scholarly journals Medical care in emergency units with risk classification: time to attendance at a hospital based on parametric models

2021 ◽  
Author(s):  
Pedro Ramos ◽  
Diego Nascimento ◽  
Robson Fernandes ◽  
Edson Guimarães ◽  
Marcelo Santana ◽  
...  
Keyword(s):  
Medical Care ◽  
Risk Classification ◽  
Parametric Models ◽  
Emergency Units ◽  
Classification Time

Knowledge-Based System supported by Chatbot to assist the Risk Classification Process of Patients in Hospital Emergency care (Preprint)

2021 ◽  
Author(s):  
Marciane Mueller ◽  
Rejane Frozza ◽  
Liane Mählmann Kipper ◽  
Ana Carolina Kessler
Keyword(s):  
Medical Care ◽  
Knowledge Base ◽  
Language Processing ◽  
Emergency Care ◽  
Risk Classification ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Hospital Emergency ◽  
Knowledge Based System ◽  
Knowledge Based

BACKGROUND This article presents the modeling and development of a Knowledge Based System, supported by the use of a virtual conversational agent called Dóris. Using natural language processing resources, Dóris collects the clinical data of patients in care in the context of urgency and hospital emergency. OBJECTIVE The main objective is to validate the use of virtual conversational agents to properly and accurately collect the data necessary to perform the evaluation flowcharts used to classify the degree of urgency of patients and determine the priority for medical care. METHODS The agent's knowledge base was modeled using the rules provided for in the evaluation flowcharts comprised by the Manchester Triage System. It also allows the establishment of a simple, objective and complete communication, through dialogues to assess signs and symptoms that obey the criteria established by a standardized, validated and internationally recognized system. RESULTS Thus, in addition to verifying the applicability of Artificial Intelligence techniques in a complex domain of health care, a tool is presented that helps not only in the perspective of improving organizational processes, but also in improving human relationships, bringing professionals and patients closer. The system's knowledge base was modeled on the IBM Watson platform. CONCLUSIONS The results obtained from simulations carried out by the human specialist allowed us to verify that a knowledge-based system supported by a virtual conversational agent is feasible for the domain of risk classification and priority determination of medical care for patients in the context of emergency care and hospital emergency.

scholarly journals Assessment and risk classification protocol for patients in emergency units

2014 ◽  
Vol 22 (2) ◽  
pp. 218-225 ◽  
Author(s):  
Michele de Freitas Neves Silva ◽  
Gabriela Novelli Oliveira ◽  
Aline Maino Pergola-Marconato ◽  
Rafael Silva Marconato ◽  
Eliete Boaventura Bargas ◽  
...  
Keyword(s):  
Content Validity ◽  
Risk Classification ◽  
University Hospital ◽  
Content Validation ◽  
Individual Assessment ◽  
Emergency Units ◽  
The Individual ◽  
Two Stages ◽  
Equivalent Method ◽  
Country Town

OBJECTIVE: to develop, validate the contents and verify the reliability of a risk classification protocol for an Emergency Unit.METHOD: the content validation was developed in a University Hospital in a country town located in the state of Sao Paulo and was carried out in two stages: the first with the individual assessment of specialists and the second with the meeting between the researchers and the specialists. The use of the protocol followed a specific guide. Concerning reliability, the concordance or equivalent method among observers was used.RESULTS: the protocol developed showed to have content validity and, after the suggested changes were made, there were excellent results concerning reliability.CONCLUSION: the assistance flow chart was shown to be easy to use, and facilitate the search for the complaint in each assistance priority.

Patterns of Medical Care among Users of Hospital Emergency Units

Medical Care ◽  
1972 ◽  
Vol 10 (1) ◽  
pp. 60-72 ◽  
Author(s):  
Jerry A. Solon ◽  
Ruth D. Rigg
Keyword(s):  
Medical Care ◽  
Hospital Emergency ◽  
Emergency Units

scholarly journals Acolhimento com classificação de risco: atuação do enfermeiro na urgência e emergência

2018 ◽  
Vol 8 (22) ◽  
pp. 22 ◽  
Author(s):  
Adriana Cunha Lima Soares ◽  
Marislei Brasileiro ◽  
Danielle Galdino de Souza
Keyword(s):  
Content Analysis ◽  
Nursing Care ◽  
Emergency Services ◽  
Risk Classification ◽  
Review Of The Literature ◽  
Emergency Units

