Georgia Correctional Health Data Sharing Project

2011 ◽  
Author(s):  
Michelle Staples-Horne
Keyword(s):  
Data Sharing ◽  
Health Data ◽  
Correctional Health

scholarly journals Community Attitudes Towards Sharing Government Health Data with Private Companies: A Scoping Review

2020 ◽  
Vol 5 (5) ◽  
Author(s):  
Jackie Street ◽  
Belinda Fabrianesi ◽  
Rebecca Bosward ◽  
Stacy Carter ◽  
Annette Braunack-Mayer
Keyword(s):  
Data Sharing ◽  
Health Care Providers ◽  
Scoping Review ◽  
Public Attitudes ◽  
Health Data ◽  
Public Confidence ◽  
Care Providers ◽  
Private Industry ◽  
Good Communication ◽  
Government Health

IntroductionLarge volumes of health data are generated through the interaction of individuals with hospitals, government agencies and health care providers. There is potential in the linkage and sharing of administrative data with private industry to support improved drug and device provision but data sharing is highly contentious. Objectives and ApproachWe conducted a scoping review of quantitative and qualitative studies examining public attitudes towards the sharing of health data, held by government, with private industry for research and development. We searched four data bases, PubMed, Scopus, Cinahl and Web of Science as well as Google Scholar and Google Advanced. The search was confined to English-only publications since January 2014 but was not geographically limited. We thematically coded included papers. ResultsWe screened 6788 articles. Thirty-six studies were included primarily from UK and North America. No Australian studies were identified. Across studies, willingness to share non-identified data was generally high with the participant’s own health provider (84-91%) and academic researchers (64-93%) but fell if the data was to be shared with private industry (14-53%). There was widespread misunderstanding of the benefits of sharing data for health research. Publics expressed concern about a range of issues including data security, misuse of data and use of data to generate profit. Conditions which would increase public confidence in sharing of data included: strict safeguards on data collection and use including secure storage, opt-in or opt-out consent mechanisms, and good communication through trusted agents. Conclusion / ImplicationsWe identified a research gap: Australian views on sharing government health data with private industry. The international experience suggests that public scepticism about data sharing with private industry will need to be addressed by good communication about public benefit of data sharing, a strong program of public engagement and information sharing conducted through trusted entities.

scholarly journals The French Health Data Hub and the German Medical Informatics Initiatives: Two National Projects to Promote Data Sharing in Healthcare

2019 ◽  
Vol 28 (01) ◽  
pp. 195-202 ◽  
Author(s):  
Marc Cuggia ◽  
Stéphanie Combes
Keyword(s):  
Data Sharing ◽  
Medical Informatics ◽  
Data Privacy ◽  
Data Use ◽  
Health Data ◽  
The European Union ◽  
Global Approach ◽  
Data Governance ◽  
Academic Hospitals ◽  
Access Data

Objective: The diversity and volume of health data have been rapidly increasing in recent years. While such big data hold significant promise for accelerating discovery, data use entails many challenges including the need for adequate computational infrastructure and secure processes for data sharing and access. In Europe, two nationwide projects have been launched recently to support these objectives. This paper compares the French Health Data Hub initiative (HDH) to the German Medical Informatics Initiatives (MII). Method: We analysed the projects according to the following criteria: (i) Global approach and ambitions, (ii) Use cases, (iii) Governance and organization, (iv) Technical aspects and interoperability, and (v) Data privacy access/data governance. Results: The French and German projects share the same objectives but are different in terms of methodologies. The HDH project is based on a top-down approach and focuses on a shared computational infrastructure, providing tools and services to speed projects between data producers and data users. The MII project is based on a bottom-up approach and relies on four consortia including academic hospitals, universities, and private partners. Conclusion: Both projects could benefit from each other. A Franco-German cooperation, extended to other countries of the European Union with similar initiatives, should allow sharing and strengthening efforts in a strategic area where competition from other countries has increased.

