A Systematic Review of Privacy Preserving Healthcare Data Sharing on Blockchain

2020 ◽  
pp. 31-37
Author(s):  
Mustafa Tanriverdi ◽  
Keyword(s):  
Data Sharing ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Health Data ◽  
Health Records ◽  
Healthcare Data ◽  
Shared Data ◽  
Quality Of Health Services ◽  
Electronic Health

Sharing the electronic health data helps to increase the accuracy of the diagnoses and to improve the quality of health services. This shared data can also be used in medical research and can reduce medical costs. However, health data are fragmented across decentralized hospitals, this prevents data sharing and puts patients’ privacy at risks. In recent years, blockchain has revealed solutions that make life easier in many areas thanks to its distributed, safe and immutable structure. There are many blockchain-based studies in the literature on providing data privacy and sharing in different areas. In some studies, blockchain has been used with technologies such as cloud computing and cryptology. In the field of healthcare blockchain-based solutions are offered for the management and sharing of Electronic health records. In these solutions, private and consortium blockchain types are generally preferred and Public Key Infrastructure (PKI) and encryption are used for data privacy. Within the scope of this study, blockchain-based studies on the privacy preserving data sharing of health data were examined. In this paper, information about the studies in the literature and potential issues that can be studied in the future were discussed. In addition, information about current blockchain technologies such as smart contracts and PKI is also given.

scholarly journals Healthchain: A novel framework on privacy preservation of electronic health records using blockchain technology

PLoS ONE ◽  
2020 ◽  
Vol 15 (12) ◽  
pp. e0243043
Author(s):  
Shekha Chenthara ◽  
Khandakar Ahmed ◽  
Hua Wang ◽  
Frank Whittaker ◽  
Zhenxiang Chen
Keyword(s):  
Electronic Health Records ◽  
Data Security ◽  
Data Privacy ◽  
Health Data ◽  
Cyber Attacks ◽  
Security Risk ◽  
Patient Privacy ◽  
Health Records ◽  
Blockchain Technology ◽  
Electronic Health

The privacy of Electronic Health Records (EHRs) is facing a major hurdle with outsourcing private health data in the cloud as there exists danger of leaking health information to unauthorized parties. In fact, EHRs are stored on centralized databases that increases the security risk footprint and requires trust in a single authority which cannot effectively protect data from internal attacks. This research focuses on ensuring the patient privacy and data security while sharing the sensitive data across same or different organisations as well as healthcare providers in a distributed environment. This research develops a privacy-preserving framework viz Healthchain based on Blockchain technology that maintains security, privacy, scalability and integrity of the e-health data. The Blockchain is built on Hyperledger fabric, a permissioned distributed ledger solutions by using Hyperledger composer and stores EHRs by utilizing InterPlanetary File System (IPFS) to build this healthchain framework. Moreover, the data stored in the IPFS is encrypted by using a unique cryptographic public key encryption algorithm to create a robust blockchain solution for electronic health data. The objective of the research is to provide a foundation for developing security solutions against cyber-attacks by exploiting the inherent features of the blockchain, and thus contribute to the robustness of healthcare information sharing environments. Through the results, the proposed model shows that the healthcare records are not traceable to unauthorized access as the model stores only the encrypted hash of the records that proves effectiveness in terms of data security, enhanced data privacy, improved data scalability, interoperability and data integrity while sharing and accessing medical records among stakeholders across the healthchain network.

scholarly journals Privacy-preserving data sharing infrastructures for medical research: systematization and comparison

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Felix Nikolaus Wirth ◽  
Thierry Meurers ◽  
Marco Johns ◽  
Fabian Prasser
Keyword(s):  
Medical Research ◽  
Data Sharing ◽  
Privacy Protection ◽  
Data Privacy ◽  
Privacy Preserving ◽  
Distributed Data ◽  
Research Gaps ◽  
Individual Level ◽  
Shared Data ◽  
Trade Offs

Abstract Background Data sharing is considered a crucial part of modern medical research. Unfortunately, despite its advantages, it often faces obstacles, especially data privacy challenges. As a result, various approaches and infrastructures have been developed that aim to ensure that patients and research participants remain anonymous when data is shared. However, privacy protection typically comes at a cost, e.g. restrictions regarding the types of analyses that can be performed on shared data. What is lacking is a systematization making the trade-offs taken by different approaches transparent. The aim of the work described in this paper was to develop a systematization for the degree of privacy protection provided and the trade-offs taken by different data sharing methods. Based on this contribution, we categorized popular data sharing approaches and identified research gaps by analyzing combinations of promising properties and features that are not yet supported by existing approaches. Methods The systematization consists of different axes. Three axes relate to privacy protection aspects and were adopted from the popular Five Safes Framework: (1) safe data, addressing privacy at the input level, (2) safe settings, addressing privacy during shared processing, and (3) safe outputs, addressing privacy protection of analysis results. Three additional axes address the usefulness of approaches: (4) support for de-duplication, to enable the reconciliation of data belonging to the same individuals, (5) flexibility, to be able to adapt to different data analysis requirements, and (6) scalability, to maintain performance with increasing complexity of shared data or common analysis processes. Results Using the systematization, we identified three different categories of approaches: distributed data analyses, which exchange anonymous aggregated data, secure multi-party computation protocols, which exchange encrypted data, and data enclaves, which store pooled individual-level data in secure environments for access for analysis purposes. We identified important research gaps, including a lack of approaches enabling the de-duplication of horizontally distributed data or providing a high degree of flexibility. Conclusions There are fundamental differences between different data sharing approaches and several gaps in their functionality that may be interesting to investigate in future work. Our systematization can make the properties of privacy-preserving data sharing infrastructures more transparent and support decision makers and regulatory authorities with a better understanding of the trade-offs taken.

