Technology
The SHARES granular consent tool builds on the open-source Office of the National Coordinator (ONC) Leading Edge Acceleration Programs (LEAP) Computable Consent Project. The SHARES software catalog features numerous components that can be deployed in different configurations to meet specific use cases and requirements, all underpinned by our modular SHARES core library. Reference architecture ‘C’, below, leverages a CDS Hooks-based integration pattern to apply FHIR security labeling and resource redaction rules to data payloads evaluated by a centralized CDS Hooks service using the HTTP request/request protocol.
![SHARES system architecture](https://www.asushares.com/wp-content/uploads/2024/05/architecture-c.jpeg)
Reference architecture ‘E’, by contrast, utilizes the same SHARES core library in an embedded mode wherein the data sharing engine executes inside a SMART-on-FHIR app launched within a healthcare system’s EHR. When integrated in this way, the engine can only “see” data accessible by the user who launched the app.
![SHARES system architecture](https://www.asushares.com/wp-content/uploads/2024/05/architecture-e.jpeg)
Aim 2
Demonstrate the feasibility of automated SUD data segmentation.
![](https://www.asushares.com/wp-content/uploads/2024/02/SHARES-Engine-Intro.pptx.png)
It manages clinical decision support (CDS) rules that support consent data sharing choices. The hosted copy is available here.
It is the FHIR consent resource administration and management UI for visually browsing, creating, and maintaining consent-related FHIR documents from a FHIR backend URL configured at runtime. The hosted copy is available here.
It is a reference implementation providing FHIR-based healthcare data sharing determination decisions and content data redaction functions based on FHIR R5 and CDS Hooks v1 and v2.
This is a prototype of what the patient portal could resemble in the future. The hosted copy is available here.
![](https://www.asushares.com/wp-content/uploads/2024/02/3Q2A7900-edited-square-small-scaled.jpg)
Dr. Preston Lee
Co-Investigator & Chief Architect
Dr. Lee, PhD, MBA, FAMIA is the author of the HL7/Logica Marketplace Standard for Trial Use (STU) specification, architect of the Skycapp health IT and CDS distribution platform, and CTO of Logica Health: the U.S. non-profit developer of the HL7/Logica FHIR Sandbox system. Dr. Lee also serves as advisor for HL7 International on HL7 Foundry and FHIR Accelerator programs and provides commercial assistance to health and wellness organizations committed to standards & interoperability.
Previously, Dr. Lee has served for VHA, Translational Genomics Research Institute, and many others to advance worldwide interoperability of healthcare data and software systems.
![A picture of Abhishek Dhadwal, shown wearing a blue shirt on a white background.](https://www.asushares.com/wp-content/uploads/2024/09/DSC_0748.jpg)
Abhishek Dhadwal
Graduate Research Assistant
Abhishek is a Computer Science (concentration : Biomedical Informatics) Masters Student tasked with deploying and testing the SHARES Consent Engine, as well as last-mile assembly of synthetic FHIR data resources, application of SHARES terminology scoring rules, and management of the production rule set. His deliverables directly affect the functional behavior of the SHARES CDS stack that allow (or prevent) SHARES use cases from running as expected.
Abhishek’s prior research involved the creation of a novel multi-modal depression detection framework that that combined real-time Mobile Crowd Sensing, task-based mechanisms, and an end-to-end machine learning pipeline.
![](https://www.asushares.com/wp-content/uploads/2024/01/Kaiser_Martha-rotated.jpg)
Martha Kaiser
Graduate Research Assistant
Martha is a Biomedical Informatics (BMI) masters student responsible for the amalgamation and transform of raw patient data into standards-compliant FHIR resource bundles, including representation of irreconciled terminology differences as are common in real-world EHR systems. Her data deliverables to the SHARES technology team establish the underlying patient data resources upon which all SHARES use cases are built.
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