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AI’s Promise in Delivering on Patient-Centricity


How pharmaceutical companies can utilize AI at key stages of the treatment journey to enhance patient-centric engagement by increasing empathy and humanity in patient interactions.

Chiraag Bhadana

Pharma marketer

Chiraag Bhadana

Pharma marketer

In an era of rapid technological advancement, artificial intelligence (AI) is reshaping industries across the board. The pharmaceutical sector, traditionally slow to adopt new technologies, is now at the cusp of an AI-driven revolution—particularly in the realm of patient engagement. This shift comes at a crucial time when patient-centricity is no longer just a buzzword but a strategic imperative for pharmaceutical companies aiming to improve health outcomes and build lasting relationships with those who use their products.

According to a survey by the Boston Consulting Group across more than 3200 patients1, more than 50% of HCPs stated that they were more likely to prescribe a medication to companies they considered “more patient-centric”. Moreover, understanding patients’ needs, preferences, and experiences allows pharma companies to develop treatments and support services that better address patients’ holistic needs, leading to improved health outcomes, medication adherence, and overall patient satisfaction.

This article will explore how pharmaceutical companies can utilize AI at key stages of the treatment journey to enhance patient-centric engagement. We will delve into specific areas where AI can support pharmaceutical companies, including patient identification, treatment onboarding, medication adherence, and fostering patient loyalty. We will highlight how AI can increase empathy and humanity in patient interactions, ultimately transforming the patient experience.

Joe DeLuca

EVP, Strategy at The Considered

Joe DeLuca

EVP, Strategy at The Considered

Growing patient identification & targeting

Pharmaceutical companies can use AI to identify and engage patients when it matters most–at diagnosis or when a treatment switch is needed. AI algorithms can process vast amounts of unstructured data, such as symptom descriptions, doctor’s notes, and online forums, to identify patients who might benefit from specific treatments. For example, IQVIA’s AI algorithm increased the precision of patient identification by 15X and HCP linkage by 10X for oncology patients eligible for a new 1L or 2L therapy.2

An AI-driven approach can also anchor HCP and DTC marketing in a single journey that breaks down silos and bridges gaps between the two audiences. According to a survey by OptimizeRx across 172 US-based HCPs3, 76% of new patients have specific questions or topics they’d like to address, yet they rate only 16% of patients as well-informed. With AI, we have an opportunity to help patients find better, more relevant information that is synchronized with HCP messaging, which can bridge the information gap and improve decision-making. Incorporating variables such as geo-targeting, social determinants, and media preferences into engagement plans enables us to go beyond a one-size-fits-all approach. With AI, this is within our grasp, and the results could be monumental; patients will be more prepared, physicians will be aligned with their patients, and shared decision-making can abound.

When targeting patients, we can now think beyond the “ad” by deploying AI assistants that provide personalized information, guide patients through symptom checkers, and help assess risk factors. These tools offer a natural and conversational way to answer questions and provide information about conditions and treatment options. Next-best actions can include follow-ups with curated content that meets the needs discovered through the assessment process, which can help overcome language and health literacy barriers. AI can be integrated with augmented reality (AR) and virtual reality (VR) to create immersive educational experiences or to connect newly diagnosed patients with others like them, no matter where they are.

Improving treatment onboarding & medication adherence

Expectation setting is critical before starting a new treatment. Without it, patients may discontinue treatment early, or worse, they may not even start. The good news is that AI can significantly improve the onboarding process and promote adherence by ensuring patients receive the information and support they need.

AI-powered tools like virtual assistants can offer a personalized onboarding experience tailored to the patient’s needs and preferences by analyzing medical histories, preferences, and potential challenges–or by answering a few targeted questions. Next, the system could curate and share content tailored in the form and language that supports deeper engagement with each person. These chatbots can adapt based on patient and caregiver needs as their journey evolves, ensuring that the information is always accessible, understandable, and highly relevant for the moment.

Add to that the ability to support patient adherence continuously. While this might seem creepy to some, there’s no arguing that non-adherence is a major problem today, accounting for an estimated 50% of treatment failures and 125,000 deaths in the U.S. yearly.4 Used in a patient-centric manner, a virtual assistant can identify and engage patients at the moments that matter when their doctors aren’t there to intervene. With applications like this, we can reduce the clinical burden on our healthcare system and potentially save lives.

If AI assistants can access relevant databases, including electronic health records, pharmacy refills, and medication consumption, problems can be spotted and managed faster. Marketers can design experiences based around known pain points, from explaining test results to encouraging better daily habits to alerting healthcare providers of worrisome changes. This proactive approach can help address adherence issues before they lead to adverse outcomes. Used wisely, tactics like these can help pharmaceutical companies holistically support patients, setting a new precedent for industry leadership.

