Evidence map›Paper›PMID 41078851›Full record

ReviewMethodist DeBakey cardiovascular journal2025

From Data to Decision: A Comprehensive Review of Real-Time Analytics and Smart Technologies in the Surgical Suite.

Jacob B Watson, Carlos Quintero-Peña, Anna C Moise, Alan B Lumsden, Stuart J Corr

Abstract readReview
In one paragraph

Review in Methodist DeBakey cardiovascular journal, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Review
  2. Article
  3. Review
  4. Review
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

5 authors.

Jacob B WatsonHouston Methodist Hospital, Houston, Texas, US.ORCID https://orcid.org/0009-0001-0706-6382
Carlos Quintero-PeñaThe Bookout Center, Houston Methodist Academic Institute, Houston Methodist, Houston, Texas, US.ORCID https://orcid.org/0000-0003-0915-8691
Anna C MoiseThe Bookout Center, Houston Methodist Academic Institute, Houston Methodist, Houston, Texas, US.ORCID https://orcid.org/0009-0002-6405-684X
Alan B LumsdenHouston Methodist DeBakey Heart & Vascular Center, Houston Methodist, Houston, Texas, US.ORCID https://orcid.org/0009-0005-9620-5274
Stuart J CorrThe Bookout Center, Houston Methodist Academic Institute, Houston, Texas, US.ORCID https://orcid.org/0000-0002-9865-5309

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Advancements in intraoperative data acquisition and analytics are transforming surgical care by integrating diverse information streams into cohesive, real-time decision-support systems. This review explores the use of physiological monitoring, medical imaging, instrument motion data, surgical video, and environmental metrics within next-generation operating rooms. Traditional vital sign monitoring is now augmented by wearable and minimally invasive technologies capable of continuously capturing respiratory rate, heart rate, skin temperature, and hemodynamic parameters such as stroke volume, cardiac output, systemic vascular resistance, and mean arterial pressure. Real-time imaging modalities including intraoperative cone-beam computed tomography, 3-dimensional fluoroscopy with image fusion, ultrasound, and Doppler systems provide dynamic anatomical context to guide precision interventions. Concurrently, computer vision and artificial intelligence tools are being applied to surgical video and kinematic data to identify procedural phases, assess performance, and recognize critical events. Predictive analytics models, trained on large procedural datasets, can anticipate adverse outcomes like excessive blood loss or prolonged operative time, enhancing intraoperative awareness. Smart operating room platforms integrate these multimodal data sources into centralized interfaces, enabling synchronized documentation, workflow coordination, and team communication. Moreover, emerging concepts such as patient-specific surgical simulation and digital twin models offer personalized guidance and outcome forecasting. Despite these advances, challenges remain in managing data overload, achieving system interoperability, ensuring regulatory compliance, and safeguarding privacy.

Indexed as

Decision Support Systems, ClinicalDecision Support TechniquesMonitoring, IntraoperativeOperating Room Information SystemsOperating RoomsSurgery, Computer-AssistedClinical Decision-MakingHumansPredictive Value of TestsSystems IntegrationWorkflowadvanced intraoperative imagingartificial intelligence in surgeryintraoperative data integrationpredictive analytics and digital twinssmart operating room (smart OR)surgical video analytics

Identifiers

PMID41078851
PMCPMC12513365

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.