Key result
An IoT architecture using BLE beacons successfully tracked the perioperative movements of over 100 patients, providing precise, automated data collection to enable future machine learning predictions of surgery duration.
Why the study?
Operating room management frequently suffers from inefficient scheduling and allocation of human and physical resources.
Does an IoT-based real-time tracking system using BLE beacons feasibly and accurately record perioperative patient flow times in the operating block?
Does an IoT-based real-time tracking system using BLE beacons feasibly and accurately record perioperative patient flow times in the operating block?
An IoT-based patient tracking system using BLE beacons is feasible for automatically and precisely recording perioperative times, providing high-quality data for future AI-driven operating room optimization.
Provides automated perioperative data for ML models; leaves open impact on OR efficiency pending prospective validation.
Operating rooms management is a critical point in healthcare organizations; inefficient scheduling and allocation of human and physical resources are often present. This study aims to automatically collect data from a real surgical scenario to develop an integrated technological-organizational model that optimizes the operating block resources.Each patient is real-time tracked and located by wearing a bracelet sensor with a unique identifier. Exploiting indoor localization, the software architecture is able to collect the time spent in every steps inside the surgical block. 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. This approach will make it possible to plan short and long-term strategies optimizing the available resources.
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Bottani et al. (2022) studied Surgical patients (n=100). Internet of Things (IoT) architecture with BLE beacons vs. Manual data collection (historical/standard practice) was evaluated on Feasibility of automated real-time tracking of patient movements in the surgical block. An IoT architecture using BLE beacons successfully tracked the perioperative movements of over 100 patients, providing precise, automated data collection to enable future machine learning predictions of surgery duration.
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