Evidence map›Paper›PMID 37405821›Full record

ArticleJMIR research protocols2023

Internet of Things and New Technologies for Tracking Perioperative Patients With an Innovative Model for Operating Room Scheduling: Protocol for a Development and Feasibility Study.

Eleonora Bottani, Valentina Bellini, Monica Mordonini, Mattia Pellegrino, Gianfranco Lombardo, Beatrice Franchi, Michelangelo Craca, Elena Bignami

Registry-linked trialOpen access · goldAbstract read
In one paragraph

Article in JMIR research protocols, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05106621 (NEW MODEL OF ORGANIZATION OF AN OPERATIVE BLOCK), which is not on this map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
1.1field-weighted citation impact, top 22% of its field
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.

NCT05106621 unknown statusnot on this map

New model of organization of an operative block (bloc-op)

TypeobservationalSponsorUniversity of ParmaRan2021 to 2022Enrolled142ConditionsArtificial Intelligence in Operating Room
3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 3 citations in OpenAlex.

  1. 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

8 authors at 1 institution in 1 country.

Eleonora Bottani *Department of Engineering and Architecture, University of Parma, Parma, Italy.ORCID https://orcid.org/0000-0002-6319-8080
Valentina Bellini *Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy.ORCID https://orcid.org/0000-0001-7993-9026
Monica Mordonini *Department of Engineering and Architecture, University of Parma, Parma, Italy.ORCID https://orcid.org/0000-0002-5916-9770
Mattia Pellegrino *Department of Engineering and Architecture, University of Parma, Parma, Italy.ORCID https://orcid.org/0000-0002-6592-7451
Gianfranco Lombardo *Department of Engineering and Architecture, University of Parma, Parma, Italy.ORCID https://orcid.org/0000-0003-1808-4487
Beatrice Franchi *Department of Engineering and Architecture, University of Parma, Parma, Italy.ORCID https://orcid.org/0000-0002-7829-6855
Michelangelo Craca *Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy.ORCID https://orcid.org/0000-0002-0077-3332
Elena Bignami *Anesthesiology, Critical Care and Pain Medicine Division, Department of Medicine and Surgery, University of Parma, Parma, Italy.ORCID https://orcid.org/0000-0001-9985-4126
University of Parma · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundManagement of operating rooms is a critical point in health care organizations because surgical departments represent a significant cost in hospital budgets. Therefore, it is increasingly important that there is effective planning of elective, emergency, and day surgery and optimization of both the human and physical resources available, always maintaining a high level of care and health treatment. This would lead to a reduction in patient waiting lists and better performance not only of surgical departments but also of the entire hospital.

objectiveThis study aims to automatically collect data from a real surgical scenario to develop an integrated technological-organizational model that optimizes operating block resources.

methodsEach patient is tracked and located in real time by wearing a bracelet sensor with a unique identifier. Exploiting the indoor location, the software architecture is able to collect the time spent for every step inside the surgical block. This method does not in any way affect the level of assistance that the patient receives and always protects their privacy; in fact, after expressing informed consent, each patient will be associated with an anonymous identification number.

resultsThe preliminary results are promising, making the study feasible and functional. Times automatically recorded are much more precise than those collected by humans and reported in the organization's information system. In addition, machine learning can exploit the historical data collection to predict the surgery time required for each patient according to the patient's specific profile. Simulation can also be applied to reproduce the system's functioning, evaluate current performance, and identify strategies to improve the efficiency of the operating block.

conclusionsThis functional approach improves short- and long-term surgical planning, facilitating interaction between the various professionals involved in the operating block, optimizing the management of available resources, and guaranteeing a high level of patient care in an increasingly efficient health care system.

trial registrationClinicalTrials.gov NCT05106621; https://clinicaltrials.gov/ct2/show/NCT05106621. INTERNATIONAL REGISTERED REPORT IDENTIFIER (IRRID): DERR1-10.2196/45477.

Indexed as

artificial intelligenceinternet of thingsmachine learningoperating roomsperioperative organization

Identifiers

PMID37405821
PMCPMC10357371
OpenAlexW4383187507

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

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.