---
title: Preventing Medication Dispensing Errors in Pharmacy Practice With Interpretable Machine Intelligence
nct_id: NCT06245044
overall_status: COMPLETED
phase: NA
sponsor: University of Michigan
study_type: INTERVENTIONAL
primary_condition: Machine Intelligence in the Pharmacy
countries: United States
canonical_url: "https://parkinsonspathways.com/agent/trials/NCT06245044.md"
clinicaltrials_gov: "https://clinicaltrials.gov/study/NCT06245044"
ct_last_update_post_date: 2025-11-26
last_seen_at: "2026-05-12T06:35:04.185Z"
source: ClinicalTrials.gov (mirrored, no enrichment)
---
# Preventing Medication Dispensing Errors in Pharmacy Practice With Interpretable Machine Intelligence

**NCT ID:** [NCT06245044](https://clinicaltrials.gov/study/NCT06245044)

## Key Facts

- **Status:** COMPLETED
- **Phase:** NA
- **Study Type:** INTERVENTIONAL
- **Target Enrollment:** 68
- **Lead Sponsor:** University of Michigan
- **Collaborators:** National Library of Medicine (NLM)
- **Conditions:** Machine Intelligence in the Pharmacy
- **Start Date:** 2024-04-11
- **Completion Date:** 2024-12-04
- **CT.gov Last Update:** 2025-11-26

## 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.

## Detailed Description

Pharmacists currently perform an independent double-check currently 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. Instead, pharmacists rely solely on reference images of the medication which they can compare to the prescription vial contents. Previous research has shown that decision support systems can effectively improve healthcare delivery efficiency and accuracy, while preventing adverse drug events. However, little is known about how MI technologies impact pharmacists' work performance and cognitive demand.

To facilitate the long-term symbiotic relationship between the pharmacists and the MI system, proper trust needs to be established. While trust has been identified as the central factor for effective human-machine teaming, issues arise when humans place unjustified trust in automated technologies do not place enough trust in them. Over trust in automation can lead to complacency and automation bias. For instance, the pharmacists may rely on the MI system to the extent that they blindly accept any recommendation by the system. Under trust can result in pharmacist disuse and potential abandonment of the MI system.

Furthermore, little is known about the timing of the MI advice on pharmacists' work performance. For example, showing the MI's advice while the pharmacist is performing the medication verification task may yield different results than showing the MI's advice after the pharmacist made their decision.

The study investigators have developed a MI system for medication images classification. The objective of this study is to examine the effectiveness of the timing of MI advice to determine if it results in lower task time, increased accuracy, and increased trust in the MI.

## Eligibility

- **Minimum age:** 18 Years
- **Sex:** ALL
- **Healthy Volunteers:** No

```
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, 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.

Exclusion Criteria:

* 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)
```

## Arms

- **No MI Help** (EXPERIMENTAL) — No MI help will be presented during the verification tasks
- **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.
- **Scenario #2** (EXPERIMENTAL) — MI help will be displayed concurrently with the filled and reference images.

## Interventions

- **No MI Help** (BEHAVIORAL) — Participants will complete the medication verification task without any MI help
- **Scenario #1** (BEHAVIORAL) — Participants will receive MI in the form of a pop-up message if their decision differs from the MI's determination.
- **Scenario #2** (BEHAVIORAL) — MI help will be displayed concurrently with the filled and reference images.

## Primary Outcomes

- **Reaction Time** _(time frame: Throughout the verification task)_ — Difference in task time measured by the number of seconds from starting the task to accepting or rejecting a medication image
- **Decision Accuracy** _(time frame: Throughout the verification task)_ — Difference in detection rate measured by the number of medication verification errors across all participants in the Arm/Group.
- **Trust Change** _(time frame: After every trial in Scenarios 1 and 2)_ — 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.
- **Trust** _(time frame: Post-intervention in Scenarios 1 and 2.)_ — 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.

## Secondary Outcomes

- **Cognitive Effort** _(time frame: Throughout the verification task)_
- **Cognitive Effort** _(time frame: Throughout the verification task)_
- **Workload** _(time frame: After completing 100 mock verification trials in each arm)_
- **Usability** _(time frame: After completing 100 mock verification trials in each arm)_

## Locations (1)

- University of Michigan, Ann Arbor, Michigan, United States

## Recent Field Changes (last 30 days)

- `status.overallStatus` — added _(2026-05-12)_
- `status.primaryCompletionDate` — added _(2026-05-12)_
- `status.completionDate` — added _(2026-05-12)_
- `status.lastUpdatePostDate` — added _(2026-05-12)_
- `design.phases` — added _(2026-05-12)_
- `design.enrollmentCount` — added _(2026-05-12)_
- `eligibility.criteria` — added _(2026-05-12)_
- `eligibility.minAge` — added _(2026-05-12)_
- `eligibility.sex` — added _(2026-05-12)_
- `outcomes.primary` — added _(2026-05-12)_
- `outcomes.secondary` — added _(2026-05-12)_
- `armsInterventions.arms` — added _(2026-05-12)_
- `armsInterventions.interventions` — added _(2026-05-12)_
- `sponsor.lead` — added _(2026-05-12)_
- `sponsor.collaborators` — added _(2026-05-12)_
- `results.hasResults` — added _(2026-05-12)_
- `locations.university of michigan|ann arbor|michigan|united states` — added _(2026-05-12)_

---

*Canonical: https://parkinsonspathways.com/agent/trials/NCT06245044.md*  
*Source data (authoritative): https://clinicaltrials.gov/study/NCT06245044*  
*This page is a raw mirror with no AI summary, no editorial enrichment, and no Parkinson's-specific filtering.*