The patient-centric translational health record

2013 ◽  
Vol 14 (4) ◽  
pp. 349-352 ◽  
Author(s):  
Amnon Shabo (Shvo)
Keyword(s):  
Health Record ◽  
Patient Centric

scholarly journals Privacy Preserving model for patient centric health record management using chaincode

2021 ◽  
Author(s):  
vinodhini mani ◽  
Prakash M
Keyword(s):  
Public Key ◽  
Record Management ◽  
Health Record ◽  
Smart Contracts ◽  
X Rays ◽  
Privacy And Security ◽  
Health Records ◽  
Chain Code ◽  
Encrypt Data ◽  
Patient Centric

Abstract Cloud computing poses a challenge to the healthcare infrastructure, as it affects privacy, confidentiality, and security rules concerning large binary objects such as x-rays and CT scan reports. However, health records are stored and accessed using an encryption hash which is stored in the interplanetary file system (IPFS), called a peer-to-peer system. But the patient’s data is sold, share for research purposes by their healthcare providers without their knowledge as it affects their privacy and security. In the healthcare industry today, customers face the issue of health record that lacks interoperability, resulting in difficulty aggregating and examining patient data. The objective of this research is to develop cybersecurity measurement approaches that ensure patient information security by protecting against cyber threats using blockchain technology based on healthcare IT. Consequently, this paper proposes an innovative solution to the problem, namely Patient-centric healthcare data management (PCHDM). It was built using IPFS, a permissioned distributed ledger system that uses Hyperledger Fabric, which stores health records, but only with the permission of the owner. A unique cryptographic public key encryption algorithm is used to encrypt IPFS data to build an electronic health record blockchain system. Our platform offers two types of solutions: (i) a solution that utilizes a database of hyper ledger fabric, which is an on-chain database, (ii) off-chain solutions which encrypt data and store it securely off-chain using IPFS. A robust blockchain solution for PCHDM will be created by encrypting the data stored in IPFS using appropriate public key cryptographic algorithms. To determine which blocks should be incorporated into the blockchain, the Byzantine Fault Tolerance is applied in the health chain architectural model. This system hosts smart contracts and application logic as well as smart contracts known as "chain code" via container technology. As part of this research, health record hashes were stored on the blockchain and the actual health data was stored off-chain in IPFS, which is the decentralized cloud storage system that achieves scalability. Due to the encryption of healthcare records with a hash, this model proves that unauthorized access is impossible because the records are more scalable, interoperable, and reliable. Stakeholders are more confident in collaborating and sharing their medical records with this model.

scholarly journals 2465

2017 ◽  
Vol 1 (S1) ◽  
pp. 18-18 ◽  
Author(s):  
Arlene Chung ◽  
Haiwei Chen ◽  
Grace Shin ◽  
Ketan Mane ◽  
Hye-Chung Kum
Keyword(s):  
Electronic Health Record ◽  
Design Process ◽  
Chronic Conditions ◽  
Usability Testing ◽  
Health Data ◽  
The Other ◽  
Personal Health ◽  
Health Record ◽  
Heat Map ◽  
Patient Centric

OBJECTIVES/SPECIFIC AIMS: The promise and potential of connected personal health records (PHRs) has not come to fruition. This may be, in part, due to the lack of user-centered design and of a patient-centric approach to curating personal health data for use by patients. Co-design with end-users could help mitigate these issues by ensuring the software meets user’s needs, and also engages patients in informatics research. Our team partnered with patients with multiple chronic conditions to co-design a patient-centric PHR. This abstract will describe our experience with the co-design process, highlight functionalities desired by patients, and showcase the final prototype. METHODS/STUDY POPULATION: We conducted 3 design sessions (90 min per session) with patients as co-designers and employed an iterative process for software development. Patients were recruited from Chapel Hill and surrounding areas. The initial design session laid the foundation for future sessions, and began with brainstorming about what patients thought their ideal version of an engaging connected PHR would look like in terms of features and functionalities. After each software iteration, our entire design team, including our patient co-designers, was shown the prototype during a subsequent design session. Once the final prototype was developed, usability testing was conducted with patient participants. Our team then conducted a final design session to debrief about the final prototype. RESULTS/ANTICIPATED RESULTS: We started with an initial group of 12 patients (6 males) who all had diabetes and an additional comorbidity such as hypertension and hyperlipidemia. Age of participants ranged from 30 to 77 years with an average age of 56. The majority of participants were Caucasian with 1 Asian and 2 African Americans. Hemoglobin A1c values ranged from 6.0% to 9.2% with approximately half having A1c values less than the goal of 7.0%. Half the patients were aware of PHRs, majority had smartphones, and all participants had access to the Internet and used email. Two of the patients were retired engineers who had prior experience with software design. The other sessions had between 7 and 8 participants at each session, and 7 patients completed the 90-minute usability testing session. There was a core group of 7 patients who were engaged in the design and testing sessions throughout the entire 9-month study. Key features of the PHR that emerged from design sessions included the following: (1) allow for annotation of data by patients (particularly important for lab values like glucose or for physical activity); (2) calendars, to do list, and reminder functions should be linked so that an entry in one of these allows for auto-population of this data within the other sections; (3) notifications whenever new data from the electronic health record or other sources are pushed to the PHR account; (4) allow for drag and drop of photos of pills/medications taken via smartphone or from other sources so that medication list has photo of actual pills or pill bottle; (5) allow for patients to customize the order of sections in the PHR dashboard so that the sections most important to the individual patient can be displayed more prominently; (6) allow for notifications from pharmacies to be pushed to the PHR (eg, confirmation of receipt of prescription requests or alert that prescription is ready to pick up); and (7) graphical display of trends over time (patients would like to select the measures and time frames to plot for display). Patients cited the importance of data provenance so that patient-entered data Versus provider or electronic health record data could be easily differentiated. Patients also highlighted the importance of having this PHR be a “one-stop shop for all their health data” and to have meaningful data dashboards for the different types of information needed to comprehensively manage their health. Patients wished for a single PHR that could easily bring together data from multiple patient portal accounts to avoid having to manage multiple accounts and passwords. They felt that heat map displays such as those used on popular fitness tracking websites were not intuitive and that the color-coding made interpretation challenging. Participants noted that engagement in the design process made them feel that they contributed towards developing software that could not only positively impact them individually but others as well. Every patient indicated the desire to participate on future design projects. Of the 19 tasks evaluated during usability testing, only 5 tasks could not be completed (eg, adding exercise to the calendar, opening the heat map, etc.). Patients felt that the overall PHR design was clean and aesthetically pleasing. Most patients felt that the site was “pretty easy to use” (6 out of 7). The majority of participants would like to use this PHR in the future (5) and would recommend this PHR to their friends/family to use (6). DISCUSSION/SIGNIFICANCE OF IMPACT: Involving patients directly in the design process for creating a patient-centric connected PHR was essential to sustaining engagement throughout the software life cycle and to informing the design of features and functionalities desired by patients with chronic conditions.

