Health Data Cooperatives – Citizen Empowerment

2014 ◽  
Vol 53 (02) ◽  
pp. 82-86 ◽  
Author(s):  
D. Kossmann ◽  
A. Brand ◽  
E. Hafen
Keyword(s):  
Precision Medicine ◽  
Health Data ◽  
Environmental Data ◽  
Functional Health ◽  
Personal Health ◽  
Research Information ◽  
Data Repositories ◽  
Cooperative Model ◽  
Targeted Interventions ◽  
Health Related

SummaryIntroduction: This article is part of a Focus Theme of Methods of Information in Medicine on Health Record Banking.Background: Healthcare is often ineffective and costs are steadily rising. This is in a large part due to the inaccessibility of medical and health data stored in multiple silos. Further -more, in most cases molecular differences between individuals that result in different susceptibilities to drugs and diseases as well as targeted interventions cannot be taken into account. Technological advances in genome sequencing and the interaction of ’omics’ data with environmental data on one hand and mobile health on the other, promise to generate the longitudinal health data that will form the basis for a more personalized, precision medicine.Objectives: For this new medicine to become a reality, however, millions of personal health data sets have to be aggregated. The value of such aggregated personal data has been recognized as a new asset class and many commercial entities are competing for this new asset (e.g. Google, Facebook, 23andMe, PatientsLikeMe). The primary source and beneficiary of personal health data is the individual. As a collective, society should be the beneficiary of both the eco -nomic and health value of these aggregated data and (health) information.Methods: We posit that empowering citi -zens by providing them with a platform to safely store, manage and share their health-related data will be a necessary element in the transformation towards a more effective and efficient precision medicine. Such health data platforms should be organized as co -operatives that are solely owned and controlled by their members and not by shareholders. Members determine which data they want to share for example with doctors or to contribute to research for the benefit of their health and that of society. Members will also decide how the revenues generated by granting third parties access to the anonymized data that they agreed to share, should be invested in research, information or education.Results: Currently no functional Health Data Cooperatives exist yet. The relative success of health data repositories such as 23andme and PatientsLikeMe indicates that citizens are willing to participate in research even if – and in contrast to the cooperative model – the commercial value of these data does not go back to the collective of users.Conclusions: In the Health Data Cooperative model, the citizens with their data would take the center stage in the healthcare system and society would benefit from the health-related and financial benefits that aggregation of these data brings.

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 Smart Mask – Wearable IoT Solution for Improved Protection and Personal Health

2021 ◽  
Author(s):  
Jarkko Hyysalo ◽  
Sandun Dasanayake ◽  
Jari Hannu ◽  
Christian Schuss ◽  
Mikko Rajanen ◽  
...  
Keyword(s):  
Large Scale ◽  
Personal Data ◽  
Health Data ◽  
Future Research ◽  
Personal Health ◽  
Public Data ◽  
Research Problems ◽  
Health Related ◽  
Treatment Plans ◽  
And Control

<div> <div> <div> <p>The use of face masks is an important way to fight the COVID-19 pandemic. In this paper, we envision the Smart Mask, an IoT supported platform and ecosystem aiming to prevent and control the spreading of COVID-19 and other respiratory viruses. The integration of sensing, materials, AI, wireless, IoT, and software will help gathering of health data and health-related event detection in real time from the user as well as from their environment. In larger scale, with the help of AI-based analysis for health data it is possible to predict and decrease medical costs with accurate diagnoses and treatment plans, where comparison of personal data to large-scale public data enables drawing up a personal health trajectory, for example. Key research problems for smart respiratory protective equipment are identified in addition to future research directions. </p> </div> </div> </div>

scholarly journals Ethical Considerations When Creating Evidence from Real World Digital Health Data

2020 ◽  
Author(s):  
C. Nebeker ◽  
Victoria Leavy ◽  
Eva Roitmann ◽  
Steven Steinhubl
Keyword(s):  
Data Sharing ◽  
Real World ◽  
Digital Health ◽  
Health Data ◽  
Personal Health ◽  
Real World Evidence ◽  
Self Study ◽  
Health Related Research ◽  
Health Related ◽  
English Speaking

AbstractBackgroundPersonal health data (PHD) are collected using digital self-tracking technologies and present opportunities to increase self-knowledge and, also biometric surveillance. PHD become “big” data and are used in health-related research studies. We surveyed consumers regarding expectations regarding consent and sharing of PHD for biomedical research.MethodsData sharing preferences were assessed via an 11-item survey. The survey link was emailed to 89539 English-speaking Withings product users. Responses were accepted for 5 weeks.Descriptive statistics were calculated using Excel and qualitative data were analyzed to provide additional context.ResultsNearly 1640 people or 5.7% of invitees responded representing 62 countries with 80% identifying as Caucasian, 75% male with 78% being college educated. The majority were agreeable to having their data shared with researchers to advance knowledge and improve health care.Participants responding to open ended items (N=247) appeared unaware that the company had access to their personal health data.ConclusionsWhile the majority of respondents were in favor of data sharing, individuals expressed concerns about the ability to de-identify data and associated risks of re-identification as well as an interest in having some control over the use of “their” data. Given consumer misconception about data ownership, access and use, efforts to increase transparency when interacting with individual digital health data must be prioritized. Moreover, the basic ethical principle of “respect for persons” demonstrated via the informed consent process will be critical in advancing the adoption of digital technologies that create real-world evidence and advance opportunities for N-of-1 self-study.

