AN INTELLIGENT MODEL FOR THE OPERATIVE BLOCK (BLOC-OP)
NEW MODEL OF ORGANIZATION OF AN OPERATIVE BLOCK (BLOC-OP)
Study Overview
Status
Status
Conditions
Conditions
Study Type
Study Type
Enrollment (Anticipated)
Enrollment
Contacts and Locations
Study Contact
Study Contact
- Name: Elena Bignami
- Phone Number: 390521703567
- Email: elenagiovanna.bignami@unipr.it
Study Locations
-
-
-
Parma, Italy, 43125
- Recruiting
- Azienda Ospedaliera-Universitaria di Parma
-
-
Participation Criteria
Eligibility Criteria
Eligibility Criteria
Ages Eligible for Study
Accepts Healthy Volunteers
Genders Eligible for Study
Sampling Method
Study Population
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?
Design Details
Number of groups / cohorts
Cohorts and Interventions
Group / CohortGroup / Cohort |
|---|
|
Surgical Patients
|
What is the study measuring?
Primary Outcome Measures
Primary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Surgical Time Prediction
Time Frame: 1 year
|
Prediction of time spend in oprating room
|
1 year
|
Secondary Outcome Measures
Secondary Outcome Measures
Outcome Measure |
Measure Description |
Time Frame |
|---|---|---|
|
Outcome evaluation
Time Frame: 1 year
|
ICU admission
|
1 year
|
|
Outcome evaluation
Time Frame: 1 year
|
Rate of surgical procedures cancellation
|
1 year
|
Collaborators and Investigators
Sponsor
Sponsor
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)
Study Start
Primary Completion (ANTICIPATED)
Primary Completion
Study Completion (ANTICIPATED)
Study Completion
Study Registration Dates
First Submitted
First Submitted
First Submitted That Met QC Criteria
First Submitted That Met QC Criteria
First Posted (ACTUAL)
First Posted
Study Record Updates
Last Update Posted (ACTUAL)
Last Update Posted
Last Update Submitted That Met QC Criteria
Last Update Submitted That Met QC Criteria
Last Verified
Last Verified
More Information
Terms related to this study
Other Study ID Numbers
Other Study ID Numbers
- 1284/2020/OSS/AOUPR
Drug and device information, study documents
Studies a U.S. FDA-regulated drug product
Studies a U.S. FDA-regulated device product
This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.