Redesigning the Barranquilla's public emergency care network to improve the patient waiting time

Tesis por compendio[ES] La oportunidad en la atención es uno de los críticos de mayor relevancia en la satisfacción de los pacientes que acuden a los servicios de Urgencias. Por tal motivo, las instituciones prestadoras de servicio y las organizaciones gubernamentales deben propender conjuntamente por una atención cada vez más oportuna a costos operacionales razonables. En el caso de la Red Pública en Servicios de Urgencias de Barrannquilla, compuesta por 8 puntos de atención y 2 hospitales, la tendencia marca un continuo crecimiento de la oportunidad en la atención con una tasa de 3,08 minutos/semestre y una probabilidad del 93,13% de atender a los pacientes después de una espera mayor a 30 minutos. Lo anterior se constituye en un síntoma inequívoco de la incapacidad de la Red para satisfacer los estándares de oportunidad establecidos por el Ministerio de Salud, hecho que podría desencadenar el desarrollo de sintomatologías de mayor complejidad, el incremento de la probabilidad de mortalidad, el requerimiento de servicios clínicos más complejos (hospitalización y cuidados intensivos) y el aumento de los costos asociados al servicio. En consecuencia, la presente tesis doctoral presenta el rediseño de la Red Pública en Servicios de Urgencias anteriormente mencionada a fin de otorgar a la población diana un servicio eficiente y altamente oportuno donde tanto las instituciones prestadoras del servicio como los organismos gubernamentales converjan efectivamente. Para ello, fue necesaria la ejecución de 4 grandes fases a través de las cuales se consolidó una propuesta orientada al desarrollo efectivo y sostenible de las operaciones de la Red. Primero, se caracterizó la Red Pública de Servicios de Urgencias en Salud considerando su comportamiento actual en términos de demanda y oportunidad de la atención. Luego, a través de una revisión sistemática de la literatura, se identificaron los enfoques metodológicos que se han implementado para la mejora de la oportunidad y otros indicadores de rendimiento asociados al servicio de Urgencias. Posteriormente, se diseñó una metodología para la creación de redes de Urgencias eficientes y sostenibles la cual luego se validó en la Red Pública sudamericana a fin de disminuir la oportunidad de atención promedio en Urgencias y garantizar la distribución equitativa de los beneficios financieros derivados de la colaboración. Finalmente, se construyó un modelo multicriterio que permitió evaluar el rendimiento de los departamentos de Urgencia e impulsó la creación de estrategias de mejora focalizadas en incrementar su respuesta ante la demanda cambiante, los críticos de satisfacción y las condiciones de operación estipuladas en la ley. Los resultados de esta aplicación evidenciaron que los pacientes que acceden a la Red tienden a esperar en promedio 201,6 min con desviación de estándar de 81,6 min antes de ser atendidos por urgencia. Por otro lado, de acuerdo con la revisión de literatura, la combinación de técnicas de investigación de operaciones, ingeniería de la calidad y analítica de datos es ampliamente recomendada para abordar este problema. En ese sentido, una metodología basada en modelos colaterales de pago, simulación de procesos y lean seis sigma fue propuesta y validada generando un rediseño de Red cuya oportunidad de atención promedio podría disminuir entre 6,71 min y 9,08 min con beneficios financieros promedio de US$29,980/nodo. En último lugar, un modelo compuesto por 8 criterios y 35 sub-criterios fue diseñado para evaluar el rendimiento general de los departamentos de Urgencias. Los resultados del modelo evidenciaron el rol crítico de la infraestructura (Peso global = 21,5i garantir la distribució equitativa dels beneficis financers derivats de la col´laboració. Finalment, es va construir un model multicriteri que va permetre avaluar el rendiment dels departaments d'Urgència i va impulsar la creació d'estratègies de millora focalitzades en incrementar la seva resposta davant la demanda canviant, els crítics de satisfacció i les condicions d'operació estipulades en la llei. Els resultats d'aquesta aplicació van evidenciar que els pacients que accedeixen a la Xarxa tendeixen a esperar de mitjana 201,6 min amb desviació d'estàndard de 81,6 min