scholarly journals The other side of the coin: harm due to the non-use of health-related data

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Kerina Jones ◽  
Graeme Laurie ◽  
Leslie Stevens ◽  
Christine Dobbs ◽  
David Ford ◽  
...  
Keyword(s):  
Clinical Care ◽  
Indirect Evidence ◽  
Health Data ◽  
Real Problem ◽  
Multiple Sources ◽  
Complex Issue ◽  
Study Approach ◽  
Related Data ◽  
Health Related ◽  
Financial Burdens

ABSTRACTObjectivesIt is widely acknowledged that breaches and misuses of health-related data can have serious implications and consequently they often carry penalties. However, harm due to the omission of health data usage, or data non use, is a subject that lacks attention. A better understanding of this other side of the coin is required before it can be addressed effectively. ApproachThis article uses an international case study approach to explore why data non use is difficult to ascertain, the sources and types of health-related data non-use, its implications for citizens and society and some of the reasons it occurs. It does this by focussing on issues with clinical care records, research data and governance frameworks and associated examples of non-use. ResultsThe non-use of health-related data is a complex issue with multiple sources and reasons contributing to it. Instances of data non-use can be associated with harm, but taken together they describe a trail of data non-use, and this may complicate and compound its impacts. Actual evidence of data non-use is sparse and harm due to data non use is difficult to prove. But although it can be nebulous, it is a real problem with largely unquantifiable consequences. There is ample indirect evidence that health data non-use is implicated in the deaths of many thousands of people and potentially £billions in financial burdens to societies.ConclusionThe most effective initiatives to address specific contexts of data non-use will be those that are cognisant of the multiple aspects to this complex issue, in order to move towards socially responsible reuse of data becoming the norm to save lives and resources.

scholarly journals Use of Patient Generated Health Data collected from consumer wearable devices in clinical care: a systematic review and meta-analysis (Preprint)

2017 ◽  
Author(s):  
Robab Abdolkhani ◽  
Kathleen Gray ◽  
Ann Borda
Keyword(s):  
Systematic Review ◽  
Life Cycle ◽  
Low Cost ◽  
Meta Analysis ◽  
Clinical Care ◽  
Self Care ◽  
Health Data ◽  
Related Data ◽  
Life Cycle Stages ◽  
Health Related

BACKGROUND PGHD (Patient Generated Health Data) are health-related data created or recorded by patients to inform their self-care. The availability of low-cost easy-to-use consumer wearable technologies has facilitated patients’ engagement in their self-care and increased production of PGHD but the uptake of this data in clinical environments has been slow. Studies showing opportunities and challenges affecting PGHD adoption and use in clinical care have not investigated these factors in detail during all stages of the PGHD life cycle. OBJECTIVE This study aims to provide deeper insight into various issues influencing the use of PGHD at each stage of its life cycle from the perspectives of key stakeholders including patients, healthcare professionals, and the health IT managers. METHODS A systematic review was undertaken on the scholarly and industry literature published from 2012 to 2017. Thematic analysis of content was applied to uncover perspectives of the key PGHD stakeholders on opportunities and challenges related to all life cycle stages of PGHD from consumer wearables. RESULTS Thirty-six papers were identified for detailed analysis. Challenges were discussed more frequently than opportunities. Most studies done in real-world settings were limited to the collection stage of PGHD life cycle that captured through consumer wearables. CONCLUSIONS There are many gaps in knowledge on opportunities and challenges affecting PGHD captured through consumer wearables in each stage of its life cycle. A conceptual framework involving all the stakeholders in overcoming various technical, clinical, cultural, and regulatory challenges affecting PGHD during its life cycle could help to advance the integration with and use of PGHD in clinical care.

