Search results

The study explores how Taiwan’s electronic health data systems can be used to build algorithms that reduce or eliminate medical errors and to advance precision medicine.

This study is a narrative review of the literature.

The body of medical knowledge has grown far too large for human clinicians to parse. In theory, electronic health records could augment clinical decision-making with electronic clinical decision support systems (CDSSs). However, computer scientists and clinicians have made remarkably little progress in building CDSSs, because health data tend to be siloed across many different systems that are not interoperable and cannot be linked using common identifiers. As a result, medicine in the USA is often practiced inconsistently with poor adherence to the best preventive and clinical practices. Poor information technology infrastructure contributes to medical errors and waste, resulting in suboptimal care and tens of thousands of premature deaths every year. Taiwan’s national health system, in contrast, is underpinned by a coordinated system of electronic data systems but remains underutilized. In this paper, the authors present a theoretical path toward developing artificial intelligence (AI)-driven CDSS systems using Taiwan’s National Health Insurance Research Database. Such a system could in theory not only optimize care and prevent clinical errors but also empower patients to track their progress in achieving their personal health goals.

While research teams have previously built AI systems with limited applications, this study provides a framework for building global AI-based CDSS systems using one of the world’s few unified electronic health data systems.

The purpose of this paper is to investigate the way technology affects the provision of prehospital emergency care, upgrading the quality of services offered and significantly reducing the risk of premature termination of the patients.

The paper presents the development of the eEKAB, a pilot emergency medical information system that simulates the main services offered by the Greek National Instant Aid Centre (EKAB). The eEKAB was developed on an agile system methodology. From a technical perspective, the features and the technology were mainly chosen to provide reliable and user-friendly interfaces that will attract many users. eEKAB is based on three important pillars for offering health care to the patients: the “On-time Incident Reporting”, the “On-time Arrival at the Incident” and “Transfer to the Health Center”. According to the literature review, the emergency medical services (EMS) systems that combine all the features are very few.

It reduces the total time of the EMS procedures and it allows for an easier management of EMS, by providing a better allocation of human resources and a better geographical distribution of ambulances. The evaluation displayed that it is a very helpful application for the ambulance drivers as it reduces the ambulance response time to arrive in the patient's location and contributes significantly to the general performance of the prehospital medical care system. Also, the survey verified the importance of implementing eEKAB on a larger scale beyond the pilot usage. It is worth mentioning that the younger ambulance drivers had a more positive view for the purpose of the application.

The paper clearly identifies implications for further research. Regarding interoperability, the mobile app cooperates with the Operational Center of EKAB, while further collaboration could be achieved with other operational ambulance handling center, mainly, of the private sector. The system can evolve to include better communications among the EKAB departments. Particularly, the ambulance crew as well as the doctors should be informed with more incident features such as the emergency signal so that they know whether to open the siren, the patient's name, etc. The authors are currently working on implementing some features to provide effective medical health services to the patient in the ambulance.

eEKAB will have very significant implications in case of its enforcement, such as the reduction of the total time of EMS procedures with a corresponding reduction of the operating costs of an accident management system and an ambulance fleet handling system while in parallel informing in time the doctors/clinics. It will provide better distribution of ambulances as well as of total human resources. It will greatly assist ambulance drivers, while reducing ambulance response time to reach the patient's location. In other words, the authors will have a better performance of the whole prehospital care system.

Providing emergency care before the hospital is of great importance for upgrading the quality of health services provided at the accident site, thus significantly reducing the risk of premature death of patients. This in itself has a significant social implication.

The paper demonstrates a solid understanding in the field of the EMS systems and the corresponding medical services offered. It proposes the development of an effective, feasible and innovative EMS information system that will improve the existing emergency health care system in Greece (EKAB). An in depth literature review and presentation of the adopted new technologies and the respective architecture take place. An evaluation and statistical validation were conducted for proving the high applicability of eEKAB in case of real-life running.

Contextual to the recognition of the complex interplay between health and behavioral aspects, integrated behavioral health (IBH) has emerged. Although this model is becoming popular in the Western world, its presence in the global context is not promising. This paper aims to explore the need for IBH in India and address its barriers to implementation and possible solutions.

We analyzed the case of IBH and its potential implications for India using the current evidence base, authors' reflections and experience of implementing similar programs.