abans de ser atesos per urgència. D'altra banda, d'acord amb la revisió de literatura, la combinació de tècniques d'investigació d'operacions, enginyeria de la qualitat i analítica de dades és àmpliament recomanada per abordar aquest problema. En aquest sentit, una metodologia basada en models col´laterals de pagament, simulació de processos i llegeixin 6 sigma va ser proposada i validada generant un redisseny de Xarxa la oportunitat d'atenció mitjana podria disminuir entre 6,71 min i 9,08 min amb beneficis financers mitjana d'US$ 29,980 / node. En darrer lloc, un model compost per 8 criteris i 35 sub-criteris va ser dissenyat per avaluar el rendiment general dels departaments d'Urgències. Els resultats de el model evidenciar el paper crític de la infraestructura (Pes global = 21,5%) en el rendiment dels departaments d'Urgència i la naturalesa interactiva de la Seguretat de l'Pacient (C + R = 12,771).[EN] Waiting time is one of the most critical measures in the satisfaction of patients admitted within emergency departments. Therefore, hospitals and governmental organizations should jointly aim to provide timely attention at reasonable costs. In the case of Barranquilla's Pubic Emergency Service Network, composed by 8 Points of care (POCs) and 2 hospitals, the trend evidences a continuous growing of the waiting time with a rate of 3,08 min/semester and a 93,13% likelihood of serving patients after waiting for more than 30 minutes. This is an unmistakable symptom of the network inability for satisfying the standards established by the Ministry of Health, which may trigger the development of more complex symptoms, increase in the death rate, requirement for more complex clinical services (hospitalization and intensive care unit) and increased service costs. This doctoral dissertation then illustrates the redesign of the aforementioned Public Emergency Service Network aiming at providing the target population with an efficient and highly timely service where both hospitals and governmental institutions effectively converge. It was then necessary to implement a 4-phase methodology consolidating a proposal oriented to the effective and sustainable development of network operations. First, the Public Emergency Service Network was characterized considering its current behavior in terms of demand and waiting time. A systematic literature review was then undertaken for identifying the methodological approaches that have been implementing for improving the waiting time and other performance indicators associated with the emergency care service. Following this, a methodology for the creation of efficient and sustainable emergency care networks was designed and later validated in the Southamerican Public network for lessening the average waiting time and ensuring the equitable distribution of profits derived from the collaboration. Ultimately, a multicriteria decision-making model was created for assessing the performance of the emergency departments and propelling the design of improvement strategies focused on bettering the response against the changing demand conditions, critical to satisfaction and operational conditions. The results evidenced that the patients accessing to the network tend to wait 201,6 min on average with a standard deviation of 81,6 min before being served by the emergency care unit. On the other hand, based on the reported literature, it is highly suggested to combine Operations Research (OR) methods, quality-based techniques, and data-driven approaches for addressing this problem. In this sense, a methodology based on collateral payment models, Discrete-event simulation, and Lean Six Sigma was proposed and validated resulting in a redesigned network whose average waiting time may diminish between 6,71 min and 9,08 min with an average profit US$29,980/node. Lately, a model comprising of 8 criteria and 35 sub-criteria was designed for evaluating the overall performance of emergency departments. The model outcomes revealed the critical role of Infrastructure (Global weight = 21,5%) in ED performance and the interactive nature of Patient Safety (C + R = 12,771).Ortíz Barrios, MÁ. (2020). Redesigning the Barranquilla's public emergency care network to improve the patient waiting time [Tesis doctoral]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/156215TESISCompendi