Understanding Data Collection Mechanisms Used by Health and Wellness Applications (Preprint)

2021 ◽  
Author(s):  
Ben Philip ◽  
Mohamed Abdelrazek ◽  
Alessio Bonti ◽  
Scott Barnett ◽  
John Grundy
Keyword(s):  
Data Collection ◽  
User Experience ◽  
Health Data ◽  
Domain Model ◽  
Health And Wellness ◽  
Related Services ◽  
Related Data ◽  
Medical Apps ◽  
App Development ◽  
Health Related

UNSTRUCTURED Our objective is to better understand health-related data collection across different mHealth app categories. This would help in developing a health domain model for mHealth apps to facilitate app development and data sharing between these apps to improve user experience and reduce redundancy in data collection. We identified app categories listed in a curated library which was then used to explore the Google Play Store for health/medical apps that were then filtered using our inclusion criteria. We downloaded and analysed these apps using a script we developed around the popular AndroGuard tool. We analysed the use of Bluetooth peripherals and built-in sensors to understand how a given app collects/generates health data. We retrieved 3,251 applications meeting our criteria, and our analysis showed that only 10.7% of these apps requested permission for Bluetooth access. We found 50.9% of the Bluetooth Service UUIDs to be known in these apps, with the remainder being vendor specific. The most common health-related services using the known UUIDs were Heart Rate, Glucose and Body Composition. App permissions show the most used device module/sensor to be the camera (20.57%), closely followed by GPS (18.39%). Our findings are consistent with previous studies in that not many health apps were found to use built-in sensors or peripherals for collecting health data. The use of more peripherals and automated data collection along with integration with other apps could increase usability and convenience which would eventually also improve user experience and data reliability.

scholarly journals All Our Data Will Be Health Data One Day: The Need for Universal Data Protection and Comprehensive Consent

10.2196/16879 ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. e16879 ◽  
Author(s):  
Christophe Olivier Schneble ◽  
Bernice Simone Elger ◽  
David Martin Shaw
Keyword(s):  
Health Status ◽  
Data Protection ◽  
Health Data ◽  
Future Health ◽  
Related Data ◽  
Legal Implications ◽  
Health Related ◽  
Behavior Health

Tremendous growth in the types of data that are collected and their interlinkage are enabling more predictions of individuals’ behavior, health status, and diseases. Legislation in many countries treats health-related data as a special sensitive kind of data. Today’s massive linkage of data, however, could transform “nonhealth” data into sensitive health data. In this paper, we argue that the notion of health data should be broadened and should also take into account past and future health data and indirect, inferred, and invisible health data. We also lay out the ethical and legal implications of our model.

scholarly journals A UKSeRP for SAIL: striking a balance

2017 ◽  
Vol 1 (1) ◽  
Author(s):  
Kerina Jones ◽  
David Ford ◽  
Caroline Brooks
Keyword(s):  
Data Linkage ◽  
Data Access ◽  
Remote Access ◽  
Data Types ◽  
Study Approach ◽  
Related Data ◽  
Access System ◽  
Data User ◽  
Health Related ◽  
The Uk

ABSTRACT ObjectivesWhilst the current expansion of health-related big data and data linkage research are exciting developments with great potential, they bring a major challenge. This is how to strike an appropriate balance between making the data accessible for beneficial uses, whilst respecting the rights of individuals, the duty of confidentiality and protecting the privacy of person-level data, without undue burden to research. ApproachUsing a case study approach, we describe how the UK Secure Research Platform (UKSeRP) for the Secure Anonymised Information Linkage (SAIL) databank addresses this challenge. We outline the principles, features and operating model of the SAIL UKSeRP, and how we are addressing the challenges of making health-related data safely accessible to increasing numbers of research users within a secure environment. ResultsThe SAIL UKSeRP has four basic principles to ensure that it is able to meet the needs of the growing data user community, and these are to: A) operate a remote access system that provides secure data access to approved data users; B) host an environment that provides a powerful platform for data analysis activities; (C) have a robust mechanism for the safe transfer of approved files in and out of the system; and (D) ensure that the system is efficient and scalable to accommodate a growing data user base. Subject to independent Information Governance approval and within a robust, proportionate Governance framework, the SAIL UKSeRP provides data users with a familiar Windows interface and their usual toolsets to access anonymously-linked datasets for research and evaluation. ConclusionThe SAIL UKSeRP represents a powerful analytical environment within a privacy-protecting safe haven and secure remote access system which has been designed to be scalable and adaptable to meet the needs of the rapidly growing data linkage community. Further challenges lie ahead as the landscape develops and emerging data types become more available. UKSeRP technology is available and customisable for other use cases within the UK and international jurisdictions, to operate within their respective governance frameworks.

