Integrated Secure Health Domain System using IoT

Fog computing plays major role in health care system. Fog performs well compare with cloud computing. Health care system needs more enhancements because there sensitivity. It is very important to secure the data in the health care system. Though there are many systems for health care security still there are issues to secure the data transfer from client to the fog by cloud computing. In the previous papers, we have discussed QOS parameters and various preventions to transmission of data from sensors to fog. IOT is most widely used in health systems to increase the performance which adopted with fog computing. In this paper, the integrated secure health domain system (ISHDS) used to overcome the system failures and providing the security for the health care data in various situations.

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Implementing and Evaluating Health as a Service in Fog and Cloud Computing using Raspberry Pi

International Journal of Intelligent Engineering and Systems ◽

10.22266/ijies2020.1231.13 ◽

2020 ◽

Vol 13 (6) ◽

pp. 142-155

Author(s):

Pratik Kanani ◽

◽

Mamta Padole ◽

Keyword(s):

Health Care ◽

Decision Making ◽

Cloud Computing ◽

Health Care System ◽

Fog Computing ◽

Raspberry Pi ◽

Transmission Delays ◽

Network Delays ◽

Qos Parameters ◽

Care System

Internet of Things (IoT) generates a myriad amount of data, which is sent over the Cloud computing infrastructure for analytics and Business Intelligence. This application scenario suffers network delays, transmission delays and delays in decision making. Due to these drawbacks, the Cloud-based IoT infrastructure is not suitable for time-critical health care applications. To overcome this problem, a smart way is introduced called “Fog Computing” - a LAN based processing approach which has multiple advantages. When IoT, Fog and Cloud Computing are combined, the resultant system’s performance is far better. Hence, the combination results in a very efficient Health Care system. Fog and Cloud Computing have their dimensions that not only support each other but also explore many new application domains. In this paper, the real-time ElectroCardioGram (ECG) based Health Care system is implemented in Cloud and Fog Computing. Different Quality of Service (QoS) parameters like memory consumption, transmission delays, computation delays, network delays, Carbon dioxide emission, data transferred and response time are measured, analyzed and improved to make the system more efficient. Based on the Fog computing characteristics and capabilities, the Raspberry Pi 3 B+ model is configured as a Health Care serving gateway by using different installation and configuration steps. Initially, the proposed system is tested for one patients ECG data analysis over cloud and Fog. In every set up all QoS parameters are measured and later the system is subjected to multiple ECG streams for varying numbers of patients to find the limitations of the Raspberry Pi node as a Fog Computing node. The obtained results show that for more number of ECG streams the Fog node is not able maintain QoS in decision making time. Every QoS parameter is explored in detail for decision-making time. In the end, the Fog computing based proposed system is concluded for its pros and cons and future aspects of the Fog node are discussed to make better systems.

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Proposed Implementation of Blockchain in British Columbia’s Health Care Data Management (Preprint)

10.2196/preprints.20897 ◽

2020 ◽

Author(s):

Danielle Cadoret ◽

Tamara Kailas ◽

Pedro Velmovitsky ◽

Plinio Morita ◽

Okechukwu Igboeli

Keyword(s):

Health Care ◽

British Columbia ◽

Data Management ◽

Health Care System ◽

Information Flow ◽

Health Care Data ◽

Canadian Health ◽

Canadian Health Care ◽

Lag Period ◽

Care System

BACKGROUND There are several challenges such as information silos and lack of interoperability with the current electronic medical record (EMR) infrastructure in the Canadian health care system. These challenges can be alleviated by implementing a blockchain-based health care data management solution. OBJECTIVE This study aims to provide a detailed overview of the current health data management infrastructure in British Columbia for identifying some of the gaps and inefficiencies in the Canadian health care data management system. We explored whether blockchain is a viable option for bridging the existing gaps in EMR solutions in British Columbia’s health care system. METHODS We constructed the British Columbia health care data infrastructure and health information flow based on publicly available information and in partnership with an industry expert familiar with the health systems information technology network of British Columbia’s Provincial Health Services Authorities. Information flow gaps, inconsistencies, and inefficiencies were the target of our analyses. RESULTS We found that hospitals and clinics have several choices for managing electronic records of health care information, such as different EMR software or cloud-based data management, and that the system development, implementation, and operations for EMRs are carried out by the private sector. As of 2013, EMR adoption in British Columbia was at 80% across all hospitals and the process of entering medical information into EMR systems in British Columbia could have a lag of up to 1 month. During this lag period, disease progression updates are continually written on physical paper charts and not immediately updated in the system, creating a continuous lag period and increasing the probability of errors and disjointed notes. The current major stumbling block for health care data management is interoperability resulting from the use of a wide range of unique information systems by different health care facilities. CONCLUSIONS Our analysis of British Columbia’s health care data management revealed several challenges, including information silos, the potential for medical errors, the general unwillingness of parties within the health care system to trust and share data, and the potential for security breaches and operational issues in the current EMR infrastructure. A blockchain-based solution has the highest potential in solving most of the challenges in managing health care data in British Columbia and other Canadian provinces.

