AIMC Topic: Hospitals

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New care pathways for supporting transitional care from hospitals to home using AI and personalized digital assistance.

Scientific reports
Transitional care may play a vital role in the sustainability of Europe's future healthcare system, offering solutions for relocating patient care from hospital to home, therefore addressing the growing demand for medical care as the population is ag...

Accuracy of Machine Learning Models to Predict In-hospital Cardiac Arrest: A Systematic Review.

Clinical nurse specialist CNS
PURPOSE/AIMS: Despite advances in healthcare, the incidence of in-hospital cardiac arrest (IHCA) has continued to rise for the past decade. Identifying those patients at risk has proven challenging. Our objective was to conduct a systematic review of...

Are Wearable ECG Devices Ready for Hospital at Home Application?

Sensors (Basel, Switzerland)
The increasing focus on improving care for high-cost patients has highlighted the potential of Hospital at Home (HaH) and remote patient monitoring (RPM) programs to optimize patient outcomes while reducing healthcare costs. This paper examines the r...

Harnessing artificial intelligence for infection control and prevention in hospitals: A comprehensive review of current applications, challenges, and future directions.

Saudi medical journal
Hospital-acquired infections (HAIs) significantly burden global healthcare systems, exacerbated by antibiotic-resistant bacteria. Traditional infection control measures often lack consistency due to variable human compliance. This comprehensive revie...

Predicting In-Hospital Fall Risk Using Machine Learning With Real-Time Location System and Electronic Medical Records.

Journal of cachexia, sarcopenia and muscle
BACKGROUND: Hospital falls are the most prevalent and fatal event in healthcare, posing significant risks to patient health outcomes and institutional care quality. Real-time location system (RTLS) enables continuous tracking of patient location, pro...

Societal factors influencing the implementation of AI-driven technologies in (smart) hospitals.

PloS one
INTRODUCTION: The introduction of AI in healthcare promises benefits, but also faces challenges. Currently, one of these challenges is the lack of information on the societal aspects of implementing AI in healthcare. This study aims to: 1) identify w...

LeFood-set: Baseline performance of predicting level of leftovers food dataset in a hospital using MT learning.

PloS one
Monitoring the remaining food in patients' trays is a routine activity in healthcare facilities as it provides valuable insights into the patients' dietary intake. However, estimating food leftovers through visual observation is time-consuming and bi...

Current Use And Evaluation Of Artificial Intelligence And Predictive Models In US Hospitals.

Health affairs (Project Hope)
Effective evaluation and governance of predictive models used in health care, particularly those driven by artificial intelligence (AI) and machine learning, are needed to ensure that models are fair, appropriate, valid, effective, and safe, or FAVES...

Monitoring of Artificial Intelligence in Hospitals.

Studies in health technology and informatics
Monitoring of artificial intelligence (AI)-based algorithms is necessary for safe implementation and will be required in upcoming regulations. This study investigates the potential for monitoring of AI in hospitals. First, by reviewing regulatory req...

[Application of Intelligent Logistics System Based on AGV Robot in Medical Consumables Management].

Zhongguo yi liao qi xie za zhi = Chinese journal of medical instrumentation
OBJECTIVE: Medical consumables are expensive, with numerous specifications and large usage, and traditional manual management models have certain drawbacks. Building an intelligent logistics management system to improve management level.