In the domain of Electronic Medical Records(EMRs),emerging technologies are crucial to addressing longstanding concerns surrounding transaction security and patient privacy.This paper explores the integration of smart...In the domain of Electronic Medical Records(EMRs),emerging technologies are crucial to addressing longstanding concerns surrounding transaction security and patient privacy.This paper explores the integration of smart contracts and blockchain technology as a robust framework for securing sensitive healthcare data.By leveraging the decentralized and immutable nature of blockchain,the proposed approach ensures transparency,integrity,and traceability of EMR transactions,effectivelymitigating risks of unauthorized access and data tampering.Smart contracts further enhance this framework by enabling the automation and enforcement of secure transactions,eliminating reliance on intermediaries and reducing the potential for human error.This integration marks a paradigm shift in management and exchange of healthcare information,fostering a secure and privacy-preserving ecosystem for all stakeholders.The research also evaluates the practical implementation of blockchain and smart contracts within healthcare systems,examining their real-world effectiveness in enhancing transactional security,safeguarding patient privacy,and maintaining data integrity.Findings from the study contribute valuable insights to the growing body of work on digital healthcare innovation,underscoring the potential of these technologies to transform EMR systems with high accuracy and precision.As global healthcare systems continue to face the challenge of protecting sensitive patient data,the proposed framework offers a forward-looking,scalable,and effective solution aligned with the evolving digital healthcare landscape.展开更多
The Internet ofMedical Things(IoMT)is mainly concernedwith the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically,whereas machine learning approaches enable th...The Internet ofMedical Things(IoMT)is mainly concernedwith the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically,whereas machine learning approaches enable these smart systems to make informed decisions.Generally,broadcasting is used for the transmission of frames,whereas congestion,energy efficiency,and excessive load are among the common issues associated with existing approaches.In this paper,a machine learning-enabled shortest path identification scheme is presented to ensure reliable transmission of frames,especially with the minimum possible communication overheads in the IoMT network.For this purpose,the proposed scheme utilises a well-known technique,i.e.,Kruskal’s algorithm,to find an optimal path from source to destination wearable devices.Additionally,other evaluation metrics are used to find a reliable and shortest possible communication path between the two interested parties.Apart from that,every device is bound to hold a supplementary path,preferably a second optimised path,for situations where the current communication path is no longer available,either due to device failure or heavy traffic.Furthermore,the machine learning approach helps enable these devices to update their routing tables simultaneously,and an optimal path could be replaced if a better one is available.The proposed mechanism has been tested using a smart environment developed for the healthcare domain using IoMT networks.Simulation results show that the proposed machine learning-oriented approach performs better than existing approaches where the proposed scheme has achieved the minimum possible ratios,i.e.,17%and 23%,in terms of end to end delay and packet losses,respectively.Moreover,the proposed scheme has achieved an approximately 21%improvement in the average throughput compared to the existing schemes.展开更多
文摘In the domain of Electronic Medical Records(EMRs),emerging technologies are crucial to addressing longstanding concerns surrounding transaction security and patient privacy.This paper explores the integration of smart contracts and blockchain technology as a robust framework for securing sensitive healthcare data.By leveraging the decentralized and immutable nature of blockchain,the proposed approach ensures transparency,integrity,and traceability of EMR transactions,effectivelymitigating risks of unauthorized access and data tampering.Smart contracts further enhance this framework by enabling the automation and enforcement of secure transactions,eliminating reliance on intermediaries and reducing the potential for human error.This integration marks a paradigm shift in management and exchange of healthcare information,fostering a secure and privacy-preserving ecosystem for all stakeholders.The research also evaluates the practical implementation of blockchain and smart contracts within healthcare systems,examining their real-world effectiveness in enhancing transactional security,safeguarding patient privacy,and maintaining data integrity.Findings from the study contribute valuable insights to the growing body of work on digital healthcare innovation,underscoring the potential of these technologies to transform EMR systems with high accuracy and precision.As global healthcare systems continue to face the challenge of protecting sensitive patient data,the proposed framework offers a forward-looking,scalable,and effective solution aligned with the evolving digital healthcare landscape.
文摘The Internet ofMedical Things(IoMT)is mainly concernedwith the efficient utilisation of wearable devices in the healthcare domain to manage various processes automatically,whereas machine learning approaches enable these smart systems to make informed decisions.Generally,broadcasting is used for the transmission of frames,whereas congestion,energy efficiency,and excessive load are among the common issues associated with existing approaches.In this paper,a machine learning-enabled shortest path identification scheme is presented to ensure reliable transmission of frames,especially with the minimum possible communication overheads in the IoMT network.For this purpose,the proposed scheme utilises a well-known technique,i.e.,Kruskal’s algorithm,to find an optimal path from source to destination wearable devices.Additionally,other evaluation metrics are used to find a reliable and shortest possible communication path between the two interested parties.Apart from that,every device is bound to hold a supplementary path,preferably a second optimised path,for situations where the current communication path is no longer available,either due to device failure or heavy traffic.Furthermore,the machine learning approach helps enable these devices to update their routing tables simultaneously,and an optimal path could be replaced if a better one is available.The proposed mechanism has been tested using a smart environment developed for the healthcare domain using IoMT networks.Simulation results show that the proposed machine learning-oriented approach performs better than existing approaches where the proposed scheme has achieved the minimum possible ratios,i.e.,17%and 23%,in terms of end to end delay and packet losses,respectively.Moreover,the proposed scheme has achieved an approximately 21%improvement in the average throughput compared to the existing schemes.
