AN INTELLIGENT MODEL FOR THE OPERATIVE BLOCK (BLOC-OP)

NEW MODEL OF ORGANIZATION OF AN OPERATIVE BLOCK (BLOC-OP)

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.

Study Overview

• Status: Recruiting
• Conditions: Artificial Intelligence in Operating Room

• Study Type: Observational
• Enrollment (Anticipated): 142

Contacts and Locations

• Study Contact: Name: Elena Bignami; Phone Number: 390521703567; Email: elenagiovanna.bignami@unipr.it

Study Locations

• Italy
  • Parma, Italy, 43125
    • Recruiting
    • Azienda Ospedaliera-Universitaria di Parma

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: 18 years and older (ADULT, OLDER_ADULT)
• Accepts Healthy Volunteers: No
• Genders Eligible for Study: All

• Sampling Method: Non-Probability Sample

Study Population

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

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
Surgical Patients

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Surgical Time Prediction	Prediction of time spend in oprating room	1 year

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Outcome evaluation	ICU admission	1 year
Outcome evaluation	Rate of surgical procedures cancellation	1 year

Collaborators and Investigators

• Sponsor: University of Parma

Publications and helpful links

General Publications

• 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.
• 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. Erratum In: J Digit Imaging. 2002 Sep;15(3):191.
• 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.
• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (ACTUAL): November 1, 2021
• Primary Completion (ANTICIPATED): November 30, 2022
• Study Completion (ANTICIPATED): November 30, 2022

Study Registration Dates

• First Submitted: October 11, 2021
• First Submitted That Met QC Criteria: October 22, 2021
• First Posted (ACTUAL): November 4, 2021

Study Record Updates

• Last Update Posted (ACTUAL): May 17, 2022
• Last Update Submitted That Met QC Criteria: May 16, 2022
• Last Verified: May 1, 2022

More Information

Terms related to this study

• Keywords: Artificial Intelligence; Perioperative Medicine; Machine Learning; Operating Room
• Other Study ID Numbers: 1284/2020/OSS/AOUPR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No