Artificial intelligence (AI) has been utilized for some time now in the pharmacy setting, but it has evolved significantly in recent years. So much so that pharmacies can better predict drug effectiveness, identify potential side effects, quicken time-to-market for emerging medications and even design new drugs. AI has the potential to better patient outcomes and create more informed pharmacists.
Increasing efficiency in pharma operations, fraud detection
AI can be a powerful tool to save time, reduce stress and avoid pharmacist burnout. We’ve already seen a surge in robotics-assisted fulfillment and pharmacy kiosks for contactless dispensation, but technology is becoming more advanced to now predict in-store foot traffic, peak service times and whether patients will pick up their prescriptions on time. Expansion of robot-powered fulfillment is gaining momentum as it helps alleviate basic inventory and bottling tasks, sync inventory more closely with pickups, minimize inventory waste and the mundane tasks of restocking unclaimed meds, which contribute to burnout.
Considering the promise that artificial intelligence tools bring to this branch of pharmaceuticals, we are likely to see more partnerships between software companies that have strong AI competency with organizations in the healthcare space specializing in clinical administration, documentation of clinical trials, and hospital documentation.
AI is also hugely beneficial for its fraud detection abilities. AI can analyze sales and prescription data to track instances of fraud. If a pharmacy is ordering from a vendor, AI can track and identify potentially fraudulent activity by looking at sales and purchasing data — and determine whether it is related to a pharmacist or a pharmacy.
AI improvements for patient safety, outcomes
Artificial intelligence is useful in identifying key trends for patients and tailoring it to their specific needs. It can provide personalized recommendations for medications that might be needed to complement their current prescriptions — such as instances where medication side effects need to be counteracted, for example. Extensive databases can be analyzed to identify safety signals and adverse drug reactions that may not have been detected during clinical trials. Sophisticated algorithms can review patients against study protocols and identify eligible candidates for clinical trials — offering faster, more informed and safer medication choices.
AI can also assist in quickly identifying which patients will need a consultation prior to dispensation. Software like Drug Utilization Review (DUR) flags whether a prescribed medication might be dangerous for patients when combined with one of their existing drugs. It allows pharmacies to stay updated on potential risks for specific medications, enabling them to provide informed counseling and monitoring to patients. This technology improves overall patient safety and outcomes.
AI-powered chatbots have also increased information access for patients regarding their medication. Chatbots serve as a critical tool for patients to receive answers to common questions about their medications quickly, without needing to schedule an in-person consultation. Robotic collaboration also frees pharmacists to practice the clinical services — such as immunization, counseling and medication management — that are integral to patients’ health.
Impacts on research and development
On the research and development (R&D) side, early drug development has already been influenced by AI. One foundational reason for the high costs of medications is because of the extensive amount of research and time that goes into development. AI can analyze data from pre-clinical and clinical studies — in real time — to identify trends to aid with future development. Researchers are using it as a tool to decipher which molecular entities should be considered for early-phase clinical trials by screening thousands of molecules for how they interact with target proteins. This significantly speeds up the evaluation process in early-phase clinical trials.
Thanks to AI algorithms, researchers can analyze genomic data, disease mechanisms and protein structures to pinpoint and validate new drug targets and determine which areas of the body a specific medication can help. This not only assists with discovery, but can also aid in designing safer clinical trials, as pre-existing data is informing the development process.
Ultimately, AI can fast-track and streamline R&D, which leads to improved patient outcomes and lower medication costs. Additionally, natural language processing (NLP) can assist with processing unstructured data in clinical trials, to make the information more digestible to analyze.
It is of course important to consider the potential bias that surrounds AI in R&D. Artificial intelligence only knows the data its been provided with from previous trials. Certain information might not be applicable to every patient, depending on the population sets it is collected from. But as AI continues to evolve and gather more data from clinical trials, it will only become a stronger tool for R&D. We’ll continue to monitor emerging trends regarding artificial intelligence and keep a close eye on how they impact the pharmaceutical industry.