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.

scholarly journals All Our Data Will Be Health Data One Day: The Need for Universal Data Protection and Comprehensive Consent (Preprint)

2019 ◽  
Author(s):  
Christophe Olivier Schneble ◽  
Bernice Simone Elger ◽  
David Martin Shaw
Keyword(s):  
Health Status ◽  
Data Protection ◽  
Health Data ◽  
Future Health ◽  
Related Data ◽  
Legal Implications ◽  
Health Related ◽  
Behavior Health

UNSTRUCTURED Tremendous growth in the types of data that are collected and their interlinkage are enabling more predictions of individuals’ behavior, health status, and diseases. Legislation in many countries treats health-related data as a special sensitive kind of data. Today’s massive linkage of data, however, could transform “nonhealth” data into sensitive health data. In this paper, we argue that the notion of health data should be broadened and should also take into account past and future health data and indirect, inferred, and invisible health data. We also lay out the ethical and legal implications of our model.

scholarly journals Geocomputation techniques for spatial analysis: are they relevant to health data?

2001 ◽  
Vol 17 (5) ◽  
pp. 1059-1071 ◽  
Author(s):  
Gilberto Câmara ◽  
Antônio Miguel Vieira Monteiro
Keyword(s):  
Neural Networks ◽  
Data Analysis ◽  
Spatial Data ◽  
Heuristic Search ◽  
Health Data ◽  
Frame Of Reference ◽  
Spatial Data Analysis ◽  
Related Data ◽  
Health Related ◽  
Computationally Intensive

Geocomputation is an emerging field of research that advocates the use of computationally intensive techniques such as neural networks, heuristic search, and cellular automata for spatial data analysis. Since increasing amounts of health-related data are collected within a geographical frame of reference, geocomputational methods show increasing potential for health data analysis. This paper presents a brief survey of the geocomputational field, including some typical applications and references for further reading.

scholarly journals A VOICE-INTERACTION-MEDIATED SMART HOME TO SUPPORT AGING IN PLACE: A NEW PARADIGM TO ENGAGE WITH HEALTH-RELATED DATA

2019 ◽  
Vol 3 (Supplement_1) ◽  
pp. S442-S442
Author(s):  
Yong K Choi ◽  
Hilaire J Thompson ◽  
George Demiris
Keyword(s):  
Smart Home ◽  
Healthy Aging ◽  
Health Management ◽  
Health Data ◽  
New Paradigm ◽  
Health Maintenance ◽  
Related Data ◽  
Health Related ◽  
Voice Interaction ◽  
The Voice

Abstract Current tools for health management such as personal health records (PHR), mobile applications, and wearable devices rely on conventional interfaces such as keyboard and mouse with screens for reading information. More recently, Internet-of-things (IoT) voice-activated smart speakers and embedded Artificial Intelligence (AI) assistants have emerged that may provide an effective user interaction platform to support healthy aging, creating a hands-free, conversational way to access and share health related data. We conducted an exploratory study with nineteen older adults (65+) who chose to evaluate a smart speaker for a 2-month as part of a larger IoT feasibility study. Three interview sessions were conducted to gather attitudes towards this technology. Participants provided feedback on future improvements or desirable features for health maintenance. A content analysis was performed to extract common themes. In general, participants expressed a positive experience with the voice interface and discussed its potential as an integral tool for their home health management. Based on these findings, we propose a voice interaction smart home platform to support healthy aging. This platform provides an intuitive voice user interface to execute commands to access, record, and query data from IoT sensors, a PHR and a hospital EHR. For example, the voice interaction platform will help individuals to dynamically explore their own health data thus eliminating manual and tedious searching of PHR data. We demonstrate how this platform can potentially improve usability issues including difficulties navigating charts and entering or retrieving health data using conventional interfaces. Finally, we highlight ethical and technical considerations.

