Secure Healthcare System Research Articles

Operation strategies of the supplier, demander, and third party under the DRG/DIP reform

In this study, we analyze the operation strategies of the supplier, demander, and third party involved in the current process of China’s health insurance reform. Specifically, we summarize the relevant literature on the reform of healthcare insurance both in China and abroad, explore the current changes in healthcare policy, and identify the main problems in the current diagnosis-related group/diagnosis-intervention packet (DRG/DIP) payment method reform. Based on our analysis, we propose strategies that can satisfy the needs of the supplier, demand side, and third parties and offer valuable suggestions to enhance the healthcare security system and promote the DRG/DIP payment method reform.

Smart Agriculture-Based Food Quality Analysis with Healthcare Security System Using Cloud Machine Learning Model

Smart Agriculture-Based Food Quality Analysis with Healthcare Security System Using Cloud Machine Learning Model

CICIoMT2024: A benchmark dataset for multi-protocol security assessment in IoMT

The Internet of Things (IoT) is increasingly integrated into daily life, particularly in healthcare, through the Internet of Medical Things (IoMT). IoMT devices support services like continuous health monitoring but raise significant cybersecurity concerns due to their vulnerability to various attacks. The complexity and data volume of IoMT network traffic requires advanced methods to enhance security and reliability. Machine Learning (ML) offers techniques to detect, prevent, and mitigate cyberattacks. However, existing benchmark datasets lack essential features for robust IoMT security solutions, such as a reduced number of real devices, a limited variety of attacks, and a lack of extensive profiling. We propose a realistic benchmark dataset for IoMT security solutions development and evaluation to address these gaps. We executed 18 attacks on an IoMT testbed with 40 devices (25 real and 15 simulated), using protocols like Wi-Fi, MQTT, and Bluetooth. Supporting technologies, including dedicated network traffic collectors and a Faraday Cage, ensured data quality. The attacks fall into five categories: DDoS, DoS, Recon, MQTT, and spoofing. We aim to establish a baseline that complements existing datasets, aiding researchers in creating secure healthcare systems using ML. Beyond simulating attacks, we capture the lifecycle of IoMT devices from network entry to exit through profiling, allowing classifiers to identify device anomalies. The resulting CICIoMT2024 dataset, published on the CIC dataset page, demonstrates that various methods can classify IoMT cyberattacks. This effort supports new IoMT security solutions and contributes to the broader field of cybersecurity in healthcare, ensuring more reliable IoMT device deployment.

The Structural Equation Modelling of First Level Health Facilities’ Performance-Based Capitation Payment in National Health Service

The BPJS Healthcare Security implemented performance-based capitation/Kapitasi Berbasis Kinerja(KBK) payments to conduct structured and ongoingmonitoring of the First Level Health Facilities/Fasilitas Kesehatan Tingkat Pertama(FKTP)'s performance throughout Indonesia. This study aimed to examinethe direct and indirect effects of FKTP capacity on KBK and FKTP performance. This study was conducted in February 2023, and Structural EquationModelling analysis was performed with secondary data from the BPJS Healthcare Security system database in 2021. Human resources were defined as theavailability of doctors in FKTP, infrastructure as credentialing value, ownership and type of FKTP, and KBK, and performance as first contact, non-specialistoutpatient referral ratios, and Chronic Disease Management Program/Program Pengelolaan Penyakit Kronis(Prolanis) participant ratio. The analytical resultsrevealed that human resources directly and positively affected the KBK. The indirect effect analysis results suggested that only human resources impactedFKTP performance. In conclusion, human resources are critical in achieving the KBK and FKTP performance.

