, so adherence becomes something people don’t have to think about. 

Medisafe CCO, Stacey Wasserman, says that they work with more than 14 million patients worldwide and can confidently say that even patients with the same conditions can have dramatically different motivations and needs.  

For example, Jye is newly diagnosed with diabetes. He has many questions and needs emotional support as he adjusts to his new normal. Meanwhile, another patient, Claire, who has lived with diabetes for twenty years, is focused on something very different. She wants to understand how to keep paying for her medications as costs continue to rise.  

For someone like Jye, a pharma company can provide simple videos on how to inject, easy access to a nurse who can guide him, and clear educational content about his condition. For someone like Claire, who already feels confident managing her disease, it’s essential to connect her with patient support programs and financial resources. AI allows brands to understand these needs in seconds, not in weeks. 

Meet patients where and when they show up 

When people hear hyper-personalization, they usually think about content. But true personalization is also about timing and channels. You want to reach patients in the moments and in the places where they want to engage with you.  

Some patients feel most comfortable using an app. Others prefer a web portal. And some simply like the convenience of a chatbot. You need a strong omnichannel strategy, so you don’t sound like a broken record, repeating the same messages across every platform a patient uses. With proper segmentation, reminders feel helpful instead of spammy. 

AI makes this possible by accurately segmenting your audience and suggesting the right channel for each situation. Our AI-powered agent already helps marketers create content end to end, and we’re now planning to take it to a more strategic level by enabling it to recommend channels as well. 

To make sure your message gets seen, you can use something as simple as Einstein Send Time Optimization. The system analyzes ninety days of interaction data and suggests the best time and day to reach your customers. 

Stay in touch 24/7 

These days, patients often start their health journey by asking virtual assistants like ChatGPT. They notice a strange symptom, then immediately want to know what it could mean, where to get help, or how to cope with the stress that comes with uncertainty.  

The problem is that these large language models, even though convenient, aren’t regulated. That forces pharma brands to come up with a way to communicate safely, reliably, and beyond the usual Monday-to-Friday, nine-to-five window. 

Life sciences brands develop chatbots with built-in guardrails to provide instant and, most importantly, accurate answers.

Recently, we built one for a client to help clinical trialists understand study protocols and explain them to patients in a way that boosts confidence in joining the trial.  With this AI assistant, trialists could address patients’ concerns immediately, rather than sending them home with unanswered questions that might lead them to drop out or decline participation. The chatbot also provided personalized education, helping trialists identify knowledge gaps, improve their communication, and promote positive patient behavior. After introducing the chatbot, patient enrollment increased by 10%. 

Tame compliance for timely answers 

As we mentioned earlier, patients aren’t passive recipients of care anymore. They’re the CEO of their own health journey. Health literacy lets patients put out brushfires before complications arise. When evidence-based guidance arrives too late, the consequences can be life-changing or life-threatening. 

In direct-to-consumer advertising, companies face strict regulations for patient-facing content. Since the medical, legal, and regulatory (MLR) process can take up to two months, pharma teams need a faster way to get campaigns into the market.  

One approach is to use AI to reuse what is already approved. Agents like eVa can connect to your DAM and generate new copy and images by mixing and matching assets that have already passed MLR. It prioritizes content that performs well and fits the message, so teams can run faster with their ideas. 

Another approach is to make sure your content won’t trigger a single revision request. AI agents like eVa give teams 99% certainty by spotting errors, suggesting improvements, and predicting the likelihood of approval before formal submission. You can even fine-tune the model to account for local market regulations, making the checks even more precise. 

Future Predictions 

With more data touchpoints and stronger models, personalization will become far more precise. Brands will be able to place the right message in front of the right people instantly. Dynamic personalization will feel less overhyped and will be expected. 

For life sciences, though, “real time” only works if the foundation is ready. You will need clean, structured data, and modular content that can be assembled on the fly to keep every interaction relevant and compliant. 

AI is incredibly powerful for analyzing patient data and personalizing experiences. But it doesn’t build trust. You need to balance it carefully with a human touch to capture nuance, communicate with empathy, and offer good quality of care.  

The same is true for creativity. As Dr. Rachel Barr explains in “Why Your Best Ideas Come After Your Worst,” you need to stare at the blank page and struggle a bit to create something original and learn how to combine knowledge in new contexts. AI then helps you scale that creativity when engaging patients. It makes sure your content can be reused, adapted, and delivered across channels and markets without losing its quality.