Security Privacy and Policy for Cryptographic Based Electronic Medical Information System

The construction and operational models of internet hospitals in China: a hospital-based survey study

BACKGROUND

China has empowered and continues to empower internet hospitals, which saw an increase in their development due to the pandemic, to fight against COVID-19. The construction and operational models of internet hospitals can be categorized as self-constructed and self-managed models, self-constructed and enterprise-run models, hospital and enterprise joint-owned models, and hosted by a third-party platform. Despite the growing importance of internet hospitals, there have been few systematic summaries of their construction and operational models. The primary purpose of the study was to understand the construction and operational models of internet hospitals in China.

METHODS

Data was collected from 39 internet hospitals and 356 medical staff between September 2020 and April 2021, via internet hospital and hospital staff surveys. T-tests were used to compare the continuous variables, while Chi-square tests were employed to compare the proportions of categorical variables. The self-perception of the internet hospitals' services was assessed using a 5-point Likert scale on 16 aspects and a root cause analysis was conducted to identify the root causes and influencing factors of current deficiencies experienced by internet hospitals.

RESULTS

Among the 39 internet hospitals, 22 (56.4%) were self-constructed and self-managed. Compared to other models of Internet hospitals, self-constructed and self-managed hospitals had lower percentages of professionals providing online services (P = 0.006), numbers of doctors outside of the entity (P = 0.006), numbers of online nurses (P = 0.004), and the ratio of online nurses to offline doctors (P < 0.001). Of the 16 aspects evaluated with regards to the medical staff's self-perception of the internet hospital services, the highest scores were given for fee transparency, fee rationality, travel cost capital, patience and responsibility, and consultation behaviors. The root causes included five aspects: human, channels, prices, services, and time.

CONCLUSIONS

While the self-constructed and self-managed model was found to be the most prevalent form of internet hospital in China, the different models of internet hospitals can have an impact on both the quantity and quality of online healthcare services. This study contributes to the existing literature on internet hospitals' construction and operational models, offering additional policy implications for telemedicine management.

A Novel Homomorphic Approach for Preserving Privacy of Patient Data in Telemedicine

Globally, the surge in disease and urgency in maintaining social distancing has reawakened the use of telemedicine/telehealth. Amid the global health crisis, the world adopted the culture of online consultancy. Thus, there is a need to revamp the conventional model of the telemedicine system as per the current challenges and requirements. Security and privacy of data are main aspects to be considered in this era. Data-driven organizations also require compliance with regulatory bodies, such as HIPAA, PHI, and GDPR. These regulatory compliance bodies must ensure user data privacy by implementing necessary security measures. Patients and doctors are now connected to the cloud to access medical records, e.g., voice recordings of clinical sessions. Voice data reside in the cloud and can be compromised. While searching voice data, a patient's critical data can be leaked, exposed to cloud service providers, and spoofed by hackers. Secure, searchable encryption is a requirement for telemedicine systems for secure voice and phoneme searching. This research proposes the secure searching of phonemes from audio recordings using fully homomorphic encryption over the cloud. It utilizes IBM's homomorphic encryption library (HElib) and achieves indistinguishability. Testing and implementation were done on audio datasets of different sizes while varying the security parameters. The analysis includes a thorough security analysis along with leakage profiling. The proposed scheme achieved higher levels of security and privacy, especially when the security parameters increased. However, in use cases where higher levels of security were not desirous, one may rely on a reduction in the security parameters.

Secure Outsourced Blockchain-Based Medical Data Sharing System Using Proxy Re-Encryption

The security and privacy of electronic health records (EHRs) have received considerable attention from healthcare workers and researchers. To ensure security, various encryption and decryption schemes as well as key management protocols have been developed. However, owing to sharing and scalability issues, additional security technologies have been proposed. Nonetheless, these technologies cause other problems, such as efficiency issues. Blockchain-based EHR management systems have been proposed to overcome computational overhead. However, because most blockchain systems are installed by outsourcing companies, EHRs may be leaked to the company. Hence, we herein propose a blockchain-based EHR management scheme with proxy re-encryption. In this scheme, we set a proxy server that re-encrypts the ciphertext between file servers, thereby solving EHR sharing issues. Furthermore, because the server is separated from the blockchain system, the outsourcing company cannot manipulate the server or access the records. In addition, the blockchain assists in access control by using smart contracts, thereby enabling secure and efficient EHR sharing. By performing security analysis, we prove that our proposed scheme solves the aforementioned security problems. In addition, we experimentally demonstrate the efficient operation of the proposed system.

WITHDRAWN: The role of medical and health policies in the health risk management system

Ahead of Print article withdrawn by publisher.

Design and Implementation of Human Motion Recognition Information Processing System Based on LSTM Recurrent Neural Network Algorithm

With the comprehensive development of national fitness, men, women, young, and old in China have joined the ranks of fitness. In order to increase the understanding of human movement, many researches have designed a lot of software or hardware to realize the analysis of human movement state. However, the recognition efficiency of various systems or platforms is not high, and the reduction ability is poor, so the recognition information processing system based on LSTM recurrent neural network under deep learning is proposed to collect and recognize human motion data. The system realizes the collection, processing, recognition, storage, and display of human motion data by constructing a three-layer human motion recognition information processing system and introduces LSTM recurrent neural network to optimize the recognition efficiency of the system, simplify the recognition process, and reduce the data missing rate caused by dimension reduction. Finally, we use the known dataset to train the model and analyze the performance and application effect of the system through the actual motion state. The final results show that the performance of LSTM recurrent neural network is better than the traditional algorithm, the accuracy can reach 0.980, and the confusion matrix results show that the recognition of human motion by the system can reach 85 points to the greatest extent. The test shows that the system can recognize and process the human movement data well, which has great application significance for future physical education and daily physical exercise.