Trilinear pairing-based cryptosystem authentication: for electronic health record security in healthcare system

Trilinear pairing-based cryptosystem authentication: for electronic health record security in healthcare system

A Secure Healthcare System for Iot Using Artificial Intelligence

A Secure Healthcare System for Iot Using Artificial Intelligence

On the Governance of the Basic Healthcare Security System and Economic Development in China in the Context of COVID-19

On the Governance of the Basic Healthcare Security System and Economic Development in China in the Context of COVID-19

Security Risk Assessment Framework for the Healthcare Industry 5.0

The relevance of Industry 5.0 confirms the collaborative relationship between humans and machines through an inclusive automation process. The healthcare industry at present is facilitated by the use of these emerging technologies, which promise a more personalized, patient-centric approach, enabling more prompt, cost-effective, and efficacious medical care to the affected. However, managing enormous data volumes, lack of standards, risks to data security, and regulatory obstacles, such as regulatory compliance, are critical issues that must be addressed to ensure that Industry 5.0 can be effectively integrated into the healthcare industry. This research assumes significance in the stated context as it seeks to reveal the gaps between security risks and threats assessments for personalized healthcare services based on Industry 5.0. The study’s investigations cite that the identification of security risks and various threats is an imperative need and must be prioritized so as to ensure optimal security for the healthcare system. Furthermore, the study peruses various security threats and security risk assessments for enhancing and safeguarding the healthcare industry. Moreover, the study also proposes a framework for security risk assessment based on Industry 5.0 (SRVFHI5.0) for the healthcare security system. A step-wise procedure is applied to validate the proposed framework and provide support for designing feasible security evaluation criteria and tools for future research. Statistical analysis was performed to evaluate the measure of the applicability of multiple criteria, the tool’s reliability, and factor analysis. This offers an adequate basis for accepting the suggested risk assessment methodology based on Healthcare Industry 5.0 for implementation as well as further research and analysis.

Retracted: Blockchain in Secure Healthcare Systems: State of the Art, Limitations, and Future Directions

Retracted: Blockchain in Secure Healthcare Systems: State of the Art, Limitations, and Future Directions

Blockchain-Powered Healthcare Systems: Enhancing Scalability and Security with Hybrid Deep Learning.

The rapid advancements in technology have paved the way for innovative solutions in the healthcare domain, aiming to improve scalability and security while enhancing patient care. This abstract introduces a cutting-edge approach, leveraging blockchain technology and hybrid deep learning techniques to revolutionize healthcare systems. Blockchain technology provides a decentralized and transparent framework, enabling secure data storage, sharing, and access control. By integrating blockchain into healthcare systems, data integrity, privacy, and interoperability can be ensured while eliminating the reliance on centralized authorities. In conjunction with blockchain, hybrid deep learning techniques offer powerful capabilities for data analysis and decision making in healthcare. Combining the strengths of deep learning algorithms with traditional machine learning approaches, hybrid deep learning enables accurate and efficient processing of complex healthcare data, including medical records, images, and sensor data. This research proposes a permissions-based blockchain framework for scalable and secure healthcare systems, integrating hybrid deep learning models. The framework ensures that only authorized entities can access and modify sensitive health information, preserving patient privacy while facilitating seamless data sharing and collaboration among healthcare providers. Additionally, the hybrid deep learning models enable real-time analysis of large-scale healthcare data, facilitating timely diagnosis, treatment recommendations, and disease prediction. The integration of blockchain and hybrid deep learning presents numerous benefits, including enhanced scalability, improved security, interoperability, and informed decision making in healthcare systems. However, challenges such as computational complexity, regulatory compliance, and ethical considerations need to be addressed for successful implementation. By harnessing the potential of blockchain and hybrid deep learning, healthcare systems can overcome traditional limitations, promoting efficient and secure data management, personalized patient care, and advancements in medical research. The proposed framework lays the foundation for a future healthcare ecosystem that prioritizes scalability, security, and improved patient outcomes.

Access Control and Privacy-Preserving Blockchain-Based System for Diseases Management

In many developing countries, the healthcare sector is facing several challenges, mainly due to the lack of personal, institutions, and medications in public health systems. Over the past decade, information and communication technology has proved its ability to improve medical quality, reduce costs, and promote data security. Developing countries can exploit these technologies to improve the healthcare process and ensure remote health monitoring, especially in rural areas. The Internet of Things and smart medical devices are widely used to provide remote patient monitoring. Current systems are based on centralized communication with cloud servers. However, this architecture increases several security and privacy risks. The adoption of a distributed architecture is required to overcome these issues. In this article, we describe a Blockchain-based system for securing Internet-of-Things (IoT) healthcare devices. In addition to data encryption, we propose to use Blockchain technology to enhance security and privacy in healthcare systems. The system is intended to allow remote patient monitoring, particularly for chronic diseases that necessitate regular monitoring. Three important characteristics were taken into account: security, scalability, and processing time. The security concerns are ensured by using the re-encryption proxy in conjunction with Blockchain to encrypt data and control access to it. To ensure Blockchain scalability, data are stored in an InterPlanetary file system (IPFS) off-chain database. We use an Ethereum Blockchain based on proof of authority (PoA) to speed up the data storage. In comparison to existing methods, the experimental system has shown a significant improvement in the security of healthcare systems.