2021 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Neonatal Life Support; Education, Implementation, and Teams; First Aid Task Forces; and the COVID-19 Working Group

The International Liaison Committee on Resuscitation initiated a continuous review of new, peer-reviewed published cardiopulmonary resuscitation science. This is the fifth annual summary of the International Liaison Committee on Resuscitation International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations; a more comprehensive review was done in 2020. This latest summary addresses the most recently published resuscitation evidence reviewed by International Liaison Committee on Resuscitation task force science experts. Topics covered by systematic reviews in this summary include resuscitation topics of video-based dispatch systems; head-up cardiopulmonary resuscitation; early coronary angiography after return of spontaneous circulation; cardiopulmonary resuscitation in the prone patient; cord management at birth for preterm and term infants; devices for administering positive-pressure ventilation at birth; family presence during neonatal resuscitation; self-directed, digitally based basic life support education and training in adults and children; coronavirus disease 2019 infection risk to rescuers from patients in cardiac arrest; and first aid topics, including cooling with water for thermal burns, oral rehydration for exertional dehydration, pediatric tourniquet use, and methods of tick removal. Members from 6 International Liaison Committee on Resuscitation task forces have assessed, discussed, and debated the quality of the evidence, according to the Grading of Recommendations Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations or good practice statements. Insights into the deliberations of the task forces are provided in Justification and Evidence-to-Decision Framework Highlights sections. In addition, the task forces listed priority knowledge gaps for further research

An intelligent recommender system based on short-term disease risk prediction for patients with chronic diseases in a telehealth environment

Clinical decisions are usually made based on the practitioners' experiences with limited support from data-centric analytic processes from medical databases. This often leads to undesirable biases, human errors and high medical costs affecting the quality of services provided to patients. Recently, the use of intelligent technologies in clinical decision making in the telehealth environment has begun to play a vital role in improving the quality of patients' lives and reducing the costs and workload involved in their daily healthcare. In the telehealth environment, patients suffering from chronic diseases such as heart disease or diabetes have to take various medical tests such as measuring blood pressure, blood sugar and blood oxygen, etc. This practice adversely affects the overall convenience and quality of their everyday living. In this PhD thesis, an effective recommender system is proposed utilizing a set of innovative disease risk prediction algorithms and models for short-term disease risk prediction to provide chronic disease patients with appropriate recommendations regarding the need to take a medical test on the coming day. The input sequence of sliding windows based on the patient's time series data, is analyzed in both the time domain and the frequency domain. The time series medical data obtained for each chronicle disease patient is partitioned into consecutive sliding windows for analysis in both the time and the frequency domains. The available time series data are readily available in time domains which can be used for analysis without any further conversion. For data analysis in the frequency domain, Fast Fourier Transformation (FFT) and Dual-Tree Complex Wavelet Transformation (DTCWT) are applied to convert the data into the frequency domain and extract the frequency information. In the time domain, four innovative predictive algorithms, Basic Heuristic Algorithm (BHA), Regression-Based Algorithm (RBA) and Hybrid Algorithm (HA) as well as a structural graph-based method (SG), are proposed to study the time series data for producing recommendations. While, in the frequency domain, three predictive classifiers, Artificial Neural Network, Least Squares-Support Vector Machine, and Naïve Bayes, are used to produce the recommendations. An ensemble machine learning model is utilized to combine all the used predictive models and algorithms in both the time and frequency domains to produce the final recommendation. Two real-life telehealth datasets collected from chronic disease patients (i.e., heart disease and diabetes patients) are utilized for a comprehensive experimental evaluation in this study. The results show that the proposed system is effective in analysing time series medical data and providing accurate and reliable (very low risk) recommendations to patients suffering from chronic diseases such as heart disease and diabetes. This research work will help provide high-quality evidence-based intelligent decision support to clinical disease patients that significantly reduces workload associated with medical checkups would otherwise have to be conducted every day in a telehealth environment

