Clinical trial NCT06245044: Preventing Medication Dispensing Errors in Pharmacy Practice with Interpretable Machine Intelligence
Official title: Preventing Medication Dispensing Errors in Pharmacy Practice with Interpretable Machine Intelligence
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Machine Intelligence in the Pharmacy
Keywords: pharmacy
medication verification
artificial intelligence
machine intelligence
Lead sponsor: University of Michigan
Sponsor class: OTHER
First posted: 2024-02-07
Enrollment: 69
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: CROSSOVER
Primary purpose: OTHER

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 criteria: 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)

Primary outcomes:
measure: Reaction time
timeFrame: 1 day - Single study visit
measure: Decision accuracy
timeFrame: 1 day - Single study visit
measure: Trust change
timeFrame: 1 day - Single study visit
measure: Trust
timeFrame: 1 day - Single study visit
Secondary outcomes:
measure: Cognitive effort
timeFrame: 1 day - Single study visit
measure: Cognitive effort
timeFrame: 1 day - Single study visit
measure: Workload
timeFrame: 1 day - Single study visit
measure: Usability
timeFrame: 1 day - Single study visit
Other outcomes:
Locations:
city: Ann Arbor
country: United States
facility: University of Michigan
state: Michigan
Study officials:
affiliation: University of Michigan
name: Corey A Lester, PharmD, PhD
role: PRINCIPAL_INVESTIGATOR
References: