A security framework for e-Health service authentication and e-Health data transmission

2009 ◽  
Author(s):  
Apaporn Boonyarattaphan ◽  
Yan Bai ◽  
Sam Chung
Keyword(s):  
Health Service ◽  
Data Transmission ◽  
Health Data ◽  
Security Framework

scholarly journals Using Bayesian Model Averaging to Analyse Hierarchical Health Data: model implementation and application to linked health service use data

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Behrooz Hassani-Mahmooei ◽  
Janneke Berecki-Gisolf ◽  
Alex Collie
Keyword(s):  
Mental Health ◽  
Health Service ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Medical Costs ◽  
Health Data ◽  
Post Injury ◽  
The Cost

ABSTRACTObjectiveThe majority of standard coding systems applied to health data are hierarchical: they start with several major categories and then each category is broken into subcategories across multiple levels. Running statistical models on these datasets, may lead to serious methodological challenges such as multicollinearity between levels or selecting suboptimal models as model space grows exponentially by adding each new level. The aim of this presentation is to introduce an analytical framework that addresses this challenge. ApproachData was from individuals who claimed Transport Accident Commission (TAC) compensation for motor vehicle accidents that occurred between 2010 and 2012 in the state of Victoria, Australia and provided consent for Pharmaceutical Benefits Scheme (PBS) and Medicare Benefits Schedule (MBS) linkage (n=738). PBS and MBS records dating from 12 months prior to injury were provided by the Department of Human Services (Canberra, Australia). Pre-injury use of health service items and pharmaceuticals were considered to indicate pre-existing health conditions. Both MBS and PBS listings have a hierarchical structure. The outcome was the cost of recovery; this was also hierarchical across four level (e.g. total, medical, consultations, and specialist). A Bayesian Model Averaging model was embedded into a data mining framework which automatically created all the cost outcomes and selected the best model after penalizing for multicollinearity. The model was run across multiple prior settings to ensure robustness. Monash University’s High Performance Computing Cluster was used for running approximately 5000 final models.ResultsThe framework successfully identified variables at different levels of hierarchy as indicators of pre-existing conditions that affect cost of recovery. For example, according to the results, on average, patients who received prescription pain or mental health related medication before the injury had 31.2% higher short-term and 36.9% higher long-term total recovery cost. For every anaesthetic in the year before the accident, post-injury hospital cost increased by 24%, for patients with anxiety it increased by 35.4%. For post-injury medical costs, every prescription of drugs used in diabetes (Category A10 in ATC) increased the cost by 8%, long term medical costs were affected by both pain and mental health. ConclusionBayesian model averaging provides a robust framework for mining hierarchically linked health data helping researchers to identify potential associations which may not have been discovered using conventional technique and also preventing them from identifying associations that are sporadic but not robust.

An Enhanced and Secure Cloud Infrastructure for e-Health Data Transmission

2019 ◽  
Author(s):  
Vasileios A. Memos ◽  
Kostas E. Psannis ◽  
Sotirios K. Goudos ◽  
Sofoklis Kyriazakos
Keyword(s):  
Data Transmission ◽  
Health Data ◽  
Cloud Infrastructure

scholarly journals Genomic health data generation in the UK: a 360 view

2021 ◽  
Author(s):  
Elizabeth Ormondroyd ◽  
Peter Border ◽  
Judith Hayward ◽  
Andrew Papanikitas
Keyword(s):  
Health Service ◽  
Large Scale ◽  
Social Issues ◽  
Genomic Medicine ◽  
Health Data ◽  
Clinical Indication ◽  
Data Generation ◽  
Future Health ◽  
Genomic Test ◽  
The Uk

AbstractIn the UK, genomic health data is being generated in three major contexts: the healthcare system (based on clinical indication), in large scale research programmes, and for purchasers of direct-to-consumer genetic tests. The recently delivered hybrid clinical/research programme, 100,000 Genomes Project set the scene for a new Genomic Medicine Service, through which the National Health Service aims to deliver consistent and equitable care informed by genomics, while providing data to inform academic and industry research and development. In parallel, a large scale research study, Our Future Health, has UK Government and Industry investment and aims to recruit 5 million volunteers to support research intended to improve early detection, risk stratification, and early intervention for chronic diseases. To explore how current models of genomic health data generation intersect, and to understand clinical, ethical, legal, policy and social issues arising from this intersection, we conducted a series of five multidisciplinary panel discussions attended by 28 invited stakeholders. Meetings were recorded and transcribed. We present a summary of issues identified: genomic test attributes; reasons for generating genomic health data; individuals’ motivation to seek genomic data; health service impacts; role of genetic counseling; equity; data uses and security; consent; governance and regulation. We conclude with some suggestions for policy consideration.

scholarly journals A Regional Collaboration of Health (ARCH): Using health survey and linked routine data to understand wellbeing

2018 ◽  
Vol 3 (4) ◽  
Author(s):  
Fatemeh Torabi ◽  
Ashley Akbari ◽  
Jane Lyons ◽  
Mathilde Castagnet ◽  
Ronan Lyons
Keyword(s):  
Health Service ◽  
Health Survey ◽  
Health Data ◽  
Health Service Utilisation ◽  
Service Utilisation ◽  
Care Hospital ◽  
Regional Collaboration ◽  
Primary Care Hospital ◽  
Personal Wellbeing ◽  
Reported Health

