The benefits of electrosurgery have been acknowledged since the early 1920s, and nowadays more than 80% of surgical procedures involve devices that apply energy to tissues. Despite its widespread use, it is currently unknown how the operator's choices with regard to instrument selection and application technique are related to complications. As such, the manner in which electrosurgery is applied can have a serious influence on the outcome of the procedure and the well-being of patients. The aim of this study is to investigate the variety of differences in usage of electrosurgical devices. Our approach is to measure these parameters to provide insight into application techniques. A sensor was developed that records the magnitude of electric current delivered to an electrosurgical device at a frequency of 10 Hz. The sensor is able to detect device activation times and a reliable estimate of the power-level settings. Data were recorded for 91 laparoscopic cholecystectomies performed by different surgeons and residents. Results of the current measurement data show differences in the way electrosurgery is applied by surgeons and residents during a laparoscopic cholecystectomy. Variations are seen in the number of activations, the activation time, and the approach for removal of the gallbladder. Analysis showed that experienced surgeons have a longer activation time than residents (3.01 vs 1.41 seconds, P <.001) and a lower number of activations (102 vs 123). This method offers the opportunity to relate application techniques to clinical outcome and to provide input for the development of a best practice model.

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.

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.