Evidence map›Paper›PMID 41357433›Full record

ReviewFrontiers in digital health2025

Transforming critical care: the digital revolution's impact on intensive care units.

Corina Vernic, Tudor Paul Tamas, Ion Petre, Sorin Ursoniu

Abstract readReview
In one paragraph

Review in Frontiers in digital health, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing 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

7 citing papers in PubMed.

  1. Article
  2. Observational
  3. Review
  4. Review
  5. Article
  6. Review
  7. Article
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

4 authors.

Corina VernicDoctoral School, Victor Babes University of Medicine and Pharmacy Timisoara, Timisoara, Romania.
Tudor Paul TamasDepartment of Functional Sciences III, Discipline of Physiology, Victor Babes University of Medicine and Pharmacy, Timisoara, Romania.
Ion PetreDepartment of Functional Sciences III, Discipline of Medical Informatics and Biostatistics, Victor Babes University of Medicine and Pharmacy, Timisoara, Romania.
Sorin UrsoniuDepartment of Functional Sciences III, Discipline of Public Health and History of Medicine, Victor Babes University of Medicine and Pharmacy, Timisoara, Romania.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Intensive care units (ICUs) represent a critical pillar of modern healthcare, combining advanced technologies and specialized care to support organ functions in critically ill patients. The recent COVID pandemic served not only as a stress test but also as a potential catalyst for further ICU digitalization advancements. Recently evolved tools and processes suggest a transformative potential for digitalization in enhancing ICU performance, optimizing resource utilization, and improving patient outcomes. Digital tools-particularly machine learning (ML) and artificial intelligence (AI)-could significantly support ICU care by facilitating real-time monitoring, predictive analytics, and semiautomated decision-making. ML models have shown promise in outperforming traditional scoring systems when predicting patient outcomes such as mortality, ICU length of stay, and readmission risks. The digitalization of nursing documentation and resource allocation processes appears to improve efficiency, reduce errors, and potentially optimize staff time for direct patient care. Innovations in infection control are increasingly leveraging AI to predict conditions like ventilator-associated pneumonia and sepsis, enabling earlier interventions and potentially enhancing antimicrobial stewardship. Closed-loop ventilation systems illustrate a shift toward intelligent, data-responsive care platforms that may improve patient safety and therapeutic precision by embedding adaptive decision-making into medical devices. The pandemic underscored the growing relevance of ICU digitalization, accelerating the development of tools such as remote monitoring, tele-ICU models, and wearable devices. These advancements have helped address unprecedented patient volumes and further illustrated the potential of AI-enabled tools to streamline ICU workflows and augment patient care. This momentum reflects a broader paradigm shift in critical care toward more proactive, algorithm-assisted medicine-where AI is positioned to complement clinical judgment in managing the complexity of ICU environments. In addition, personalized digital recovery pathways are being explored to support post-ICU rehabilitation, although significant challenges remain in addressing patients' physical and psychological recovery needs. Altogether, these recent evolutions underscore the potentially transforming role of digitalization in enhancing ICU care quality and safety parameters, improving resource utilization, supporting better patient outcomes, and helping meet the evolving expectations of patients and their families.

Indexed as

digitalizationICU care metricsindicators and metricsmachine learningquality and safety

Identifiers

PMID41357433
PMCPMC12678268

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

None linked

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.