This paper identifies contextual factors, including increased instances of non-communicable diseases and psychosocial and cultural determinants of health, that necessitate the implementation of IBH programs in India. The key features of different IBH models and their applicability are outlined. The current status of IBH and potential challenges in implementation in India in terms of human resources and other factors are delineated. We also discuss the potential models for implementing IBH in India.

Integrating behavioral health in primary care is considered an effective and sustainable model to promote health and well-being across various target populations. Towards this end, this paper is the first to discuss the contextual factors of IBH in India. It is a significant addition to the knowledge base on IBH and its possible implementation barriers and strategies in low- and middle-income countries.

The study aims to develop a multilayer high-effective ensemble of ensembles predictive model (stacking ensemble) using several hyperparameter optimized ensemble machine learning (ML) methods (bagging and boosting ensembles) trained with high-volume data points retrieved from Internet of Things (IoT) emission sensors, time-corresponding meteorology and traffic data.

For a start, the study experimented big data hypothesis theory by developing sample ensemble predictive models on different data sample sizes and compared their results. Second, it developed a standalone model and several bagging and boosting ensemble models and compared their results. Finally, it used the best performing bagging and boosting predictive models as input estimators to develop a novel multilayer high-effective stacking ensemble predictive model.

Results proved data size to be one of the main determinants to ensemble ML predictive power. Second, it proved that, as compared to using a single algorithm, the cumulative result from ensemble ML algorithms is usually always better in terms of predicted accuracy. Finally, it proved stacking ensemble to be a better model for predicting PM_2.5 concentration level than bagging and boosting ensemble models.

A limitation of this study is the trade-off between performance of this novel model and the computational time required to train it. Whether this gap can be closed remains an open research question. As a result, future research should attempt to close this gap. Also, future studies can integrate this novel model to a personal air quality messaging system to inform public of pollution levels and improve public access to air quality forecast.

The outcome of this study will aid the public to proactively identify highly polluted areas thus potentially reducing pollution-associated/ triggered COVID-19 (and other lung diseases) deaths/ complications/ transmission by encouraging avoidance behavior and support informed decision to lock down by government bodies when integrated into an air pollution monitoring system

This study fills a gap in literature by providing a justification for selecting appropriate ensemble ML algorithms for PM_2.5 concentration level predictive modeling. Second, it contributes to the big data hypothesis theory, which suggests that data size is one of the most important factors of ML predictive capability. Third, it supports the premise that when using ensemble ML algorithms, the cumulative output is usually always better in terms of predicted accuracy than using a single algorithm. Finally developing a novel multilayer high-performant hyperparameter optimized ensemble of ensembles predictive model that can accurately predict PM_2.5 concentration levels with improved model interpretability and enhanced generalizability, as well as the provision of a novel databank of historic pollution data from IoT emission sensors that can be purchased for research, consultancy and policymaking.

Due to their limited capacity for adaptation and dependence on natural resources for economic growth, developing countries (DCs) tend to be more prone to climate change. It is argued that climate finance (CF) is a significant financial innovation to mitigate the negative effects of climate variation. However, the heterogeneous impacts of CF on environmental sustainability (ES) and social welfare (SW) have been masked. Thus, this study aims to investigate the heterogeneous effects of CF on ES and SW in 80 CF receipt DCs from 2002 to 2018. This study also aims to investigate the effects of CF on ES and SW based on population size, income heterogeneity and the type of CF.

The method of moments quantile regression (MMQR) with fixed effects is utilized. Alternatively, the fully modified least square (FMOLS) and dynamic least square (DOLS) estimators are used for the robustness test.

The findings revealed that DCs with the lowest and middle quantiles of EF, carbon dioxide (CO₂) emissions and human development exhibit large beneficial impacts of CF on ES and SW. In contrast, the positive effects of CF on ES breakdown for countries with the largest distributions of EF and CO₂ emissions. Besides, the impacts of CF on ES and SW depend on income heterogeneity, population size and the type of CF.

This study calls for a framework to integrate CF into all economic development decisions to strengthen climate-resilient SW and ES in DCs.

To the best of the authors’ knowledge, this is the first study to investigate the effects of CF on ES and SW in a wide range of DCs. Thus, it complements existing related literature focusing on the effects of CF on ES and SW.

