Artificial Intelligence in Pharmacy Practice: Enhancing Efficiency and Clinical Decision-Making

Artificial intelligence (AI) is widely discussed in health care, but it is already being applied in practical, workflow-driven ways throughout pharmacy practice. Rather than functioning as a clinical decision-making authority, these tools are embedded within existing systems to help manage increasing complexity in medication-related care. Their primary value is not to replace pharmacist judgment but to enhance it by organizing large volumes of clinical data, identifying meaningful patterns, and improving prioritization of patient care activities. In this context, AI functions as a supportive layer that improves efficiency while preserving the pharmacist’s central role in therapeutic decision-making.¹

One of the most established uses for AI in pharmacy practice is predictive risk modeling using electronic health record (EHR) data. These learning systems analyze large data sets to identify patterns associated with outcomes such as hospital readmission, clinical deterioration, and medication nonadherence. Rather than producing treatment recommendations, they generate risk scores that stratify patients by likelihood of adverse outcomes.¹ For example, a patient newly started on insulin with an inconsistent refill history and multiple missed outpatient appointments may be flagged as high risk for poor glycemic control or nonadherence. This does not initiate a clinical action but serves as a signal for closer pharmacist review. In practice, this allows pharmacists to shift from reactive chart review to earlier prioritization of patients who may benefit from medication reconciliation, counseling, or follow-up interventions.

A similar approach is being used in hospitals through machine learning–based early warning systems for sepsis integrated into the EHR. For example, the Targeted Real-time Early Warning System (TREWS) is an AI-based tool designed to identify patients at risk for sepsis earlier in the course of illness.² These systems continuously analyze patient-specific data, including vital signs, laboratory values, medication information, and clinical documentation, to detect patterns associated with early clinical deterioration before traditional recognition methods identify sepsis. In a study evaluating TREWS across 5 hospitals over a 2-year period, the system identified 82% of retrospectively confirmed sepsis cases. In addition, 89% of generated alerts were reviewed by a physician or advanced practice provider, indicating strong integration into clinical workflow and high provider utilization of the system.²

For instance, a patient hospitalized with pneumonia may not yet meet established sepsis criteria, but concerning trends, such as an increasing heart rate, rising white blood cell count, and decreasing blood pressure, may collectively trigger a high-risk alert. Although these systems may support earlier recognition of clinical deterioration and initiation of treatment, they are not intended to serve as diagnostic tools and may still produce false-positive alerts. Instead, they function as clinical support tools that prompt additional assessment and assist health care providers identify patients who may require closer evaluation.

Beyond risk prediction, AI is also being used to address a long-standing challenge in pharmacy practice: unstructured clinical documentation. Much of the medication-related information needed for clinical decision-making is documented within narrative notes rather than organized data fields in the EHR. Natural language processing tools can review progress notes, discharge summaries, and consult documentation to identify important information such as medication initiations, discontinuations, dose adjustments, and adverse drug events. This is especially valuable during transitions of care, when discrepancies between intended and documented medication regimens are common and manual chart review can be both time-consuming and prone to omission.³

Moreover, AI is being used in pharmacy operations to help predict medication demand and identify potential shortages earlier. These systems analyze factors such as prescribing trends, seasonal patterns, and medication utilization data to help pharmacies better anticipate inventory needs. This can support more proactive inventory management and reduce reliance on last-minute ordering, particularly during periods of fluctuating medication demand or potential supply shortages.

AI may also support patient care by helping patients better understand and manage their medications through digital health applications, automated reminders, and personalized educational tools. These technologies can provide guidance on when and how to take medications, send refill or adherence reminders, and support medication monitoring through wearable devices and mobile health platforms. Some AI-based tools may also help improve communication with health care professionals and identify patients who may benefit from earlier follow-up or adherence interventions.¹

Ultimately, AI in pharmacy should be viewed as a supportive technology rather than a source of clinical authority. These systems can improve efficiency by organizing complex clinical data, identifying relevant patterns earlier, and helping pharmacists prioritize patient care activities. However, pharmacists remain responsible for interpreting findings and making all final therapeutic decisions. As AI continues to evolve, its greatest contribution to pharmacy practice will likely be optimizing workflows, reducing cognitive burden, and allowing pharmacists to devote more attention to patient care, clinical reasoning, and interdisciplinary collaboration.

REFERENCES

1. Chalasani SH, Syed J, Ramesh M, Patil V, Pramod Kumar TM. Artificial intelligence in the field of pharmacy practice: a literature review. Explor Res Clin Soc Pharm. 2023;12(100346):100346. doi:10.1016/j.rcsop.2023.100346

2. Henry KE, Adams R, Parent C, et al. Factors driving provider adoption of the TREWS machine learning-based early warning system and its effects on sepsis treatment timing. Nat Med. 2022;28(7):1447-1454. doi:10.1038/s41591-02201895-z

3. Cimino JJ, Bright TJ, Li J. Medication reconciliation using natural language processing and controlled terminologies. Stud Health Technol Inform. 2007;129(pt 1):679-683.