IntroductionMonitoring social wellbeing and its relationship to health service utilisation by means of appropriate measurement tools can potentially provide a complementary view for influencing service development. Aspects of wellbeing have been collected in the Welsh Health Survey (WHS) while routine health data captures health service utilisation. Objectives and ApproachWHS was used to link self-reported wellbeing to health outcomes, prior to linking to routinely collected data. Initially, a measure for personal wellbeing was developed using the four personal wellbeing questions defined by The Office of National Statistics (ONS), included in national surveys from 2011 onward. We conducted regression analysis to identify potential predictors of personal wellbeing scores our model included self-reported lifestyle behaviour, self-reported health, education, work, household and general demographics. Links to primary care, hospital and emergency department datasets are being developed to provide insight into the relationship between wellbeing, multi-morbidity and health service utilisation. ResultsFour wellbeing questions had similar scoring patterns across age groups which is different to most health indicators that tend to show a marked health decline with increasing age. There is a difference between mean wellbeing score for males and females. Our finding showed that self-reported of ‘excellent’ or ‘very good’ general health has the largest positive effect on wellbeing while positive viewpoint on self-health has the second largest effect on our model. In addition, being retired from a paid job, eating at least one or 5+ portion of fruit and vegetables and giving up smoking have positive impact on population wellbeing. In contrast, not being able to work, intermediate household occupancy, consuming alcohol in last 12-months, having long-standing illness, showed a negative impact on wellbeing. Conclusion/ImplicationsThis project established robust methodology on utilizing survey and routine health data for monitoring and evaluation purposes. We also evaluated the linkability of survey data The latest release of National Survey for Wales (NSW) will cover a combination of self-reported health measures and aims for a higher linkage consent rate.

scholarly journals SOCIAL ISOLATION AND LONELINESS: A LATENT CLASS APPROACH TO COSTING HEALTH SERVICE ENGAGEMENT IN AGING POPULATIONS

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S535-S535
Author(s):  
Elaine Douglas ◽  
David Bell
Keyword(s):  
Health Service ◽  
Social Isolation ◽  
Latent Class ◽  
Health Data ◽  
Service Usage ◽  
Population Groups ◽  
Administrative Health Data ◽  
Service Engagement ◽  
Health Service Usage ◽  
The Impact

Abstract Social isolation and loneliness are associated with poorer health status and poorer health outcomes. Little is known the impact on health service usage, and its inherent cost, although it is considered to be higher. Latent class analysis (LCA) was used to determine profiles (population groups) of loneliness and social isolation in older people (aged 50+, n=1,057) using model-fit criteria. Loneliness was measured using the UCLA Loneliness Scale and social isolation used a measure of social networks and social contact. We then analysed the socio-demographic, perceived health, and health behaviour of these profiles using descriptive statistics and logistic regression. The survey data (HAGIS, 2016/17) were linked to retrospective administrative health data to investigate patterns of repeat prescription use (from 2009) and health service usage (from 2005) and their associated costs. Our results highlight the distinction and inter-relation between social isolation and loneliness (including associations with socio-demographic and health characteristics), and the variation in health service usage and costs between the population groups. LCA profiles may help focussed targeting of these groups for health interventions. Further, the data-driven approach of LCA may overcome some of the limitations of indices of social isolation and loneliness. As such, this will extend the existing methodological approaches to quantitative analyses of social isolation and loneliness and demonstrate the benefits of using linked administrative health data. Significantly, this study incorporates the social and financial cost of social isolation and loneliness on health and its implications for health services.

COVID-19 Pandemic and Indigenous Representation in Public Health Data

2021 ◽  
Vol 111 (S3) ◽  
pp. S208-S214
Author(s):  
Kimberly R. Huyser ◽  
Aggie J. Yellow Horse ◽  
Alena A. Kuhlemeier ◽  
Michelle R. Huyser
Keyword(s):  
Public Health ◽  
Health Service ◽  
Alaska Native ◽  
Health Data ◽  
Indian Health Service ◽  
Data Set ◽  
Indian Health ◽  
Public Health Data ◽  
Indigenous Representation ◽  
Tribal Governments

Public Health 3.0 calls for the inclusion of new partners and novel data to bring systemic change to the US public health landscape. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has illuminated significant data gaps influenced by ongoing colonial legacies of racism and erasure. American Indian and Alaska Native (AI/AN) populations and communities have been disproportionately affected by incomplete public health data and by the COVID-19 pandemic itself. Our findings indicate that only 26 US states were able to calculate COVID-19‒related death rates for AI/AN populations. Given that 37 states have Indian Health Service locations, we argue that public health researchers and practitioners should have a far larger data set of aggregated public health information on AI/AN populations. Despite enormous obstacles, local Tribal facilities have created effective community responses to COVID-19 testing, tracking, and vaccine administration. Their knowledge can lead the way to a healthier nation. Federal and state governments and health agencies must learn to responsibly support Tribal efforts, collect data from AI/AN persons in partnership with Indian Health Service and Tribal governments, and communicate effectively with Tribal authorities to ensure Indigenous data sovereignty. (Am J Public Health. 2021;111(S3): S208–S214. https://doi.org/10.2105/AJPH.2021.306415 )

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