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

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.

Personal Health Record Use Is on the Upswing

2010 ◽  
Vol 38 (5) ◽  
pp. 8
Author(s):  
ANNE C. ZIEGER
Keyword(s):  
Personal Health Record ◽  
Personal Health ◽  
Health Record

Cloud Based Framework for Ethiopian Personal Health Record System

2018 ◽  
Vol 6 (7) ◽  
pp. 1256-1260
Author(s):  
Rakeb Daba Tugie ◽  
Gagandeep .
Keyword(s):  
Personal Health Record ◽  
Personal Health ◽  
Health Record ◽  
Record System

Multiauthority Access Control With Anonymous Authentication for Personal Health Record

2021 ◽  
Vol 8 (1) ◽  
pp. 156-167
Author(s):  
Leyou Zhang ◽  
Yadi Ye ◽  
Yi Mu
Keyword(s):  
Access Control ◽  
Personal Health Record ◽  
Personal Health ◽  
Health Record ◽  
Anonymous Authentication
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