O estudo teve por objetivo analisar a assistência de enfermagem aos pacientes atendidos com classificação de risco, segundo a literatura. Por meio de revisão integrativa da literatura foram identificados 912 artigos dos quais se selecionou 14, considerando os critérios: artigos completos; publicados no período entre 2012 a 2015; disponíveis no idioma português e inglês; indexados nas bases de dados da Biblioteca Virtual em Saúde. Todo o material foi analisado por meio da técnica Análise de Conteúdo. Emanaram-se duas categorias temáticas: A Humanização no Atendimento e O Papel do Enfermeiro nas Unidades de Urgência e Emergência. Conclui-se que o Acolhimento com Classificação de Risco seja um dos instrumentos fundamentais de auxílio para alcance da qualidade no atendimento “de porta” dos serviços de emergências dos hospitais brasileiros.Descritores: Enfermagem, Classificação, Emergência, Manchester, Triagem, Urgência.Embracement with risk classification: the nurse's action in urgency and emergencyAbstract: The purpose of this study was to analyze nursing care for patients treated with risk classification, according to the literature. Through the integrated review of the literature of identifiers 912 of which 14 were selected, considering the criteria: complete articles; Published in the period between 2012 to 2015; Available not Portuguese and English; Indexed in the databases of the Virtual Health Library. All the material was analyzed through the technique. Content analysis. Two thematic categories emerged: Humanization in care and the Role of nurses in emergency and emergency units. It is concluded that the Reception with Risk Classification of one of the fundamental instruments of assistance to reach the quality without door attendance of the emergency services of the Brazilian hospitals.Descriptors: Nursing, Classification, Emergency, Manchester, Screening, Urgency.Acogimiento con clasificación de riesgo: actuación del enfermero en urgencia y emergenciaResumen: El estudio tuvo por objetivo analisar la asistencia de enfermería a pacientes atendidos con clasificación de riesgo, segundo la literatura. Por medio de revisión integrativa de la literatura se identificaron 912 articulos de los que se seleccionó 14, considerando los criterios: artículos completos; Publicadados en el período entre 2012 a 2015; Disponible en português e inglés; Indexados en las bases de datos de la Biblioteca Virtual en Salud. Todo el material se analizó por medio de la técnica Análisis de Contenido. Se organizaron dos categorias temáticas: La humanización en el atendimiento y el papel del enfermero en las unidades de urgencia y emergencia. Se concluye que el Acogimiento con Clasificación de Riesgo es uno de los instrumentos fundamentales de auxilio para alcanzar la calidad en la atención "de puerta" de los servicios de emergencias de los hospitales brasileños.Descriptores: Enfermería, Clasificación, Emergencia, Manchester, Selección, Urgencia.

scholarly journals EMBRACEMENT ANALYSIS OF THE RISK CLASSIFICATION IN THE EMERGENCY UNITS

2019 ◽  
Vol 23 ◽  
Author(s):  
Mariana Tomé Gouveia ◽  
Suélida Rafaela de Melo ◽  
Maria Wanessa da Silva Costa ◽  
José Madson Medeiros Souza ◽  
Laisa Ribeiro de Sá ◽  
...  
Keyword(s):  
Risk Classification ◽  
Emergency Units

scholarly journals Medical Care in Emergency Units with Risk Classification: Time to Attendance at Hospital based on Parametric Models

2019 ◽  
Vol 20 (3) ◽  
pp. 571
Author(s):  
Pedro L. Ramos ◽  
Diego C. Nascimento ◽  
Robson Fernandes ◽  
Edson Guimarães ◽  
M. Santana ◽  
...  
Keyword(s):  
Regression Models ◽  
Explanatory Variable ◽  
Patient Flow ◽  
Parametric Models ◽  
Time To Event ◽  
Manchester Triage System ◽  
Explanatory Variables ◽  
Emergency Units ◽  
Improve Patient ◽  
Parametric Regression Models

The present work intends to study the effectiveness of applying Manchester Triage System to improve patient flow in a Brazilian hospital, which allow a more welcoming and decisive service.  Thus, time to event techniques are applied based on parametric regression models with the objective of investigating indicators for the emergency/urgency sector and thus, contributing to better operational efficiency. The results show that different explanatory variables such as classification, age, period, among others, influence in the time of attendance. At the end, we provide a simple model that can be used to predict such time under different explanatory variable for a particular Brazilian hospital.

The Collaborative Medical and Behavioral Management of Chronic Cough

2009 ◽  
Vol 19 (2) ◽  
pp. 49-57
Author(s):  
Brian E. Petty ◽  
Seth H. Dailey
Keyword(s):  
Medical Care ◽  
Chronic Cough ◽  
Therapeutic Management ◽  
Ambulatory Setting ◽  
Behavioral Management ◽  
Speech Language Pathologists ◽  
Speech Language Pathologist ◽  
The Many ◽  
Frequent Reason

Abstract Chronic cough is the most frequent reason cited by patients for seeking medical care in an ambulatory setting and may account for 10% to 38% of a pulmonologist's practice. Because chronic cough can be caused by or correlated with a wide array of disorders and behaviors, the diagnosis of etiologic factors and determination of appropriate therapeutic management in these cases can prove to be daunting for the physician and speech-language pathologist alike. This article will describe the phenomenon of chronic cough, discuss the many etiologic factors to consider, and review some of the more common ways in which speech-language pathologists and physicians collaborate to treat this challenging condition.

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