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

Award Number: R01LM015213
ORGANIZATION: NATIONAL LIBRARY OF MEDICINE
OPDIV: NIH
AWARD CLASS: DISCRETIONARY
AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)
PERIOD OF PERFORMANCE START DATE: 05/10/2026
PERIOD OF PERFORMANCE END DATE: 05/09/2030

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


Issue Date FY	Funding FY	Legal Entity Name	Legal Entity Address	Legal Entity City	Legal Entity State	Legal Entity Zip Code	Legal Entity COUNTY	Legal Entity COUNTRY	Assistance Listing	Award Code	Budget Year	Action Date	Action Type	Action Amount

Issue Date FY: 2026 ( Subtotal = $1,364,793 )
2026	2026	REGENTS OF THE UNIVERSITY OF MICHIGAN	1109 GEDDES AVE STE 3300	ANN ARBOR	MI	48109	WASHTENAW	USA	Medical Library Assistance	000	1	5/12/2026	NEW	$1,364,793
														Subtotal = $1,364,793

Grand Total All Awards = $1,364,793

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

Award Number: R01LM015213

ORGANIZATION: NATIONAL LIBRARY OF MEDICINE

OPDIV: NIH

AWARD CLASS: DISCRETIONARY

AWARD ACTIVITY TYPE: SCIENTIFIC/HEALTH RESEARCH (INCLUDES SURVEYS)

PERIOD OF PERFORMANCE START DATE: 05/10/2026

PERIOD OF PERFORMANCE END DATE: 05/09/2030

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