Preventing Medication Dispensing Errors in Pharmacy Practice With Interpretable Machine Intelligence

NCT06245044 | Completed Results

• 2 other identifiers: HUM002412235R01LM013624
• Study Type: interventional
• Target: 68
• Locations: 1 country
• Sites: 1

Brief Summary

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 of the timing of machine intelligence (MI) advice on to determine if it results in lower task time, increased accuracy, and increased trust in the MI.

Conditions

Machine Intelligence in the Pharmacy

Keywords

pharmacy; medication verification; artificial intelligence; machine intelligence

Outcome Measures

Primary Outcomes (4)

• Reaction Time

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

  Throughout the verification task

• Decision Accuracy

  Difference in detection rate measured by the number of medication verification errors across all participants in the Arm/Group.

  Throughout the verification task

• Trust Change

  Participants will complete 100 mock medication verification trials in each of the study arms (i.e., Scenario 1, Scenario 2, and No Help). After each trial in Scenario 1 and Scenario 2, participants will use a visual analog scale (VAS) to respond to the question: "How much do you trust the AI advice?" The endpoints of the 100-point VAS are 'Not at all' to 'Completely trust'. Participants indicate their level of trust in the MI advice after every trial on a scale from 1-100, with higher scores indicating greater levels of trust. The trust change, as measured by the visual analog scale, will be calculated using the following formula: Trust change (i) = Trust(i) - Trust(i - 1), where i=2, 3, ..., 100. To compute a single, summarized value for the Trust Change variable within a specific scenario, the individual Trust Change scores measured from the trials are averaged. This averaging method provides a comprehensive measure of how trust shifted across the duration of the scenario.

  After every trial in Scenarios 1 and 2

• Trust

  Trust will be assessed using the Muir \& Moray's (1996) Trust in Automation scale. Scores range from 0 to 100 with higher scores indicating greater levels of trust.

  Post-intervention in Scenarios 1 and 2.

Secondary Outcomes (4)

• Cognitive Effort

  Throughout the verification task

• Cognitive Effort

  Throughout the verification task

• Workload

  After completing 100 mock verification trials in each arm

• Usability

  After completing 100 mock verification trials in each arm

Study Arms (3)

No MI Help

EXPERIMENTAL

No MI help will be presented during the verification tasks

Behavioral: No MI Help; Behavioral: Scenario #1; Behavioral: Scenario #2

Scenario #1

EXPERIMENTAL

MI help will be presented in the form of a pop-up message the participant's decision differs from the MI's determination.

Behavioral: No MI Help; Behavioral: Scenario #1; Behavioral: Scenario #2

Scenario #2

EXPERIMENTAL

MI help will be displayed concurrently with the filled and reference images.

Behavioral: No MI Help; Behavioral: Scenario #1; Behavioral: Scenario #2

Interventions

No MI Help BEHAVIORAL

Participants will complete the medication verification task without any MI help

No MI Help; Scenario #1; Scenario #2

Scenario #1 BEHAVIORAL

Participants will receive MI in the form of a pop-up message if their decision differs from the MI's determination.

No MI Help; Scenario #1; Scenario #2

Scenario #2 BEHAVIORAL

MI help will be displayed concurrently with the filled and reference images.

No MI Help; Scenario #1; Scenario #2

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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, Edge, Opera, Safari, 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.

You may not qualify if:

• Participated in Wave 1 or Wave 2
• Eyeglasses
• Uncorrected cataracts, intraocular implants, glaucoma, or permanently dilated pupil
• Require a screen reader/magnifier or other assistive technology to use the computer
• Eye movement or alignment abnormalities (lazy eye, strabismus, nystagmus)

Sponsors & Collaborators

• University of Michigan: lead
• National Library of Medicine (NLM): collaborator

Study Sites (1)

University of Michigan

Ann Arbor, Michigan, 48109, United States

Location

Results Point of Contact

• Title: Dr. Corey Lester
• Organization: University of Michigan

lesterca@umich.edu

734-647-8849

Study Officials

• Corey A Lester, PharmD, PhD

  University of Michigan

  PRINCIPAL INVESTIGATOR

Publication Agreements

• PI is Sponsor Employee: No
• Restrictive Agreement: No

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: NONE
• Purpose: OTHER
• Intervention Model: CROSSOVER
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Assistant Professor of Clinical Pharmacy

Study Record Dates

• First Submitted: January 5, 2024
• First Posted: February 7, 2024
• Study Start: April 11, 2024
• Primary Completion: December 4, 2024
• Study Completion: December 4, 2024
• Last Updated: November 26, 2025
• Results First Posted: November 26, 2025

Record last verified: 2025-09

Locations

US (1)