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IT-enabled medicine dispensing in the community pharmacy: an action design research project

J. A. S. Barata et al.

Health Systems2026https://doi.org/10.1080/20476965.2025.2591042article
AJG 2ABDC C
Weight
0.50

What the paper says

This paper presents a community pharmacy information system to assist with medication dispensing and monitoring medication adherence. The proposed prototype uses artificial intelligence (AI), cloud, and mobile technology to support patient medication records, reduce medication errors when preparing pillboxes, and provide personalized information to end-users. Action design research was selected to understand how innovative dispensing processes can be deployed in community pharmacies. The results include design guidelines for AI-enabled medicine dispensing and an evaluation of digital transformation success factors in this vital healthcare sector. AI-enabled systems can contribute to (1) prevent errors in filling pillbox compartments, (2) provide an additional cross-check in medication dispensing, and (3) identify medication adherence problems in more demanding scenarios of institutions with multiple patients. However, there are also relevant challenges, making the replacement of non-critical manual tasks, complementary checkpoints, and pre-validation stages of medicine dispensing the most promising use cases for artificial intelligence adoption.

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https://doi.org/https://doi.org/10.1080/20476965.2025.2591042

Or copy a formatted citation

@article{j.2026,
  title        = {{IT-enabled medicine dispensing in the community pharmacy: an action design research project}},
  author       = {J. A. S. Barata et al.},
  journal      = {Health Systems},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1080/20476965.2025.2591042},
}

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IT-enabled medicine dispensing in the community pharmacy: an action design research project

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.