Evidence map›Paper›PMID 39469850›Full record

ReviewRevista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia2025

Strengths and weaknesses of computerized clinical decision support systems: insights from a digital control center (C3 COVID-19) for early and personalized treatment for COVID-19.

T F Aiello, C Teijon-Lumbreras, A Gallardo-Pizarro, P Monzó-Gallo, A Martinez-Urrea, G Cuervo, A Del Rio, M Hernández-Meneses, L Morata, J Mensa and 2 more

Abstract readReview
In one paragraph

Review in Revista espanola de quimioterapia : publicacion oficial de la Sociedad Espanola de Quimioterapia, 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

12 authors.

T F Aiello
C Teijon-Lumbreras
A Gallardo-Pizarro
P Monzó-Gallo
A Martinez-Urrea
G Cuervo
A Del Rio
M Hernández-Meneses
L Morata
J Mensa
A Soriano
C Garcia-VidalCarolina Garcia-Vidal, Infectious Diseases Department, Hospital Clinic of Barcelona-IDIBAPS, University of Barcelona. Carrer de Villarroel 170, 08036, Barcelona, Spain. cgarciav@clinic.cat.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Clinical Decision Support Systems (CDSS) are computer-based tools that leverage the analysis of large volumes of health data to assist healthcare professionals in making clinical decisions, whether preventive, diagnostic, or therapeutic. This review examines the impact of CDSS on clinical practice, highlighting both their potential benefits and their limitations and challenges. We detail the experience of clinical medical professionals in the development of a virtual control center for COVID-19 patients (C3 COVID-19) in Spain during the SARS-CoV-2 pandemic. This tool enabled real-time monitoring of clinical data for hospitalized COVID-19 patients, optimizing personalized and informed medical decision-making. CDSS can offer significant advantages, such as improving the quality of inpatient care, promoting evidence-based clinical and therapeutic decision-making, facilitating treatment personalization, and enhancing healthcare system efficiency and productivity. However, the implementation of CDSS presents challenges, including the need for physicians to become familiar with the systems and software, and the necessity for ongoing updates and technical support of the systems.

Indexed as

COVID-19Decision Support Systems, ClinicalPrecision MedicineClinical Decision-MakingHumansSARS-CoV-2SpainArtificial IntelligenceClinical Decision Support SystemsCOVID-19

Identifiers

PMID39469850
PMCPMC11758872

What OpenQuestion holds

Textmetadata
LicenceCC BY-NC
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.