Operating Room (OR) scheduling is crucial to allow efficient use of ORs. Currently, the predicted durations of surgical procedures are unreliable and the OR schedulers have to follow the progress of the procedures in order to update the daily planning accordingly. The OR schedulers often acquire the needed information through verbal communication with the OR staff, which causes undesired interruptions of the surgical process. The aim of this study was to develop a system that predicts in real-time the remaining procedure duration and to test this prediction system for reliability and usability in an OR. The prediction system was based on the activation pattern of one single piece of equipment, the electrosurgical device. The prediction system was tested during 21 laparoscopic cholecystectomies, in which the activation of the electrosurgical device was recorded and processed in real-time using pattern recognition methods. The remaining surgical procedure duration was estimated and the optimal timing to prepare the next patient for surgery was communicated to the OR staff. The mean absolute error was smaller for the prediction system (14 min) than for the OR staff (19 min). The OR staff doubted whether the prediction system could take all relevant factors into account but were positive about its potential to shorten waiting times for patients. The prediction system is a promising tool to automatically and objectively predict the remaining procedure duration, and thereby achieve optimal OR scheduling and streamline the patient flow from the nursing department to the OR.@en

The unavailability of the required surgical instruments at the start of a procedure is undesirable. It causes delays and stress in the operating room (OR), which can lead to additional risks for the patients. Issues with availability of surgical instruments may become visible just before the start of the procedure but are induced earlier in the delivery process. Therefore, efficient and safe supply chain management is essential. Just in time ( JIT) is a concept widely applied in industrial sectors to improve efficiency and quality. The aim of this study is to design a JIT process for the delivery of surgical instruments and to assess the potential risks. The JIT delivery process of surgical instruments was designed for a Dutch hospital working with an external Central Sterile Supply Department (CSSD). Hazards (ie, sources of potential adverse events) were identified according to the Healthcare Failure Mode and Effects Analysis (HFMEA) methodology. The risks of applying JIT principles to the delivery of surgical instruments were compared to the risks involved with the current situation of the hospital at the time of this study (supply-driven delivery). The results showed that the total (high-)risk score of the JIT situation was similar to that of the current situation, although the number of (high-risk) hazards was slightly higher. However, almost half of the hazards were ‘controlled’ (when actions to remove the hazard were taken later in the process), in contrast to the current situation in which only about 10% of the (high-risk) hazards were ‘controlled’. The JIT delivery of surgical instruments is expected to present less risks compared to the current situation. The multiple requirements for information technology support and a higher level of trust between the CSSD and OR department should be taken into account in order to improve the supply chain management of surgical instruments.@en

Efficiency in the Operating Room (OR) is a topic of growing interest. Planning of care is a crucial element to ensure optimal use of the ORs. Currently, OR scheduling is considered as a complex task based on predictions of surgery duration. The latter are often based on average times, but turn out to be inaccurate in practice because of various factors (such as complexity, patient's characteristics, unexpected events, etc). The aim of this study is to develop a prediction system that estimates in real-time the remaining duration of a surgical procedure. The prediction system was based on monitoring the progress of a procedure by recording the activation of a single piece of equipment in the OR, the electrosurgical device. Support Vector Machines was then used as a classifier to predict the remaining surgical procedure duration and thereby the optimal timing to start preparing the next patient for surgery. The classifier was trained with data on the activation of the electrosurgical device during 55 laparoscopic cholecystectomies. The performance tests showed a mean error rate about 0.2, which means that about 80% of the procedures were classified correctly. The real-time prediction system is a promising tool to improve OR planning and decrease unnecessary patients' waiting times.

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