Advancing MTM Through Pharmacist and AI Collaboration

Medication Therapy Management (MTM) has become a critical lever in the fight against medication errors and non-adherence, a stubborn challenge that costs the U.S. over $500 billion a year and drives up to 25% of hospitalizations.¹ By combining pharmacist-led medication reviews with targeted interventions, MTM addresses the human and economic toll of medication-related issues . As artificial intelligence brings powerful analytics and predictive capabilities, the challenge lies in empowering pharmacists to transform that intelligence into care that is measurably more efficient, precise, and effective.

How AI Supports Better Medication Therapy Management

AI is making its way into every corner of healthcare, and MTM is no different. It's working alongside pharmacists to make care smarter, faster, and more personal. Modern AI systems, from machine learning algorithms to advanced chatbots, can sift through massive amounts of patient data (medication fill histories, medical claims, lab results, clinical notes) to spot patterns and risks that a human might miss. For instance, AI can help predict which patients are at the highest risk of non-adherence or of developing a side effect, allowing pharmacists to prioritize those patients for MTM outreach. AI can also rapidly check for complex drug interactions or contraindications across a patient’s entire regimen, potentially faster and more comprehensively than traditional methods.

One emerging use case for Quantiles in MTM is its ability to rapidly analyze complex patient data and surface the insights pharmacists need most. By analyzing vast and complex claims and patient data, Quantiles can identify those at highest risk for medication non-adherence, identify patients who may need closer follow-up, uncover care gaps, and more. This level of analysis not only gives MTM teams a clearer, evidence-based foundation for patient care decisions but can also streamlines documentation.

Future Benefits to Come

AI has the potential to reduce much of MTM’s manual workload . For instance, an algorithm could rapidly analyze a patient’s refill records and clinical information to generate a “medication risk score,” revealing the likelihood of non-adherence to an anticoagulant or identifying a hazardous overlap in blood pressure therapies. The AI could also generate a draft medication action plan for the pharmacist to review, saving time in crafting recommendations.

In a case-based validation study, the authors found that using AI in MTM could enhance patient safety, lower healthcare costs, and reduce the time pharmacists spend on creating care plans.² Busy healthcare teams could thus manage more patients more efficiently with AI handling data analysis in the background. There’s also evidence that AI could enable more personalized medication recommendations by analyzing vast datasets.³

With AI managing complex data in the background, MTM teams can deliver more personalized, efficient, and impactful care.

The recent CMS Health Tech Ecosystem Initiative marks another step toward a future where AI serves both providers and patients directly. Imagine an AI-powered chatbot on a patient’s phone, available 24/7 to answer questions like “What should I do if I missed my cholesterol pill?” It could send tailored trustworthy information and offer coaching that was once only available from a clinician. By offering accurate, personalized guidance, these tools can replace online guesswork with trusted advice that supports medication understanding and adherence.

Despite the headlines, AI is nowhere near a cure-all. It’s brilliant at processing data, but in a clinical setting, it has real limits. In the study mentioned above, ChatGPT flagged potential problems but sometimes missed the mark on offering the right alternative therapy or a specific recommendation without more details.² In short, it can spot that something might be wrong, but not always the best way to fix it. And because current AI works by recognizing patterns, not by truly understanding a patient as a person, pharmacists remain essential.

Expanding the Pharmacist’s Impact Through AI

Medications affect every patient differently, and engaging them about their treatment requires trust and understanding. Despite rapid advances, AI cannot replace the empathy, insight, and nuanced decision-making of a pharmacist, nor the flexibility and emotional intelligence that are often essential in MTM encounters. A pharmacist can read between the lines, sense fear or confusion, and respond with compassion, uncovering barriers like side effects, depression, or cost concerns, and solving them with empathy and creativity that no algorithm can currently match.

Even the best AI needs oversight

In healthcare, AI is prone to errors or biases if not carefully managed, and a pharmacist must validate any AI-generated recommendations. For example, if an AI tool suggested discontinuing a certain medication, the pharmacist must check if that’s truly safe and aligns with the patient’s clinical picture. “Pharmacists should use [AI] mindfully and make the final decision about therapy, since they understand every piece of a patient’s case,” observed the American Pharmacists Association in a recent article.⁴ In practical terms, this means AI might flag an interaction between Drug A and Drug B, but the pharmacist knows that in this patient’s case, Drug B is absolutely necessary and the interaction can be managed with monitoring, something an algorithm’s black-and-white rules might not handle gracefully.

