Framework for Development Triages through Mobile Applications

The emergency triage is being implemented due to the congestion of the emergency services, for several reasons, such as: easy access to the patient, demanding an immediate diagnostic an medical aid, prioritizing severely ill patients rather than patients with minor problems that make improper use of the emergency areas. The objective is building a system, integrated to the main system of health organization, for management of emergency triage. To do this, we have analysed and developed a system which allows us to evaluate patients through a mobile application. Results suggest that the integration of emergency triage to mobile application, helps to improve and optimize resource management and decrease the response time. In conclusion, this system optimizes the resources implemented as well as an increase of customer satisfaction

Skin and soft tissue infections in hospitalized and critically ill patients: a nationwide population-based study

<p>Abstract</p> <p>Background</p> <p>The proportional distributions of various skin and soft tissue infections (SSTIs) with/without intensive care are unclear. Among SSTI patients, the prevalence and significance of complicating factors, such as comorbidities and infections other than skin/soft tissue (non-SST infections), remain poorly understood. We conducted this population-based study to characterize hospitalized SSTI patients with/without intensive care and to identify factors associated with patient outcome.</p> <p>Methods</p> <p>We analyzed first-episode SSTIs between January 1, 2005 and December 31, 2007 from the hospitalized claims data of a nationally representative sample of 1,000,000 people, about 5% of the population, enrolled in the Taiwan National Health Insurance program. We classified 18 groups of SSTIs into three major categories: 1) superficial; 2) deeper or healthcare-associated; and 3) gangrenous or necrotizing infections. Multivariate logistic regression models were applied to identify factors associated with intensive care unit (ICU) admission and hospital mortality.</p> <p>Results</p> <p>Of 146,686 patients ever hospitalized during the 3-year study period, we identified 11,390 (7.7%) patients having 12,030 SSTIs. Among these SSTI patients, 1,033 (9.1%) had ICU admission and 306 (2.7%) died at hospital discharge. The most common categories of SSTIs in ICU and non-ICU patients were "deeper or healthcare-associated" (62%) and "superficial" (60%) infections, respectively. Of all SSTI patients, 45.3% had comorbidities and 31.3% had non-SST infections. In the multivariate analyses adjusting for demographics and hospital levels, the presence of several comorbid conditions was associated with ICU admission or hospital mortality, but the results were inconsistent across most common SSTIs. In the same analyses, the presence of non-SST infections was consistently associated with increased risk of ICU admission (adjusted odds ratios [OR] 3.34, 95% confidence interval [CI] 2.91-3.83) and hospital mortality (adjusted OR 5.93, 95% CI 4.57-7.71).</p> <p>Conclusions</p> <p>The proportional distributions of various SSTIs differed between ICU and non-ICU patients. Nearly one-third of hospitalized SSTI patients had non-SST infections, and the presence of which predicted ICU admission and hospital mortality.</p

Ten golden lessons from Republic of China (Taiwan), the best country to save lives during 300 days battle against Covid-19

Almost 1.81 million lives were officially lost by Covid-19 (WORLDOMETERS, 2020) until last 31thDecember 2020. It was one year with intense global battle against the pandemic, with most countries eagle to learn from benchmark nations able to save lives. A new methodology developed by Silva (2020b), with fifteen phases, showed that among 108 well-evaluated countries, the top six benchmark countries are from Asia with emphasis on Vietnam, Taiwan and Thailand. To complement Silva (2020b) study, this article aims to investigate the performance and the best management practices adopted in Taiwan to save lives, during the first 300 days facing the pandemic. The research is descriptive, uses an online questionnaire with bibliographic and documentary approaches. The Fatality Total Index (FTI) developed by Silva (2020b p. 563) was used to compare Taiwan performance against 43 finalist countries. Some results are: 1) Taiwan`s FTI300 is the lowest (0,0020), confirming that the National Government has learned from the past, and is able to integrate and support main actors of the nation to prevent, prepare and fight against the Covid-19; 2) for 109 respondents living in Taiwan, the ten main policy measures adopted by the National Government that saved lives against the virus are: international travel control (78%), effective public-private collaboration (61%), public information campaigns (52%), integration with mass media (51%), increase the medical and personal equipment capacity (49%), combat fake news (47%), public event cancellations (45%), improve intensive care unit structure (28%), support the expansion of the testing system (20%), and schools closures (16%). At the final, ten golden lessons are described, most of them from the 225 policies, measures, programs, projects, strategies, and innovative products or services identified in Taiwan, with the majority led by Public Sector (56%), Corporations (29%), followed by Others (6%), Start Up (4%) and Universities (4%)