The process of sourcing and preparing electronic health records data to implement a machine-learning algorithm for early identification of maternal cardiovascular risk (Preprint)

2021 ◽  
Author(s):  
Nawar Shara ◽  
Kelley M. Anderson ◽  
Noor Falah ◽  
Maryam F. Ahmad ◽  
Darya Tavazoei ◽  
...  
Keyword(s):  
Machine Learning ◽  
Cardiovascular Risk ◽  
Electronic Health Records ◽  
Patient Care ◽  
Early Identification ◽  
Health Data ◽  
Patient Specific ◽  
Patient Records ◽  
Health Records ◽  
Electronic Health

BACKGROUND Healthcare data are fragmenting as patients seek care from diverse sources. Consequently, patient care is negatively impacted by disparate health records. Machine learning (ML) offers a disruptive force in its ability to inform and improve patient care and outcomes [6]. However, the differences that exist in each individual’s health records, combined with the lack of health-data standards, in addition to systemic issues that render the data unreliable and that fail to create a single view of each patient, create challenges for ML. While these problems exist throughout healthcare, they are especially prevalent within maternal health, and exacerbate the maternal morbidity and mortality (MMM) crisis in the United States. OBJECTIVE Maternal patient records were extracted from the electronic health records (EHRs) of a large tertiary healthcare system and made into patient-specific, complete datasets through a systematic method so that a machine-learning-based (ML-based) risk-assessment algorithm could effectively identify maternal cardiovascular risk prior to evidence of diagnosis or intervention within the patient’s record. METHODS We outline the effort that was required to define the specifications of the computational systems, the dataset, and access to relevant systems, while ensuring data security, privacy laws, and policies were met. Data acquisition included the concatenation, anonymization, and normalization of health data across multiple EHRs in preparation for its use by a proprietary risk-stratification algorithm designed to establish patient-specific baselines to identify and establish cardiovascular risk based on deviations from the patient’s baselines to inform early interventions. RESULTS Patient records can be made actionable for the goal of effectively employing machine learning (ML), specifically to identify cardiovascular risk in pregnant patients. CONCLUSIONS Upon acquiring data, including the concatenation, anonymization, and normalization of said data across multiple EHRs, the use of a machine-learning-based (ML-based) tool can provide early identification of cardiovascular risk in pregnant patients. CLINICALTRIAL N/A

scholarly journals Clinical Research Informatics Contributions from 2015

2016 ◽  
Vol 25 (01) ◽  
pp. 219-223
Author(s):  
R. Choquet ◽  
C. Daniel ◽  
Keyword(s):  
Clinical Research ◽  
Data Privacy ◽  
Design Stage ◽  
Editorial Team ◽  
Legal Requirements ◽  
Double Blind ◽  
Blind Review ◽  
Healthcare Data ◽  
Clinical Research Informatics ◽  
Electronic Health

Summary Objectives: To summarize key contributions to current research in the field of Clinical Research Informatics (CRI) and to select best papers published in 2015. Method: A bibliographic search using a combination of MeSH and free terms search over PubMed on Clinical Research Informatics (CRI) was performed followed by a double-blind review in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was finally organized to conclude on the selection of best papers. Results: Among the 579 returned papers published in the past year in the various areas of Clinical Research Informatics (CRI) - i) methods supporting clinical research, ii) data sharing and interoperability, iii) re-use of healthcare data for research, iv) patient recruitment and engagement, v) data privacy, security and regulatory issues and vi) policy and perspectives - the full review process selected four best papers. The first selected paper evaluates the capability of the Clinical Data Interchange Standards Consortium (CDISC) Operational Data Model (ODM) to support the representation of case report forms (in both the design stage and with patient level data) during a complete clinical study lifecycle. The second selected paper describes a prototype for secondary use of electronic health records data captured in non-standardized text. The third selected paper presents a privacy preserving electronic health record linkage tool and the last selected paper describes how big data use in US relies on access to health information governed by varying and often misunderstood legal requirements and ethical considerations. Conclusions: A major trend in the 2015 publications is the analysis of observational, “nonexperimental” information and the potential biases and confounding factors hidden in the data that will have to be carefully taken into account to validate new predictive models. In addiction, researchers have to understand complicated and sometimes contradictory legal requirements and to consider ethical obligations in order to balance privacy and promoting discovery.

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