Abstract
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.
Highlights
Health care big data being collected today for patients typically comprises heterogeneous data sets collected as: electronic health records (EHR) of patient history, wearable and other sensor data, genetics, environmental factors, medical imaging, clinical diagnosis of signs/symptoms/outcomes, and laboratory results
Health care data comprises heterogeneous data sets collected from multiple sources, such as patient information, health claims, billing info, and user demographic data
We addressed the issue of long delays in data accessibility using a semi-automated honest broker solution within an online health application implementation
Summary
Health care big data being collected today for patients typically comprises heterogeneous data sets collected as: electronic health records (EHR) of patient history, wearable and other sensor data, genetics, environmental factors, medical imaging, clinical diagnosis of signs/symptoms/outcomes, and laboratory results. The American Medical Informatics Association (AMIA) Genomics and Translational Bioinformatics Working Group has identified knowledge discovery and data mining as important components of clinical research informatics and next-generation clinical decision support Building upon these advances, researchers and clinicians in ophthalmology and other fields of medicine can enhance existing knowledge relating to studies of disease management (diagnosis, prevention, early prediction, personalized treatment) for quality health care. Researchers and clinicians in ophthalmology and other fields of medicine can enhance existing knowledge relating to studies of disease management (diagnosis, prevention, early prediction, personalized treatment) for quality health care They can potentially analyze/visualize any accessible (protected) data sets to pursue medical breakthroughs in the areas of personalized medicine, and big data knowledge discovery.
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