Abstract Introduction This study aimed at evaluating the impact of the implementation of a cognitive robot (Robot Laura™) on processes related to the identification and care of patients with risk of sepsis in a clinical-surgical unit of a private hospital in Curitiba-PR. Methods The study data were obtained from the retrospective review of medical records of patients identified with infection and/or sepsis, in the period of six months before and after the implementation of such technology in the hospital. In addition, the Average Attendance Time (AAT) was obtained from the autonomous reading of the robot. Results The average time/median until antibiotic prescription from the first identified sign of infection, with or without sepsis, was 390/77 and 109/58 minutes, respectively, in the six months before and after implementation of the technology. However, this difference was not statistically significant (p = 0.85). Regarding AAT, it was possible to observe a reduction from 305 to 280 minutes when comparing the periods of six months before and after the implementation of the technology (p = 0.02). Conclusion Technologies such as this may be promising in helping healthcare professionals to identify risky situations for patients, as well as in assisting them to optimize the care required. However, further studies, with a greater number of subjects and with different scenarios, are necessary to consistently validate the results found.

Abstract Introduction: Despite of more than a hundred years of electrosurgery, only a few electrosurgical equipment manufacturers have developed methods to regulate the active power delivered to the patient, usually around an arbitrary setpoint. In fact, no manufacturer has a method to measure the active power actually delivered to the load. Measuring the delivered power and computing it fast enough so as to avoid injury to the organic tissue is challenging. If voltage and current signals can be sampled in time and discretized in the frequency domain, a simple and very fast multiplication process can be used to determine the active power. Methods This paper presents an approach for measuring active power at the output power stage of electrosurgical units with mathematical shortcuts based on a simple multiplication procedure of discretized variables – frequency domain vectors – obtained through Discrete Fourier Transform (DFT) applied on time-sampled voltage and current vectors. Results Comparative results between simulations and a practical experiment are presented – all being in accordance with the requirements of the applicable industry standards. Conclusion An analysis is presented comparing the active power analytically obtained through well-known voltage and current signals against a computational methodology based on vector manipulation using DFT only for time-to-frequency domain transformation. The greatest advantage of this method is to determine the active power of noisy and phased out signals with neither complex DFT or ordinary transform methodologies nor sophisticated computing techniques such as convolution. All results presented errors substantially lower than the thresholds defined by the applicable standards.