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In the era of cloud computing and big data analysis, how to efficiently share and utilize medical information scattered across various care providers has become a critical problem. This paper proposes a new framework for sharing medical data in a secure and privacy-preserving way. This framework holistically integrates multi-authority attribute based encryption, blockchain and smart contract, as well as software defined networking to define and enforce sharing policies. Specifically in our framework, patients' medical records are encrypted and stored in hospital databases, where strict access controls are enforced with attribute based encryption coupled with privacy level classification. Our framework leverages blockchain technology to connect scattered private databases from participating hospitals for efficient and secure data provision, smart contracts to enable the business logic of clinical data usage, and software defined networking to revoke sharing privileges. The performance evaluation of our prototype demonstrates that the associated computation costs are reasonable in practice. 

Healthcare network and computing infrastructure is rapidly changing from closed environments to open environments that incorporate new devices and new application scenarios. Home-based healthcare is such an example of leveraging pervasive sensors and analyzing sensor data (often in real-time) to guide therapy or intervene. In this paper, we address the challenges in regulatory compliance when designing and deploying healthcare applications on a heterogeneous cloud environment. We propose the CareNet framework, consisting of a set of APIs and secure data transmission mechanisms, to facilitate the specification of home-based healthcare services running on the software-defined infrastructure (SDI). This work is a collaboration among computer scientists, medical researchers, healthcare IT and healthcare providers, and its goal is to reduce the gap between the availability of SDI and meeting regulatory compliance in healthcare applications. Our prototype demonstrates the feasibility of the framework and serves as testbed for novel experimental studies of emerging healthcare applications. 

Healthcare network and computing infrastructure is rapidly changing from closed environments to open environments that incorporate new devices and new application scenarios. Home-based healthcare is such an example of leveraging pervasive sensors and analyzing sensor data (often in real-time) to guide therapy or intervene. In this paper, we address the challenges in regulatory compliance when designing and deploying healthcare applications on a heterogeneous cloud environment. We propose CareNet framework, consisting of a set of abstraction and APIs, to allow the specification of compliance requirements. This work is a collaboration among computer scientists, medical researchers, healthcare IT and healthcare providers, and its goal is to reduce the gap between the availability of software defined infrastructure and meeting regulatory compliance in healthcare applications. 

Abstract Objective In response to COVID-19, the informatics community united to aggregate as much clinical data as possible to characterize this new disease and reduce its impact through collaborative analytics. The National COVID Cohort Collaborative (N3C) is now the largest publicly available HIPAA limited dataset in US history with over 6.4 million patients and is a testament to a partnership of over 100 organizations. Materials and Methods We developed a pipeline for ingesting, harmonizing, and centralizing data from 56 contributing data partners using 4 federated Common Data Models. N3C data quality (DQ) review involves both automated and manual procedures. In the process, several DQ heuristics were discovered in our centralized context, both within the pipeline and during downstream project-based analysis. Feedback to the sites led to many local and centralized DQ improvements. Results Beyond well-recognized DQ findings, we discovered 15 heuristics relating to source Common Data Model conformance, demographics, COVID tests, conditions, encounters, measurements, observations, coding completeness, and fitness for use. Of 56 sites, 37 sites (66%) demonstrated issues through these heuristics. These 37 sites demonstrated improvement after receiving feedback. Discussion We encountered site-to-site differences in DQ which would have been challenging to discover using federated checks alone. We have demonstrated that centralized DQ benchmarking reveals unique opportunities for DQ improvement that will support improved research analytics locally and in aggregate. Conclusion By combining rapid, continual assessment of DQ with a large volume of multisite data, it is possible to support more nuanced scientific questions with the scale and rigor that they require.