SECURING IOT HEALTHCARE APPLICATIONS AND BLOCKCHAIN: ADDRESSING SECURITY ATTACKS

The Internet of Things (IoT) describes the connection of bodily devices as "things" that can communicate with other systems and devices through the Internet and exchange statistics (data or information), facilitating the exchange of data with other systems and devices. These devices have sensors, software, and various components designed to exchange data seamlessly within the IoT network. Securing and protecting the data transmitted over the Internet from unauthorized access is imperative to ensuring the integrity and confidentiality of the information. IoT Smart health monitoring systems, integral components of the IoT landscape, are susceptible to various attacks. These include denial of service (DoS), fingerprint, router, select, forwarding, sensor, and replay attacks, all of which pose significant threats to the security of these systems. As such, there is a pressing need to address and mitigate the vulnerabilities associated with IoT healthcare applications. This paper aims to explore the significant role of IoT devices in healthcare systems and provide an in-depth review of attacks that threaten the security of IoT healthcare applications. The study analyses the existing literature on the vulnerabilities present in smart health monitoring systems and the potential application of blockchain technology as a robust solution to enhance the security of IoT healthcare applications. This research reveals critical vulnerabilities in IoT healthcare applications and highlights blockchain's effectiveness in mitigating them, providing insights for robust security measures and strategic decision-making in secure healthcare systems. This paper provides valuable insight and recommendations for policymakers, researchers, and practitioners involved in the domain of the IoT healthcare system.

Care4U: Integrated healthcare systems based on blockchain

During the COVID-19 crisis, the need to stay at home has increased dramatically. In addition, the number of sick people, especially elderly persons, has increased exponentially. In such a scenario, home monitoring of patients can ensure remote healthcare at home using advanced technologies such as the Internet of Medical Things (IoMT). The IoMT can monitor and transmit sensitive health data; however, it may be vulnerable to various attacks. In this paper, an efficient healthcare security system is proposed for IoMT applications. In the proposed system, the medical sensors can transmit sensed encrypted health data via a mobile application to the doctor for privacy. Then, three consortium blockchains are constructed for load balancing of transactions and reducing transaction latency. They store the credentials of system entities, doctors' prescriptions and recommendations according to the data transmitted via mobile applications, and the medical treatment process. Besides, cancelable biometrics are used for providing authentication and increasing the security of the proposed medical system. The investigational results show that the proposed system outperforms existing work where the proposed model consumed less processing time by values of 18%, 22%, and 40%, and less energy for processing a 200 ​KB file by values of 9%, 13%, and 17%. Finally, the proposed model consumed less memory usage by values of 7%, 7%, and 18.75%. From these results, it is clear that the proposed system gives a very reliable and secure performance for efficiently securing medical applications.

Multimodal Fusion-Based Image Hiding Algorithm for Secure Healthcare System

The development of artificial intelligence (AI) plays a significant role of multimedia applications, especially in the healthcare domain. However, it has brought about the problem of sensitive information leakage. To address these challenges, an interesting multimodal fusion-based robust image hiding algorithm is proposed in this paper. Firstly, fused image is considered as mark image generated from MRI and CT images using non-subsampled shearlet transform (NSST). Secondly, we employed principal component analysis (PCA) to compute the appropriate coefficients of cover image for embedding purpose. Thirdly, fused mark image is Arnold cat map encoded to address the security issue hidden mark media. Finally, the fusion of fractional dual-tree complex wavelet transform (Fr-DTCWT) and randomized singular value decomposition (RSVD) is utilized to conceal encrypted fused mark media inside host image. Our findings show that the proposed algorithm outperforms some of the recent techniques in terms of high robustness and invisibility.

