Jianzhi Deng, Ying Chen, Xiaoyu Zhang, Yuehan Zhou, Bin Xiong
Abstract
Pharmacy Intravenous Admixture Services (PIVAS) are places dedicated to the centralized dispensing of intravenous drugs, usually managed and operated by professional pharmacists and pharmacy technicians, and are an integral part of modern healthcare. However, the workflow of PIVAS has some problems, such as low efficiency and error-prone. This study aims to improve the efficiency of drug dispensing, reduce the rate of manual misjudgment, and minimize drug errors by conducting an in-depth study of the entire workflow of PIVAS and applying image recognition technology to the drug checking and dispensing process. Firstly, through experimental comparison, a target detection model suitable for drug category recognition is selected in the drug-checking process of PIVAS, and it is improved to improve the recognition accuracy and speed of intravenous drug categories. Secondly, a corner detection model for drug dosage recognition was studied in the drug dispensing stage to further increase drug dispensing accuracy. Then the PIVAS drug category recognition system and PIVAS drug dosage recognition system were designed and implemented.
Introduction
Pharmacy intravenous admixture services (PIVAS), is in line with GMP standards, according to the characteristics of the drug designed for the operation of the environment, by the trained pharmacy technicians, in strict accordance with the operation procedures, including intravenous nutritional fluid, cytotoxic drugs and antibiotics and other intravenous drug dispensing, for clinical drug therapy and rational use of services. According to the Code of Pharmacy Management for Medical Institutions issued by the State Ministry of Health in 2002, medical institutions should establish intravenous drug dispensing centers (PIVAS) according to clinical needs [1] for the dispensing of anticancer chemotherapeutic drugs and total parenteral nutrition (TPN). The Code of Quality Management for Pharmacy Intravenous Infusion Services, issued by the Ministry of Health in 2010, also provides the requirements and standard operating procedures of PIVAS with a description. In recent years, significant progress has been made in the establishment of PIVAS, which has become an important part of hospital pharmacies [2]. PIVAS plays an important role in occupational safety by centralizing the dispensing of drugs that were previously distributed in different wards [3].
Results
In terms of verifying the accuracy and universality of the drug dosage recognition model, this paper has done comparative experiments of drug dosage recognition in different scenarios. The experimental results are shown in Tables 5 and 6. In the table, groups 1–4 is the control group, and the scale of the syringe is visible, group 5–10 is the experimental group, in which the black piston position of the syringe in groups 5 and 6 is located between the scales and the position is blurred, the scale in the syringe in groups 7 and 8 is affected by the light and the angle and the scale is not clear, and the scale in the syringe in group 9 and 10 is occluded. The "None" in the table means that it is not observable by human eyes. Tables 5 and 6 records the recognized dosages of colorless and colored drugs after drawing from syringes of different capacities.
Conclusion
Summarize
PIVAS offers several advantages in terms of occupational protection and drug safety. However, intravenous infusions are associated with a higher incidence of adverse reactions than oral administration. Therefore, IV infusion safety is critical to treatment outcomes. The application of image recognition technology can improve drug safety and reduce drug errors. Before we introduced image recognition technology, the main workflow of PIVAS was manual. Although some intelligent methods have been introduced recently, they have little effect and waste a lot of time instead. The method proposed in this study may become an ideal choice for large hospitals with heavy workloads, huge time pressures, and strict quality control requirements.
Citation: Deng J, Chen Y, Zhang X, Zhou Y, Xiong B (2024) Intelligent supervision of PIVAS drug dispensing based on image recognition technology. PLoS ONE 19(4): e0298109. https://doi.org/10.1371/journal.pone.0298109
Editor: Ramada Rateb Khasawneh, Yarmouk University, JORDAN
Received: October 29, 2023; Accepted: January 13, 2024; Published: April 4, 2024
Copyright: © 2024 Deng et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Data Availability: All relevant data are within the manuscript and its Supporting Information files.
Funding: This research was supported by the National Natural Science Foundation of China (No. 42174080).The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Competing interests: The authors have declared that no competing interests exist.
https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0298109#abstract0
