Dissertation submitted in fulfillment of the requirements for the Degree of Master of Technology: Emergency Medical Care, Durban University of Technology, 2012. / Background In the emergency setting, the onus is on the individual practitioner’s ability to make critical decisions at critical moments in order to provide the best level of care to their patient. In order to ensure that these decisions fall in line with the best interests of the patient, the South African paramedic requires a better understanding of how to arrive at such a decision; they need to understand the clinical decision making process. This study focused on South African paramedic clinical decision making with specific reference to acute traumatic pain management, with the aim of determining the factors which influence South African paramedic clinical decision making by revealing the current method of pain management employed by South African paramedics, how they view the priority of pain management in the continuum of care and if there were any context specific factors influencing their clinical decision making. Methods A mixed method design was used to determine the factors contributing to the clinical decision making process of South African paramedics in the acute pain management of patients with acute traumatic pain. A mixture of qualitative and quantitative approaches was utilized by means of a research questionnaire as well as in-depth interviews. The questionnaires were targeted at all South African paramedics while the in-depth interviews were conducted with five participants who had been purposefully selected from the questionnaire respondents. The data analysis was conducted in a descriptive manner in order to inform the explanatory nature of the answers to the research questions and objective. Results The results provided insight into the current methods and clinical decision making processes employed by South African paramedics in the management of patients’ experiencing acute traumatic pain. The study determined that the South African paramedic’s clinical decision making process involves three key phases in the acute traumatic pain management setting, the assessment phase, the initiation/pain management phase and the conclusion/re-evaluation phase, with each phase utilizing different decision making models, the intuitive/humanist model, the hypothetico- deductive model and a model which combined both of the aforementioned models. In addition to this, numerous factors such as the provision of care in order to facilitate further management and transportation to an appropriate facility, which influenced clinical decision making, were identified. Amongst South African paramedics, pain management was identified as coming second only to the interventions required to manage immediately life threatening conditions in terms of the prioritization of treatment. Recommendations A variety of recommendations which included the need to further the development of clinical decision making and pain management through research and education as well as considerations for investigation into the potential expansion of South African paramedic scope of practice in the pain management environment were made.

No description available.

Bag-valve mask (BVM) ventilation maintains a patient's oxygenation and ventilation until a more definitive artificial airway can be established. In the prehospital setting of a traffic collision or medical aid scene this is performed by an Emerency Medical Technician or medical first responder. Few studies have looked at the effectiveness of Bag-valve masks (BVM) or the complication rate of ventilating an unprotected airway. The purpose and goal of this study is to educate both medical first responders and basic emergency medical technicians.

No description available.

The health applications aimed at monitoring patients remotely have reached great proportions with the advancement of wireless networks. This paper presents a study of performance evaluation of biosignal traffic, which was simulated the transmission of patient data in emergency situations. The simulation scenario considered the transmission of signals from an ambulance through wireless network and collected in a medical monitoring center. On the way to the hospital, while the mobile broadcast biosignals moved between areas covered by different network technologies, featuring vertical handover situation. Based on the minimum QoS requirements prevailing in the scientific community, the most important parameters in healthcare applications such as loss rate, delay, throughput and jitter were evaluated. Was still considered a minimum bandwidth