AN INTELLIGENT MODEL FOR THE OPERATIVE BLOCK

NCT ID: NCT05106621

Last Updated: 2022-05-17

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

UNKNOWN

Total Enrollment

142 participants

Study Classification

OBSERVATIONAL

Study Start Date

2021-11-01

Study Completion Date

2022-11-30

Brief Summary

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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.

Detailed Description

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Conditions

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Artificial Intelligence in Operating Room

Study Design

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Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

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Surgical Patients

No interventions assigned to this group

Eligibility Criteria

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Inclusion Criteria

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

Exclusion Criteria

refusal of the patient to the study in question.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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University of Parma

OTHER

Sponsor Role lead

Responsible Party

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Elena Giovanna Bignami

Chief of 2^ UO Anesthesua and Intensive Care, Full Professor of University of Parma

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Azienda Ospedaliera-Universitaria di Parma

Parma, , Italy

Site Status RECRUITING

Countries

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Italy

Central Contacts

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Elena Bignami

Role: CONTACT

390521703567

Facility Contacts

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Elena Giovanna Bignami, MD Professor

Role: primary

+390521703567

References

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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.

Reference Type BACKGROUND
PMID: 1554148 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 12415466 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 21115393 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 20428601 (View on PubMed)

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.

Reference Type BACKGROUND
PMID: 25105749 (View on PubMed)

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.

Reference Type DERIVED
PMID: 37405821 (View on PubMed)

Other Identifiers

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1284/2020/OSS/AOUPR

Identifier Type: -

Identifier Source: org_study_id

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