Document Type
Journal Article
Publication Title
Applied Sciences
Publisher
MDPI
School
School of Science / ECU Security Research Institute
RAS ID
28869
Abstract
The current healthcare sector is facing difficulty in satisfying the growing issues, expenses, and heavy regulation of quality treatment. Surely, electronic medical records (EMRs) and protected health information (PHI) are highly sensitive, personally identifiable information (PII). However, the sharing of EMRs, enhances overall treatment quality. A distributed ledger (blockchain) technology, embedded with privacy and security by architecture, provides a transparent application developing platform. Privacy, security, and lack of confidence among stakeholders are the main downsides of extensive medical collaboration. This study, therefore, utilizes the transparency, security, and efficiency of blockchain technology to establish a collaborative medical decision-making scheme. This study considers the experience, skill, and collaborative success rate of four key stakeholders (patient, cured patient, doctor, and insurance company) in the healthcare domain to propose a local reference-based consortium blockchain scheme, and an associated consensus gathering algorithm, proof-of-familiarity (PoF). Stakeholders create a transparent and tenable medical decision to increase the interoperability among collaborators through PoF. A prototype of PoF is tested with multichain 2.0, a blockchain implementing framework. Moreover, the privacy of identities, EMRs, and decisions are preserved by two-layer storage, encryption, and a timestamp storing mechanism. Finally, superiority over existing schemes is identified to improve personal data (PII) privacy and patient-centric outcomes research (PCOR).
DOI
10.3390/app9071370
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.
Comments
Yang, J., Onik, M. M. H., Lee, N.-Y., Ahmed, M., & Kim, C.-S. (2019). Proof-of-familiarity: A privacy-preserved blockchain scheme for collaborative medical decision-making. Applied Sciences, 9(7), article 1370. https://doi.org/10.3390/app9071370