Intelligent Systems for Sustainable Person-Centered Healthcare

This open access book establishes a dialog among the medical and intelligent system domains for igniting transition toward a sustainable and cost-effective healthcare. The Person-Centered Care (PCC) positions a person in the center of a healthcare system, instead of defining a patient as a set of diagnoses and treatment episodes. The PCC-based conceptual background triggers enhanced application of Artificial Intelligence, as it dissolves the limits of processing traditional medical data records, clinical tests and surveys. Enhanced knowledge for diagnosing, treatment and rehabilitation is captured and utilized by inclusion of data sources characterizing personal lifestyle, and health literacy, and it involves insights derived from smart ambience and wearables data, community networks, and the caregivers’ feedback. The book discusses intelligent systems and their applications for healthcare data analysis, decision making and process design tasks. The measurement systems and efficiency evaluation models analyze ability of intelligent healthcare system to monitor person health and improving quality of life

2021 International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations: Summary From the Basic Life Support; Advanced Life Support; Neonatal Life Support; Education, Implementation, and Teams; First Aid Task Forces; and the COVID-19 Working Group

The International Liaison Committee on Resuscitation initiated a continuous review of new, peer-reviewed published cardiopulmonary resuscitation science. This is the fifth annual summary of the International Liaison Committee on Resuscitation International Consensus on Cardiopulmonary Resuscitation and Emergency Cardiovascular Care Science With Treatment Recommendations; a more comprehensive review was done in 2020. This latest summary addresses the most recently published resuscitation evidence reviewed by International Liaison Committee on Resuscitation task force science experts. Topics covered by systematic reviews in this summary include resuscitation topics of video-based dispatch systems; head-up cardiopulmonary resuscitation; early coronary angiography after return of spontaneous circulation; cardiopulmonary resuscitation in the prone patient; cord management at birth for preterm and term infants; devices for administering positive-pressure ventilation at birth; family presence during neonatal resuscitation; self-directed, digitally based basic life support education and training in adults and children; coronavirus disease 2019 infection risk to rescuers from patients in cardiac arrest; and first aid topics, including cooling with water for thermal burns, oral rehydration for exertional dehydration, pediatric tourniquet use, and methods of tick removal. Members from 6 International Liaison Committee on Resuscitation task forces have assessed, discussed, and debated the quality of the evidence, according to the Grading of Recommendations Assessment, Development, and Evaluation criteria, and their statements include consensus treatment recommendations or good practice statements. Insights into the deliberations of the task forces are provided in Justification and Evidence-to-Decision Framework Highlights sections. In addition, the task forces listed priority knowledge gaps for further research

Classification of electroencephalogram (EEG) for lower limb movement of post stroke patients using artificial neural network (ANN)

Nowadays, many neurological conditions happen suddenly, such as stroke or spinal cord injury. This can cause chronic gait function impairment due to functional deficits in motor control. Current physiotherapy techniques such as functional electrical stimulation (FES) can be used to reconstruct some skills needed for movements of daily life. However, FES system provides only a limited degree of motor function recovery and has no mechanism for reflecting a patient’s motor intentions, hence requires novel therapies. Brain-Computer Interfaces (BCI) provides the means to decode mental states and activate devices according to user intentions. However, conventional BCI cannot be used fully, due to the lack of accuracy, and need some improvement. In addition to that, the integration of BCI with lower extremity FES systems has received less attention compared to the BCI-FES systems with upper extremity. The discussion of this thesis was divided into two parts, which were the BCI part as input and the functional electrical stimulator (FES) controller part as the output for this system. For BCI part, the main processes involved are brainwave signals classification and mapping process. Here the signal has been classed will be applied to match the appropriate rehabilitation exercise. Whereas for the FES part, the signal from the mapping system will be controlled by the controller to ensure that the target knee angle is achieved to make the rehabilitation process more effective. As a conclusion, patients can be classified into two classes based on their alpha and beta signals status and these must undergone rehabilitation sessions according to their post-stroke level. So the results proved that the ANN model developed was able to classify the post-stroke severity. Also, the result had proven that the BCI fuzzy-based mapping system in this study was able to work perfectly into mapping the post-stroke patient with a suitable exercise according to their post-stroke level

