scholarly journals Sharing health data through hybrid cloud for self-management

2015 ◽  
Author(s):  
Yan Hu ◽  
Cong Peng ◽  
Guohua Bai
Keyword(s):  
Health Data ◽  
Self Management ◽  
Hybrid Cloud

PinCloud: Effective Personalization and Self-management of Health Data and Care Services

2015 ◽  
pp. 707-710
Author(s):  
Alexandros Mourouzis ◽  
Marinos Themistocleous ◽  
Ilias Maglogiannis ◽  
Ioanna Chouvarda ◽  
Nikos Maglaveras
Keyword(s):  
Health Data ◽  
Self Management ◽  
Care Services

scholarly journals Secure Record Linkage of Large Health Data Sets: Evaluation of a Hybrid Cloud Model

10.2196/18920 ◽  
2020 ◽  
Vol 8 (9) ◽  
pp. e18920
Author(s):  
Adrian Paul Brown ◽  
Sean M Randall
Keyword(s):  
Cloud Computing ◽  
Record Linkage ◽  
Social Issues ◽  
Cloud Model ◽  
Synthetic Data ◽  
Health Data ◽  
Data Sets ◽  
Hybrid Cloud ◽  
Cloud Infrastructure ◽  
Amazon Web Services

Background The linking of administrative data across agencies provides the capability to investigate many health and social issues with the potential to deliver significant public benefit. Despite its advantages, the use of cloud computing resources for linkage purposes is scarce, with the storage of identifiable information on cloud infrastructure assessed as high risk by data custodians. Objective This study aims to present a model for record linkage that utilizes cloud computing capabilities while assuring custodians that identifiable data sets remain secure and local. Methods A new hybrid cloud model was developed, including privacy-preserving record linkage techniques and container-based batch processing. An evaluation of this model was conducted with a prototype implementation using large synthetic data sets representative of administrative health data. Results The cloud model kept identifiers on premises and uses privacy-preserved identifiers to run all linkage computations on cloud infrastructure. Our prototype used a managed container cluster in Amazon Web Services to distribute the computation using existing linkage software. Although the cost of computation was relatively low, the use of existing software resulted in an overhead of processing of 35.7% (149/417 min execution time). Conclusions The result of our experimental evaluation shows the operational feasibility of such a model and the exciting opportunities for advancing the analysis of linkage outputs.

scholarly journals Secure Record Linkage of Large Health Data Sets: Evaluation of a Hybrid Cloud Model (Preprint)

2020 ◽  
Author(s):  
Adrian Paul Brown ◽  
Sean M Randall
Keyword(s):  
Cloud Computing ◽  
Record Linkage ◽  
Social Issues ◽  
Cloud Model ◽  
Synthetic Data ◽  
Health Data ◽  
Data Sets ◽  
Hybrid Cloud ◽  
Cloud Infrastructure ◽  
Amazon Web Services

BACKGROUND The linking of administrative data across agencies provides the capability to investigate many health and social issues with the potential to deliver significant public benefit. Despite its advantages, the use of cloud computing resources for linkage purposes is scarce, with the storage of identifiable information on cloud infrastructure assessed as high risk by data custodians. OBJECTIVE This study aims to present a model for record linkage that utilizes cloud computing capabilities while assuring custodians that identifiable data sets remain secure and local. METHODS A new hybrid cloud model was developed, including privacy-preserving record linkage techniques and container-based batch processing. An evaluation of this model was conducted with a prototype implementation using large synthetic data sets representative of administrative health data. RESULTS The cloud model kept identifiers on premises and uses privacy-preserved identifiers to run all linkage computations on cloud infrastructure. Our prototype used a managed container cluster in Amazon Web Services to distribute the computation using existing linkage software. Although the cost of computation was relatively low, the use of existing software resulted in an overhead of processing of 35.7% (149/417 min execution time). CONCLUSIONS The result of our experimental evaluation shows the operational feasibility of such a model and the exciting opportunities for advancing the analysis of linkage outputs.

scholarly journals A Mobile App to Support Self-management of Chronic Kidney Disease: Development Study

10.2196/29197 ◽  
2021 ◽  
Vol 8 (4) ◽  
pp. e29197
Author(s):  
Talar W Markossian ◽  
Jason Boyda ◽  
Jennifer Taylor ◽  
Bella Etingen ◽  
François Modave ◽  
...  
Keyword(s):  
Chronic Kidney Disease ◽  
Kidney Disease ◽  
Focus Group ◽  
Primary Care Physicians ◽  
Self Care ◽  
Health Data ◽  
Mobile App ◽  
Self Management ◽  
Smart Device ◽  
Care Providers