required for transmission of vital signs, taking into account rates of known samples to physicians signs such as electrocardiogram (ECG), blood pressure, heart rate, body temperature and rate of oxygen saturation blood. To evaluate the performance, were carried computer simulations using an implementation of the IEEE 802.21 standard for the simulator NS-2. The simulated scenario used the networks of Wi-Fi and WiMAX technologies, mobile with multiple interfaces and nodes cargo, which made transmissions with constant rates. The results showed that the network technologies in use can meet the minimum QoS requirements for medical applications. / As aplicações de saúde voltadas para monitoramento de pacientes a distância têm atingido grandes proporções com o avanço das redes sem fio. Este trabalho apresenta um estudo de avaliação de desempenho do tráfego de biosinais, no qual foi simulado a transmissão de dados de pacientes em situações de emergência. O cenário de simulação considerou a transmissão dos sinais a partir de uma ambulância, através de rede sem fio e coletados em um centro de monitoramento médico. No percurso até o hospital, o móvel transmitiu biosinais enquanto transitou entre áreas cobertas por tecnologias de rede distintas, caracterizando situação de handover vertical. Com base nos requisitos mínimos de QoS praticados na comunidade científica, foram avaliados os parâmetros mais importantes em aplicações de saúde como taxa de perdas, atraso, vazão e jitter. Ainda foi considerada uma largura de banda mínima necessária para transmissão de sinais vitais, levando-se em conta as taxas de amostragens conhecidas para sinais médicos como Eletrocardiograma (ECG), Pressão arterial, Frequência cardíaca, Temperatura do corpo e Taxa de saturação de oxigênio no sangue. Para avaliar o desempenho, foram realizadas simulações computacionais com o uso de uma implementação do padrão IEEE 802.21 para o simulador NS-2. O cenário simulado utilizou as redes das tecnologiasWi-Fi eWiMAX, dispositivo móvel com múltipla interface e nós de carga, os quais realizaram transmissões com taxas constantes. Os resultados mostraram que as tecnologias de rede em uso podem atender aos requisitos mínimos de QoS para aplicações médicas.

[ES] El triaje de los incidentes de urgencias y emergencias extrahospitalarias representa un reto difícil, debido a las limitaciones temporales y a la incertidumbre. Además, errores en este proceso pueden tener graves consecuencias para los pacientes. Por lo tanto, cualquier herramienta o estrategia novedosa que mejore estos procesos ofrece un valor sustancial en términos de atención al paciente y gestión global de los incidentes. La hipótesis en la que se basa esta tesis es que el Aprendizaje Automático, concretamente el Aprendizaje Profundo, puede mejorar estos procesos proporcionando estimaciones de la gravedad de los incidentes, mediante el análisis de millones de datos derivados de llamadas de emergencia de la Comunitat Valenciana (España) que abarcan desde 2009 hasta 2019. Por tanto, esta tesis profundiza en el diseño y desarrollo de modelos basados en Aprendizaje Profundo Multitarea que aprovechan los datos multimodales asociados a eventos de urgencias y emergencias extrahospitalarias. Nuestro objetivo principal era predecir si el incidente suponía una situación de riesgo vital, la demora admisible de la respuesta y si era competencia del sistema de emergencias o de atención primaria. Utilizando datos disponibles entre 2009 y 2012, se observaron mejoras sustanciales en las métricas macro F1, con ganancias del 12.5% para la clasificación de riesgo vital, del 17.5% para la demora en la respuesta y del 5.1% para la clasificación por jurisdicción, en comparación con el protocolo interno de triaje de la Comunidad Valenciana. Sin embargo, los sistemas, los protocolos de triaje y las prácticas operativas evolucionan de forma natural con el tiempo. Los modelos que mostraron un rendimiento excelente con el conjunto de datos inicial de 2009 a 2012 no demostraron la misma eficacia cuando se evaluaron con datos posteriores que abarcaban de 2014 a 2019. Estos últimos habían sufrido modificaciones en comparación con los anteriores, que dieron lugar a variaciones en las distribuciones de probabilidad, caracterizadas e investigadas meticulosamente en esta tesis. Continuando con nuestra investigación, nos centramos en la incorporación de técnicas de Aprendizaje Continuo Profundo en nuestros desarrollos. Gracias a ello, pudimos mitigar sustancialmente los efectos adversos consecuencia de los cambios distribucionales sobre el rendimiento. Los resultados indican que, si bien las fluctuaciones