The reality is that this future is already upon us. A study involving AllazoHealth’s AI engine demonstrated that AI could accurately predict adherence risk and recommend specific interventions to improve adherence. This approach led to a 4.6X increased duration of therapy for patients targeted by AI interventions compared to those who were not.5 According to Clinical Research News6, AI can highlight the best channel and time to engage patients to help them take their medication, ultimately raising adherence rates from 50% to nearly 70%. These interventions are especially beneficial for patients with chronic conditions who require consistent medication management. But this example is just the beginning–and pharma marketers would be wise to put on their design thinking and strategic foresight caps to imagine potential future use cases. 

Developing long term brand loyalty

AI can also strengthen patient loyalty, ensuring all patients feel valued and heard. Throughout their treatment journey, natural language processing (NLP) can analyze unstructured patient feedback within branded experiences (including online forums, apps, and surveys) to analyze sentiment and extract insights. These data help capture patient experiences in the patient’s own words, identify underlying concerns, and shine a light on areas for improvement. Social listening tools such as Talkwalker by Blue Silk™ GPT7 allow for real-time monitoring and analysis of customer sentiment, tone, context of discussions, and even what’s being said about competitors. By addressing concerns and highlighting positive experiences, companies can strengthen patient loyalty and encourage continued adoption throughout the brand’s lifecycle.

Addressing privacy & regulatory concerns

Ensuring compliance with data privacy regulations, such as GDPR8 and HIPAA9, is crucial to protect patient confidentiality and prevent unauthorized access or misuse of sensitive data. Companies must also ensure transparency in AI algorithms to substantiate marketing claims and avoid misleading patients.

A core concern today is that AI algorithms are currently perceived as “black boxes,” making it difficult to explain how decisions are made. Regulators will require increased transparency in AI systems to ensure marketing claims are substantiated and unbiased before proceeding. Undoubtedly, this will be a major hurdle to overcome–as misinformation in healthcare can be deadly. Additionally, it will be important to implement fairness testing and mitigation strategies to prevent biased outputs and ensure equitable treatment for all patients.

Finally, while AI can automate many tasks, meaningful human oversight and control are necessary, especially for high-risk applications that could impact patient safety or treatment decisions. Establishing clear governance frameworks and audit trails can help assign accountability and maintain ethical AI practices.

The path ahead

AI can revolutionize patient engagement by enhancing patient identification, onboarding, adherence, loyalty, and advocacy. Companies can also significantly improve patient outcomes and satisfaction, ushering in a new era beyond business as usual.

However, the risks are as great as the opportunities. AI developers, governments, and industry must establish open and transparent communication as they seek to create health-related AI products that engage with patients. This is a crucial point. Frankly, it is here–at the early stages of these conversations–where patient engagement needs to begin. Giving the people whose very lives are at stake a seat at the table can empower a new healthcare model previously thought impossible.

The above article is one of a series of articles exploring AI's role and its implications for the biopharmaceutical industry. Co-authored by Chiraag Bhadana (Pharma Marketer) & Joe DeLuca (EVP, Strategy at The Considered). Read the previous article here.


  1. BCG. (2020). What Patients Want: Is Pharma Delivering? Retrieved from https://www.bcg.com/publications/2020/what-patients-want-is-pharma-delivering
  2. IQVIA. (n.d.). AI Case Study: How IQVIA Increased Target Patient Identification 15x. Retrieved from https://www.iqvia.com/library/case-studies/ai-case-study-how-iqvia-increased-target-patient-identification-15x
  3. OptimizeRx Survey: Conversations That Convert: Physician Survey on Opportunities for HCP and DTC Marketing Synchronization © 2024
  4. US Pharmacist. (n.d.). Medication Adherence: The Elephant in the Room. Retrieved from https://www.uspharmacist.com/article/medication-adherence-the-elephant-in-the-room
  5. AllazoHealth. (n.d.). How Pharma Advances Adherence. Retrieved from https://allazohealth.com/resources/how-pharma-advances-adherence/
  6. Clinical Research News. (2021, May 21). How Clinical AI Can Help Improve Medication Adherence. Retrieved from https://www.clinicalresearchnewsonline.com/news/2021/05/21/how-clinical-ai-can-help-improve-medication-adherence
  7. Talkwalker. (n.d.). Next-Generation Social Listening with AI. Retrieved from https://www.talkwalker.com/blog/next-generation-social-listening-ai
  8. GDPR Info. (n.d.). General Data Protection Regulation (GDPR). Retrieved from https://gdpr-info.eu/
  9. U.S. Department of Health and Human Services. (n.d.). Health Insurance Portability and Accountability Act (HIPAA). Retrieved from https://www.hhs.gov/hipaa/index.html
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