Predictive analytics framework for electronic health records with machine learning advancements : optimising hospital resources utilisation with predictive and epidemiological models

The primary aim of this thesis was to investigate the feasibility and robustness of predictive machine-learning models in the context of improving hospital resources’ utilisation with data- driven approaches and predicting hospitalisation with hospital quality assessment metrics such as length of stay. The length of stay predictions includes the validity of the proposed methodological predictive framework on each hospital’s electronic health records data source. In this thesis, we relied on electronic health records (EHRs) to drive a data-driven predictive inpatient length of stay (LOS) research framework that suits the most demanding hospital facilities for hospital resources’ utilisation context. The thesis focused on the viability of the methodological predictive length of stay approaches on dynamic and demanding healthcare facilities and hospital settings such as the intensive care units and the emergency departments. While the hospital length of stay predictions are (internal) healthcare inpatients outcomes assessment at the time of admission to discharge, the thesis also considered (external) factors outside hospital control, such as forecasting future hospitalisations from the spread of infectious communicable disease during pandemics. The internal and external splits are the thesis’ main contributions. Therefore, the thesis evaluated the public health measures during events of uncertainty (e.g. pandemics) and measured the effect of non-pharmaceutical intervention during outbreaks on future hospitalised cases. This approach is the first contribution in the literature to examine the epidemiological curves’ effect using simulation models to project the future hospitalisations on their strong potential to impact hospital beds’ availability and stress hospital workflow and workers, to the best of our knowledge. The main research commonalities between chapters are the usefulness of ensembles learning models in the context of LOS for hospital resources utilisation. The ensembles learning models anticipate better predictive performance by combining several base models to produce an optimal predictive model. These predictive models explored the internal LOS for various chronic and acute conditions using data-driven approaches to determine the most accurate and powerful predicted outcomes. This eventually helps to achieve desired outcomes for hospital professionals who are working in hospital settings

Digital Transformation in Healthcare

This book presents a collection of papers revealing the impact of advanced computation and instrumentation on healthcare. It highlights the increasing global trend driving innovation for a new era of multifunctional technologies for personalized digital healthcare. Moreover, it highlights that contemporary research on healthcare is performed on a multidisciplinary basis comprising computational engineering, biomedicine, biomedical engineering, electronic engineering, and automation engineering, among other areas

Digital Health Care in Taiwan

This open access book introduces the National Health Insurance (NHI) system of Taiwan with a particular emphasis on its application of digital technology to improve healthcare access and quality. The authors explicate how Taiwan integrates its strong Information and Communications Technology (ICT) industry with 5G to construct an information system that facilitates medical information exchange, collects data for planning and research, refines medical claims review procedures and even assists in fighting COVID-19. Taiwan's NHI, launched in 1995, is a single-payer system funded primarily through payroll-based premiums. It covers all citizens and foreign residents with the same comprehensive benefits without the long waiting times seen in other single-payer systems. Though premium rate adjustment and various reforms were carried out in 2010, the NHI finds itself at a crossroads over its financial stability. With the advancement of technologies and an aging population, it faces challenges of expanding coverage to newly developed treatments and diagnosis methods and applying the latest innovations to deliver telemedicine and more patient-centered services. The NHI, like the national health systems of other countries, also needs to address the privacy concerns of the personal health data it collects and the issues regarding opening this data for research or commercial use. In this book, the 12 chapters cover the history, characteristics, current status, innovations and future reform plans of the NHI in the digital era. Topics explored include: Income Strategy Payment Structure Pursuing Health Equity Infrastructure of the Medical Information System Innovative Applications of the Medical Information Applications of Big Data and Artificial Intelligence Digital Health Care in Taiwan is essential reading for academic researchers and students in healthcare administration, health policy, health systems research, and health services delivery, as well as policymakers and public officials in relevant government departments. It also would appeal to academics, practitioners, and other professionals in public health, health sciences, social welfare, and health and biotechnology law