A matter of ethical and personal accountability

Pharmacists are trained to uphold patient safety and will err on the side of caution, whereas an AI has no innate sense of responsibility. As of now, AI has no bedside manner and it won’t convince a reluctant patient to try a new inhaler or celebrate with them when their blood pressure is finally under control. Pharmacists do this every day, and it’s that human connection that helps patients keep going.

MTM is as much about coaching a patient and tailoring solutions to their life as it is about the medicines themselves.

In MTM, this means a collaborative model in which AI manages the data-heavy work by aggregating records, flagging potential issues, and generating preliminary recommendations , while pharmacists apply their clinical expertise and interpersonal skills to make final decisions and guide patients. This partnership can actually elevate the role of the pharmacist. Freed from some clerical tasks, pharmacists can focus more on direct patient care, the part of the job that truly requires their level of training. The combination delivers faster, more consistent results without sacrificing safety, quality, or individualized care.

How Do We Train Pharmacists to Use AI in MTM?

Because collaboration between AI and pharmacists is the future, training is critical. Pharmacists need the skills to use AI tools that can effectively interpret healthcare data and apply the insights to patient care. The shift starts in pharmacy education, as leading schools integrate informatics and data science into the foundation of their programs. For example, the USC Alfred E. Mann School of Pharmacy and Pharmaceutical Sciences has integrated data science and bioinformatics into their graduate and undergraduate curricula.⁵ These programs expose future pharmacists to the concepts of algorithm-driven decision support, data analytics, and the potential pitfalls (like bias or data privacy issues) that come with AI. As AI becomes more prevalent, we can expect such training to become standard, ensuring new pharmacists are as comfortable working with an AI recommendation engine as they are working with a medication ordering system.

Continuing Education for Pharmacists on AI Tools and Best Practices

Professional development must continue long after graduation. Practicing pharmacists may need continuing education on using specific AI platforms that their employer or health plan adopts. This could take the form of certification programs or workshops on “AI in Pharmacy Practice.” Already, some pharmacy organizations are discussing “boot camps” or training modules for healthcare leaders and providers on AI fundamentals.⁶ The idea isn't to turn pharmacists into data scientists, but to give them a foundational understanding of AI’s capabilities and limitations. For instance, a pharmacist should understand which data points an AI tool used to flag a drug interaction, so they can verify its accuracy if needed. Training should also cover ethical and privacy considerations, since using AI often involves handling sensitive patient data. Pharmacists must ensure tools comply with privacy laws and that any patient-facing AI maintains confidentiality.

Building Interprofessional Teams for AI-Enabled MTM

Pharmacists should be involved alongside physicians, nurses, and IT specialists to ensure AI is implemented smoothly in medication management. If pharmacists are part of AI tool development or pilot programs, they can help shape the algorithms with their on-the-ground insights. Some forward-thinking health systems have even created Pharmacy Residency programs in informatics, preparing pharmacists who can champion technology-enabled medication management.⁷ The goal is a workforce of pharmacists who are tech-savvy clinicians, capable of harnessing AI for tasks like patient risk stratification, medication reconciliation automation, and outcome tracking, all while applying their clinical judgment and communication prowess.

Training Pharmacists to Confidently Work Alongside AI in Clinical Practice

Training should also nurture the human abilities that make AI partnerships effective. That means learning how to communicate AI findings to patients and colleagues in a clear way, and how to appropriately override the AI when it’s wrong. Health systems might implement simulation exercises where pharmacists practice with AI-generated suggestions and decide when to accept or reject them.

Training pharmacists to work confidently with AI creates a future where technology and human expertise advance patient care together.

Building a culture of Pharmacist–AI collaboration opens new possibilities for care. Pharmacists can work alongside AI as an ally that amplifies their skills, with leaders already showcasing its potential to elevate the profession.⁸ Once pharmacists and technicians see AI handling time-consuming tasks like resolving prior authorizations, they’re often excited to use it, knowing it frees them for more clinical care.

Advancing MTM with AI While Keeping Care Human

The future of MTM will be a blend of high-tech and high-touch. More sophisticated AI will be built into pharmacy management platforms, from MTM software that generates prioritized patient problem lists using predictive algorithms to virtual assistants that document encounters and suggest next steps in real time. These tools could make MTM services more proactive and continuous rather than reactive or periodic. Recent AI regulations are already paving the way for faster adoption, including potential prescribing capabilities .

Yet even as AI processes information at a speed and scale no human can match, it cannot replace the relational skills, intuition, and ethical judgment that define great pharmacy practice. By weaving AI into the everyday work of pharmacists, MTM can become both more efficient and more humane.