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The adoption of big data analytics in healthcare applications is overwhelming not only because of the huge volume of data being analyzed, but also because of the heterogeneity and sensitivity of the data. Effective and efficient analysis and visualization of secure patient health records are needed to e.g., find new trends in disease management, determining risk factors for diseases, and personalized medicine. In this paper, we propose a novel community cloud architecture to help clinicians and researchers to have easy/increased accessibility to data sets from multiple sources, while also ensuring security compliance of data providers is not compromised. Our cloud-based system design configuration with cloudlet principles ensures application performance has high-speed processing, and data analytics is sufficiently scalable while adhering to security standards (e.g., HIPAA, NIST). Through a case study, we show how our community cloud architecture can be implemented along with best practices in an ophthalmology case study which includes health big data (i.e., Health Facts database, I2B2, Millennium) hosted in a campus cloud infrastructure featuring virtual desktop thin-clients and relevant Data Classification Levels in storage.
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Increasing protected data accessibility for age-related cataract research using a semi-automated honest broker
Ophthalmology researchers are becoming increasingly reliant on protected data sets to find new trends and enhance patient care. However, there is an inherent lack of trust in the current healthcare community ecosystem between the data custodians (i.e., health care organizations and hospitals) and data consumers (i.e., researchers and clinicians). This typically results in a manual governance approach that causes slow data accessibility for researchers due to concerns such as ensuring auditability for any authorization of data consumers, and assurance to ensure compliance with health data security standards. In this paper, we address this issue of long-drawn data accessibility by proposing a semi-automated “honest broker” framework that can be implemented in an online health application. The framework establishes trust between the data consumers and the custodians by: 1. improving the eiciency in compliance checking for data consumer requests using a risk assessment technique; 2. incorporating auditability for consumers to access protected data by including a custodian-in-the-loop only when essential; and 3. increasing the speed of large-volume data actions (such as view, copy, modify, and delete) using a popular common data model. Via an ophthalmology case study involving an age-related cataract research use case in a community cloud testbed, we demonstrate how our solution approach can be implemented in practice to improve timely data access and secure computation of protected data for ultimately achieving data-driven eye health insights.
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- Award ID(s):
- 1827177
- PAR ID:
- 10119290
- Date Published:
- Journal Name:
- Journal for modeling in ophthalmology
- Volume:
- 2
- Issue:
- 3
- ISSN:
- 2468-3922
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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