de rendimiento no se eliminan por completo, pueden mantenerse dentro de un rango manejable. En particular, con respecto a la métrica F1, cuando las variaciones distribucionales son ligeras o moderadas, el comportamiento se mantiene estable, sin variar más de un 2.5%. Además, nuestra tesis demuestra la viabilidad de construir herramientas auxiliares que permitan a los operadores interactuar con estos complejos modelos. En consecuencia, sin interrumpir el flujo de trabajo de los profesionales, se hace posible proporcionar retroalimentación mediante predicciones de probabilidad para cada clase de etiqueta de gravedad y tomar las medidas pertinentes. Por último, los resultados de esta tesis tienen implicaciones directas en la gestión de las urgencias y emergencias extrahospitalarias en la Comunidad Valenciana, al integrarse el modelo final resultante en los centros de atención de llamadas. Este modelo utilizará los datos proporcionados por los operadores telefónicos para calcular automáticamente las predicciones de gravedad, que luego se compararán con las generadas por el protocolo de triaje interno. Cualquier disparidad entre estas predicciones desencadenará la derivación del incidente a un coordinador médico, que supervisará su tratamiento. Por lo tanto, nuestra tesis, además de realizar importantes contribuciones al campo de la Investigación en Aprendizaje Automático Biomédico, también conlleva implicaciones sustanciales para mejorar la gestión de las urgencias y emergencias extrahospitalarias en el contexto de la Comunidad Valenciana. / [CA] El triatge dels incidents d'urgències i emergències extrahospitalàries representa un repte difícil, a causa de les limitacions temporals i de la incertesa. A més, els errors en aquest procés poden tindre greus conseqüències per als pacients. Per tant, qualsevol eina o estratègia innovadora que millore aquests processos ofereix un valor substancial en termes d'atenció al pacient i gestió global dels incidents. La hipòtesi en què es basa aquesta tesi és que l'Aprenentatge Automàtic, concretament l'Aprenentatge Profund, pot millorar significativament aquests processos proporcionant estimacions de la gravetat dels incidents, mitjançant l'anàlisi de milions de dades derivades de trucades d'emergència de la Comunitat Valenciana (Espanya) que abasten des de 2009 fins a 2019. Per tant, aquesta tesi aprofundeix en el disseny i desenvolupament de models basats en Aprenentatge Profund Multitasca que aprofiten dades multimodals d'incidents mèdics d'urgències i emergències extrahospitalàries. El nostre objectiu principal era predir si l'incident suposava una situació de risc vital, la demora admissible de la resposta i si era competència del sistema d'emergències o d'atenció primària. Utilitzant dades disponibles entre 2009 i 2012, es van observar millores substancials en les mètriques macro F1, amb guanys del 12.5% per a la classificació de risc vital, del 17.5% per a la demora en la resposta i del 5.1% per a la classificació per jurisdicció, en comparació amb el protocol intern de triatge de la Comunitat Valenciana. Tanmateix, els protocols de triatge i les pràctiques operatives evolucionen de forma natural amb el temps. Els models que van mostrar un rendiment excel·lent amb el conjunt de dades inicial de 2009 a 2012 no van demostrar la mateixa eficàcia quan es van avaluar amb dades posteriors que abastaven de 2014 a 2019. Aquestes últimes havien sofert modificacions en comparació amb les anteriors, que van donar lloc a variacions en les distribucions de probabilitat, caracteritzades i investigades minuciosament en aquesta tesi. Continuant amb la nostra investigació, ens vam centrar en la incorporació de tècniques d'Aprenentatge Continu als nostres desenvolupaments. Gràcies a això, vam poder mitigar substancialment els efectes adversos sobre el rendiment conseqüència dels canvis distribucionals. Els resultats indiquen que, si bé les fluctuacions de rendiment no s'eliminen completament al llarg del temps, poden mantenir-se dins d'un rang manejable. En particular, respecte a la mètrica F1, quan les variacions distribucionals són lleugeres o moderades, el comportament es manté estable, sense variar més d'un 2.5%. A més, la nostra tesi demostra la viabilitat de construir eines auxiliars que permeten als operadors interactuar amb aquests models complexos. En conseqüència, sense interrompre el flux de treball dels professionals, es fa possible proporcionar retroalimentació mitjançant prediccions de probabilitat per a cada classe d'etiqueta de gravetat i prendre les mesures pertinents. Finalment, els resultats d'aquesta tesi tenen implicacions directes en la gestió de