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Designing Human-Centered AI to Prevent Medication Dispensing Errors: Focus Group Study With Pharmacists.

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  • 1Department of Clinical Pharmacy, College of Pharmacy, University of Michigan, Ann Arbor, MI, United States.

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Pharmacists prefer a hybrid artificial intelligence (AI) model for medication dispensing verification, where AI assists and pharmacists intervene based on risk. The AI prototype prioritizes interpretability, trust, and user-friendly design for enhanced human-machine collaboration.

Keywords:
SEIPSSystems Engineering Initiative for Patient Safetyartificial intelligencecommunicationdesigndesign methodsdevelopmentengineeringfocus groupshuman-computer interactionmedication errorsmorbiditymortalitypatient safetysafetytooluser-centereduser-centered design methodsvisualization

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Area of Science:

  • Pharmacy Practice
  • Human-Computer Interaction
  • Artificial Intelligence in Healthcare

Background:

  • Medication dispensing errors pose significant global health risks, impacting patient safety and healthcare costs.
  • Current verification methods like barcode scanning have limitations.
  • Artificial intelligence (AI) offers potential for improved accuracy and efficiency in pharmacy verification.

Purpose of the Study:

  • To gather pharmacists' feedback for designing a user-centered AI prototype for medication dispensing verification.
  • To inform the initial user interface and iterative design of the AI system.

Main Methods:

  • A multidisciplinary team engaged 8 pharmacists in 3 focus group rounds.
  • A Bayesian neural network was used to predict National Drug Codes (NDC).
  • Content analysis of transcribed discussions guided by human-machine teaming frameworks informed AI design.

Main Results:

  • Pharmacists favored a hybrid AI-pharmacist teaming model for verification.
  • Key design needs included enhanced AI interpretability (e.g., checkmarks, probability scores) and simplicity.
  • The final prototype featured visual aids for comparison, probability histograms, and clear decision options (accept, reject, unsure).

Conclusions:

  • A human-centered AI prototype for dispensing verification was developed in partnership with pharmacists.
  • The design emphasizes AI interpretability, trust, and human-machine collaboration, positioning AI as an assistive tool.
  • This study demonstrates a process for creating user-centric AI in pharmacy, focusing on collaboration and confidence visualization.