Situational Awareness for Novel Epidemic Response - Local Development build (v0.1.0). See the Directory of published versions

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The Situational Awareness for Novel Epidemic Response Implementation Guide enables transmission of high level situational awareness information from inpatient facilities to centralized data repositories to support the treatment of novel influenza-like illness.

Fighting COVID-19 with FHIR®

About This Guide

This is a draft implementation guide to promote discussion with leaders in the Health IT industry, and very much a work in progress. All content in this guide is subject to discussion and change.

The goal of publishing this guide is to encourage the creation of a community interested in extremely rapid development of interfaces that can support communication Bed and other resourcees to Public Health in this time of crisis.

This implementation guide provides the FHIR Implementation materials associated with the SANER project.

Organization of This Guide

This guide is organized into four main sections:

1. Volume I: Overview - Provides an overview of the challenges this effort is trying to address.
   1. Technology Environment - Describes the technology environment for uninitiated.
   2. Architecture - Illustrates the Microservice Architecture
   3. Security Considerations - Documents security concerns and mitigations
   4. Use Cases - Illustrates key use cases
   5. Actors and Transactions - Provides an overview of technical components
   6. Measures used for Reporting - Defines measures used by CDC and FEMA as FHIR Measure resources
2. Volume II: Transactions
   1. Query Measure [PULL-TX]
   2. Produce Measure [PUSH-TX]
   3. Communicate Results [REPORT-TX]
3. Volume III: Content Profiles
   1. Public Health Measure
   2. Measure Group Attributes
   3. Measure Criteria
   4. [Measure Population Alternate Criteria(StructureDefinition-MeasurePopulationAlternateCriteria.html)
   5. Measured Item Description
   6. Audit Event Write
   7. Audit Event Read
4. Volume IV: Terminology
   1. Code Systems
      1. Bed Type
      2. Device Field Names
      3. Group System
      4. Location Field Names
      5. Measure Rate Aggregation
      6. Population System
   2. Value Sets
      1. Bed Location Operational Status
      2. Bed Location Types
      3. Bed Property
      4. Bed Type
      5. Confirmed COVID19 Diagnosis
      6. Confirmed COVID19 Diagnosis Values
      7. COVID19 Diagnostic Test
      8. ED or Overflow Locations
      9. Fields
      10. Groups
      11. ICU Locations
      12. Inpatient Locations
      13. Location Fields
      14. Measure Rate Aggregation Values
      15. Measure Status
      16. Occupied Bed
      17. Patient Died
      18. Populations
      19. Suspected Or Diagnosed COVID19
      20. Ventilator Devices
      21. Ventilator Devices With Tracheostomy
5. Volume V: Examples
   1. Measure: CDC Patient Impact and Hospital Capacity
   2. Measure: FEMA Daily Hospital COVID19 Reporting
   3. Location: New Jersey
   4. Organization: NJ Department of Health

Click on any of the links above, head on over the table of contents, or if you are looking for a specific artifact, check out the index.

You can also download:

• this entire guide,
• the definition resources in json, xml, ttl, or csv format, or
• the example resources in json, xml or ttl format.

The source code for this Implementation Guide can be found on https://github.com/AudaciousInquiry/saner-ig.

About Audacious Inquiry

Audacious Inquiry (Ai) is an industry-shaping health information technology and policy company that provides bold solutions for connected healthcare. Nationally recognized for its work to facilitate health data interoperability, Ai is a trusted partner to CMS, ONC, state Hospital Associations and Medicaid agencies across the country. The company delivers a cloud-based software as a service platform that is the catalyst for secure and smart health information exchange among physicians, hospitals, health plans, ACOs, MCOs, and public health agencies across 12 US states. Ai is raising the bar for how health data is shared, managed, and protected.