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

Corey Lester30 enrolled

Overview

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.

Study Type

INTERVENTIONAL

Allocation

RANDOMIZED

Purpose

OTHER

Masking

NONE

Enrollment

Conditions

Machine Intelligence in the Pharmacy

Interventions

No MI HelpBEHAVIORAL

Participants will complete the medication verification task without any MI help

Interpretable MIBEHAVIORAL

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

Uninterpretable MIBEHAVIORAL

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

Eligibility

Sex: ALLMin age: 18 Years

Medical Language ↔ Plain English

Inclusion Criteria: 1. Licensed pharmacist in the United States 2. Age 18 years and older at screening 3. PC/Laptop with Microsoft Windows 10 or Mac (Macbook, iMac) with MacOS with Google Chrome or Firefox web browser installed on the device 4. Screen resolution of 1024x968 pixels or more 5. A laptop integrated webcam or USB webcam is also required for the eye tracking purpose. Exclusion Criteria: 1. Eyeglasses with more than one power (bifocals, trifocals, progressives, layered lenses, or regression lenses) 2. Cataracts, intraocular implants, glaucoma, or permanently dilated pupil 3. Require a screen reader/magnifier or other assistive technology to use the computer 4. Eye surgery (e.g., corneal) 5. Eye movement or alignment abnormalities (lazy eye, strabismus, nystagmus)

Locations (1)

University of Michigan

Ann Arbor, Michigan, United States

Outcomes

Primary Outcomes

Cognitive effort

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

Time frame: 1 day - Single study visit

Decision accuracy

Difference in detection rate measured by number of medication verification errors

Time frame: 1 day - Single study visit

Trust change

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.

Time frame: 1 day - Single study visit

Secondary Outcomes

Reaction time

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

Time frame: 1 day - Single study visit

Data from ClinicalTrials.gov

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