scholarly journals Personalized Consent Flow in Contemporary Data Sharing for Medical Research: A Viewpoint

2017 ◽  
Vol 2017 ◽  
pp. 1-4 ◽  
Author(s):  
Ester A. Rake ◽  
Marleen M. H. J. van Gelder ◽  
David C. Grim ◽  
Barend Heeren ◽  
Lucien J. L. P. G. Engelen ◽  
...  
Keyword(s):  
Medical Research ◽  
Personal Health Record ◽  
Health Data ◽  
Ethical Review ◽  
Personal Health ◽  
Review Committee ◽  
Semistructured Interviews ◽  
Research Platform ◽  
Share Data ◽  
Data Source

Background. Health data personally collected by individuals with wearable devices and smartphones is becoming an important data source for healthcare, but also for medical research. Objective. To describe a new consent model that allows people to control their personally collected health data and determine to what extent they want to share these for research purposes. Methods. We developed, in close collaboration with patients, researchers, healthcare professionals, privacy experts, and an accredited Medical Ethical Review Committee, an innovative concept called “personalized consent flow” within a research platform connected to a personal health record. The development was an iterative process with informal meetings, semistructured interviews, and surveys. The final concept of the personalized consent flow was reviewed by patients and improved and approved by the same patients in a focus group. Results. This concept could result in optimal control for individual users, since they will answer questions about how they will share data. Furthermore, it enables users to collect data for specific studies and add expiration dates to their data. This work facilitates further discussion about dynamic and personalized consent. A pilot study with the personalized consent model is currently being carried out.

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 Trust and privacy in the context of user-generated health data

2017 ◽  
Vol 4 (1) ◽  
pp. 205395171770467 ◽  
Author(s):  
Kirsten Ostherr ◽  
Svetlana Borodina ◽  
Rachel Conrad Bracken ◽  
Charles Lotterman ◽  
Eliot Storer ◽  
...  
Keyword(s):  
General Public ◽  
Well Being ◽  
Health Data ◽  
Third Party ◽  
Personal Health ◽  
Clinical Settings ◽  
Start Up ◽  
Semistructured Interviews ◽  
Defined Benefits ◽  
Social Sharing

This study identifies and explores evolving concepts of trust and privacy in the context of user-generated health data. We define “user-generated health data” as data captured through devices or software (whether purpose built or commercially available) and used outside of traditional clinical settings for tracking personal health data. The investigators conducted qualitative research through semistructured interviews (n = 32) with researchers, health technology start-up companies, and members of the general public to inquire why and how they interact with and understand the value of user-generated health data. We found significant results concerning new attitudes toward trust, privacy, and sharing of health data outside of clinical settings that conflict with regulations governing health data within clinical settings. Members of the general public expressed little concern about sharing health data with the companies that sold the devices or apps they used, and indicated that they rarely read the “terms and conditions” detailing how their data may be exploited by the company or third-party affiliates before consenting to them. In contrast, interviews with researchers revealed significant resistance among potential research participants to sharing their user-generated health data for purposes of scientific study. The widespread rhetoric of personalization and social sharing in “user-generated culture” appears to facilitate an understanding of user-generated health data that deemphasizes the risk of exploitation in favor of loosely defined benefits to individual and social well-being. We recommend clarification and greater transparency of regulations governing data sharing related to health.

scholarly journals Is a Mobile Personal Health Record Effective Tool for Managing Patient-Generated Health Data?

Iproceedings ◽  
2017 ◽  
Vol 3 (1) ◽  
pp. e11
Author(s):  
Jae-Ho Lee ◽  
Yura Lee ◽  
Yurang Park ◽  
Ji-Young Kim ◽  
Jeong-Hoon Kim ◽  
...  
Keyword(s):  
Personal Health Record ◽  
Health Data ◽  
Personal Health ◽  
Health Record

scholarly journals Development of a Mobile Personal Health Record Application Designed for Emergency Care in Korea; Integrated Information from Multicenter Electronic Medical Records

2020 ◽  
Vol 10 (19) ◽  
pp. 6711 ◽  
Author(s):  
Yuri Choi ◽  
June-sung Kim ◽  
In Ho Kwon ◽  
Taerim Kim ◽  
Su Min Kim ◽  
...  
Keyword(s):  
Emergency Care ◽  
Medical Records ◽  
Information Exchange ◽  
Health Information Exchange ◽  
System Development ◽  
Personal Health Record ◽  
Health Data ◽  
Mobile App ◽  
Personal Health ◽  
Tertiary Hospitals

Collecting patient’s medical data is essential for emergency care. Although hospital-tethered personal health records (PHRs) can provide accurate data, they are not available as electronic information when the hospital does not develop and supply PHRs. The objective of this research was to evaluate whether a mobile app can assemble health data from different hospitals and enable interoperability. Moreover, we identified numerous barriers to overcome for putting health data into one place. The new mobile PHR (mPHR) application was developed and evaluated according to the four phases of the system development life cycle: defining input data and functions, developing a prototype, developing a mobile application, and implementation testing. We successfully introduced the FirstER (First for Emergency Room) platform on 23 September 2019. Additionally, validation in three tertiary hospitals has been carried out since the launch date. From 14 October to 29 November 2019, 1051 cases registered with the FirstER, and the total download count was 15,951 records. We developed and successfully implemented the mPHR service, which can be used as a health information exchange tool in emergency care, by integrating medical records from three different tertiary hospitals. By recognizing the significance and limitations of this service, it is necessary to study the development and implementation of mPHR services that are more suitable for emergency care.

