Impact of Analytics Applying Artificial Intelligence and Machine Learning on Enhancing Intensive Care Unit - A Narrative Review

Aug 4, 2026

The intensive care unit (ICU) is essential for delivering specialized care to patients with severe illness or injury, yet ICU admission requires rapid, skilled decisions and effective use of scarce resources. This narrative review examines how analytics, artificial intelligence, and machine learning can strengthen ICU management by supporting triage, benchmarking effective practices, optimizing resource allocation, and improving clinical decision processes from continuous monitoring data. Evidence from 2016–2023 indicates improved critical care delivery and patient outcomes through data analysis.

LanguageEnglish

CategoryHealthcare

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Introduction

ICU admission challenges

Need for analytics in critical care

Aim

Methods

Databases searched (2016–2023)

Results

AI/ML for benchmarking and practice identification

Conclusions

Data analysis improving outcomes and care delivery

Galician medical journal“Online First”, e-GMJ2023-A06 DOI: 10.21802/e-GMJ2023-A06 Review | Internal Medicine Impact of Analytics Applying Artificial Intelligence and Machine Learning on Enhancing Intensive Care Unit: A Narrative Review Gopal Singh Charan 1*, Ashok Singh Charan2, Mandeep Singh Khurana3 , Gursharn Singh Narang3 Abstract Introduction. The intensive care unit (ICU) plays a pivotal role in providing specialized care to patients with severe illnesses or injuries. As a critical aspect of healthcare, ICU admissions demand immediate attention and skilled care from healthcare professionals. However, the intricacies involved in this process necessitate analytical solutions to ensure effective management and optimal patient outcomes. Aim. The aim of this review was to highlight the enhancement of the ICUs through the application of analytics, artificial intelligence, and machine learning. Methods. The review approach was carried out through databases such as MEDLINE, Embase, Web of Science, Scopus, Taylor & Francis, Sage, ProQuest, Science Direct, CINAHL, and Google Scholar. These databases were chosen due to their potential to offer pertinent and comprehensive coverage of the topic while reducing the likelihood of overlooking certain publications. The studies for this review involved the period from 2016 to 2023 . Results. Artificial intelligence and machine learning have been instrumental in benchmarking and identifying effective practices to enhance ICU care. These advanced technologies have demonstrated significant improvements in various aspects. Conclusions. Artificial intelligence, machine learning, and data analysis techniques significantly improved critical care, patient outcomes, and healthcare delivery. Keywords Artificial Intelligence; Machine Learning; Resource Allocation; ICU Admission; Clinical Decision Processes; Analytics 1 SGRD College of Nursing, SGRD University of Health Sciences, Amritsar, Punjab, India 2 Institute of Respiratory Disease, SMS Medical College, Jaipur, Rajasthan, India 3 Department of Pediatrics, SGRD Institute of Medical Sciences & Research, SGRD University of Health Sciences, Amritsar, Punjab, India *Corresponding author: [pedslove@gmail.com](pedslove@gmail.com) Copyright ©Gopal Singh Charan, Ashok Singh Charan, Mandeep Singh Khurana, Gursharn Singh Narang, 2023 Introduction The intensive care unit (ICU) plays a pivotal role in providing specialized care to patients with severe illnesses or injuries. As a critical aspect of healthcare, ICU admissions demand immediate attention and skilled care from healthcare professionals. However, the intricacies involved in this process necessitate analytical solutions to ensure Publication history: Received: July 17, 2023 Revisions Requested: August 15, 2023 Revision Received: August 21, 2023 Accepted: August 24, 2023 Published Online: November 6, 2023 effective management and optimal patient outcomes. ICU admissions are of paramount importance due to critical conditions of patients involved. These individuals require intensive monitoring, specialized interventions, and advanced life support systems to stabilize their health and facilitate recovery. Timely allocation of resources and interventions is crucial during this phase, as any delay can lead to increased morbidity and mortality rates. The complexity of ICU admissions arises from multiple factors, requiring analytical solutions. Assessing patient acuity and illness severity is pivotal in determining the appropriateness of ICU admission. Vital signs, laboratory results, and organ dysfunction are Impact of Analytics Applying Artificial Intelligence and Machine Learning on Enhancing Intensive Care Unit: A Narrative Review—2/13 crucial in triaging patients. Analytical models can aid in developing robust triage protocols, prioritizing critically ill patients while ensuring equitable access to care for others. Guidelines such as the Grading o

FAQs

What is the goal of this narrative review?

The review aims to highlight how analytics, artificial intelligence, and machine learning can enhance intensive care unit care.

Which sources were used to conduct the review?

The review used multiple databases including MEDLINE, Embase, Web of Science, Scopus, Taylor & Francis, Sage, ProQuest, ScienceDirect, CINAHL, and Google Scholar for studies from 2016 to 2023.

How can AI and machine learning improve ICU outcomes?

AI and machine learning can support triage and benchmarking of effective practices, optimize resource allocation, and enhance clinical decision-making using continuously collected patient and physiological data.

Aug 4, 2026

Upload by Ava Thompson