ArticleHealthcare (Basel, Switzerland)2022
Design and Implementation of a Comprehensive AI Dashboard for Real-Time Prediction of Adverse Prognosis of ED Patients.
Article in Healthcare (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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Who cites it
11 citing papers in PubMed.
- Using artificial intelligence to predict adverse outcomes in emergency department patients with hyperglycemic crises in real time.BMC endocrine disorders · 2023Trial
- Real-World Barriers to and Facilitators of Implementing AI-Based Clinical Decision Support Systems: Scoping Review.JMIR medical informatics · 2026Article
- Nutritional detoxification strategies in aquaculture: bioactive feed additives mitigating oxidative stress and xenobiotic toxicity in fish.Fish physiology and biochemistry · 2026Review
- Technological Strategies for the Patient Experience in Emergency Departments: Scoping Review.JMIR medical informatics · 2026Article
- EmergInsight: a real-time dashboard for optimizing emergency care through data visualization and analytics.JAMIA open · 2026Article
- Technological advancements and the use of key performance indicators in hospital management: a critical-conceptual review.Frontiers in health services · 2026Review
- A systematic review of clinicians' acceptance and use of clinical decision support systems over time.NPJ digital medicine · 2025Article
- Designing Health Care Provider-Centered Emergency Department Interventions: Participatory Design Study.JMIR formative research · 2025Article
- Article
- Implementing Explainable Machine Learning Models for Practical Prediction of Early Neonatal Hypoglycemia.Diagnostics (Basel, Switzerland) · 2024Article
- Leveraging artificial intelligence to advance implementation science: potential opportunities and cautions.Implementation science : IS · 2024Article
Corrections and comments
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Authors and funding
8 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The emergency department (ED) is at the forefront of medical care, and the medical team needs to make outright judgments and treatment decisions under time constraints. Thus, knowing how to make personalized and precise predictions is a very challenging task. With the advancement of artificial intelligence (AI) technology, Chi Mei Medical Center (CMMC) adopted AI, the Internet of Things (IoT), and interaction technologies to establish diverse prognosis prediction models for eight diseases based on the ED electronic medical records of three branch hospitals. CMMC integrated these predictive models to form a digital AI dashboard, showing the risk status of all ED patients diagnosed with any of these eight diseases. This study first explored the methodology of CMMC's AI development and proposed a four-tier AI dashboard architecture for ED implementation. The AI dashboard's ease of use, usefulness, and acceptance was also strongly affirmed by the ED medical staff. The ED AI dashboard is an effective tool in the implementation of real-time risk monitoring of patients in the ED and could improve the quality of care as a part of best practice. Based on the results of this study, it is suggested that healthcare institutions thoughtfully consider tailoring their ED dashboard designs to adapt to their unique workflows and environments.
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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.