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"Gesundheitswesen"
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Conceptualising public health : historical and contemporary struggles over key concepts
In Germanic and Nordic languages, the term for 'public health' literally translates to 'people's health', for example Volksgesundheit in German, folkhälsa in Swedish and kansanterveys in Finnish. Covering a period stretching from the late nineteenth century to the present day, this book discusses how understandings and meanings of public health have developed in their political and social context, identifying ruptures and redefinitions in its conceptualisation. It analyses the multifaceted and interactive rhetorical play through which key concepts have been used as political tools, on the one hand, and shaped the understanding and operating environment of public health, on the other. Focusing on the blurred boundaries between the social and the medico-scientific realms, from social hygiene to population policy, Conceptualising Public Health explores the sometimes contradictory and paradoxical normative aims associated with the promotion of public health -- Provided by the publisher.
Book
Methodological problems of big data and artificial intelligence in the medical specialists training
The emergence of big data and artificial intelligence firstly in healthcare has caused considerable excitement, stating the need to improve approaches to diagnosis, prognosis, and treatment. Despite enthusiasm, the methodological assumptions underlying the movement of big data and artificial intelligence in medicine are rarely studied. This article outlines the methodological problems facing this movement. In particular, the following topics were considered: the theory of large data congestion, the limits of the algorithms action, and the phenomenology of the disease. These methodological issues demonstrate several important roles for these technologies that must be considered and studied before they are integrated into the healthcare system.
Journal Article
Resistance to Medical Artificial Intelligence
Artificial intelligence (AI) is revolutionizing healthcare, but little is known about consumer receptivity to AI in medicine. Consumers are reluctant to utilize healthcare provided by AI in real and hypothetical choices, separate and joint evaluations. Consumers are less likely to utilize healthcare (study 1), exhibit lower reservation prices for healthcare (study 2), are less sensitive to differences in provider performance (studies 3A–3C), and derive negative utility if a provider is automated rather than human (study 4). Uniqueness neglect, a concern that AI providers are less able than human providers to account for consumers’ unique characteristics and circumstances, drives consumer resistance to medical AI. Indeed, resistance to medical AI is stronger for consumers who perceive themselves to be more unique (study 5). Uniqueness neglect mediates resistance to medical AI (study 6), and is eliminated when AI provides care (a) that is framed as personalized (study 7), (b) to consumers other than the self (study 8), or (c) that only supports, rather than replaces, a decision made by a human healthcare provider (study 9). These findings make contributions to the psychology of automation and medical decision making, and suggest interventions to increase consumer acceptance of AI in medicine.
Journal Article
Stationary distribution Markov chain for Covid-19 pandemic
Coronavirus disease (Covid-19) is a new disease found in the late 2019. The first case was reported on December 31, 2019 in Wuhan, China and spreading all over the countries. The disease was quickly spread to all over the countries. There are 206,900 cases confirmed by March 18, 2020 causing 8,272 death. It was predicted that the number of confirmed cases will continue to increase. On January 30, 2020, World Health Organization (WHO) declared this as Public Health Emergency of International Concern (PHEIC). There are a lot of researchers which discuss pandemic spreading caused by virus with mathematical modelling. In this paper, we discuss a long-term prediction over the Covid-19 spreading using stationary distribution Markov chain. The aim of this paper is to analyze the prediction of infected people in long-term by analyzing the Covid-19 daily cases in an observation interval. By analyzing the daily cases of Covid-19 worldwide from December 31, 2019 until April 16, 2020, result shows that 61.43% of probability that the Covid-19 daily case will incline in long-term, 32.14% of chance will decline, and 6.43% of chance will stagnant.
Journal Article
Fully Printed PEDOT:PSS-based Temperature Sensor with High Humidity Stability for Wireless Healthcare Monitoring
Facile fabrication and high ambient stability are strongly desired for the practical application of temperautre sensor in real-time wearable healthcare. Herein, a fully printed flexible temperature sensor based on cross-linked poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) was developed. By introducing the crosslinker of (3-glycidyloxypropyl)trimethoxysilane (GOPS) and the fluorinated polymer passivation (CYTOP), significant enhancements in humidity stability and temperature sensitivity of PEDOT:PSS based film were achieved. The prepared sensor exhibited excellent stability in environmental humidity ranged from 30% RH to 80% RH, and high sensitivity of −0.77% °C
−1
for temperature sensing between 25 °C and 50 °C. Moreover, a wireless temperature sensing platform was obtained by integrating the printed sensor to a printed flexible hybrid circuit, which performed a stable real-time healthcare monitoring.
