Background Health care professionals are required to maintain accurate health records of patients. Furthermore, these records should be shared across different health care organizations for professionals to have a complete review of medical history and avoid missing important information. Nowadays, health care providers use electronic health records (EHRs) as a key to the implementation of these goals and delivery of quality care. However, there are technical and legal hurdles that prevent the adoption of these systems, such as concerns about performance and privacy issues. Objective This study aimed to build and evaluate an experimental blockchain for EHRs, named HealthChain, which overcomes the disadvantages of traditional EHR systems. Methods HealthChain is built based on consortium blockchain technology. Specifically, three organizations, namely hospitals, insurance providers, and governmental agencies, form a consortium that operates under a governance model, which enforces the business logic agreed by all participants. Every peer node hosts an instance of the distributed ledger consisting of EHRs and an instance of chaincode regulating the permissions of participants. Designated orderers establish consensus on the order of EHRs and then disseminate blocks to peers. Results HealthChain achieves functional and nonfunctional requirements. It can store EHRs in a distributed ledger and share them among different participants. Moreover, it demonstrates superior features, such as privacy preservation, security, and high throughput. These are the main reasons why HealthChain is proposed. Conclusions Consortium blockchain technology can help to build new EHR systems and solve the problems that prevent the adoption of traditional systems.

Because traffic flow data has complex spatial dependence and temporal correlation, it is a challenging problem for researchers in the field of Intelligent Transportation to accurately predict traffic flow by analyzing spatio-temporal traffic data. Based on the idea of spatio-temporal data fusion, fully considering the correlation of traffic flow data in the time dimension and the dependence of spatial structure, this paper proposes a new spatio-temporal traffic flow prediction model based on Graph Neural Network (GNN), which is called Bidirectional-Graph Recurrent Convolutional Network (Bi-GRCN). First, aiming at the spatial dependence between traffic flow data and traffic roads, Graph Convolution Network (GCN) which can directly analyze complex non-Euclidean space data is selected for spatial dependence modeling, to extract the spatial dependence characteristics. Second, considering the temporal dependence of traffic flow data on historical data and future data in its time-series period, Bidirectional-Gate Recurrent Unit (Bi-GRU) is used to process historical data and future data at the same time, to learn the temporal correlation characteristics of data in the bidirectional time dimension from the input data. Finally, the full connection layer is used to fuse the extracted spatial features and the learned temporal features to optimize the prediction results so that the Bi-GRCN model can better extract the spatial dependence and temporal correlation of traffic flow data. The experimental results show that the model can not only effectively predict the short-term traffic flow but also get a good prediction effect in the medium- and long-term traffic flow prediction.

BACKGROUND The maintenance of accurate health records of patients is a requirement of health care professionals. Furthermore, these records should be shared across different health care organizations in order for professionals to have a complete review of medical history and avoid missing important information. Nowadays, health care providers use electronic health records (EHRs) as a key to accomplishment of these jobs and delivery of quality care. However, there are technical and legal hurdles that prevent the adoption of these systems, such as the concern about performance and privacy issues. OBJECTIVE The aim of this paper is to build and evaluate an experimental blockchain for EHRs, named HealthChain, which addresses the disadvantages of traditional EHR systems. METHODS HealthChain is built based on consortium blockchain technology. Specifically, three stakeholders, namely hospitals, insurance providers, and governmental agencies, form a consortium that operates under a governance model, which enforces the business logic agreed by all participants. Peer nodes host instance of the distributed ledger consisting of EHRs, and instance of chaincode regulating the permissions of participants; designated orderers establish consensus on the order of EHRs and then disseminate blocks to peers. RESULTS HealthChain achieves the functional and non-functional requirements. While it can store EHRs in distributed ledger and share them among different participants, it demonstrates superior features, such as privacy preserving, security, and high throughout. These are the main reasons why HealthChain is proposed. CONCLUSIONS Consortium blockchain technology can help build EHR system and solve the problems that prevent the adoption of traditional ones.