scholarly journals Fitting patient-generated health data for women into their personal health plans: VA has an app for that!

2020 ◽  
Vol 5 (1) ◽  
Author(s):  
Kathleen L. Frisbee
Keyword(s):  
Health Data ◽  
Personal Health ◽  
Health Plans

(Preprint)

2018 ◽  
Author(s):  
Ram Dixit ◽  
Sahiti Myneni
Keyword(s):  
Cancer Survivors ◽  
Mental Models ◽  
Health Systems ◽  
Information Needs ◽  
Health Data ◽  
Information Architecture ◽  
Personal Health ◽  
User Centered Design ◽  
Card Sort ◽  
Connected Health

BACKGROUND Connected Health technologies are a promising solution for chronic disease management. However, the scope of connected health systems makes it difficult to employ user-centered design in their development, and poorly designed systems can compound the challenges of information management in chronic care. The Digilego Framework addresses this problem with informatics methods that complement quantitative and qualitative methods in system design, development, and architecture. OBJECTIVE To determine the accuracy and validity of the Digilego information architecture of personal health data in meeting cancer survivors’ information needs. METHODS We conducted a card sort study with 9 cancer survivors (patients and caregivers) to analyze correspondence between the Digilego information architecture and cancer survivors’ mental models. We also analyzed participants’ card sort groups qualitatively to understand their conceptual relations. RESULTS We observed significant correlation between the Digilego information architecture and cancer survivors’ mental models of personal health data. Heuristic analysis of groups also indicated informative discordances and the need for patient-centric categories relating health tracking and social support in the information architecture. CONCLUSIONS Our pilot study shows that the Digilego Framework can capture cancer survivors’ information needs accurately; we also recognize the need for larger studies to conclusively validate Digilego information architectures. More broadly, our results highlight the importance of complementing traditional user-centered design methods and innovative informatics methods to create patient-centered connected health systems.

scholarly journals The funhouse mirror: the I in personalised healthcare

2021 ◽  
Vol 17 (1) ◽  
Author(s):  
Mira W. Vegter ◽  
Hub A. E. Zwart ◽  
Alain J. van Gool
Keyword(s):  
Precision Medicine ◽  
Personal Data ◽  
Health Data ◽  
Personal Health ◽  
Lifestyle Interventions ◽  
Phenotypic Data ◽  
Individual Health ◽  
Data Source ◽  
Tracking Devices ◽  
Personalised Healthcare

AbstractPrecision Medicine is driven by the idea that the rapidly increasing range of relatively cheap and efficient self-tracking devices make it feasible to collect multiple kinds of phenotypic data. Advocates of N = 1 research emphasize the countless opportunities personal data provide for optimizing individual health. At the same time, using biomarker data for lifestyle interventions has shown to entail complex challenges. In this paper, we argue that researchers in the field of precision medicine need to address the performative dimension of collecting data. We propose the fun-house mirror as a metaphor for the use of personal health data; each health data source yields a particular type of image that can be regarded as a ‘data mirror’ that is by definition specific and skewed. This requires competence on the part of individuals to adequately interpret the images thus provided.

scholarly journals Public Interest, Health Research and Data Protection Law: Establishing a Legitimate Trade-Off between Individual Control and Research Access to Health Data

Laws ◽  
2020 ◽  
Vol 9 (1) ◽  
pp. 6 ◽  
Author(s):  
Mark J. Taylor ◽  
Tess Whitton
Keyword(s):  
Public Interest ◽  
Data Protection ◽  
Health Research ◽  
Health Data ◽  
Personal Health ◽  
Positive Contribution ◽  
Data Governance ◽  
The Public ◽  
Individual Control ◽  
Data Protection Law

The United Kingdom’s Data Protection Act 2018 introduces a new public interest test applicable to the research processing of personal health data. The need for interpretation and application of this new safeguard creates a further opportunity to craft a health data governance landscape deserving of public trust and confidence. At the minimum, to constitute a positive contribution, the new test must be capable of distinguishing between instances of health research that are in the public interest, from those that are not, in a meaningful, predictable and reproducible manner. In this article, we derive from the literature on theories of public interest a concept of public interest capable of supporting such a test. Its application can defend the position under data protection law that allows a legal route through to processing personal health data for research purposes that does not require individual consent. However, its adoption would also entail that the public interest test in the 2018 Act could only be met if all practicable steps are taken to maximise preservation of individual control over the use of personal health data for research purposes. This would require that consent is sought where practicable and objection respected in almost all circumstances. Importantly, we suggest that an advantage of relying upon this concept of the public interest, to ground the test introduced by the 2018 Act, is that it may work to promote the social legitimacy of data protection legislation and the research processing that it authorises without individual consent (and occasionally in the face of explicit objection).