This study aims to demonstrate the evaluation of an incentive scheme to encourage pregnant people to set a quit-smoking date.

The paper outlines a collaborative approach, working with pregnant people, clinicians, tobacco dependency practitioners and academics to gain insights into their perspectives and experiences. Quantitative and qualitative data were analysed.

The incentive scheme and appropriate support from clinicians have been shown to encourage pregnant people to set a quit date. The tobacco dependency practitioners helped remove barriers, such as the perception of the stigmatisation of smoking when pregnant. The practitioners also helped pregnant people make informed decisions to support successful behaviour change. The impact of the scheme resulted in improved infant health indicators. The scheme’s evaluation also supported establishing stakeholder knowledge exchange and learning processes.

This is a single-site study among a relatively small group of people designed to achieve a specific evaluation objective. Caution in generalising to wider settings should be exercised.

This study highlights the efficacy of an incentive scheme, complemented with support from clinicians, and the significance of knowledge exchange and collaboration between stakeholders in health care with significance in similar settings.

The paper details the incentive scheme input, actions, output, outcomes and impact involving a wider range of stakeholders, including the emotional consequences for participants, clinicians and academics.

While science has researched the impact of air pollution on human health, the economic dimension of it has been less researched so far. Renewable energy consumption is an important factor in determining the level of life expectancy and reducing health expenditure. Thus, this study aims to investigate the impact of renewable energy, carbon emissions, health expenditure and urbanization on life expectancy in G-7 countries over the period of 2000–2019.

This study has adopted a novel Method of Moments Quantile Regression (MMQR). Furthermore, as a robustness check for MMQR, the fully modified ordinary least square, dynamic ordinary least squares and fixed effect ordinary least square estimators have been used.

The results indicated that renewable energy consumption, health expenditure and urbanization lead to an increase in life expectancy across all quantiles (5th to 95th), whereas higher carbon dioxide emissions reduce life expectancy at birth across all the quantiles (5th to 95th).

The empirical findings conclude that governments should recognize their potential in renewable energy sources and devise policies such as tax-related regulations, or relevant incentives to encourage further investments in this field.

This paper in comparison to the other research studies used MMQR to investigate the impact of factors affecting life expectancy. Also, to the best of the authors’ knowledge, so far no study has investigated the impact of renewable energy on life expectancy in G-7 countries.

This study aims to evaluate the quality of a speech and language therapy (SLT) swallow service provided to adults with intellectual disability (ID) by exploring the process and outcome factors; to explore the process of what is done to and for the patient including identifying dysphagia, choking and pneumonia risk, typical interventions and management; examine clinical outcomes; and explore the relationship of outcomes with risk factors, satisfaction with the service and the impact of the service on the number of choking incidents and admissions to acute hospital with swallow concerns.

There were three specific work packages: (1) collecting and scrutinising patient data from the clinical record for adults with ID referred to the SLT swallow service over a six-month period. The researcher created aims, defined the limits to achieve the aims, designed a standardised data collection form, set out where data was in the clinical record, piloted, set limits for collection and trained reviewers; (2) gathering experience and satisfaction surveys from patients, caregivers and referrers over the six-month study period; and (3) monitoring choking adverse incident reports and hospital admission with swallow concerns for the whole ID population.

Choking and hospital admission were the main reasons for referral, and pneumonia risk significantly predicted dysphagia impairment. The research highlighted common dysphagia risk factors, interventions and recommendations for this population. The SLT swallow service is a quality service that is highly valued by patients, their caregivers and referrers. The service achieves significant clinical improvements, helps identify dysphagia and provides management to reduce associated risks.

This study found common dysphagia risk factors, interventions and recommendations; it also found that the therapy outcome measures/Royal College of Speech and Language Therapy online outcome tool was a meaningful outcome measure, and that pneumonia risk significantly predicted dysphagia impairment, all of which could inform the identified dysphagia research priorities for this population.

Naming usual care in treatment and recommendations could help ensure a fair service and could help form quality indicators. People with ID, their caregivers and staff generated valuable ideas for improvement, and further involvement work could create a logic model for the service. Other future work could explore the use of screening tools, increase multidisciplinary team working, improve access to instrumental assessments, raise awareness of swallowing and promote important oral health and medication reviews. By using this information to shape quality improvement work and policies, one can work toward addressing high health-related inequalities and preventable deaths associated with dysphagia in this vulnerable population.

