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 Type
OBSERVATIONAL
Enrollment
142
Azienda Ospedaliera-Universitaria di Parma
Parma, Italy
RECRUITINGSurgical Time Prediction
Prediction of time spend in oprating room
Time frame: 1 year
Outcome evaluation
ICU admission
Time frame: 1 year
Outcome evaluation
Rate of surgical procedures cancellation
Time frame: 1 year
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