scholarly journals Mental health professionals’ perceptions on patients control of data sharing

2020 ◽  
Vol 26 (3) ◽  
pp. 2011-2029 ◽  
Author(s):  
Julia Ivanova ◽  
Adela Grando ◽  
Anita Murcko ◽  
Michael Saks ◽  
Mary Jo Whitfield ◽  
...  
Keyword(s):  
Mental Health ◽  
Health Information ◽  
Data Sharing ◽  
Mental Health Professionals ◽  
Health Professionals ◽  
Health Care Professionals ◽  
Health Data ◽  
Structured Interviews ◽  
Timely Access ◽  
The Impact

Integrated mental and physical care environments require data sharing, but little is known about health professionals’ perceptions of patient-controlled health data sharing. We describe mental health professionals’ views on patient-controlled data sharing using semi-structured interviews and a mixed-method analysis with thematic coding. Health information rights, specifically those of patients and health care professionals, emerged as a key theme. Behavioral health professionals identified patient motivations for non-sharing sensitive mental health records relating to substance use, emergency treatment, and serious mental illness (94%). We explore conflicts between professional need for timely access to health information and patient desire to withhold some data categories. Health professionals’ views on data sharing are integral to the redesign of health data sharing and informed consent. As well, they seek clarity about the impact of patient-controlled sharing on health professionals’ roles and scope of practice.

scholarly journals On the Coronavirus (COVID-19) Outbreak and the Smart City Network: Universal Data Sharing Standards Coupled with Artificial Intelligence (AI) to Benefit Urban Health Monitoring and Management

Healthcare ◽  
2020 ◽  
Vol 8 (1) ◽  
pp. 46 ◽  
Author(s):  
Zaheer Allam ◽  
David S. Jones
Keyword(s):  
Artificial Intelligence ◽  
Data Sharing ◽  
Urban Health ◽  
Health Monitoring ◽  
Smart City ◽  
Political Influence ◽  
Health Data ◽  
The Other ◽  
City Network ◽  
Global Understanding

As the Coronavirus (COVID-19) expands its impact from China, expanding its catchment into surrounding regions and other countries, increased national and international measures are being taken to contain the outbreak. The placing of entire cities in ‘lockdown’ directly affects urban economies on a multi-lateral level, including from social and economic standpoints. This is being emphasised as the outbreak gains ground in other countries, leading towards a global health emergency, and as global collaboration is sought in numerous quarters. However, while effective protocols in regard to the sharing of health data is emphasised, urban data, on the other hand, specifically relating to urban health and safe city concepts, is still viewed from a nationalist perspective as solely benefiting a nation’s economy and its economic and political influence. This perspective paper, written one month after detection and during the outbreak, surveys the virus outbreak from an urban standpoint and advances how smart city networks should work towards enhancing standardization protocols for increased data sharing in the event of outbreaks or disasters, leading to better global understanding and management of the same.

scholarly journals Implementation of a Health Data-Sharing Infrastructure Across Diverse Primary Care Organizations

2014 ◽  
Vol 37 (2) ◽  
pp. 164-170 ◽  
Author(s):  
Allison M. Cole ◽  
Kari A. Stephens ◽  
Gina A. Keppel ◽  
Ching-Ping Lin ◽  
Laura-Mae Baldwin
Keyword(s):  
Primary Care ◽  
Data Sharing ◽  
Health Data

scholarly journals CrowdMed-II: a blockchain-based framework for efficient consent management in health data sharing

2022 ◽  
Author(s):  
Chaochen Hu ◽  
Chao Li ◽  
Guigang Zhang ◽  
Zhiwei Lei ◽  
Mira Shah ◽  
...  
Keyword(s):  
Data Sharing ◽  
Service Providers ◽  
Heterogeneous Data ◽  
Health Data ◽  
Smart Contracts ◽  
Management Framework ◽  
Execution Costs ◽  
Traditional Health ◽  
Centralized Management ◽  
Health Service Providers