It may be useful to raise awareness that adults with ID age earlier and that one should not exclude them from older people’s services because of an age threshold, often set higher than their life expectancy. This study highlighted a possible inequality of access issue for adults with ID who do not have direct care or day care staff. There is a need to increase access to awareness training so caregivers and general practitioners can recognise swallow difficulties and know how to make a referral for a swallow assessment.

Overall, the evaluation of the swallow service to adults with ID suggests that SLT have a quality service for adults with ID that is highly valued and provides significant clinical improvements. By building on these strengths, SLT could extend the reach, influence and impact of their services to help those adults with ID who have emerging swallow difficulties or who do not access the service.

The use of energy efficient cookstoves (EECS) has been promoted for a long and considered as instrumental in the efforts to mitigate the multiple social, economic and environmental consequences of traditional biomass cookstoves. Despite this, the adoption of EECS in pre-urban and rural Ethiopia is still very low. In response to this, the government of Ethiopia, in collaboration with international development organizations, implemented numerous initiatives aimed at improving the availability and use of EECS as part of the effort to support sustainable development. However, very little is known about the impact of the introduced EECS on improving the welfare of women. The purpose of this study is, therefore, to assess the impact of improved cookstoves projects on the welfare of women in Yaya Gullele district, Ethiopia.

A mixed research design was adopted to conduct the study. The quantitative data for the study were collected using a structured questionnaire by interviewing 388 randomly selected respondents. The data were analysed using both descriptive and inferential data analysis techniques, including the propensity score matching model using STATA.

Results of the study revealed that adoption of EECS has reduced fuel wood expense (124.65 Ethiopian Birr [ETB]/week), reduced five-year stove expense (404.67 ETB) and increased regular savings (116.58 ETB/month), which contributed for an increased annual income of participants (5,594.42 ETB). The result of the study also indicated that the use of EECS enabled the beneficiary women to reduce the amount of fuel wood use by 29.4 kg/week, which in turn reduced forest degradation and emission by 2.34 tons of CO₂e/year/household. Besides, it reduced the drudgery on women in terms of reduced time to cook (53 min/day), reduced time to collect fuelwood and prepare food (3.95 h/week) and reduced frequency of fuelwood collection trips (3.42 trips/week). The study results, in general, indicated that the adoption of EECS improved the welfare of women in the study area, where the majority of women have been suffering from the burden of using traditional stoves and associated impacts for a long.

Energy is central to most of the development-related challenges and opportunities every country faces today. The result of the study implied that policies and strategies intended at improving the availability and use of EECS as part of the effort to support sustainable development need to consider integrating such context-referenced, locally manageable and affordable EECS into the clean developmental strategies of the country.

Insights from this study can support development practitioners and policymakers to make informed decisions regarding future interventions in the use of energy efficient that have the potential to several economic, social and environmental positive development outcomes.

This research intends to investigate the determinants that affect consumers’ purchase intention of electric vehicles (EVs) in Malaysia using an extended theory of planned behaviour (TPB).

Survey data were collected with a sample size of 306. The research used SmartPLS 4.0 structural equation modelling tool to analyse the data. Reliability and validity tests (discriminant and convergent validity) were used and subsequently assessed the measurement and structural models. Mediation analysis was conducted to identify the role of the latent constructs.

The findings indicated that a green purchase attitude plays a complete mediation role in the effect of environmental knowledge on the purchase intention of EVs. In the same notion, the effect of price perception and availability of charging facilities on the purchase intention of EVs passes completely through perceived behavioural control. However, the subjective norm was an insignificant mediator of the impact between government support and EV purchase intention.

This paper helps to examine the latent constructs that impact purchase intention using environmental knowledge, government support, price perception and the availability of charging facilities. Successful green marketing and a sustainable consumerism framework are seen as a booster to promote the usage of EVs in Malaysia.

An extended TPB model has been employed in this research to study the effects of the above-mentioned constructs. The results show that most of the extended constructs are significant in explaining the purchase intention. The empirical results address the gap in the consumer green attitude and provide insight into this area of study.