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OpenTrials
Completed

NCT Number: NCT06795477

Preventing Medication Dispensing Errors in Pharmacy Practice with Interpretable Machine Intelligence: Wave 2

Pharmacists currently perform an independent double-check to identify drug-selection errors before they can reach the patient. However, the use of machine intelligence (MI) to support this cognitive decision-making work by pharmacists does not exist in practice. This research is being conducted to examine the effectiveness machine intelligence (MI) advice on to determine if its impact on pharmacists' work performance and cognitive demand.

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Key information

Conditions

Machine Intelligence in the Pharmacy

Age range

18 year and older

Sex eligibility

All sexes

Study type

Interventional

Phase

Not applicable

Primary location

University of Michigan

Ann Arbor, Michigan, 48109, United States

Who can participate

Healthy volunteers accepted: No

Only the study team can determine whether someone qualifies for participation.

Inclusion criteria

  • Licensed pharmacist in the United States
  • Age 18 years and older at screening
  • PC/Laptop with Microsoft Windows 10 or Mac (Macbook, iMac) with MacOS with Google Chrome or Firefox web browser installed on the device
  • Screen resolution of 1024x968 pixels or more
  • A laptop integrated webcam or USB webcam is also required for the eye tracking purpose.

Exclusion criteria

  • Eyeglasses with more than one power (bifocals, trifocals, progressives, layered lenses, or regression lenses)
  • Cataracts, intraocular implants, glaucoma, or permanently dilated pupil
  • Require a screen reader/magnifier or other assistive technology to use the computer
  • Eye surgery (e.g., corneal)
  • Eye movement or alignment abnormalities (lazy eye, strabismus, nystagmus)

Treatment and study plan

No MI Help

Behavioral

Participants will complete the medication verification task without any MI help

Interpretable MI

Behavioral

Participants receive interpretable machine intelligence assistance to complete the medication verification tasks.

Uninterpretable MI

Behavioral

Participants receive uninterpretable (i.e., black-box) machine intelligence assistance to complete the medication verification tasks.

Primary outcomes

  1. Cognitive effort

    Time frame: 1 day - Single study visit

    Difference in cognitive effort measured by duration of fixation and fixation count

  2. Decision accuracy

    Time frame: 1 day - Single study visit

    Difference in detection rate measured by number of medication verification errors

  3. Trust change

    Time frame: 1 day - Single study visit

    Difference in trust as measured by visual analog scale will be calculated based on AI advice accuracy. Participants will indicate their level of trust in the AI advice after every trial on a scale from 1-100, with higher scores indicating greater levels of trust.

Secondary outcomes

  1. Reaction time

    Time frame: 1 day - Single study visit

    Difference in task time measured by the number of seconds from starting the task to accepting or rejecting a medication image

Sponsors and collaborators

Lead sponsor

Corey Lester

Other

Collaborators

  • National Library of Medicine (NLM)

Registry information

Important dates

Study start
2023
Primary completion
2023
Study completion
2023
First posted
Jan 28, 2025
Registry last updated
Jan 28, 2025

OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.

View the official ClinicalTrials.gov record (opens in a new tab)

This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.

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