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%)

Application and comparison of scoring indices to predict outcomes in patients with healthcare-associated pneumonia

Introduction: Healthcare-associated pneumonia HCAP is a relatively new category of pneumonia. It refers to infections that occur prior to hospital admission in patients with specific risk factors following contact or exposure to a healthcare environment. There is currently no scoring index to predict the outcomes of HCAP patients. We applied and compared different community acquired pneumonia CAP scoring indices to predict 30-day mortality and 3-day and 14-day intensive care unit ICU admission in patients with HCAP. Methods: We conducted a retrospective cohort study based on an inpatient database from six medical centers, recruiting a total of 444 patients with HCAP between 1 January 2007 and 31 December 2007. Pneumonia severity scoring indices including PSI pneumonia severity index, CURB 65 confusion, urea, respiratory rate, blood pressure , age 65, IDSA/ATS Infectious Diseases Society of America/American Thoracic Society, modified ATS rule, SCAP severe community acquired pneumonia, SMART-COP systolic blood pressure, multilobar involvement, albumin, respiratory rate, tachycardia, confusion, oxygenation, pH, SMRT- CO systolic blood pressure, multilobar involvement, respiratory rate, tachycardia, confusion, oxygenation, and SOAR systolic blood pressure, oxygenation, age, respiratory rate were calculated for each patient. Patient characteristics, co-morbidities, pneumonia pathogen culture results, length of hospital stay LOS, and length of ICU stay were also recorded. Results: PSI > 90 has the highest sensitivity in predicting mortality, followed by CURB-65 >= 2 and SCAP > 9 SCAP score area under the curve AUC: 0.71, PSI AUC: 0.70 and CURB-65 AUC: 0.66. Compared to PSI, modified ATS, IDSA/ATS, SCAP, and SMART-COP were easy to calculate. For predicting ICU admission Day 3 and Day 14, modified ATS AUC: 0.84, 0.82 , SMART-COP AUC: 0.84, 0.82, SCAP AUC: 0.82, 0.80 and IDSA/ ATS AUC: 0.80, 0 .79 performed better statistically significant difference than PSI, CURB- 65, SOAR and SMRT-CO. Conclusions: The utility of the scoring indices for risk assessment in patients with healthcare-associated pneumonia shows that the scoring indices originally designed for CAP can be applied to HCAP

Smart MCI Tracking and Tracing System Based on Colored Active RFID TriageTags

With regards of quick response, its importance can’t be ingored during the mass‐casualty incident (MCI) event. This paper focuses on the application of a mass‐casualty incidents system in tracking and tracing with the use of the colored active Radio Frequency Identification (RFID) triage tag to make information of each vicim visual at the base of operations as soon as possible. Its main funtion mentioned in this paper is to traige the victims with the means of active colored tag. Meanwhile, the injuried information will be saved as data in PDA reader. In the process of the victim’s arrival in the hospital emergency department and the treatment data sheet will be sent back by the hospital information system, so that this system will get the RFID triage tag ID. This system of tracing and tracking is called as a smart MCIs management system

Wavelet-based filtration procedure for denoising the predicted CO2 waveforms in smart home within the Internet of Things

The operating cost minimization of smart homes can be achieved with the optimization of the management of the building's technical functions by determination of the current occupancy status of the individual monitored spaces of a smart home. To respect the privacy of the smart home residents, indirect methods (without using cameras and microphones) are possible for occupancy recognition of space in smart homes. This article describes a newly proposed indirect method to increase the accuracy of the occupancy recognition of monitored spaces of smart homes. The proposed procedure uses the prediction of the course of CO2 concentration from operationally measured quantities (temperature indoor and relative humidity indoor) using artificial neural networks with a multilayer perceptron algorithm. The mathematical wavelet transformation method is used for additive noise canceling from the predicted course of the CO2 concentration signal with an objective increase accuracy of the prediction. The calculated accuracy of CO2 concentration waveform prediction in the additive noise-canceling application was higher than 98% in selected experiments.Web of Science203art. no. 62

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

Ecosystem-Driven Design of In-Home Terminals Based on Open Platform for the

Abstract—In-home healthcare services based on the Internet-of-Things (IoT) have great business potentials. To turn it into reality, a business ecosystem should be established first. Technical solutions should therefore aim for a cooperative ecosystem by meeting the interoperability, security, and system integration requirements. In this paper, we propose an ecosystem-driven design strategy and apply it in the design of an open-platform-based in-home healthcare terminal. A cooperative business ecosystem is formulated by merging the traditiona

tieval: An Evaluation Framework for Temporal Information Extraction Systems

Temporal information extraction (TIE) has attracted a great deal of interest over the last two decades, leading to the development of a significant number of datasets. Despite its benefits, having access to a large volume of corpora makes it difficult when it comes to benchmark TIE systems. On the one hand, different datasets have different annotation schemes, thus hindering the comparison between competitors across different corpora. On the other hand, the fact that each corpus is commonly disseminated in a different format requires a considerable engineering effort for a researcher/practitioner to develop parsers for all of them. This constraint forces researchers to select a limited amount of datasets to evaluate their systems which consequently limits the comparability of the systems. Yet another obstacle that hinders the comparability of the TIE systems is the evaluation metric employed. While most research works adopt traditional metrics such as precision, recall, and $F_1$, a few others prefer temporal awareness -- a metric tailored to be more comprehensive on the evaluation of temporal systems. Although the reason for the absence of temporal awareness in the evaluation of most systems is not clear, one of the factors that certainly weights this decision is the necessity to implement the temporal closure algorithm in order to compute temporal awareness, which is not straightforward to implement neither is currently easily available. All in all, these problems have limited the fair comparison between approaches and consequently, the development of temporal extraction systems. To mitigate these problems, we have developed tieval, a Python library that provides a concise interface for importing different corpora and facilitates system evaluation. In this paper, we present the first public release of tieval and highlight its most relevant features.Comment: 10 page

Wearable, Multimodal, Biosignal Acquisition System for Potential Critical and Emergency Applications

For emergency or intensive-care units (ICUs), patients with unclear consciousness or unstable hemodynamics often require aggressive monitoring by multiple monitors. Complicated pipelines or lines increase the burden on patients and inconvenience for medical personnel. Currently, many commercial devices provide related functionalities. However, most devices measure only one biological signal, which can increase the budget for users and cause difficulty in remote integration. In this study, we develop a wearable device that integrates electrocardiography (ECG), electroencephalography (EEG), and blood oxygen machines for medical applications with the hope that it can be applied in the future. We develop an integrated multiple-biosignal recording system based on a modular design. The developed system monitors and records EEG, ECG, and peripheral oxygen saturation (SpO2) signals for health purposes simultaneously in a single setting. We use a logic level converter to connect the developed EEG module (BR8), ECG module, and SpO2 module to a microcontroller (Arduino). The modular data are then smoothly encoded and decoded through consistent overhead byte stuffing (COBS). This developed system has passed simulation tests and exhibited proper functioning of all modules and subsystems. In the future, the functionalities of the proposed system can be expanded with additional modules to support various emergency or ICU applications