Evidence map›Paper›PMID 41068363›Full record

Trial reportScientific reports2025

An AIoT enabled system for optimizing data retrieval in the intensive care unit evaluated in a randomized crossover pilot trial.

How-Yang Tseng, Wen-Sheng Feng, Chieh-Lung Chen, Jiun-Yi Lin, Ching-Yao Chou, Chih-Hao Chen, Chi-Kang Teng, Yu-Chao Lin, Yi-Cheng Shen, Yu-Chang Fu and 4 more

Abstract readRandomized Controlled Trial
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

14 authors.

How-Yang TsengDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Wen-Sheng FengDepartment of Biomedical Informatics, China Medical University, Taichung, Taiwan.
Chieh-Lung ChenDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Jiun-Yi LinDepartment of Biomedical Informatics, China Medical University, Taichung, Taiwan.
Ching-Yao ChouDigital Transformation Technology Office, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Chih-Hao ChenDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Chi-Kang TengDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Yu-Chao LinDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Yi-Cheng ShenDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Yu-Chang FuDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Wu-Huei HsuDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Chih-Yen TuDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan.
Wei-Cheng ChenDivision of Pulmonary and Critical Care, Department of Internal Medicine, China Medical University Hospital, China Medical University, Taichung, Taiwan. pion501@gmail.com.
Der-Yang ChoSchool of Medicine, China Medical University, Taichung, Taiwan. dycho@tool.caaumed.org.tw.

Funding

China Medical University Hospital DMR-HHC-113-5
6 · The paper itself

Abstract

Healthcare providers (HCPs) in the intensive care unit (ICU) frequently face information overload, which can result in cognitive fatigue and decision-making errors. This study compares the efficiency and accuracy of data collection between an artificial intelligence of things (AIoT)-enabled ICU command center (CC) and a hospital information system (HIS). A randomized crossover pilot trial was conducted with ICU-trained HCPs, who collected data from the most critically ill ICU patients, selected based on their Acute Physiologic Assessment and Chronic Health Evaluation-II (APACHE II) score, using either the CC-HIS or HIS-CC sequence. Data collection time, accuracy, and subgroup differences were evaluated. The effect of increased data volume on time and accuracy was also assessed. The study enrolled 21 HCPs to collect data from five ICU patients. After excluding incomplete or insufficient data, 184 data sets were analyzed. The results showed that the CC significantly reduced data collection time by 41.8% (p < 0.0001), with a mean reduction of 6.75 min per patient (8.5 vs. 15.2 min, p < 0.0001). Furthermore, the CC improved data accuracy by 2.07% (93.8% vs. 95.9%, p = 0.0002). These findings indicate that the AIoT-enabled CC improves both the efficiency and accuracy in data collection in the ICU.

Indexed as

Artificial IntelligenceHospital Information SystemsInformation Storage and RetrievalIntensive Care UnitsAgedAPACHECritical IllnessCross-Over StudiesFemaleHumansMaleMiddle AgedPilot ProjectsArtificial intelligenceData accuracyData collectionHospital information systemIntensive care unitInternet of thingsPrimary care physicians

Identifiers

PMID41068363
PMCPMC12511305

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Registered trials

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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.