scholarly journals National Swiss Personalized Health Network: A Semantic-Driven Three Pillars Strategy to Enable Health Data secondary usage Interoperability for Research (Preprint)

10.2196/27591 ◽  
2021 ◽  
Author(s):  
Christophe Gaudet-Blavignac ◽  
Jean Louis Raisaro ◽  
Vasundra Touré ◽  
Sabine Österle ◽  
Katrin Crameri ◽  
...  
Keyword(s):  
Health Data ◽  
Health Network ◽  
Secondary Usage ◽  
Personalized Health

scholarly journals Designing a personalized health dashboard: an interdisciplinary and participatory approach. (Preprint)

2020 ◽  
Author(s):  
Miriam Weijers ◽  
Caroline Bastiaenen ◽  
Frans Feron ◽  
Kay Schröder
Keyword(s):  
Health Care ◽  
Developmental Process ◽  
Health Data ◽  
Design Model ◽  
Children's Health ◽  
Children’S Health ◽  
Health Determinants ◽  
Nested Design ◽  
Entry Points ◽  
Personalized Health

BACKGROUND Within Dutch Child Health Care (CHC), a 360⁰CHILD-profile is designed to enhance prevention and transformation towards Personalized Health Care. From a personalized preventive perspective, it is of fundamental importance to timely identify children with emerging health problems interrelated to multiple health determinants. While digitalization of children’s health data is now realized, the accessibility of data is a major challenge for CHC-professionals, let alone for parents/youth. Therefore, the idea was initiated from CHC-practice to develop a novel approach to make relevant information accessible at a glance. OBJECTIVE This paper describes the stepwise development of a dashboard, as an example of using a design model to achieve visualization of a comprehensive overview of theoretically structured health data. METHODS Developmental process is based on the nested design model with involvement of relevant stakeholders in a real-life context. This model considers immediate upstream validation within four cascading design levels: Domain Problem and Data Characterization, Operation and Data Type Abstraction, Visual Encoding and Interaction Design, Algorithm Design. This model also includes impact oriented downstream validation, which can be initiated after delivering the prototype. RESULTS A comprehensible 360°CHILD-profile is developed: an online accessible visualization of CHC-data based on the theoretical concept of the International Classification of Functioning, Disability and Health. This dashboard provides caregivers and parents/youth with a holistic view on children’s health and “entry points” for preventive, individualized health plans. CONCLUSIONS Describing this developmental process offers guidance on how to utilize the nested design model within a health care context. CLINICALTRIAL

scholarly journals DCC Terminology Service—An Automated CI/CD Pipeline for Converting Clinical and Biomedical Terminologies in Graph Format for the Swiss Personalized Health Network

2021 ◽  
Vol 11 (23) ◽  
pp. 11311
Author(s):  
Philip Krauss ◽  
Vasundra Touré ◽  
Kristin Gnodtke ◽  
Katrin Crameri ◽  
Sabine Österle
Keyword(s):  
Semantic Web ◽  
Security Requirements ◽  
Snomed Ct ◽  
Service Architecture ◽  
Health Network ◽  
Related Data ◽  
Semantic Web Technology ◽  
Icd 10 ◽  
Health Related ◽  
Personalized Health

One goal of the Swiss Personalized Health Network (SPHN) is to provide an infrastructure for FAIR (Findable, Accessible, Interoperable and Reusable) health-related data for research purposes. Semantic web technology and biomedical terminologies are key to achieving semantic interoperability. To enable the integrative use of different terminologies, a terminology service is a important component of the SPHN Infrastructure for FAIR data. It provides both the current and historical versions of the terminologies in an SPHN-compliant graph format. To minimize the usually high maintenance effort of a terminology service, we developed an automated CI/CD pipeline for converting clinical and biomedical terminologies in an SPHN-compatible way. Hospitals, research infrastructure providers, as well as any other data providers, can download a terminology bundle (currently composed of SNOMED CT, LOINC, UCUM, ATC, ICD-10-GM, and CHOP) and deploy it in their local terminology service. The distributed service architecture allows each party to fulfill their local IT and security requirements, while still having an up-to-date interoperable stack of SPHN-compliant terminologies. In the future, more terminologies and mappings will be added to the terminology service according to the needs of the SPHN community.

scholarly journals Research on the Secure Medical Big Data Ecosystem Based on Hadoop

2020 ◽  
Author(s):  
Xiangfeng Zhang ◽  
Yanmei Wang
Keyword(s):  
Information System ◽  
Big Data ◽  
Health Information System ◽  
Health Data ◽  
Personal Health ◽  
Medical Institutions ◽  
Data Platform ◽  
Data Ecosystem ◽  
Medical Big Data ◽  
Personalized Health