scholarly journals Visualizing Knowledge Evolution Trends and Research Hotspots of Personal Health Data Research: Bibliometric Analysis (Preprint)

2021 ◽  
Author(s):  
Jianxia Gong ◽  
Vikrant Sihag ◽  
Qingxia Kong ◽  
Lindu Zhao
Keyword(s):  
Health Care ◽  
Human Factors ◽  
Computer Science ◽  
Bibliometric Analysis ◽  
Focal Point ◽  
Empirical Studies ◽  
Health Data ◽  
Personal Health ◽  
Knowledge Evolution ◽  
Research Hotspots

BACKGROUND The recent surge in clinical and nonclinical health-related data has been accompanied by a concomitant increase in personal health data (PHD) research across multiple disciplines such as medicine, computer science, and management. There is now a need to synthesize the dynamic knowledge of PHD in various disciplines to spot potential research hotspots. OBJECTIVE The aim of this study was to reveal the knowledge evolutionary trends in PHD and detect potential research hotspots using bibliometric analysis. METHODS We collected 8281 articles published between 2009 and 2018 from the Web of Science database. The knowledge evolution analysis (KEA) framework was used to analyze the evolution of PHD research. The KEA framework is a bibliometric approach that is based on 3 knowledge networks: reference co-citation, keyword co-occurrence, and discipline co-occurrence. RESULTS The findings show that the focus of PHD research has evolved from medicine centric to technology centric to human centric since 2009. The most active PHD knowledge cluster is developing knowledge resources and allocating scarce resources. The field of computer science, especially the topic of artificial intelligence (AI), has been the focal point of recent empirical studies on PHD. Topics related to psychology and human factors (eg, attitude, satisfaction, education) are also receiving more attention. CONCLUSIONS Our analysis shows that PHD research has the potential to provide value-based health care in the future. All stakeholders should be educated about AI technology to promote value generation through PHD. Moreover, technology developers and health care institutions should consider human factors to facilitate the effective adoption of PHD-related technology. These findings indicate opportunities for interdisciplinary cooperation in several PHD research areas: (1) AI applications for PHD; (2) regulatory issues and governance of PHD; (3) education of all stakeholders about AI technology; and (4) value-based health care including “allocative value,” “technology value,” and “personalized value.”

Personal health data: A systematic mapping study

2018 ◽  
Vol 118 ◽  
pp. 86-98 ◽  
Author(s):  
Maria Karampela ◽  
Sofia Ouhbi ◽  
Minna Isomursu
Keyword(s):  
Health Data ◽  
Personal Health ◽  
Systematic Mapping Study ◽  
Mapping Study ◽  
Systematic Mapping

Exploring Type-and-Identity-Based Proxy Re-Encryption Scheme to Securely Manage Personal Health Records

2010 ◽  
Vol 1 (2) ◽  
pp. 1-21
Author(s):  
Luan Ibraimi ◽  
Qiang Tang ◽  
Pieter Hartel ◽  
Willem Jonker
Keyword(s):  
Access Control ◽  
Health Data ◽  
Third Party ◽  
Personal Health ◽  
Encryption Scheme ◽  
Health Records ◽  
Web Based ◽  
The Third ◽  
Access Control Policies ◽  
Identity Based

Commercial Web-based Personal-Health Record (PHR) systems can help patients to share their personal health records (PHRs) anytime from anywhere. PHRs are very sensitive data and an inappropriate disclosure may cause serious problems to an individual. Therefore commercial Web-based PHR systems have to ensure that the patient health data is secured using state-of-the-art mechanisms. In current commercial PHR systems, even though patients have the power to define the access control policy on who can access their data, patients have to trust entirely the access-control manager of the commercial PHR system to properly enforce these policies. Therefore patients hesitate to upload their health data to these systems as the data is processed unencrypted on untrusted platforms. Recent proposals on enforcing access control policies exploit the use of encryption techniques to enforce access control policies. In such systems, information is stored in an encrypted form by the third party and there is no need for an access control manager. This implies that data remains confidential even if the database maintained by the third party is compromised. In this paper we propose a new encryption technique called a type-and-identity-based proxy re-encryption scheme which is suitable to be used in the healthcare setting. The proposed scheme allows users (patients) to securely store their PHRs on commercial Web-based PHRs, and securely share their PHRs with other users (doctors).

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