Blockchain-based architecture design for personal health record (Preprint)

2021 ◽  
Author(s):  
Thiago Bulhões ◽  
Lucas Shinoda ◽  
Ramon Moreno ◽  
Marco Gutierrez
Keyword(s):  
Personal Health Record ◽  
Distributed Databases ◽  
Health Data ◽  
System Response ◽  
Personal Health ◽  
Health Record ◽  
Architecture Design ◽  
Smart Contracts ◽  
Order Of Magnitude ◽  
Health Database

BACKGROUND The importance of blockchain-based architectures for personal health record (PHR) lies in the fact that they are thought and developed to allow patients to control and at least partly collect their health data. Ideally, these systems should provide the full control of such data for the respective owner. In spite of this importance, most of the works focus more on describing how blockchain models can be used in a PHR scenario than whether these models are in fact feasible and robust enough to support a large number of users. OBJECTIVE Toward a consistent, reproducible and comparable PHR system, we build a novel ledger-oriented architecture out of a permissioned distributed network, providing patients with a manner to securely collect, store, share and manage their health data. We also emphasize the importance of suitable ledgers and smart contracts to operate the blockchain network as well as discuss the necessity of standardizing evaluation metrics to compare related works. METHODS We adopted the Hyperledger Fabric platform to implement our blockchain-based architecture design and the Hyperledger Caliper framework to provide a detailed assessment of our system under workload, ranging from 100 to 2,500 simultaneous record submissions, and using throughput and average latency as primary metrics. We also create a health database, a cryptographic unit and a server to complement the blockchain network. RESULTS Smart contracts that write on the ledger have throughputs, measured in transactions per seconds (tps), in an order of magnitude close to 10^2 tps while those contracts that only read have rates close to 10^3 tps. Smart contracts that write also have latencies, measured in seconds (s), in an order of magnitude close to 10^1 s while that only read have delays close to 10^0 s. In particular, smart contracts that retrieve, list and view history have throughputs varying, respectively, from 1,100 to 1,300 tps, 650 to 750 tps and 850 to 950 tps, impacting the overall system response if they are equally requested under the same workload. CONCLUSIONS To the best of our knowledge, we are the first to evaluate, using Hyperledger Caliper, the performance of a PHR blockchain architecture and also the first to evaluate each smart contract separately. Nevertheless, blockchain systems achieve performances far below the traditional distributed databases achieve, indicating the assessment of blockchain solutions for PHR is a major concern to be addressed before putting them into a real production.

The Lifespan Personal Health Record

1991 ◽  
Vol 11 (4_suppl) ◽  
pp. S74-S76 ◽  
Author(s):  
Ben T. Williams ◽  
Harriet Imrey ◽  
Richard G. Williams
Keyword(s):  
Personal Health Record ◽  
Data Entry ◽  
Health Data ◽  
Patient Record ◽  
Personal Health ◽  
Health Record ◽  
Computer Based ◽  
Sentinel Events

A system for entry of health data in a computer-based patient record by lay individuals is described. The lay user is supported in data entry and data clarification, as well as by system-supported summarization of the data in context to show relationships, highlight sentinel events, and assist in evaluation of alternative decisions and actions as needed.

scholarly journals Effects of Patient-Generated Health Data: Comparison of Two Versions of Long-Term Mobile Personal Health Record Usage Logs

Healthcare ◽  
2021 ◽  
Vol 10 (1) ◽  
pp. 53
Author(s):  
Do-Hoon Kim ◽  
Yura Lee ◽  
Ji Seon Oh ◽  
Dong-Woo Seo ◽  
Kye Hwa Lee ◽  
...  
Keyword(s):  
Health Management ◽  
Personal Health Record ◽  
Estimating Equation ◽  
Health Data ◽  
Generalized Estimating Equation ◽  
Personal Health ◽  
Health Record ◽  
Data Comparison ◽  
Adult Females

Patient-generated health data (PGHD) can be managed easily by a mobile personal health record (mPHR) and can increase patient engagement. This study investigated the effect of PGHD functions on mPHR usage. We collected usage log data from an mPHR app, My Chart in My Hand (MCMH), for seven years. We analyzed the number of accesses and trends for each menu by age and sex according to the version-up. Generalized estimating equation (GEE) analysis was used to determine the likelihood of continuous app usage according to the menus and version-up. The total number of users of each version were 15,357 and 51,553, respectively. Adult females under 50 years were the most prevalent user group (30.0%). The “My Chart” menu was the most accessed menu, and the total access count increased by ~10 times after the version-up. The “Health Management” menu designed for PGHD showed the largest degree of increase in its likelihood of continuous usage after the version-up (1.245; p < 0.0001) across menus (range: 0.925–1.050). Notably, improvement of PGHD management in adult females over 50 years is needed.

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