Decision Algorithms for Emergency Neurology

2021 ◽  
2021 ◽  
pp. 194187442110166
Author(s):  
Tarini Goyal ◽  
John C. Probasco ◽  
Carl A. Gold ◽  
Joshua P. Klein ◽  
Natalie R. Weathered ◽  
...  

Background and Purpose: Neurohospitalists play an important role in, and have been variably affected by, the ongoing COVID-19 pandemic. In this study, we survey neurohospitalists to characterize practice changes and the impact of the pandemic on their well-being. Methods: A 22-item survey was distributed to neurohospitalists through the Neurohospitalist Society and the American Academy of Neurology Neurohospitalist, Stroke & Vascular Neurology, and Critical Care & Emergency Neurology Sections. Results: After 2 weeks of collection, 123 responses were received, with 57% of respondents practicing in academic settings, 23% in private practice, and 7% in community hospitals. A minority of neurohospitalists (8%) were redeployed to care for COVID-19 or non-COVID-19 medicine patients. The most common neurologic diagnoses they reported in COVID-19 patients were delirium (85%), cerebrovascular events (75%), and seizure (35%); however, most neurohospitalists (59%) had evaluated fewer than 10 patients with COVID-19. Respondents observed that fewer patients with unrelated neurological diseases were admitted to the hospital compared to before the pandemic. Neurohospitalists experienced changes in administrative (27%), educational (15%), and research duties (11%), and had overall worse well-being and work-life balance (77%). Conclusions: The most common neurologic diagnoses seen in COVID-19 patients by neurohospitalists in this sample are delirium, cerebrovascular disease, and seizure. Though the majority of survey respondents reported not being primary frontline providers, they report key clinical and operational roles during the pandemic, and report worse well-being as compared to before the pandemic. Our data suggests that there are opportunities to improve neurohospitalists’ experience through flexible work practices and providing family care support.


Author(s):  
C. López-Casado ◽  
C. J. Pérez del Pulgar ◽  
E. Fernández ◽  
V. F. Muñoz ◽  
A. Castro-Tirado

This paper proposes the design and development of a scheduler for the GLORIA telescope network. This network, which main objective is to make astronomy closer to citizens in general, is formed by 18 telescopes spread over four continents and both hemispheres. Part of the management of this network is made by the network scheduler. It receives the observation requests made by the GLORIA users and then sends it to the most suitable telescope. A key module of the network scheduler is the telescope decision algorithm that makes possible to choose the best telescope, and thus avoiding offering an observation to a telescope that cannot execute it. This paper shows two different telescope decision algorithms: the first one is only based on weather forecast, meanwhile the second one uses fuzzy logic and information from each network telescope. Both algorithms were deployed in the GLORIA network. The achieved results coupled with a comparative of their performance is shown. Moreover, the network scheduler architecture, based on a hybrid distributed-centralized schema, is detailed.


2018 ◽  
Vol 210 ◽  
pp. 05016
Author(s):  
Mariusz Chmielewski ◽  
Damian Frąszczak ◽  
Dawid Bugajewski

This paper discusses experiences and architectural concepts developed and tested aimed at acquisition and processing of biomedical data in large scale system for elderly (patients) monitoring. Major assumptions for the research included utilisation of wearable and mobile technologies, supporting maximum number of inertial and biomedical data to support decision algorithms. Although medical diagnostics and decision algorithms have not been the main aim of the research, this preliminary phase was crucial to test capabilities of existing off-the-shelf technologies and functional responsibilities of system’s logic components. Architecture variants contained several schemes for data processing moving the responsibility for signal feature extraction, data classification and pattern recognition from wearable to mobile up to server facilities. Analysis of transmission and processing delays provided architecture variants pros and cons but most of all knowledge about applicability in medical, military and fitness domains. To evaluate and construct architecture, a set of alternative technology stacks and quantitative measures has been defined. The major architecture characteristics (high availability, scalability, reliability) have been defined imposing asynchronous processing of sensor data, efficient data representation, iterative reporting, event-driven processing, restricting pulling operations. Sensor data processing persist the original data on handhelds but is mainly aimed at extracting chosen set of signal features calculated for specific time windows – varying for analysed signals and the sensor data acquisition rates. Long term monitoring of patients requires also development of mechanisms, which probe the patient and in case of detecting anomalies or drastic characteristic changes tune the data acquisition process. This paper describes experiences connected with design of scalable decision support tool and evaluation techniques for architectural concepts implemented within the mobile and server software.


2016 ◽  
Vol 36 (06) ◽  
pp. 481-482
Author(s):  
Kevin Sheth

Sign in / Sign up

Export Citation Format

Share Document