Journal Article
BEHRT: Transformer for Electronic Health Records
Today, despite decades of developments in medicine and the growing interest in precision healthcare, vast majority of diagnoses happen once patients begin to show noticeable signs of illness. Early indication and detection of diseases, however, can provide patients and carers with the chance of early intervention, better disease management, and efficient allocation of healthcare resources. The latest developments in machine learning (including deep learning) provides a great opportunity to address this unmet need. In this study, we introduce BEHRT: A deep neural sequence transduction model for electronic health records (EHR), capable of simultaneously predicting the likelihood of 301 conditions in one’s future visits. When trained and evaluated on the data from nearly 1.6 million individuals, BEHRT shows a striking improvement of 8.0–13.2% (in terms of average precision scores for different tasks), over the existing state-of-the-art deep EHR models. In addition to its scalability and superior accuracy, BEHRT enables personalised interpretation of its predictions; its flexible architecture enables it to incorporate multiple heterogeneous concepts (e.g., diagnosis, medication, measurements, and more) to further improve the accuracy of its predictions; its (pre-)training results in disease and patient representations can be useful for future studies (i.e., transfer learning).
Journal Article
SARS-CoV-2 vaccines in development
Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) was first reported in late 2019 in China and is the causative agent of the coronavirus disease 2019 (COVID-19) pandemic. To mitigate the effects of the virus on public health, the economy and society, a vaccine is urgently needed. Here I review the development of vaccines against SARS-CoV-2. Development was initiated when the genetic sequence of the virus became available in early January 2020, and has moved at an unprecedented speed: a phase I trial started in March 2020 and there are currently more than 180 vaccines at various stages of development. Data from phase I and phase II trials are already available for several vaccine candidates, and many have moved into phase III trials. The data available so far suggest that effective and safe vaccines might become available within months, rather than years.
The development of vaccines against SARS-CoV-2 is reviewed, including an overview of the development process, the different types of vaccine candidate, and data from animal studies as well as phase I and II clinical trials in humans.
Journal Article
The Bright and Dark Sides of Technostress
Today’s healthcare workers, specifically nurses, are experiencing technostress associated with the use of healthcare information technology (HIT). Technostress is often characterized by IS researchers as negative, or as being on the “dark side” of technology. However, a broader reading of the stress literature suggests that technostress may be both positive and negative, and can therefore have a “bright side” in addition to a dark side. The objective of this study is to conceptualize a holistic technostress process that includes positive and negative components of technostress embedded in two subprocesses: the techno-eustress subprocess and the techno-distress subprocess, respectively. The study instantiates this holistic technostress model through a sequential mixed-methods research design in the context of HIT. Phase 1 of the design is a qualitative, interpretive case study involving interviews with 32 nurses. Based on the findings from the case study, the paper builds a research model that operationalizes the concepts embedded in the holistic technostress model and identifies contextually relevant challenge and hindrance technostressors and outcomes. In Phase 2, the research model is empirically validated by analyzing survey data collected from 402 nurses employed in the United States. Results reveal that several challenge and hindrance technostressors are related to positive and negative psychological responses, respectively, and that such responses are related to job satisfaction and attrition, which impact turnover intention. Contributions to theory and practice are also discussed.
Journal Article
Emerging MDR-Pseudomonas aeruginosa in fish commonly harbor oprL and toxA virulence genes and blaTEM, blaCTX-M, and tetA antibiotic-resistance genes
This study aimed to investigate the prevalence, antibiogram of
Pseudomonas
aeruginosa
(
P.
aeruginosa
), and the distribution of virulence genes (
opr
L
,
exo
S,
phz
M, and
tox
A) and the antibiotic-resistance genes (
bla
T
EM
,
tet
A, and
bla
CTX-M
). A total of 285 fish (165
Oreochromis
niloticus
and 120
Clarias
gariepinus
) were collected randomly from private fish farms in Ismailia Governorate, Egypt. The collected specimens were examined bacteriologically.
P
.
aeruginosa
was isolated from 90 examined fish (31.57%), and the liver was the most prominent infected organ. The antibiogram of the isolated strains was determined using a disc diffusion method, where the tested strains exhibited multi-drug resistance (MDR) to amoxicillin, cefotaxime, tetracycline, and gentamicin. The PCR results revealed that all the examined strains harbored (
opr
L and
tox
A) virulence genes, while only 22.2% were positive for the
phz
M gene. On the contrary, none of the tested strains were positive for the
exo
S gene. Concerning the distribution of the antibiotic resistance genes, the examined strains harbored
bla
TEM
,
bla
CTX-M
,
and
tet
A genes with a total prevalence of 83.3%, 77.7%, and 75.6%, respectively. Experimentally infected fish with
P.
aeruginosa
displayed high mortalities in direct proportion to the encoded virulence genes and showed similar signs of septicemia found in the naturally infected one. In conclusion,
P.
aeruginosa
is a major pathogen of
O.
niloticus
and
C.
gariepinus.
opr
L and
tox
A genes are the most predominant virulence genes associated with
P.
aeruginosa
infection. The
bla
CTX
-M
,
bla
TEM
, and
tet
A genes are the main antibiotic-resistance genes that induce resistance patterns to cefotaxime, amoxicillin, and tetracycline, highlighting MDR
P.
aeruginosa
strains of potential public health concern.
Journal Article