Data privacy and security breaches are a critical concern with AI in health care.
The National Coordinating Council for Medication Error Reporting and Prevention defines a medication error as “any preventable event that may cause or lead to inappropriate medication use or patient harm, while the medication is in the control of the health care professional, patient, or consumer. Such events may be related to professional practice, health care products, procedures, and systems including prescribing; order communication; product labeling, packaging and nomenclature; compounding; dispensing; distribution; administration; education; monitoring; and use.”1
One of every 30 patients experiences medication-related harm. The global cost of medication errors, not including lost wages and productivity, is estimated at $42 billion.2
Kathleen Kenny, PharmD, RPh, earned her doctoral degree from the University of Colorado Health Sciences Center in Aurora. She has more than 30 years of experience as a community pharmacist and works as a clinical medical writer based in Homosassa, Florida.
The role of artificial intelligence (AI) in pharmacy is significant in its ability to improve patient care, including dose selection and reduced medication errors. These errors cause patient harm and increase health care costs, including hospitalizations. AI can identify and prevent medication errors by analyzing patient data, predicting drug interactions, and providing real-time alerts.3
AI can take several forms, including natural language processing (NLP), machine learning algorithms, and data mining, among others. NLP analyzes unstructured data, such as clinical notes, laboratory reports, and patient narratives, by using algorithms to identify key details, extract this information, and transform it into structured information that can then be used for analysis and decision-making. This information may include patient demographics, diagnosis, medications, and treatment plans.4
Machine learning algorithms can analyze large data sets of patient information, including clinical guidelines, clinical trials, postmarketing surveillance, and case reports.3 Health care professionals can use these data to predict adverse reactions, optimize treatment plans, and improve patient outcomes.5
Finally, data mining is simply pattern analysis within large data sets. This improves the pharmacy’s ability to identify potential drug interactions, optimize inventory, and detect fraudulent activity.6
These AI strategies could have various implications for medication safety, particularly in reducing potential medication errors. For example, machine learning algorithms are trained to recognize drug-drug interactions by analyzing data-mined drug attributes, patient medication history, and patient-specific documented adverse effects and drug interactions.5 Additionally, by combining machine learning with pharmacogenomics and ongoing pharmacovigilance, AI can effectively predict potential adverse drug reactions in patients.7
AI tools could also help monitor medication adherence. By analyzing patient data, AI can identify trends and potential obstacles for patients to maintain adherence. Data from smartphone apps, smart pill bottles, and other technology could also be incorporated into this analysis.8 Finally, AI can personalize medical treatments by considering such factors as current medications, comorbid conditions, lifestyle choices, and patient preferences.9
Outside of medication safety efforts, AI can help pharmacies forecast the demand for medications, track inventory, and optimize the supply chain by using predictive analytics. These benefits improve patient care, reduce costs, increase efficiency, and enhance decision-making. AI tools can also help identify potential new therapeutic targets by analyzing biological data and using algorithms and machine learning to identify connections that may reveal new areas of treatment.
Despite the significant potential of AI in pharmacy, it is crucial to also be mindful and intentional when identifying opportunities and implementing these tools. The quality of the data, user acceptance of these tools, and ethical implications must all be considered.
Appropriate training of AI models requires high quality data. Metrics for assessing health care data quality include accuracy, completeness, and consistency. Health care professionals can ensure data quality by employing stringent protocols, using automated error detection, and fostering an environment of accuracy and transparency.
Some concerns regarding the use of AI in health care include potential bias in algorithms leading to unequal treatment, lack of transparency into the reasoning behind the decisions made, and potential for mistreatment due to flawed data. Additionally, some believe there will be an overreliance on AI that will lead to a reduction in human health care professionals, potentially leading to reluctant uptake.
Finally, data privacy and security breaches are a critical concern with AI in health care. The leakage or misuse of sensitive data, such as diagnoses, reports, treatments, and genetic information, could have devastating consequences for individuals.
AI enhances the pharmacist’s role and will continue to do so by providing decision support, streamlining medication management, and automating routine tasks. AI may take over more complex tasks as time goes on, freeing pharmacists to provide human judgment and patient interaction.
AI is quickly evolving in pharmacy practice. Through the use of AI algorithms and machine learning, pharmacists can analyze large volumes of data to predict and detect adverse drug reactions and drug-drug interactions, assess the safety and efficacy of medications, and provide patient-centered recommendations.
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