Background Chronic kidney disease (CKD) is a common and costly condition that is usually accompanied by multiple comorbidities including type 2 diabetes, hypertension, and obesity. Proper management of CKD can delay or prevent kidney failure and help mitigate cardiovascular disease risk, which increases as kidney function declines. Smart device apps hold potential to enhance patient self-management of chronic conditions including CKD. Objective The objective of this study was to develop a mobile app to facilitate self-management of nondialysis-dependent CKD. Methods Our stakeholder team included 4 patients with stage 3-4 nondialysis-dependent CKD; a kidney transplant recipient; a caretaker; CKD care providers (pharmacists, a nurse, primary care physicians, a nephrologist, and a cardiologist); 2 health services and CKD researchers; a researcher in biomedical informatics, nutrition, and obesity; a system developer; and 2 programmers. Focus groups and in-person interviews with the patients and providers were conducted using a focus group and interview guide based on existing literature on CKD self-management and the mobile app quality criteria from the Mobile App Rating Scale. Qualitative analytic methods including the constant comparative method were used to analyze the focus group and interview data. Results Patients and providers identified and discussed a list of requirements and preferences regarding the content, features, and technical aspects of the mobile app, which are unique for CKD self-management. Requirements and preferences centered along themes of communication between patients and caregivers, partnership in care, self-care activities, adherence to treatment regimens, and self-care self-efficacy. These identified themes informed the features and content of our mobile app. The mobile app user can enter health data including blood pressure, weight, and blood glucose levels. Symptoms and their severity can also be entered, and users are prompted to contact a physician as indicated by the symptom and its severity. Next, mobile app users can select biweekly goals from a set of predetermined goals with the option to enter customized goals. The user can also keep a list of medications and track medication use. Our app includes feedback mechanisms where in-range values for health data are depicted in green and out-of-range values are depicted in red. We ensured that data entered by patients could be downloaded into a user-friendly report, which could be emailed or uploaded to an electronic health record. The mobile app also includes a mechanism that allows either group or individualized video chat meetings with a provider to facilitate either group support, education, or even virtual clinic visits. The CKD app also includes educational material on CKD and its symptoms. Conclusions Patients with CKD and CKD care providers believe that a mobile app can enhance CKD self-management by facilitating patient-provider communication and enabling self-care activities including treatment adherence.

scholarly journals Using Patient-Generated Health Data From Mobile Technologies for Diabetes Self-Management Support

2014 ◽  
Vol 8 (1) ◽  
pp. 74-82 ◽  
Author(s):  
Shantanu Nundy ◽  
Chen-Yuan E. Lu ◽  
Patrick Hogan ◽  
Anjuli Mishra ◽  
Monica E. Peek
Keyword(s):  
Health Data ◽  
Mobile Technologies ◽  
Self Management ◽  
Management Support

scholarly journals Meaningful Integration of Data from Heterogeneous Health Services and Home Environment Based on Ontology

Sensors ◽  
2019 ◽  
Vol 19 (8) ◽  
pp. 1747 ◽  
Author(s):  
Cong Peng ◽  
Prashant Goswami
Keyword(s):  
Home Environment ◽  
Open Data ◽  
Health Data ◽  
Self Management ◽  
Formal Ontology ◽  
Data Interoperability ◽  
Semantic Web Technologies ◽  
Ontology Language ◽  
Integration Architecture ◽  
Hl7 Fhir

The development of electronic health records, wearable devices, health applications and Internet of Things (IoT)-empowered smart homes is promoting various applications. It also makes health self-management much more feasible, which can partially mitigate one of the challenges that the current healthcare system is facing. Effective and convenient self-management of health requires the collaborative use of health data and home environment data from different services, devices, and even open data on the Web. Although health data interoperability standards including HL7 Fast Healthcare Interoperability Resources (FHIR) and IoT ontology including Semantic Sensor Network (SSN) have been developed and promoted, it is impossible for all the different categories of services to adopt the same standard in the near future. This study presents a method that applies Semantic Web technologies to integrate the health data and home environment data from heterogeneously built services and devices. We propose a Web Ontology Language (OWL)-based integration ontology that models health data from HL7 FHIR standard implemented services, normal Web services and Web of Things (WoT) services and Linked Data together with home environment data from formal ontology-described WoT services. It works on the resource integration layer of the layered integration architecture. An example use case with a prototype implementation shows that the proposed method successfully integrates the health data and home environment data into a resource graph. The integrated data are annotated with semantics and ontological links, which make them machine-understandable and cross-system reusable.

Using Patient Avatars to Promote Health Data Sharing Applications: Perspectives and Regulatory Challenges

2016 ◽  
Vol 23 (2) ◽  
pp. 175-194
Author(s):  
Alan Dahi ◽  
Nikolaus Forgó ◽  
Sarah Jensen ◽  
Marc Stauch
Keyword(s):  
Data Sharing ◽  
Economic Benefits ◽  
Health Data ◽  
Self Management ◽  
Healthcare Provision ◽  
Data Breaches ◽  
Benefits And Costs ◽  
Regulatory Challenges

The potential of ict to address problems in modern healthcare is considerable, and an ict-driven revolution in healthcare appears imminent. Such developments may be viewed largely in positive terms. Thus they should result in enhanced treatment and care options, empowering patients — including by permitting greater self-management of illness outside hospital, while offering economic benefits and costs savings over traditional healthcare provision. However, the new possibilities also present manifold risks, such as of data breaches, encroachments on subject autonomy, as well as of other harms. This article considers some of the key regulatory challenges against the background of the progress of the current eu Commission-sponsored ‘MyHealthAvatar’ project.

Sign in / Sign up

Export Citation Format

Share Document