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| ID | Type | Description | Link |
|---|---|---|---|
| 5R01LM013624 | U.S. NIH Grant/Contract | View source |
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| Name | Class |
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| National Library of Medicine (NLM) | NIH |
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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.
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.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| No MI Help | Experimental | No MI help will be presented during the verification tasks |
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| 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. |
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| Scenario #2 | Experimental | MI help will be displayed concurrently with the filled and reference images. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| No MI Help | Behavioral | Participants will complete the medication verification task without any MI help |
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| Measure | Description | Time Frame |
|---|---|---|
| Reaction Time | Difference in task time measured by the number of seconds from starting the task to accepting or rejecting a medication image | Throughout the verification task |
| Decision Accuracy | Difference in detection rate measured by the number of medication verification errors across all participants in the Arm/Group. | Throughout the verification task |
| Trust Change | 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. | After every trial in Scenarios 1 and 2 |
| Trust | 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. | Post-intervention in Scenarios 1 and 2. |
| Measure | Description | Time Frame |
|---|---|---|
| Cognitive Effort | 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. | Throughout the verification task |
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Inclusion Criteria:
Exclusion Criteria:
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| Name | Affiliation | Role |
|---|---|---|
| Corey A Lester, PharmD, PhD | University of Michigan | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Michigan | Ann Arbor | Michigan | 48109 | United States |
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| ID | Title | Description |
|---|---|---|
| FG000 | Pharmacists | Licensed pharmacist were recruited to participate in a mock verification task. In this crossover design trial, each participant received all the three study interventions. The order of the arms was randomized. |
| Title | Milestones | Reasons Not Completed | ||||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| No MI help |
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| Scenario 1 |
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| Scenario 2 |
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| ID | Title | Description |
|---|---|---|
| BG000 | Pharmacists | Licensed pharmacists in the United States with medication dispensing experience who are 18 or older. |
| Units | Counts |
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| Participants |
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| Title | Description | Population Description | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Denominator Units Selected | Denominators | Classes |
|---|---|---|---|---|---|---|---|---|---|
| Age, Categorical | Count of Participants |
| Type | Title | Description | Population Description | Reporting Status | Anticipated Posting Date | Parameter Type | Dispersion Type | Unit of Measure | Calculate Percentage | Time Frame | Units Analyzed | Denominator Units Selected | Arm/Group Information | Denominators | Classes | Analyses | ||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| Primary | Reaction Time | Difference in task time measured by the number of seconds from starting the task to accepting or rejecting a medication image | Posted | Mean | Standard Deviation | millisecond (ms) | Throughout the verification task |
|
1 day
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| ID | Title | Description | Deaths (Affected) | Deaths (At Risk) | Serious Events (Affected) | Serious Events (At Risk) | Other Events (Affected) | Other Events (At Risk) |
|---|---|---|---|---|---|---|---|---|
| EG000 | No MI Help | Participants will complete the medication verification task without any MI help |
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| Title | Organization | Phone | Extension | |
|---|---|---|---|---|
| Dr. Corey Lester | University of Michigan | 734-647-8849 | lesterca@umich.edu |
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| Type | Includes Protocol | Includes SAP | Includes ICF | Document Label | Document Date | Document Uploaded Date | Document File Name |
|---|---|---|---|---|---|---|---|
| Prot_SAP | Yes | Yes | No | Study Protocol and Statistical Analysis Plan | Aug 26, 2025 | Sep 9, 2025 | Prot_SAP_000.pdf |
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| Scenario #1 | Behavioral | Participants will receive MI in the form of a pop-up message if their decision differs from the MI's determination. |
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| Scenario #2 | Behavioral | MI help will be displayed concurrently with the filled and reference images. |
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| Cognitive Effort | 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. | Throughout the verification task |
| Workload | 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. | After completing 100 mock verification trials in each arm |
| Usability | 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. | After completing 100 mock verification trials in each arm |
| Participants |
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| Age, Continuous | Mean | Standard Deviation | years |
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| Sex: Female, Male | Two participants did not indicate their sex on the demographics survey. | Count of Participants | Participants |
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| Ethnicity (NIH/OMB) | Count of Participants | Participants |
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| Race (NIH/OMB) | Count of Participants | Participants |
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| Region of Enrollment | Number | participants |
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MI help will be displayed concurrently with the filled and reference images. |
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| Primary | Decision Accuracy | Difference in detection rate measured by the number of medication verification errors across all participants in the Arm/Group. | Posted | Number | Number of errors | Throughout the verification task |
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| Primary | Trust Change | 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. | This Outcome Measure was pre-specified to be only assessed for Scenarios 1 and 2. No data were collected for this Outcome Measure for the "No MI Help" scenario. | Posted | Mean | Standard Deviation | units on a scale | After every trial in Scenarios 1 and 2 |
|
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| Primary | Trust | 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. | This Outcome Measure was pre-specified to be only assessed for Scenarios 1 and 2. No data were collected for this Outcome Measure for the "No MI Help" scenario. | Posted | Mean | Standard Deviation | scores on a scale | Post-intervention in Scenarios 1 and 2. |
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| Secondary | Cognitive Effort | 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. | Complete eye tracking data was not available for analysis for one participant. The MI plot was pre-specified to be only assessed for Scenarios 1 and 2. No data were collected for the Outcome Measure MI plot for the "No MI Help". | Posted | Median | Inter-Quartile Range | Number of fixations | Throughout the verification task |
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| Secondary | Cognitive Effort | 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. | Complete eye tracking data was not available for one participant. The MI plot was pre-specified to be only assessed for Scenarios 1 and 2. No data were collected for the Outcome Measure MI plot for the "No MI Help". | Posted | Median | Inter-Quartile Range | millisecond (ms) | Throughout the verification task |
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| Secondary | Workload | 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. | Posted | Mean | Standard Deviation | score on a scale | After completing 100 mock verification trials in each arm |
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| Secondary | Usability | 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. | Posted | Mean | Standard Deviation | score on a scale | After completing 100 mock verification trials in each arm |
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| 0 |
| 50 |
| 0 |
| 50 |
| 0 |
| 50 |
| EG001 | Scenario #1 | MI help will be presented in the form of a pop-up message the participant's decision differs from the MI's determination. | 0 | 50 | 0 | 50 | 0 | 50 |
| EG002 | Scenario #2 | MI help will be displayed concurrently with the filled and reference images. | 0 | 50 | 0 | 50 | 0 | 50 |
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| Area of Interest: Reference Image |
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| Area of Interest: MI plot |
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| Area of Interest: Reference Image |
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| Area of Interest: MI Plot |
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