# Preventing medication dispensing errors in pharmacy practice with interpretable machine intelligence

> **NIH NIH R01** · UNIVERSITY OF MICHIGAN AT ANN ARBOR · 2024 · $288,814

## Abstract

PROJECT SUMMARY
Medical errors are the 3rd leading cause of death in the United States behind cancer and cardiovascular
disease. The largest proportion of medical errors involve medications. 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.
Machine intelligence (MI) can be employed to assist in the verification process to help avoid dangerous and
costly pharmacy dispensing errors.4–6 However for the human-MI partnership to function optimally, the MI
should be capable of conveying accurate information that encourages providers to make sound cognitive
decisions such that optimal trust is maintained, and temporal and cognitive demand is reduced. Imperative to
this goal is to design MI from which interpretable information can be extracted, convey this information in an
effective manner and calibrate user's trust in MI as either over-trust or under-trust can lead to near miss and
incident errors.
This proposed project will further our knowledge for designing interpretable MI outputs and inform the
development of MI 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 interpretable MI
methods in the context of medication images classification and designs effective MI advice and reasoning that
lead to lower cognitive demand and increased trust in the MI. Our hypothesis is that interpretable MI will lead to
improved work performance and more calibrated trust compared to uninterpretable M. The objectives of this
proposal are to: 1) design interpretable machine intelligence to double-check dispensed medication images in
real-time; 2) evaluate changes in pharmacy staff trust due to the long-term use of interpretable machine
intelligence; and 3) determine the effect of interpretable machine intelligence on long-term pharmacy staff work
performance.

## Key facts

- **NIH application ID:** 10814211
- **Project number:** 5R01LM013624-04
- **Recipient organization:** UNIVERSITY OF MICHIGAN AT ANN ARBOR
- **Principal Investigator:** Raed Al Kontar
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2024
- **Award amount:** $288,814
- **Award type:** 5
- **Project period:** 2021-07-01 → 2026-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10814211

## Citation

> US National Institutes of Health, RePORTER application 10814211, Preventing medication dispensing errors in pharmacy practice with interpretable machine intelligence (5R01LM013624-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10814211. Licensed CC0.

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