Key result
Real-time tracking of perioperative patients using a bracelet sensor automatically recorded times that were more precise than those collected by humans, demonstrating feasibility.
Why the study?
Effective operating room planning and resource optimization are increasingly critical to reduce patient waiting lists and improve hospital performance while maintaining high care standards.
Does real-time tracking using IoT bracelet sensors improve the precision of recorded times and optimize operating room scheduling in perioperative patients compared to human recording?
Does real-time tracking using IoT bracelet sensors improve the precision of recorded times and optimize operating room scheduling in perioperative patients compared to human recording?
An IoT-based tracking system using bracelet sensors for perioperative patients is feasible and provides more precise time tracking than manual human recording, potentially optimizing operating room scheduling.
Caution against routine adoption; leaves open whether IoT tracking optimizes OR scheduling.
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. CLINICALTRIAL ClinicalTrials.gov NCT05106621; https://clinicaltrials.gov/ct2/show/NCT05106621 INTERNATIONAL REGISTERED REPORT DERR1-10.2196/45477
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Bottani et al. (2023) studied Perioperative patients. Bracelet sensor tracking (Internet of Things) vs. Human data collection was evaluated on Time spent for every step inside the surgical block. Real-time tracking of perioperative patients using a bracelet sensor automatically recorded times that were more precise than those collected by humans, demonstrating feasibility.
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