Background: Management of operating rooms is a critical point in health care organizations because surgical departments represent a significant cost in hospital budgets. Therefore, it is increasingly important that there is effective planning of elective, emergency, and day surgery and optimization of both the human and physical resources available, always maintaining a high level of care and health treatment. This would lead to a reduction in patient waiting lists and better performance not only of surgical departments but also of the entire hospital.
Objective: This study aims to automatically collect data from a real surgical scenario to develop an integrated technological-organizational model that optimizes operating block resources.
Methods: Each patient is tracked and located in real time by wearing a bracelet sensor with a unique identifier. Exploiting the indoor location, the software architecture is able to collect the time spent for every step inside the surgical block. This method does not in any way affect the level of assistance that the patient receives and always protects their privacy; in fact, after expressing informed consent, each patient will be associated with an anonymous identification number.
Results: The preliminary results are promising, making the study feasible and functional. Times automatically recorded are much more precise than those collected by humans and reported in the organization's information system. In addition, machine learning can exploit the historical data collection to predict the surgery time required for each patient according to the patient's specific profile. Simulation can also be applied to reproduce the system's functioning, evaluate current performance, and identify strategies to improve the efficiency of the operating block.
Conclusions: This functional approach improves short- and long-term surgical planning, facilitating interaction between the various professionals involved in the operating block, optimizing the management of available resources, and guaranteeing a high level of patient care in an increasingly efficient health care system.
Trial registration: ClinicalTrials.gov NCT05106621; https://clinicaltrials.gov/ct2/show/NCT05106621.
International registered report identifier (irrid): DERR1-10.2196/45477.
Keywords: artificial intelligence; internet of things; machine learning; operating rooms; perioperative organization.
©Eleonora Bottani, Valentina Bellini, Monica Mordonini, Mattia Pellegrino, Gianfranco Lombardo, Beatrice Franchi, Michelangelo Craca, Elena Bignami. Originally published in JMIR Research Protocols (https://www.researchprotocols.org), 05.07.2023.