Preventing Medication Dispensing Errors in Pharmacy Practice with Risk-sensitive Artificial Intelligence

About This Grant

PROJECT SUMMARY Preventable medication errors are a global problem that can cause significant patient harm and annually incur costs of $42 billion worldwide. In the United States, 3 million outpatient medical appointments, 1 million emergency department visits, and 125,000 hospital admissions each year are the result of medication errors. Medication errors result in 3 million outpatient medical appointments, 1 million emergency department visits, and 125,000 hospital admissions each year. Astoundingly, over 4 billion prescriptions are dispensed every year in the United States alone. Although dispensing error rates are generally low at 0.06%, the sheer volume of dispensed medications translates to 2.4 million incorrectly dispensed medications each year. In the pharmacy, dispensing errors arise when pharmacists do not detect that the medication filled inside a prescription vial is different from the medication ordered on the prescription’s label. These dispensing errors can result in patient harm, added strain on the healthcare system, and costly legal action against the pharmacy. Artificial intelligence (AI) can be employed to assist in the verification process to help avoid dangerous and costly pharmacy dispensing errors. However, for the human-AI partnership to function optimally, the AI should be capable of determining the relative risks of medication errors (e.g., warfarin vs. vitamin C, kidney function, pregnancy status) while encouraging providers to make sound cognitive decisions such that optimal trust is maintained (i.e., catching errors), and temporal and cognitive demand is reduced (i.e., improving efficiency and avoiding alert fatigue). Risk-sensitive classification is critical when misclassification errors widely vary in frequency and severity. Imperative to this goal is to design AI from which risk-sensitive information can be extracted and conveyed to calibrate user’s trust in AI as either over-trust or under-trust can lead to near miss and incident errors. This proposed project will further our knowledge for designing risk- sensitive AI outputs and inform the development of AI models that encourage pharmacy staff to make sound clinical decisions that lead to better patient outcomes while improving work-life at lower costs of care. This study develops risk-sensitive AI methods in the context of medication images classification and designs effective AI advice and reasoning that lead to lower cognitive demand and increased trust in the AI. Our hypothesis is that risk-sensitive AI will lead to improved pharmacist work performance and more calibrated trust. The objectives of this proposal are to: 1) design risk-sensitive artificial intelligence to double-check dispensed medication images in real-time; 2) evaluate changes in pharmacy staff trust due to the use of risk- sensitive artificial intelligence; and 3) determine the effect of risk-sensitive artificial intelligence on pharmacy staff work performance.