NCT05106621

Brief Summary

Perioperative medicine is characterized by a very delicate path; it is composed, in fact, of a series of highly specialized clinical measures managed by various professionals (surgeons, anesthetists, intensivists, nurses, etc.), who work together to ensure the best quality of all phases of the path (preoperative , intra and postoperative). On the other hand, it is necessary to underline the huge resources needed to provide surgical services. Organizational optimization, based on specific analyzes, could lead to a more careful management of resources in this area, avoiding waste due to early closure of the operating room or unexpected extension of the same. In recent years, precisely to respond to the need to analyze large quantities of information, the use of artificial intelligence techniques, and in particular of machine learning, is becoming increasingly popular, a branch of artificial intelligence that aims, through the use of algorithms and statistical model, to infer new knowledge in a way automatic. Such technologies appear to possess excellent analytical skills both in the clinical and, above all, organizational fields. The data that are emerging in the literature on this issue, although still the first in this regard, seem to confirm this hypothesis.

Trial Health

43
At Risk

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Trial has exceeded expected completion date
Enrollment
142

participants targeted

Target at P50-P75 for all trials

Timeline
Completed

Started Nov 2021

Geographic Reach
1 country

1 active site

Status
unknown

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

First Submitted

Initial submission to the registry

October 11, 2021

Completed
21 days until next milestone

Study Start

First participant enrolled

November 1, 2021

Completed
3 days until next milestone

First Posted

Study publicly available on registry

November 4, 2021

Completed
1.1 years until next milestone

Primary Completion

Last participant's last visit for primary outcome

November 30, 2022

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

November 30, 2022

Completed
Last Updated

May 17, 2022

Status Verified

May 1, 2022

Enrollment Period

1.1 years

First QC Date

October 11, 2021

Last Update Submit

May 16, 2022

Conditions

Artificial Intelligence in Operating Room

Keywords

Perioperative MedicineArtificial IntelligenceMachine LearningOperating Room

Outcome Measures

Primary Outcomes (1)

  • Surgical Time Prediction

    Prediction of time spend in oprating room

    1 year

Secondary Outcomes (2)

  • Outcome evaluation

    1 year

  • Outcome evaluation

    1 year

Study Arms (1)

Surgical Patients

Eligibility Criteria

Age18 Years+
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64), Older Adult (65+)
Sampling MethodNon-Probability Sample
Study Population

all patients undergoing abdominal, thoracic, urological, vascular, orthopedic and gynecological and plastic surgery

You may qualify if:

  • all patients undergoing surgery who sign the informed consent form will be included.

You may not qualify if:

  • refusal of the patient to the study in question.

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Azienda Ospedaliera-Universitaria di Parma

Parma, 43125, Italy

RECRUITING

Related Publications (6)

  • Evans RS, Burke JP, Classen DC, Gardner RM, Menlove RL, Goodrich KM, Stevens LE, Pestotnik SL. Computerized identification of patients at high risk for hospital-acquired infection. Am J Infect Control. 1992 Feb;20(1):4-10. doi: 10.1016/s0196-6553(05)80117-8.

    PMID: 1554148BACKGROUND

  • Redfern RO, Langlotz CP, Abbuhl SB, Polansky M, Horii SC, Kundel HL. The effect of PACS on the time required for technologists to produce radiographic images in the emergency department radiology suite. J Digit Imaging. 2002 Sep;15(3):153-60. doi: 10.1007/s10278-002-0024-5. Epub 2002 Nov 6.

    PMID: 12415466BACKGROUND

  • Lee TT, Liu CY, Kuo YH, Mills ME, Fong JG, Hung C. Application of data mining to the identification of critical factors in patient falls using a web-based reporting system. Int J Med Inform. 2011 Feb;80(2):141-50. doi: 10.1016/j.ijmedinf.2010.10.009. Epub 2010 Nov 5.

    PMID: 21115393BACKGROUND

  • Martins M. Use of comorbidity measures to predict the risk of death in Brazilian in-patients. Rev Saude Publica. 2010 Jun;44(3):448-56. doi: 10.1590/s0034-89102010005000003. Epub 2010 Apr 30.

    PMID: 20428601BACKGROUND

  • Izad Shenas SA, Raahemi B, Hossein Tekieh M, Kuziemsky C. Identifying high-cost patients using data mining techniques and a small set of non-trivial attributes. Comput Biol Med. 2014 Oct;53:9-18. doi: 10.1016/j.compbiomed.2014.07.005. Epub 2014 Jul 22.

    PMID: 25105749BACKGROUND

  • Bottani E, Bellini V, Mordonini M, Pellegrino M, Lombardo G, Franchi B, Craca M, Bignami E. 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. JMIR Res Protoc. 2023 Jul 5;12:e45477. doi: 10.2196/45477.

    PMID: 37405821DERIVED

Central Study Contacts

Elena Bignami

CONTACT

390521703567elenagiovanna.bignami@unipr.it

Study Design

Study Type
observational
Observational Model
COHORT
Time Perspective
PROSPECTIVE
Sponsor Type
OTHER
Responsible Party
PRINCIPAL INVESTIGATOR
PI Title
Chief of 2^ UO Anesthesua and Intensive Care, Full Professor of University of Parma

Study Record Dates

First Submitted

October 11, 2021

First Posted

November 4, 2021

Study Start

November 1, 2021

Primary Completion

November 30, 2022

Study Completion

November 30, 2022

Last Updated

May 17, 2022

Record last verified: 2022-05

Locations

IT(1)