ArticleFrontiers in medicine2025
The effect of an IoT-based intelligent infusion monitoring system on the intravenous infusion safety of hospitalized patients: a retrospective study.
Article in Frontiers in medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Background: Intravenous infusion is a key part of nursing practice, and its scientific reliability and safety directly affect nursing quality and patient satisfaction. Currently, most hospitals still use traditional gravity infusion, with inherent limitations that burden nursing staff. Therefore, information management is an effective strategy to enhance medication safety. Aim: This study aimed to explore the effect of intelligent infusion monitoring systems in improving patient infusion safety. Methods: The study used a retrospective study design. A collaborative development of an intelligent infusion monitoring system using Internet of Things (IoT) technology was conducted. Data from two pilot wards were retrospectively analyzed, including on-time infusion completion rates, infusion-related complications, and patient satisfaction 1 year before and after system implementation. Additionally, infusion abnormalities and nurses' system satisfaction were recorded during application. Results: After the application of the intelligent infusion monitoring system, the on-time completion rates of patient infusions significantly improved in both Ward A and Ward B independently, as well as in both wards combined. The occurrence of complications related to infusion significantly reduced in Ward A and in both wards combined. Patient satisfaction also improved after the system application. Most infusion abnormality alerts (74.33%, 85,322 cases) involved blockages or rate abnormalities, with a mean response time of 2.12 min. Nurses' satisfaction with the system was 75% (33/44). Conclusion: The application of the intelligent infusion monitoring system effectively improved the on-time completion rates of intravenous infusions and effectively reduced the incidence of complications related to infusion. Through real-time monitoring and alert functions, the system facilitated the timely detection and management of infusion abnormalities, enhancing the quality of infusion management for patients, ensuring infusion safety, and improving patient satisfaction. However, there is still room for improvement in the intelligence and convenience of the system. Therefore, future efforts should focus on further research and investment in intelligent infusion equipment to improve the safety of clinical patient infusions and enhance nursing efficiency.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.