scholarly journals Added Value from Secondary Use of Person Generated Health Data in Consumer Health Informatics

2017 ◽  
Vol 26 (01) ◽  
pp. 160-171
Author(s):  
P.-Y. Hsueh ◽  
Y.-K. Cheung ◽  
S. Dey ◽  
K. K. Kim ◽  
F. J. Martin-Sanchez ◽  
...  
Keyword(s):  
Health Informatics ◽  
Health Data ◽  
Added Value ◽  
Patient Centered ◽  
Related Data ◽  
Secondary Use ◽  
Care Plans ◽  
Making Sense ◽  
The Status ◽  
Health Related

Summary Introduction: Various health-related data, subsequently called Person Generated Health Data (PGHD), is being collected by patients or presumably healthy individuals as well as about them as much as they become available as measurable properties in their work, home, and other environments. Despite that such data was originally just collected and used for dedicated predefined purposes, more recently it is regarded as untapped resources that call for secondary use. Method: Since the secondary use of PGHD is still at its early evolving stage, we have chosen, in this paper, to produce an outline of best practices, as opposed to a systematic review. To this end, we identified key directions of secondary use and invited protagonists of each of these directions to present their takes on the primary and secondary use of PGHD in their sub-fields. We then put secondary use in a wider perspective of overarching themes such as privacy, interpretability, interoperability, utility, and ethics. Results: We present the primary and secondary use of PGHD in four focus areas: (1) making sense of PGHD in augmented Shared Care Plans for care coordination across multiple conditions; (2) making sense of PGHD from patient-held sensors to inform cancer care; (3) fitting situational use of PGHD to evaluate personal informatics tools in adaptive concurrent trials; (4) making sense of environment risk exposure data in an integrated context with clinical and omics-data for biomedical research. Discussion: Fast technological progress in all the four focus areas calls for a societal debate and decision-making process on a multitude of challenges: how emerging or foreseeable results transform privacy; how new data modalities can be interpreted in light of clinical data and vice versa; how the sheer mass and partially abstract mathematical properties of the achieved insights can be interpreted to a broad public and can consequently facilitate the development of patient-centered services; and how the remaining risks and uncertainties can be evaluated against new benefits. This paper is an initial summary of the status quo of the challenges and proposals that address these issues. The opportunities and barriers identified can serve as action items individuals can bring to their organizations when facing challenges to add value from the secondary use of patient-generated health data.

Electronic Health Records Aggregators (EHRagg )

2020 ◽  
Vol 59 (02/03) ◽  
pp. 096-103
Author(s):  
Belén Prados-Suárez ◽  
Carlos Molina Fernández ◽  
Carmen Peña Yañez
Keyword(s):  
Heterogeneous Data ◽  
Health Data ◽  
Data Sources ◽  
Data Systems ◽  
Related Data ◽  
Heterogeneous Data Sources ◽  
Health Related ◽  
Long Time ◽  
Unified View ◽  
Fair Principles

Abstract Background Integration of health data systems is an open problem. Most of the active initiatives are based on the use of standards. However, achieving a widely and generalized compliment of such standards still seems a costly task that will take a long time to be completed. Even more, most of the standards are proposed for a specific use, without integrating other needs. Objectives We propose an alternative to get a unified view of health-related data, valid for several uses, that unites heterogeneous data sources. Methods Our proposal integrates developments made so far to automatically learn how to extract and convert data from different health-related systems. It enables the creation of a single multipurpose point of access. Results We present the EhRagg notion and its related concepts. EHRagg is defined as a middleware that, following the FAIR principles, integrates health data sources offering a unified view over them.

Sign in / Sign up

Export Citation Format

Share Document