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Proposed Implementation of Blockchain in British Columbia’s Health Care Data Management

Journal of Medical Internet Research ◽

10.2196/20897 ◽

2020 ◽

Vol 22 (10) ◽

pp. e20897

Author(s):

Danielle Cadoret ◽

Tamara Kailas ◽

Pedro Velmovitsky ◽

Plinio Morita ◽

Okechukwu Igboeli

Keyword(s):

Health Care ◽

British Columbia ◽

Data Management ◽

Health Care System ◽

Information Flow ◽

Health Care Data ◽

Canadian Health ◽

Canadian Health Care ◽

Lag Period ◽

Care System

Background There are several challenges such as information silos and lack of interoperability with the current electronic medical record (EMR) infrastructure in the Canadian health care system. These challenges can be alleviated by implementing a blockchain-based health care data management solution. Objective This study aims to provide a detailed overview of the current health data management infrastructure in British Columbia for identifying some of the gaps and inefficiencies in the Canadian health care data management system. We explored whether blockchain is a viable option for bridging the existing gaps in EMR solutions in British Columbia’s health care system. Methods We constructed the British Columbia health care data infrastructure and health information flow based on publicly available information and in partnership with an industry expert familiar with the health systems information technology network of British Columbia’s Provincial Health Services Authorities. Information flow gaps, inconsistencies, and inefficiencies were the target of our analyses. Results We found that hospitals and clinics have several choices for managing electronic records of health care information, such as different EMR software or cloud-based data management, and that the system development, implementation, and operations for EMRs are carried out by the private sector. As of 2013, EMR adoption in British Columbia was at 80% across all hospitals and the process of entering medical information into EMR systems in British Columbia could have a lag of up to 1 month. During this lag period, disease progression updates are continually written on physical paper charts and not immediately updated in the system, creating a continuous lag period and increasing the probability of errors and disjointed notes. The current major stumbling block for health care data management is interoperability resulting from the use of a wide range of unique information systems by different health care facilities. Conclusions Our analysis of British Columbia’s health care data management revealed several challenges, including information silos, the potential for medical errors, the general unwillingness of parties within the health care system to trust and share data, and the potential for security breaches and operational issues in the current EMR infrastructure. A blockchain-based solution has the highest potential in solving most of the challenges in managing health care data in British Columbia and other Canadian provinces.

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Looking beyond Administrative Health Care Data: The Role of Socioeconomic Status in Predicting Future High-cost Patients with Mental Health and Addiction: Voir au-delà des données administratives des soins de santé : le rôle du statut socio-économique pour prédire les patients à coût élevé en santé mentale et dépendance

The Canadian Journal of Psychiatry ◽

10.1177/07067437211004882 ◽

2021 ◽

pp. 070674372110048

Author(s):

Claire de Oliveira ◽

Luke Mondor ◽

Walter P. Wodchis ◽

Laura C. Rosella

Keyword(s):

Mental Health ◽

Health Care ◽

Socioeconomic Status ◽

Mental Illness ◽

Health Care System ◽

Health Care Use ◽

Public Health Care ◽

Income Quintile ◽

Health Care Data ◽

Care System

Introduction: Previous research has shown that the socioeconomic status (SES)–health gradient also extends to high-cost patients; however, little work has examined high-cost patients with mental illness and/or addiction. The objective of this study was to examine associations between individual-, household- and area-level SES factors and future high-cost use among these patients. Methods: We linked survey data from adult participants (ages 18 and older) of 3 cycles of the Canadian Community Health Survey to administrative health care data from Ontario, Canada. Respondents with mental illness and/or addiction were identified based on prior mental health and addiction health care use and followed for 5 years for which we ascertained health care costs covered under the public health care system. We quantified associations between SES factors and becoming a high-cost patient (i.e., transitioning into the top 5%) using logistic regression models. For ordinal SES factors, such as income, education and marginalization variables, we measured absolute and relative inequalities using the slope and relative index of inequality. Results: Among our sample, lower personal income (odds ratio [ OR] = 2.11, 95% confidence interval [CI], 1.54 to 2.88, for CAD$0 to CAD$14,999), lower household income ( OR = 2.11, 95% CI, 1.49 to 2.99, for lowest income quintile), food insecurity ( OR = 1.87, 95% CI, 1.38 to 2.55) and non-homeownership ( OR = 1.34, 95% CI, 1.08 to 1.66), at the individual and household levels, respectively, and higher residential instability (OR = 1.72, 95% CI, 1.23 to 2.42, for most marginalized), at the area level, were associated with higher odds of becoming a high-cost patient within a 5-year period. Moreover, the inequality analysis suggested pro-high-SES gradients in high-cost transitions. Conclusions: Policies aimed at high-cost patients with mental illness and/or addiction, or those concerned with preventing individuals with these conditions from becoming high-cost patients in the health care system, should also consider non-clinical factors such as income as well as related dimensions including food security and homeownership.

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