scholarly journals Hyperledger Healthchain: Patient-Centric IPFS-Based Storage of Health Records

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 3003
Author(s):  
Vinodhini Mani ◽  
Prakash Manickam ◽  
Youseef Alotaibi ◽  
Saleh Alghamdi ◽  
Osamah Ibrahim Khalaf
Keyword(s):  
Data Storage ◽  
Storage System ◽  
Security And Privacy ◽  
Health Record ◽  
Byzantine Fault Tolerance ◽  
Patient Privacy ◽  
Health Records ◽  
Healthcare Data ◽  
Blockchain Technology ◽  
Patient Centric

Blockchain-based electronic health system growth is hindered by privacy, confidentiality, and security. By protecting against them, this research aims to develop cybersecurity measurement approaches to ensure the security and privacy of patient information using blockchain technology in healthcare. Blockchains need huge resources to store big data. This paper presents an innovative solution, namely patient-centric healthcare data management (PCHDM). It comprises the following: (i) in an on-chain health record database, hashes of health records are stored as health record chains in Hyperledger fabric, and (ii) off-chain solutions that encrypt actual health data and store it securely over the interplanetary file system (IPFS) which is the decentralized cloud storage system that ensures scalability, confidentiality, and resolves the problem of blockchain data storage. A security smart contract hosted through container technology with Byzantine Fault Tolerance consensus ensures patient privacy by verifying patient preferences before sharing health records. The Distributed Ledger technology performance is tested under hyper ledger caliper benchmarks in terms of transaction latency, resource utilization, and transaction per second. The model provides stakeholders with increased confidence in collaborating and sharing their health records.

The Collaborative Network Approach: a model for advancing patient-centric research for Castleman disease and other rare diseases

2019 ◽  
Vol 3 (1) ◽  
pp. 97-105
Author(s):  
Mary Zuccato ◽  
Dustin Shilling ◽  
David C. Fajgenbaum
Keyword(s):  
Rare Diseases ◽  
Treatment Options ◽  
Castleman Disease ◽  
High Impact ◽  
Collaborative Network ◽  
Network Approach ◽  
Grant Applications ◽  
Research Questions ◽  
Novel Strategy ◽  
Patient Centric

Abstract There are ∼7000 rare diseases affecting 30 000 000 individuals in the U.S.A. 95% of these rare diseases do not have a single Food and Drug Administration-approved therapy. Relatively, limited progress has been made to develop new or repurpose existing therapies for these disorders, in part because traditional funding models are not as effective when applied to rare diseases. Due to the suboptimal research infrastructure and treatment options for Castleman disease, the Castleman Disease Collaborative Network (CDCN), founded in 2012, spearheaded a novel strategy for advancing biomedical research, the ‘Collaborative Network Approach’. At its heart, the Collaborative Network Approach leverages and integrates the entire community of stakeholders — patients, physicians and researchers — to identify and prioritize high-impact research questions. It then recruits the most qualified researchers to conduct these studies. In parallel, patients are empowered to fight back by supporting research through fundraising and providing their biospecimens and clinical data. This approach democratizes research, allowing the entire community to identify the most clinically relevant and pressing questions; any idea can be translated into a study rather than limiting research to the ideas proposed by researchers in grant applications. Preliminary results from the CDCN and other organizations that have followed its Collaborative Network Approach suggest that this model is generalizable across rare diseases.

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