An energy efficient and secure model using chaotic levy flight deep Q-learning in healthcare system

Fog computing is a modern research trend that brings cloud computing services closer to the network edge. It serves as middleware between cloud and Internet of Things (IoT) devices by bringing application services and computing resources closer to the device generating the data. However, there are several real-time issues that have not been completely resolved, including load balancing, network utilization, energy consumption, traffic overhead, computational cost, latency, and so on. To address these issues, we propose a novel DDQ-CLF algorithm-based fog server classification model. The proposed fog-enabled secure healthcare system comprises three main layers: the IoT layer, the fog layer, and the cloud layer. In the IoT layer, medical healthcare data containing confidential details of patients is collected through sensors connected to smartphones. In the fog layer, the load balancer agent plays a main role in determining the suitable fog server to process the incoming request based on server characteristics and predefined conditions. Finally, the health status results obtained from the fog layer are communicated to the cloud through a proxy server. These procedures make the system more secure for communicating confidential information through the network layers. The performance of the proposed DDQ-CLF algorithm is evaluated using different indicators, such as traffic overhead, energy consumption, computational cost, makespan, latency, network utilization, LBL, and ARU. The simulation results demonstrate that the proposed DDQ-CLF algorithm achieves a superior performance rate compared to other techniques in terms of all metrics, particularly with the best LBL of about 73.21 %.

Review Analysis of Cyber Security in Healthcare System: A Systematic Approach of Modern Development

The healthcare industry provides medical devices such as pharmaceuticals. The third-party vendor can also pose a risk to the organization. Cyber security if they are not properly vetted and do not have adequate security measures in place. These will help to mitigate and other cyber security risks, healthcare organizations should implement a range of security measures. Regular security assessments in healthcare organizations should conduct a regular security assessment to identify vulnerable in their systems and network. Distributed dental or services attacks where criminals overload the healthcare system. Health care system servers with traffic, causing them to crash and preventive. Lag mate users from accessing the system's network, stealing data, and causing damage to the system. Ensure that these tools are updated regularly to protect against the latest threats. Regularly check the backup data and critical data and store them in a secure location. Monitoring network activity to detect and respond to any potential security incidents conduct regularly.

A Review on Data Breaches in Healthcare Security Systems

A Review on Data Breaches in Healthcare Security Systems

Trilinear Pairing-Based Cryptosystem Authentication: For Electronic Health Record Security in Healthcare System

Trilinear Pairing-Based Cryptosystem Authentication: For Electronic Health Record Security in Healthcare System

Data Security in Healthcare Systems: Integration of Information Security and Information Management

Effective management of patient data is critical for improving the quality of care and patient outcomes in healthcare systems. However, ensuring the confidentiality, integrity, and availability of patient data while complying with regulatory requirements can be challenging. To address this challenge, this work proposes an artificial intelligence (AI)-enabled framework that integrates information security (IS) and information management (IM) capabilities into a unified solution for improving the overall functionality of healthcare systems. The proposed framework leverages AI algorithms to automate managerial transactions of healthcare systems, while ensuring they are secure against possible threats. By automating these tasks, the framework can reduce the burden on healthcare staff, improve the accuracy and speed of information processing, and reduce the risk of human error. Our framework provides accurate and timely information to healthcare providers, enabling them to make informed decisions and provide better care to patients.

Enhancing cyber security in health care industry by using ISO 27001 accreditation

Healthcare Industry plays a pivotal role in every one's life and with rapid advancements in cyber-attack vectors, threat actors and their strategies it has in-need created a necessity and a challenge to the numerous organizations and to the Governments as well, to stand guard and secure the institutes and the data stored with them. To design a secure healthcare system involves several considerations to protect sensitive patient data and ensure the confidentiality, integrity, and availability of information. Lot of work has been published on cyber security along with importance of protecting the Personally Identifiable Information (PII) and patient health records stored in hospitals, and also comparisons were made between paid or licensed tools and open source; however, implementation of the tools in real time was not in place where the financial limitations are a real concern and security is a need. This paper has given insight into important parameters such as risk assessment and security policies etc.