Triagem de pedidos de assistência médica

Nesta dissertação foi avaliada a capacidade de efectuar a triagem de pedidos de assistência médica recorrendo a técnicas de Data Mining. Com base na revisão da literatura decidiu-se seguir a metodologia de Cios et. al (2000), tendo-se explorado diversas abordagens. Uma das principais razões para a escolha desta metodologia foi o facto de se verificar que é a mais utilizada em estudos na área da saúde. Os dados utilizados consistem em 2.070.227 pedidos de assistência médica com as variáveis Ano, Mês, Dia, Dia da Semana, Hora, Distrito, Concelho, Prioridade, Tipo de Ocorrência, Faixa Etária e Sexo, sendo a variável Prioridade o nível de triagem atribuído, podendo este assumir um de quatro valores Emergentes, Urgente, Pouco-urgente e Nãourgente. O tratamento de dados médicos exige cuidados que vão além dos requisitos habituais neste tipo de trabalhos. Para além da dificuldade na obtenção de dados por questões de confidencialidade, é importante que o resultado seja transparente e perceptível e cuidadosamente avaliado. Nesse sentido, foram aplicados os algoritmos árvores de decisão (J48), o Naïve Bayes e Máquinas de Vectores de Suporte (SMO e LibSVM) considerando a escala real de quatro níveis (Emergente, Urgente, Pouco-urgente e Não-urgente). Foi igualmente considerada uma escala de dois níveis, derivada a partir da escala real. As medidas de avaliação utilizadas foram a taxa de acerto, sensibilidade e especificidade. Os resultados mostram que as técnicas de Data Mining são mais eficazes a efectuar a triagem considerando apenas dois níveis. Igualmente se demonstrou nas diferentes abordagens que as Máquinas de Vectores de Suporte são mais eficazes que as restantes técnicas utilizadas.In this dissertation was evaluated the ability to perform the screening of medical assistance requests using Data Mining techniques. Based on the literature review it was decided to follow the methodology of Cios et. al (2000), and several approaches have been explored. One of the main reasons for choosing this methodology was the fact that it is used most frequently in healthcare studies. The data consists of 2,070,227 requests of medical assistance and it features the following variables: Year, Month, Day, Day of the Week, Hour, District, County, Priority, Type of Occurrence, Age Group and Gender. The variable for Priority is the level of triage attributed, which may assume one of four values: Emergent, Urgent, Less Urgent and Nonurgent. The processing of medical data demands a supplementary degree of caution when comparing to other kinds of data. In addition to the difficulties of obtaining sensitive and confidential information, it is important that the results are transparent, perceptible and carefully evaluated. In this regard, the following algorithms are applied: Decision Tree (J48), the Naïve Bayes and Support Vector Machines (SMO and LibSVM), considering the four-levels of the real scale: Emergent, Urgent, Less Urgent and Nonurgent. A two-level scale was also derived from the original scale. The evaluation measures used were: Accuracy, Sensitivity and Specificity. The results show that Data Mining techniques are more effective performing triage considering only two levels. It has also been demonstrated in the different approaches investigated that the Support Vector Machines are more effective than the other techniques analyzed