NCT06245044

Preventing Medication Dispensing Errors in Pharmacy Practice With Interpretable Machine Intelligence

• Status: Completed
• Phase: N/A
• Study type: Interventional
• Enrollment: 68 (actual)
• Sponsor: University of Michigan · Academic / Other
• Sex: All
• Age: 18 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Machine Intelligence in the Pharmacy

Interventions

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

Timeline

• Start date: 2024-04-11
• Primary completion: 2024-12-04
• Completion: 2024-12-04
• First posted: 2024-02-07
• Last updated: 2025-11-26
• Results posted: 2025-11-26

Locations

1 site across 1 country: United States