An Open-Source, Interoperable Architecture for Generating Real-Time Surgical Team Cognitive Alerts from Heart-Rate Variability Monitoring.

Clinical alarm and decision support systems that lack clinical context may create non-actionable nuisance alarms that are not clinically relevant and can cause distractions during the most difficult moments of a surgery. We present a novel, interoperable, real-time system for adding contextual awareness to clinical systems by monitoring the heart-rate variability (HRV) of clinical team members. We designed an architecture for real-time capture, analysis, and presentation of HRV data from multiple clinicians and implemented this architecture as an application and device interfaces on the open-source OpenICE interoperability platform. In this work, we extend OpenICE with new capabilities to support the needs of the context-aware OR including a modularized data pipeline for simultaneously processing real-time electrocardiographic (ECG) waveforms from multiple clinicians to create estimates of their individual cognitive load. The system is built with standardized interfaces that allow for free interchange of software and hardware components including sensor devices, ECG filtering and beat detection algorithms, HRV metric calculations, and individual and team alerts based on changes in metrics. By integrating contextual cues and team member state into a unified process model, we believe future clinical applications will be able to emulate some of these behaviors to provide context-aware information to improve the safety and quality of surgical interventions.

Automated False Alarm Reduction in a Real-Life Intensive Care Setting Using Motion Detection.

BACKGROUND: Contemporary monitoring systems are sensitive to motion artifacts and cause an excess of false alarms. This results in alarm fatigue and hazardous alarm desensitization. To reduce the number of false alarms, we developed and validated a novel algorithm to classify alarms, based on automatic motion detection in videos. METHODS: We considered alarms generated by the following continuously measured parameters: arterial oxygen saturation, systolic blood pressure, mean blood pressure, heart rate, and mean intracranial pressure. The movements of the patient and in his/her surroundings were monitored by a camera situated at the ceiling. Using the algorithm, alarms were classified into RED (true), ORANGE (possibly false), and GREEN alarms (false, i.e., artifact). Alarms were reclassified by blinded clinicians. The performance was evaluated using confusion matrices. RESULTS: A total of 2349 alarms from 45 patients were reclassified. For RED alarms, sensitivity was high (87.0%) and specificity was low (29.6%) for all parameters. As the sensitivities and specificities for RED and GREEN alarms are interrelated, the opposite was observed for GREEN alarms, i.e., low sensitivity (30.2%) and high specificity (87.2%). As RED alarms should not be missed, even at the expense of false positives, the performance was acceptable. The low sensitivity for GREEN alarms is acceptable, as it is not harmful to tag a GREEN alarm as RED/ORANGE. It still contributes to alarm reduction. However, a 12.8% false-positive rate for GREEN alarms is critical. CONCLUSIONS: The proposed system is a step forward toward alarm reduction; however, implementation of additional layers, such as signal curve analysis, multiple parameter correlation analysis and/or more sophisticated video-based analytics are needed for improvement.

Soluções para a fadiga de alarmes em unidades de terapia intensiva: algoritmo de gestão de alarmes / Solutions for alarm fatigue in intensive care units: alarm management algorithm

Objetivo: Propor um Algoritmo de Gestão de alarmes de monitores multiparametricos em UTIs, usando Logica Fuzzy e o programa MATLAB, que objetiva a redução da Fadiga de Alarmes em UTI, pela seleção inteligente das prioridades no atendimento aos alarmes. A gestão inteligente dos alarmes visa evitar o problema da Fadiga de Alarmes, que leva a ignorar, silenciar ou retardar o atendimento aos pacientes em UTI. Método: quanti-qualitativa, com pesquisa observacional descritiva. Resultados: O estudo mostra que a modelagem pela Logica Fuzzy consegue emular o raciocínio humano dos profissionais de saúde da UTI na tomada de decisões de atendimento a pacientes alarmados. Conclusão: a Logica Fuzzy poderá ser eficiente na gestão de parâmetros fisiológicos alarmados em UTI, pelo uso de Algoritmos "Smart Alarms" ponderando sobre a prioridade de atendimento ao paciente

Purpose: To propose a management algorithm multiparameter monitors alarms in ICUs, using Fuzzy Logic and MATLAB program, which aims to reduce the Alarm Fatigue in ICU, the intelligent selection of priorities in meeting the alarms.. The intelligent alarm management aims to avoid the problem of Fatigue Alarm, which leads to ignore, mute or delay patient care in the ICU. Method: quantitative and qualitative, with descriptive observational research Results: The study shows that modeling by Fuzzy Logic can emulate the human reasoning of health professionals in the ICU decision-making services to patients alarmed. Conclusion: Fuzzy Logic can be efficient in managing alarmed physiological parameters in the ICU, by the use of algorithms "Smart Alarms" pondering the priority of patient care

Objetivo: Proponer un multiparamétrico algoritmo de gestión monitorea las alarmas en las UCI, utilizando el programa MATLAB, que tiene como objetivo reducir la fatiga de alarma en la UCI, pela selección inteligente de las prioridades en el cumplimiento de las alarmas y Lógica Fuzzy. La gestión de alarmas inteligentes pretende evitar el problema de la fatiga de alarma, lo que lleva a ignorar, silenciar o retrasar la atención al paciente en la UCI. Método: cuantitativa y cualitativa, con la investigación observacional descriptivo Resultados: El estudio muestra que el modelado por la Lógica Fuzzy puede emular el razonamiento humano de profesionales de la salud en los servicios de toma de decisión de la UCI de pacientes alarmados. Conclusión: Lógica Fuzzy puede ser eficiente en la gestión de los parámetros fisiológicos alarmados en la UCI, utilizando algoritmos de gestión inteligentes ponderando la prioridad de la atención al paciente