Evidence map›Paper›PMID 41725889›Full record

ArticleMDM policy & practice

A Discrete-Event Simulation Model to Configure Operating Rooms for Robotic Cardiac Surgery.

Martina Doneda, Elena Elzi, Alfonso Agnino, Ascanio Graniero, Laura Giroletti, Matteo Parrinello, Giovanni Albano, Gabriele Tunesi, Ettore Lanzarone

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in MDM policy & practice. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  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

9 authors.

Martina DonedaDepartment of Management, Information and Production Engineering, University of Bergamo, Dalmine (BG), Italy.ORCID https://orcid.org/0000-0002-1660-3100
Elena ElziDepartment of Management, Information and Production Engineering, University of Bergamo, Dalmine (BG), Italy.
Alfonso AgninoDivision of Robotic and Minimally Invasive Cardiac Surgery, Humanitas Gavazzeni Hospital, Bergamo, Italy.ORCID https://orcid.org/0000-0003-4863-569X
Ascanio GranieroDivision of Robotic and Minimally Invasive Cardiac Surgery, Humanitas Gavazzeni Hospital, Bergamo, Italy.ORCID https://orcid.org/0000-0001-5178-9409
Laura GirolettiDivision of Robotic and Minimally Invasive Cardiac Surgery, Humanitas Gavazzeni Hospital, Bergamo, Italy.ORCID https://orcid.org/0009-0004-4067-2781
Matteo ParrinelloDivision of Cardiac Anesthesia, Humanitas Gavazzeni Hospital, Bergamo, Italy.ORCID https://orcid.org/0009-0006-3276-0276
Giovanni AlbanoDivision of Cardiac Anesthesia, Humanitas Gavazzeni Hospital, Bergamo, Italy.
Gabriele TunesiClinical Engineer and Building Management, Humanitas Gavazzeni Hospital, Bergamo, Italy.
Ettore LanzaroneDepartment of Management, Information and Production Engineering, University of Bergamo, Dalmine (BG), Italy.ORCID https://orcid.org/0000-0001-8816-9086

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Highlights: Discrete event simulation (DES) is used for the first time to improve the performance of robotic cardiac surgery (RCS), an application that presents unique challenges.The flexible DES model for RCS can parametrize various factors related to both operating rooms and procedures.The impact of these factors is evaluated on a set of KPIs.New insights into the positioning of equipment and personnel in the OR are provided, allowing to formulate informed recommendations for RCS providers.

Indexed as

discrete-event simulationoperating room designoperations managementrobotic cardiac surgery

Identifiers

PMID41725889
PMCPMC12923941

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.