Cloud-Based Digital Twins’ Storage in Emergency Healthcare

Abstract

In a medical emergency situation, real-time patient data sharing may improve the survivability of a patient. In this paper, we explore how Digital Twin (DT) technology can be used for real-time data storage and processing in emergency healthcare. We investigated various enabling technologies, including cloud platforms, data transmission formats, and storage file formats, to develop a feasible DT storage solution for emergency healthcare. Through our analysis, we found Amazon AWS to be the most suitable cloud platform due to its sophisticated real-time data processing and analytical tools. Additionally, we determine that the MQTT protocol is suitable for real-time medical data transmission, and FHIR is the most appropriate medical file storage format for emergency healthcare situations. We propose a cloud-based DT storage solution, in which real-time medical data are transmitted to AWS IoT Core, processed by Kinesis Data Analytics, and stored securely in AWS HealthLake. Despite the feasibility of the proposed solution, challenges such as insufficient access control, lack of encryption, and vendor conformity must be addressed for successful practical implementation. Future work may involve Hyperledger Fabric technology and HTTPS protocol to enhance security, while the maturation of DT technology is expected to resolve vendor conformity issues. By addressing these challenges, our proposed DT storage solution has the potential to improve data accessibility and decision-making in emergency healthcare settings.

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“…Amazon Athena and Amazon Quick Sight are used for data analysis. AWS TwinMaker also supports the importation of multi-source and heterogeneous data [89,96] Consistent rendering performance in the AWS TwinMaker scene is not supported due to hardware dependence. The AWS TwinMaker scene (provides a visual context for the data connected to the service) rendering performance is hardware-dependent.…”

“…Amazon Kinesis Data Stream/analytics facilitates real-time data stream processing. AWS TwinMaker offers alerts based on data visualization to detect deviations from expected data or forecasts [89,92]. Devices not directly connected to the Internet can access AWS IoT Core through a physical hub [92].…”

“…AWS provides ML models and AI services, such as Amazon SageMaker, enabling the development of predictive models. Integration with Lambda and SageMaker enhances data processing and decision-making capabilities [89].…”

“…, such as CloudTrail and Amazon CloudWatch, can be used to set logging requirements to ensure sensitive data sent through API Gateway are captured. Storage modules (such as S3 and Glacier) are encrypted using management keys to ensure data privacy[89]. AWS supports more than 89 security standards and protocols than any other competitor.…”