Preventing Medication Dispensing Errors in Pharmacy Practice With Interpretable Machine Intelligence

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

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

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 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.

Participants' eye movements were tracked using a browser-based online eye tracking system. The outcome measure is the difference in cognitive effort as measured by fixation count in the defined areas of interest: fill image, reference image, or MI plot. Higher fixation rates indicate repeated interest in a certain area.

Participants' eye movements were tracked using a browser-based online eye tracking system. The outcome measure is the difference in cognitive effort as measured by the duration of fixations in the defined areas of interest: fill image, reference image, or MI plot. Longer fixation duration indicates a higher cognitive load.

Participants will complete 100 mock medication verification trials in each of the 3 arms. The workload of each arm will be measured by the NASA Task Load Index (TLX). The 5 TLX dimensions assessed are: mental demand, effort, temporal demand, performance, and frustration. For each dimension, participants will indicate their response to a single question. For 4 of the dimensions, the endpoints of the Likert scale are 'very low' and 'very high'. The performance dimension is reverse-scored, and the endpoints are 'perfect' and 'failure'. Participants then complete 10 pairwise comparisons of the dimensions by indicating which dimension they consider to be a more important factor (e.g., effort vs frustration).

Each category score multiplied by its respective pairwise comparison count is summed and divided by 10 to get an overall weighted workload score. The result is an overall workload score between 1 and 20, with higher scores indicating higher workload.

Participants will complete 100 mock medication verification trials in each of the 3 arms (No MI Help, Scenario 1, and Scenario 2). After completing 100 trials, participants will assess the mock verification interface using the System Usability Scale (SUS). The SUS is comprised of 10 statements that participants indicate their agreement with using a 5-point Likert scale ranging from strongly agree to strongly disagree. Odd-numbered questions have a positive response and even-numbered questions are reverse-scored. Scores are summed and multiplied by 2.5 to get a final SUS score. SUS scores range from 0 to 100 with higher scores indicating greater usability. An average SUS score is considered to be 68. Anything below 50 is "Not Acceptable. Scores between 51-70 are considered "Marginal", those above 71 are considered "Acceptable", and those at 80 or above are indicative of high usability.