les urgències i emergències extrahospitalàries a la Comunitat Valenciana, al integrar-se el model final resultant als centres d'atenció de telefonades. Aquest model utilitzarà les dades proporcionades pels operadors telefònics per calcular automàticament les prediccions de gravetat, que després es compararan amb les generades pel protocol de triatge intern. Qualsevol disparitat entre aquestes prediccions desencadenarà la derivació de l'incident a un coordinador mèdic, que supervisarà el seu tractament. Per tant, és evident que la nostra tesi, a més de realitzar importants contribucions al camp de la Investigació en Aprenentatge Automàtic Biomèdic, també comporta implicacions substancials per a millorar la gestió de les urgències i emergències extrahospitalàries en el context de la Comunitat Valenciana. / [EN] Triage for out-of-hospital emergency incidents represents a tough challenge, primarily due to time constraints and uncertainty. Furthermore, errors in this process can have severe consequences for patients. Therefore, any novel tool or strategy that enhances these processes can offer substantial value in terms of patient care and overall management of out-of-hospital emergency medical incidents. The hypothesis upon which this thesis is based is that Machine Learning, specifically Deep Learning, can improve these processes by providing estimations of the severity of incidents, by analyzing millions of data derived from emergency calls from the Valencian Region (Spain) spanning from 2009 to 2019. Hence, this thesis delves into designing and developing Deep Multitask Learning models that leverage multimodal out-of-hospital emergency medical data. Our primary objective was to predict whether the incident posed a life-threatening situation, the admissible response delay, and whether it fell under the jurisdiction of the emergency system or primary care. Using data available from 2009 to 2012, the results obtained were promising. We observed substantial improvements in macro F1-scores, with gains of 12.5% for life-threatening classification, 17.5% for response delay, and 5.1% for jurisdiction classification, compared to the in-house triage protocol of the Valencian Region. However, systems, dispatch protocols, and operational practices naturally evolve over time. Models that exhibited excellent performance with the initial dataset from 2009 to 2012 did not demonstrate the same efficacy when evaluated on data spanning from 2014 to 2019. This later dataset had undergone modifications compared to the earlier one, which led to dataset shifts, which we have meticulously characterized and investigated in this thesis. Continuing our research, we incorporated Deep Continual Learning techniques in our developments. As a result, we could substantially mitigate the adverse performance effects consequence of dataset shifts. The results indicate that, while performance fluctuations are not completely eliminated, they can be kept within a manageable range. In particular, with respect to the F1-score, when distributional variations fall within the light to moderate range, the performance remains stable, not varying by more than 2.5%. Furthermore, our thesis demonstrates the feasibility of building auxiliary tools that enable dispatchers to interact with these complex deep models. Consequently, without disrupting professionals' workflow, it becomes possible to provide feedback through probability predictions for each severity label class and take appropriate actions based on these predictions. Finally, the outcomes of this thesis hold direct implications for the management of out-of-hospital emergency medical incidents in the Valencian Region. The final model resulting from our research is slated for integration into the emergency medical dispatch centers of the Valencian Region. This model will utilize data provided by dispatchers to automatically compute severity predictions, which will then be compared with those generated by the in-house triage protocol. Any disparities between these predictions will trigger the referral of the incident to a physician coordinator, who will oversee its handling. Therefore, it is evident that our thesis, in addition to making significant contributions to the field of Biomedical Machine Learning Research, also carries substantial implications for enhancing the management of out-of-hospital emergencies in the context of the Valencian Region. / Ferri Borredà, P. (2024). Deep Continual Multimodal Multitask Models for Out-of-Hospital Emergency Medical Call Incidents Triage Support in the Presence of Dataset Shifts [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/203192