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 Image credit: aksonsat | stock.adobe.com 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 About the Author
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 Tools
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 Potential Error-Reducing Applications
These […]
Artificial Intelligence Has Implications for Medication Safety
