Machine Intelligence in the PharmacyNCT06795477
Preventing Medication Dispensing Errors in Pharmacy Practice with Interpretable Machine Intelligence: Wave 2
A study testing No MI Help, Interpretable MI and Uninterpretable MI in people with Machine Intelligence in the Pharmacy, run by Corey Lester at 1 site in United States.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Machine Intelligence in the Pharmacy
- What is being tested?
- No MI Help, Interpretable MI and Uninterpretable MI
- Type of treatment
- Lifestyle or therapy programme
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 30 participants
- Where
- 1 siteUnited States
- Study dates
- 1 Feb 2023to 12 May 2023
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- 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 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.
Who cannot join
- Eyeglasses with more than one power (bifocals, trifocals, progressives, layered lenses, or regression lenses)
- Cataracts, intraocular implants, glaucoma, or permanently dilated pupil
- Require a screen reader/magnifier or other assistive technology to use the computer
- Eye surgery (e.g., corneal)
- Eye movement or alignment abnormalities (lazy eye, strabismus, nystagmus)
Trial locations and contacts
1 site in 1 country.
About this study
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.
- Conditions
- Machine Intelligence in the Pharmacy
- Being tested
- No MI Help, Interpretable MI, Uninterpretable MI
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
No MI HelpBehavioural
Participants will complete the medication verification task without any MI help
Interpretable MIBehavioural
Participants receive interpretable machine intelligence assistance to complete the medication verification tasks.
Uninterpretable MIBehavioural
Participants receive uninterpretable (i.e., black-box) machine intelligence assistance to complete the medication verification tasks.
| Study group | Role | What they receive |
|---|---|---|
| Interpretable MI | Experimental | Participants receive interpretable machine intelligence to complete the medication verification task. Receives: Behavioral: No MI Help; Behavioral: Interpretable MI |
| Uninterpretable MI | Experimental | Participants receive uninterpretable (i.e., black-box) machine intelligence to complete the medication verification task. Receives: Behavioral: No MI Help; Behavioral: Uninterpretable MI |
What is being measured
The primary outcome is the main question the study is designed to answer.
Cognitive effort
Difference in cognitive effort measured by duration of fixation and fixation count
Measured: 1 day - Single study visit
Decision accuracy
Difference in detection rate measured by number of medication verification errors
Measured: 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.
Measured: 1 day - Single study visit
Secondary outcomes (1)
Reaction time
Difference in task time measured by the number of seconds from starting the task to accepting or rejecting a medication image
Measured: 1 day - Single study visit
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted28 Jan 2025
- Study start1 Feb 2023
- Primary completion12 May 2023
- Study completion12 May 2023
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (2)Papers the sponsor has linked to this study.
- Other trials for Machine Intelligence in the PharmacyStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Enrolment
- 30 participants (actual)
Sponsor and investigators
- Lead sponsor
- Corey Lester
- Sponsor type
- University, hospital or other
- Collaborators
- National Library of Medicine (NLM)
- Responsible party
- Sponsor-investigator, Corey Lester, University of Michigan
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06795477
- Sponsor's protocol ID
- HUM00213493
- Other IDs
- 5R01LM013624
- Status last verified
- Jan 2025
- First submitted
- 21 Jan 2025
- Last updated
- 28 Jan 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
Publications
Lester C, Rowell B, Zheng Y, Co Z, Marshall V, Kim JY, Chen Q, Kontar R, Yang XJ. Effect of Uncertainty-Aware AI Models on Pharmacists' Reaction Time and Decision-Making in a Web-Based Mock Medication Verification Task: Randomized Controlled Trial. JMIR Med Inform. 2025 Apr 18;13:e64902. doi: 10.2196/64902.
PubMed 40249341Tsai CC, Kim JY, Chen Q, Rowell B, Yang XJ, Kontar R, Whitaker M, Lester C. Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled Trial. J Med Internet Res. 2025 Jan 31;27:e59946. doi: 10.2196/59946.
PubMed 39888668
Study at a glance
From the registry record, last updated by the sponsor on 28 Jan 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Preventing Medication Dispensing Errors in Pharmacy Practice with Interpretable Machine Intelligence: Wave 2
- ClinicalTrials.gov ID
- NCT06795477
- Acronym / sponsor ID
- HUM00213493
- Conditions
- Machine Intelligence in the Pharmacy
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 30 participants (actual)
- Lead sponsor
- Corey LesterUniversity, hospital or other
- Collaborators
- National Library of Medicine (NLM)
- Study start
- 1 Feb 2023 (actual)
- Primary completion
- 12 May 2023 (actual)
- Study completion
- 12 May 2023 (actual)
- First posted
- 28 Jan 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Primary purpose
- Other
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- None (open label)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Interpretable MI | Experimental | Behavioral: No MI Help, Behavioral: Interpretable MIParticipants receive interpretable machine intelligence to complete the medication verification task. |
| Uninterpretable MI | Experimental | Behavioral: No MI Help, Behavioral: Uninterpretable MIParticipants receive uninterpretable (i.e., black-box) machine intelligence to complete the medication verification task. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (5 listed)
- 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 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.
Key exclusion criteria (5 listed)
- Eyeglasses with more than one power (bifocals, trifocals, progressives, layered lenses, or regression lenses)
- Cataracts, intraocular implants, glaucoma, or permanently dilated pupil
- Require a screen reader/magnifier or other assistive technology to use the computer
- Eye surgery (e.g., corneal)
- Eye movement or alignment abnormalities (lazy eye, strabismus, nystagmus)
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| No MI HelpParticipants will complete the medication verification task without any MI help | Behavioural | Interpretable MI, Uninterpretable MI |
| Interpretable MIParticipants receive interpretable machine intelligence assistance to complete the medication verification tasks. | Behavioural | Interpretable MI |
| Uninterpretable MIParticipants receive uninterpretable (i.e., black-box) machine intelligence assistance to complete the medication verification tasks. | Behavioural | Uninterpretable MI |
Primary outcome
Cognitive effort
Time frame: 1 day - Single study visit
Difference in cognitive effort measured by duration of fixation and fixation count
Decision accuracy
Time frame: 1 day - Single study visit
Difference in detection rate measured by number of medication verification errors
Trust change
Time frame: 1 day - Single study visit
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.
1 secondary outcome
Reaction time
Time frame: 1 day - Single study visit
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Jan 2025.
- Derived from this study: PubMed 40249341Lester C, Rowell B, Zheng Y, Co Z, Marshall V, Kim JY, Chen Q, Kontar R, Yang XJ. Effect of Uncertainty-Aware AI Models on Pharmacists' Reaction Time and Decision-Making in a Web-Based Mock Medication Verification Task: Randomized Controlled Trial. JMIR Med Inform. 2025 Apr 18;13:e64902. doi: 10.2196/64902.
- Derived from this study: PubMed 39888668Tsai CC, Kim JY, Chen Q, Rowell B, Yang XJ, Kontar R, Whitaker M, Lester C. Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Pharmacists: Randomized Controlled Trial. J Med Internet Res. 2025 Jan 31;27:e59946. doi: 10.2196/59946.
Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing No MI Help, Interpretable MI and Uninterpretable MI in people with Machine Intelligence in the Pharmacy, run by Corey Lester at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 5 inclusion and 5 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 28 Jan 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Cognitive effort (time frame: 1 day - Single study visit). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06795477, which also lists 2 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 28 Jan 2025; AzTrials last refreshed its copy on 27 Oct 2026.