scholarly journals Smart Mask – Wearable IoT Solution for Improved Protection and Personal Health

2021 ◽  
Author(s):  
Jarkko Hyysalo ◽  
Sandun Dasanayake ◽  
Jari Hannu ◽  
Christian Schuss ◽  
Mikko Rajanen ◽  
...  
Keyword(s):  
Large Scale ◽  
Personal Data ◽  
Health Data ◽  
Future Research ◽  
Personal Health ◽  
Public Data ◽  
Research Problems ◽  
Health Related ◽  
Treatment Plans ◽  
And Control

<div> <div> <div> <p>The use of face masks is an important way to fight the COVID-19 pandemic. In this paper, we envision the Smart Mask, an IoT supported platform and ecosystem aiming to prevent and control the spreading of COVID-19 and other respiratory viruses. The integration of sensing, materials, AI, wireless, IoT, and software will help gathering of health data and health-related event detection in real time from the user as well as from their environment. In larger scale, with the help of AI-based analysis for health data it is possible to predict and decrease medical costs with accurate diagnoses and treatment plans, where comparison of personal data to large-scale public data enables drawing up a personal health trajectory, for example. Key research problems for smart respiratory protective equipment are identified in addition to future research directions. </p> </div> </div> </div>

(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 Effect of Baseline Patient and Caregiver Mindfulness on Dementia Outcomes

2021 ◽  
pp. 1-23
Author(s):  
Ashley D. Innis ◽  
Magdalena I. Tolea ◽  
James E. Galvin
Keyword(s):  
Quality Of Life ◽  
Cognitive Performance ◽  
Rating Scales ◽  
Present Moment ◽  
Health Related Quality ◽  
Targeted Interventions ◽  
Related Quality ◽  
Health Related ◽  
Caregiver Rating

Background: Mindfulness is the practice of awareness and living in the present moment without judgment. Mindfulness-based interventions may improve dementia-related outcomes. Before initiating interventions, it would be beneficial to measure baseline mindfulness to understand targets for therapy and its influence on dementia outcomes. Objective: This cross-sectional study examined patient and caregiver mindfulness with patient and caregiver rating scales and patient cognitive performance and determined whether dyadic pairing of mindfulness influences patient outcomes. Methods: Individuals (N = 291) underwent comprehensive evaluations, with baseline mindfulness assessed using the 15-item Applied Mindfulness Process Scale (AMPS). Correlation, regression, and mediation models tested relationships between patient and caregiver mindfulness and outcomes. Results: Patients had a mean AMPS score of 38.0±11.9 and caregivers had a mean AMPS score of 38.9±11.5. Patient mindfulness correlated with activities of daily living, behavior and mood, health-related quality of life, subjective cognitive complaints, and performance on episodic memory and attention tasks. Caregiver mindfulness correlated with preparedness, care confidence, depression, and better patient cognitive performance. Patients in dyads with higher mindfulness had better cognitive performance, less subjective complaints, and higher health-related quality of life (all p-values<0.001). Mindfulness effects on cognition were mediated by physical activity, social engagement, frailty, and vascular risk factors. Conclusion: Higher baseline mindfulness was associated with better patient and caregiver outcomes, particularly when both patients and caregivers had high baseline mindfulness. Understanding the baseline influence of mindfulness on the completion of rating scales and neuropsychological test performance can help develop targeted interventions to improve well-being in patients and their caregivers.

scholarly journals A System for Phenotype Harmonization in the NHLBI Trans-Omics for Precision Medicine (TOPMed) Program

2021 ◽  
Author(s):  
Adrienne M Stilp ◽  
Leslie S Emery ◽  
Jai G Broome ◽  
Erin J Buth ◽  
Alyna T Khan ◽  
...  
Keyword(s):  
Precision Medicine ◽  
Association Studies ◽  
Controlled Vocabulary ◽  
National Institutes Of Health ◽  
Design Data ◽  
Data Set ◽  
Data Repositories ◽  
Phenotype Data ◽  
National Heart Lung ◽  
Centralized System

Abstract Genotype-phenotype association studies often combine phenotype data from multiple studies to increase power. Harmonization of the data usually requires substantial effort due to heterogeneity in phenotype definitions, study design, data collection procedures, and data set organization. Here we describe a centralized system for phenotype harmonization that includes input from phenotype domain and study experts, quality control, documentation, reproducible results, and data sharing mechanisms. This system was developed for the National Heart, Lung and Blood Institute’s Trans-Omics for Precision Medicine program, which is generating genomic and other omics data for &gt;80 studies with extensive phenotype data. To date, 63 phenotypes have been harmonized across thousands of participants from up to 17 studies per phenotype (participants recruited 1948-2012). We discuss challenges in this undertaking and how they were addressed. The harmonized phenotype data and associated documentation have been submitted to National Institutes of Health data repositories for controlled-access by the scientific community. We also provide materials to facilitate future harmonization efforts by the community, which include (1) the code used to generate the 63 harmonized phenotypes, enabling others to reproduce, modify or extend these harmonizations to additional studies; and (2) results of labeling thousands of phenotype variables with controlled vocabulary terms.

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.

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