updated the intro and added logical and schema diagrams

content/patterns/emerging-disease-detection/_index.adoc

@@ -6,6 +6,8 @@ summary: This pattern is based on a demo implementation of an automated data pip
 products:
 - Red Hat OpenShift Container Platform
 - Red Hat OpenShift Serverless
+- Red Hat Single Sign-On
+- Red Hat AMQ Streams
 industries:
 - medical
 aliases: /emerging-disease-detection/
@@ -46,24 +48,52 @@ Detection of disease states like sepsis, stroke, pulmonary embolism, and heart a
 
 To demonstrate the effectiveness of the solution this pattern focuses on the specific problem of Sepsis. Sepsis is the body's extreme response to an infection. It is a life-threatening medical emergency. Sepsis is a costly and life threatening condition that can result in multi-organ failure. Beating conditions like sepsis requires rapid detection and mitigation of risks. With the immune system compromised, recovery at home is often preferred to minimize the risk for cross-infections, yet medical teams often lack the capability to perform constant surveillance for emerging risks across their patient cohorts, especially in rural settings. In this session, we will demonstrate an early warning system driven by Clinical AI at the Edge, fed by at-home post-operative monitoring and automated Clinical care escalation and coordination processes. 
 
-Sepsis is a costly and life threatening condition that can result in multi-organ failure. Beating conditions like sepsis requires rapid detection and mitigation of risks. With the immune system compromised, recovery at home is often preferred to minimize the risk for cross-infections, yet medical teams often lack the capability to perform constant surveillance for emerging risks across their patient cohorts, especially in rural settings. In this demonstration, we will follow a vulnerable, post-operative patient, Alani, as she recovers from surgery in her home setting. 
-
 == Technology Highlights:
 * Event-Driven Architecture
-* Mobile Engagement 
-* Machine Learning
-* BPM+ Process Automation
+* Data Science on OpenShift
+* Process Automation
+
+== Solution Discussion and Demonstration
+In this demonstration, we will follow a vulnerable, post-operative patient, Alani, as she recovers from surgery in her home setting. The pattern demonstrates an early warning system driven by Clinical AI at the Edge, fed by at-home post-operative monitoring and automated Clinical care escalation and coordination processes.
+
+This architecture pattern demonstrates three strengths:
+
+* Enabling healthcare at the edge
+** Taking healthcare past the edge of the cloud to support care processes in Low Bandwidth environments.
+* AI integrated into healthcare team workflows
+** BPM+ Health workflows support plug & play AI modules embedded into clinical workflows as Clinical Decision Support.
+* Event driven, Intelligent automation
+** Clinical best practices & processes automated using BPM+ Health authored workflows using FHIR API endpoints.
+
+For a thorough explanation of this solution in the context of Sepsis detection please consider reviewing the following 25 minute video. 
+
+// video link to a presentation on the use case
+.Overview of the solution in Sepsis Detection
+video::VHjpKIeviFE[youtube]
+
+== Pattern Architecture Overview
+
+As data arrives from Alani's various monitoring devices her latest metrics are collected in the FHIR database. Debezium is an open source distributed platform for change data capture. It will create an observation bundle for streaming to the AI model. This, in turn, will create a risk assessment and provide that to the process automation for review with Alani's doctor or the on-call doctor that is available. Their assessment may trigger further process workflows. 
+
 
 
 //figure 1 originally
 .Overview of the solution reference architecture
-image::emerging-disease-detection/edd-reference-architecture.png[Reference Architecture]
+image::emerging-disease-detection/edd-reference-architecture.png[link="/images/emerging-disease-detection/edd-reference-architecture.png"]
 
 In the following figure, logically, this solution can be viewed as being composed of an automation component, unified management including secrets management, and the clusters under management, all running on top of a user-chosen mixture of on-premise data centers and public clouds.
 
 //figure 2 originally
-.Sepsis Detection Workflow
-image::emerging-disease-detection/edd-sepsis-detection-ml-workflow.png[ML Workflow]
+.Sepsis Data Architecture
+image::emerging-disease-detection/edd-data-architecture.png[link="/images/emerging-disease-detection/edd-data-architecture.png", width=940]
+
+//figure 3 logical
+.Logical Architecture
+image::emerging-disease-detection/edd-logical-architecture-legend.png[link="/images/emerging-disease-detection/edd-logical-architecture-legend.png", width=940]
+
+//figure 4 Schema
+.Data Flow Architecture
+image::emerging-disease-detection/edd-schema-dataflow.png[link="/images/emerging-disease-detection/edd-schema-dataflow.png", width=940]
 
 [id="about-technology"]
 == About the technology
@@ -79,6 +109,12 @@ A declarative application continuous delivery tool for Kubernetes based on the A
 https://www.redhat.com/en/technologies/management/ansible[Red Hat Ansible Automation Platform]::
 Provides an enterprise framework for building and operating IT automation at scale across hybrid clouds including edge deployments. It enables users across an organization to create, share, and manage automation, from development and operations to security and network teams.
 
+https://www.redhat.com/en/technologies/jboss-middleware/amq[Red Hat AMQ Streams]::
+Red Hat AMQ streams is a massively scalable, distributed, and high-performance data streaming platform based on the Apache Kafka project. It offers a distributed backbone that allows microservices and other applications to share data with high throughput and low latency. Red Hat AMQ Streams is available in the Red Hat AMQ product.
+
+https://marketplace.redhat.com/en-us/products/red-hat-single-sign-on[Red Hat Single Sign-On]::
+Based on the Keycloak project, Red Hat Single Sign-On enhances security by enabling you to secure your web applications with Web single sign-on (SSO) capabilities based on popular standards such as SAML 2.0, OpenID Connect and OAuth 2.0.
+
 Hashicorp Vault::
 Provides a secure centralized store for dynamic infrastructure and applications across clusters, including over low-trust networks between clouds and data centers.