Abstract This paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. Since traditional healing activities take place in medical institutions, patient users must travel to these institutions to learn about their treatment status. The personalized health information system designed for this purpose enables patient users to understand their treatment and rehabilitation status anytime and anywhere. The above is a consideration from the perspective of the patient user, from the perspective of personal health data. Because traditional medical health data is scattered throughout different independent medical institutions, and these databases are heterogeneous. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. The characteristics of blockchain without a central server make the system without a single point In case of failure, the stability of the system is well maintained. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data is stored and analysed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

scholarly journals Research on intelligent medical big data system based on Hadoop and blockchain

2021 ◽  
Vol 2021 (1) ◽  
Author(s):  
Xiangfeng Zhang ◽  
Yanmei Wang
Keyword(s):  
Information System ◽  
Big Data ◽  
Health Information ◽  
Health Information System ◽  
Health Data ◽  
Medical Rehabilitation ◽  
Data Platform ◽  
Data Ecosystem ◽  
Medical Big Data ◽  
Personalized Health

AbstractIn order to improve the intelligence of the medical system, this paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. In order to improve the efficiency of traditional medical rehabilitation activities and enable patients to maximize their understanding of their treatment status, this paper designs a personalized health information system that allows patient users to understand their treatment and rehabilitation status anytime and anywhere, and all medical health data distributed in different independent medical institutions to ensure that these data are stored independently. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data are stored and analyzed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

scholarly journals Research on intelligent medical big data system based on Hadoop and blockchain

2020 ◽  
Author(s):  
Xiangfeng Zhang ◽  
Yanmei Wang
Keyword(s):  
Information System ◽  
Big Data ◽  
Health Information ◽  
Health Information System ◽  
Health Data ◽  
Medical Rehabilitation ◽  
Data Platform ◽  
Data Ecosystem ◽  
Medical Big Data ◽  
Personalized Health

Abstract In order to improve the intelligence of the medical system, this paper designs and implements a secure medical big data ecosystem on top of the Hadoop big data platform. It is designed against the background of the increasingly serious trend of the current security medical big data ecosystem. In order to improve the efficiency of traditional medical rehabilitation activities and enable patients to maximize their understanding of their treatment status, this paper designs a personalized health information system that allows patient users to understand their treatment and rehabilitation status anytime and anywhere, and all medical health data Distributed in different independent medical institutions to ensure that these data are stored independently. As a distributed accounting technology for multi-party maintenance and backup information security, blockchain is a good breakthrough point for innovation in medical data sharing. In this paper, the system realizes the personal health data centre on the Hadoop big data platform, and the original distributed data is stored and analysed centrally through the data synchronization module and the independent data acquisition system. Utilizing the advantages of the Hadoop big data platform, the personalized health information system for stroke has designed to provide personalized health management services for patients and facilitate the management of patients by medical staff.

scholarly journals Reporting Genetic Findings to Individual Research Participants: Guidelines From the Swiss Personalized Health Network

2020 ◽  
Vol 11 ◽  
Author(s):  
Alessandro Blasimme ◽  
Caroline Brall ◽  
Effy Vayena
Keyword(s):  
Expert Panel ◽  
Private Insurance ◽  
Genetic Research ◽  
Advisory Group ◽  
University Hospitals ◽  
Data Infrastructure ◽  
Health Network ◽  
Research Participants ◽  
Research Findings ◽  
Personalized Health

In 2017 the Swiss federal government established the Swiss Personalized Health Network (SPHN), a nationally coordinated data infrastructure for genetic research. The SPHN advisory group on Ethical, Legal, and Social Implications (ELSI) was tasked with the creation of a recommendation to ensure ethically responsible reporting of genetic research findings to research participants in SPHN-funded studies. Following consultations with expert stakeholders, including geneticists, pediatricians, sociologists, university hospitals directors, patient representatives, consumer protection associations, and insurers, the ELSI advisory group issued its recommendation on “Reporting actionable genetic findings to research participants” in May 2020. In this paper we outline the development of this recommendation and the provisions it contains. In particular, we discuss some of its key features, namely: (1) that participation in SPHN-funded studies as a research subject is conditional to accepting that medically relevant genetic research findings will be reported; (2) that a Multidisciplinary Expert Panel (MEP) should be created to support researchers’ decision-making processes about reporting individual genetic research findings; (3) that such Multidisciplinary Expert Panel will make case-by-case decisions about whether to allow reporting of genetic findings, instead of relying on a pre-defined list of medically relevant variants; (4) that research participants shall be informed of the need to disclose genetic mutations when applying for private insurance, which may influence individual decisions about participation in research. By providing an account of the procedural background and considerations leading to the SPHN recommendation on “Reporting actionable genetic findings to research participants,” we seek to promote a better understanding of the proposed guidance, as well as to contribute to the global dialog on the reporting of genetic research findings.

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