AbstractThe healthcare industry faces serious problems with health data. Firstly, health data is fragmented and its quality needs to be improved. Data fragmentation means that it is difficult to integrate the patient data stored by multiple health service providers. The quality of these heterogeneous data also needs to be improved for better utilization. Secondly, data sharing among patients, healthcare service providers and medical researchers is inadequate. Thirdly, while sharing health data, patients’ right to privacy must be protected, and patients should have authority over who can access their data. In traditional health data sharing system, because of centralized management, data can easily be stolen, manipulated. These systems also ignore patient’s authority and privacy. Researchers have proposed some blockchain-based health data sharing solutions where blockchain is used for consensus management. Blockchain enables multiple parties who do not fully trust each other to exchange their data. However, the practice of smart contracts supporting these solutions has not been studied in detail. We propose CrowdMed-II, a health data management framework based on blockchain, which could address the above-mentioned problems of health data. We study the design of major smart contracts in our framework and propose two smart contract structures. We also introduce a novel search contract for searching patients in the framework. We evaluate their efficiency based on the execution costs on Ethereum. Our design improves on those previously proposed, lowering the computational costs of the framework. This allows the framework to operate at scale and is more feasible for widespread adoption.

scholarly journals Accelerating Health Data Sharing: A Solution Based on the Internet of Things and Distributed Ledger Technologies (Preprint)

2019 ◽  
Author(s):  
Xiaochen Zheng ◽  
Shengjing Sun ◽  
Raghava Rao Mukkamala ◽  
Ravi Vatrapu ◽  
Joaquín Ordieres-Meré
Keyword(s):  
Health Care ◽  
Internet Of Things ◽  
Data Sharing ◽  
Data Access ◽  
Health Data ◽  
Security And Privacy ◽  
Access Management ◽  
Related Data ◽  
Distributed Ledger ◽  
Health Related

BACKGROUND Huge amounts of health-related data are generated every moment with the rapid development of Internet of Things (IoT) and wearable technologies. These big health data contain great value and can bring benefit to all stakeholders in the health care ecosystem. Currently, most of these data are siloed and fragmented in different health care systems or public and private databases. It prevents the fulfillment of intelligent health care inspired by these big data. Security and privacy concerns and the lack of ensured authenticity trails of data bring even more obstacles to health data sharing. With a decentralized and consensus-driven nature, distributed ledger technologies (DLTs) provide reliable solutions such as blockchain, Ethereum, and IOTA Tangle to facilitate the health care data sharing. OBJECTIVE This study aimed to develop a health-related data sharing system by integrating IoT and DLT to enable secure, fee-less, tamper-resistant, highly-scalable, and granularly-controllable health data exchange, as well as build a prototype and conduct experiments to verify the feasibility of the proposed solution. METHODS The health-related data are generated by 2 types of IoT devices: wearable devices and stationary air quality sensors. The data sharing mechanism is enabled by IOTA’s distributed ledger, the Tangle, which is a directed acyclic graph. Masked Authenticated Messaging (MAM) is adopted to facilitate data communications among different parties. Merkle Hash Tree is used for data encryption and verification. RESULTS A prototype system was built according to the proposed solution. It uses a smartwatch and multiple air sensors as the sensing layer; a smartphone and a single-board computer (Raspberry Pi) as the gateway; and a local server for data publishing. The prototype was applied to the remote diagnosis of tremor disease. The results proved that the solution could enable costless data integrity and flexible access management during data sharing. CONCLUSIONS DLT integrated with IoT technologies could greatly improve the health-related data sharing. The proposed solution based on IOTA Tangle and MAM could overcome many challenges faced by other traditional blockchain-based solutions in terms of cost, efficiency, scalability, and flexibility in data access management. This study also showed the possibility of fully decentralized health data sharing by replacing the local server with edge computing devices.

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