Dispensing error rates in pharmacy: A systematic review and meta-analysis

Um, Irene S.; Clough, Alexander; Tan, Edwin C.K.

doi:10.1016/j.sapharm.2023.10.003

Cited by 5 publications

(1 citation statement)

References 54 publications

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“…Medication dispensing error can be defined as "any deviations of the prescription order," including but not limited to dispensing the wrong dosage, strength, dose form, or pharmaceutical ingredient [11]. The estimated rate of medication dispensing errors is 2.4 in 100 prescriptions [12] in community pharmacies, with the most common types being wrong ingredient, wrong strength, and labeling errors [12,13]. These errors can result in unfavorable therapeutic outcomes, which can have adverse effects on patient safety and outcomes.…”

Section: The Effect Of Artificial Intelligence Helpfulness and Uncert...mentioning

confidence: 99%

The Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Humans: A Randomized Controlled Trial (Preprint)

Tsai,

Kim,

Chen

et al. 2024

Preprint

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BACKGROUND Clinical decision support systems (CDSS) leveraging artificial intelligence (AI) are increasingly integrated into healthcare practices, including pharmacy medication verification. Communicating uncertainty in an AI prediction is viewed as an important mechanism for boosting human collaboration and trust. Yet, little is known about the effects on human cognition as a result of interacting with such types of AI advice. OBJECTIVE To evaluate the cognitive interaction patterns of pharmacists during medication product verification when using an AI prototype. Moreover, we examine the impact of AI’s assistance — both helpful and unhelpful — and the communication of uncertainty of AI-generated results on pharmacists’ cognitive interaction with the prototype and their performance. METHODS In a randomized controlled trial, 30 pharmacists from professional networks each performed 200 medication verification tasks while their eye movements were recorded using a virtual eye tracker. Participants completed 100 verifications without AI assistance and 100 with AI assistance (either with simple help without uncertainty information, or advanced help which displays AI uncertainty). Fixation patterns (first and last areas fixated, number of fixations, fixation duration, and dwell times) were analyzed in relation to AI help type and helpfulness. RESULTS Pharmacists shifted 19-26% of their total fixations to AI-generated regions when these were available, suggesting integration of AI advice in decision-making. AI assistance did not reduce the number of fixations on fill images, which remained the primary focus area. Unhelpful AI advice led to longer dwell times on reference and fill images, indicating increased cognitive processing. Displaying AI uncertainty led to longer cognitive processing times as measured by dwell times in original images. CONCLUSIONS Unhelpful AI increases cognitive processing time in the original images. Transparency in AI is needed in “black-box” systems, but showing more information can add cognitive burden. Therefore, the communication of uncertainty should be optimized and integrated into clinical workflows using user-centered design to avoid increasing cognitive load or impeding clinicians’ original workflow.

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Section: The Effect Of Artificial Intelligence Helpfulness and Uncert...mentioning

confidence: 99%

The Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Humans: A Randomized Controlled Trial (Preprint)

Tsai,

Kim,

Chen

et al. 2024

Preprint

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Suitability of Hair Testing in Medical Dispensing Errors: A Fatal Case of Gliclazide Poisoning

Riess,

Klinger,

Roussel

et al. 2024

Drug Testing and Analysis

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A patient was prescribed a new treatment, 40 mg furosemide. Her pharmacist mistakenly dispensed 40 mg gliclazide instead. After 3 weeks of treatment, the patient was found in a coma, was hospitalised and died after a week. Hair sample was collected during post‐mortem examination and tested for gliclazide on three 2 cm sections, starting at the root end. Hair strands were decontaminated, segmented and incubated in the presence of diazepam‐d5, and a solid–liquid extraction has been performed. Finally, toxicological analyses were performed by UFLC Shimadzu Prominence ‐ MS/MS Sciex 6500 QTRAP. Gliclazide was found on the proximal (238 pg/mg), median (77 pg/mg) and distal (69 pg/mg) segments. The concentration in the proximal segment was largely higher than in the other two, which demonstrates repeated intake of gliclazide by the patient during at least the last 2 months before death. The lower concentrations in the medial and distal segments, as well as a part of the concentration in the proximal segment, may be linked to external hair contamination. These could be due to either radial sweat diffusion, possibly occurring in the last week hospitalisation of the patient, or to biological fluids contamination during post‐mortem examination. This case illustrates the opportunity to confirm slow and fatal chronic poisoning by gliclazide using hair analysis and shows the benefit of hair testing for the investigation of medical or dispensing errors.

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Comparing Patient Satisfaction with Automated Drug Dispensing System and Traditional Drug Dispensing System: A Cross-Sectional Study

Amirthalingam,

Alruwaili,

Albalawi

et al. 2024

PPA

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Dispensing error rates in pharmacy: A systematic review and meta-analysis

Cited by 5 publications

References 54 publications

The Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Humans: A Randomized Controlled Trial (Preprint)

The Effect of Artificial Intelligence Helpfulness and Uncertainty on Cognitive Interactions with Humans: A Randomized Controlled Trial (Preprint)

Suitability of Hair Testing in Medical Dispensing Errors: A Fatal Case of Gliclazide Poisoning

Comparing Patient Satisfaction with Automated Drug Dispensing System and Traditional Drug Dispensing System: A Cross-Sectional Study

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