Intelligent Systems for Sustainable Person-Centered Healthcare

This open access book establishes a dialog among the medical and intelligent system domains for igniting transition toward a sustainable and cost-effective healthcare. The Person-Centered Care (PCC) positions a person in the center of a healthcare system, instead of defining a patient as a set of diagnoses and treatment episodes. The PCC-based conceptual background triggers enhanced application of Artificial Intelligence, as it dissolves the limits of processing traditional medical data records, clinical tests and surveys. Enhanced knowledge for diagnosing, treatment and rehabilitation is captured and utilized by inclusion of data sources characterizing personal lifestyle, and health literacy, and it involves insights derived from smart ambience and wearables data, community networks, and the caregivers’ feedback. The book discusses intelligent systems and their applications for healthcare data analysis, decision making and process design tasks. The measurement systems and efficiency evaluation models analyze ability of intelligent healthcare system to monitor person health and improving quality of life

Comparison of Correlation for Asian Shariah Indices Using DCC-GARCH and Rolling Window Correlation.

This paper aims to compare the capability of correlation in capturing the volatility using rolling window correlation and Dynamic Conditional Correlation - Generalized Autoregressive Conditional Heteroscedasticity (DCC-GARCH) approach. This study will perform a DCC-GARCH to estimate the dynamic conditional correlation between the Asian Shariah indices. The Asian Shariah index comprises FTSE SGX Asia Shariah 100, FTSE Bursa Malaysia Emas Shariah Index, FTSE Greater China Shariah Index, and FTSE Stock Exchange of Thailand (SET) Shariah Index. The correlation estimation considers the FTSE SGX Asia Shariah 100 as a proxy. The World Health Organization (WHO) declared the Coronavirus 2019 (COVID-19) as pandemic on 11th March 2020. Therefore, the data used covers six months before and after 11th March 2020, from 11th September 2019 until 11th September 2020. The output of both effected correlations towards the Covid-19 will be evaluated based on their ability to capture the time-varying changes through graph plotting. The empirical findings show that the DCC-GARCH is better at capturing the highly changes volatility than the rolling window correlation

Developing and evaluating a complex intervention in stroke: using very early mobilisation as an example

Background: Complex interventions, those that incorporate multiple interacting components, are difficult to define, measure and implement. The aim of this research was to develop and evaluate the complex intervention, very early mobilisation (VEM) in acute stroke care. The clinical effectiveness and cost-effectiveness of VEM were evaluated whilst simultaneously considering the implications for future implementation. Methods: A mixed methods approach was used: systematic review, predictive modelling, observational study design, individual patient data meta-analysis, qualitative methods and economic evaluation. Statistical models to accurately predict mobility post-stroke were developed. A multicentre observational study was conducted to establish pre-implementation activity levels of acute stroke patients. Data from two completed and comparable feasibility trials were used to estimate the clinical and economic impact of VEM. A qualitative process evaluation was conducted to identify the barriers and facilitators to implementing VEM, if shown to be effective. Results: Two predictive models were developed with age and stroke type common factors to both. Pre-implementation activity levels were low. Patients who underwent VEM were 3-times more likely to be independent at 3 months than were standard care (SC) patients. The incremental cost-effectiveness ratio associated with VEM in comparison to SC indicated VEM to be potentially cost-effective from a societal perspective. Barriers and facilitators identified for each stage of the stroke pathway and a set of HCPs’ beliefs towards VEM were formulated. Conclusions: This research has adhered to current guidance provided by the Medical Research Council to develop and evaluate VEM. The clinical effectiveness and cost-effectiveness of VEM were estimated. The ongoing A Very Early Rehabilitation Trial phase III will provide definitive evidence for the effectiveness of VEM and the wider consequences for stroke care. This research has provided the support and the foundations for the development of a clear implementation strategy for VEM