Merge pull request #208142 from msjasteppe/iot-concepts-update

Light updates.

Author: liljack17

articles/healthcare-apis/iot/iot-connector-machine-learning.md

@@ -5,7 +5,7 @@ author: msjasteppe
 ms.service: healthcare-apis
 ms.subservice: fhir
 ms.topic: conceptual
-ms.date: 03/25/2022
+ms.date: 08/16/2022
 ms.author: jasteppe
 ---
 
@@ -15,7 +15,7 @@ In this article, we'll explore using the MedTech service and Azure Machine Learn
 
 ## MedTech service and Azure Machine Learning Service reference architecture
 
-MedTech service enables IoT devices seamless integration with Fast Healthcare Interoperability Resources (FHIR®) services. This reference architecture is designed to accelerate adoption of Internet of Medical Things (IoMT) projects. This solution uses Azure Databricks for the Machine Learning (ML) compute. However, Azure ML Services with Kubernetes or a partner ML solution could fit into the Machine Learning Scoring Environment.
+The MedTech service enables IoT devices seamless integration with Fast Healthcare Interoperability Resources (FHIR®) services. This reference architecture is designed to accelerate adoption of Internet of Medical Things (IoMT) projects. This solution uses Azure Databricks for the Machine Learning (ML) compute. However, Azure ML Services with Kubernetes or a partner ML solution could fit into the Machine Learning Scoring Environment.
 
 The four line colors show the different parts of the data journey.
 
@@ -31,15 +31,15 @@ The four line colors show the different parts of the data journey.
 1. Data from IoT device or via device gateway sent to Azure IoT Hub/Azure IoT Edge.
 2. Data from Azure IoT Edge sent to Azure IoT Hub.
 3. Copy of raw IoT device data sent to a secure storage environment for device administration.
-4. PHI IoMT payload moves from Azure IoT Hub to the MedTech service. Multiple Azure services are represented by 1 MedTech service icon.
+4. PHI IoMT payload moves from Azure IoT Hub to the MedTech service. Multiple Azure services are represented by the MedTech service icon.
 5. Three parts to number 5: 
- a. MedTech service request Patient resource from FHIR service. 
- b. FHIR service sends Patient resource back to the MedTech service. 
- c. IoT Patient Observation is record in FHIR service.
+ a. The MedTech service requests Patient resource from the FHIR service. 
+ b. The FHIR service sends Patient resource back to the MedTech service. 
+ c. IoT Patient Observation is record in the FHIR service.
 
 **Machine Learning and AI Data Route – Steps 6 through 11**
 
-6. Normalized ungrouped data stream sent to Azure Function (ML Input).
+6. Normalized ungrouped data stream sent to an Azure Function (ML Input).
 7. Azure Function (ML Input) requests Patient resource to merge with IoMT payload.
 8. IoMT payload with PHI is sent to an event hub for distribution to Machine Learning compute and storage.
 9. PHI IoMT payload is sent to Azure Data Lake Storage Gen 2 for scoring observation over longer time windows.
@@ -68,4 +68,4 @@ In this article, you've learned about the MedTech service and Machine Learning s
 >[!div class="nextstepaction"]
 >[MedTech service overview](iot-connector-overview.md)
 
-(FHIR®) is a registered trademark of [HL7](https://hl7.org/fhir/) and is used with the permission of HL7.
+FHIR® is a registered trademark of Health Level Seven International, registered in the U.S. Trademark Office and is used with their permission.

articles/healthcare-apis/iot/iot-connector-power-bi.md

@@ -5,7 +5,7 @@ author: msjasteppe
 ms.service: healthcare-apis
 ms.subservice: fhir
 ms.topic: conceptual
-ms.date: 03/25/2021
+ms.date: 08/16/2021
 ms.author: jasteppe
 ---
 
@@ -15,13 +15,13 @@ In this article, we'll explore using the MedTech service and Microsoft Power Bus
 
 ## MedTech service and Power BI reference architecture
 
-The reference architecture below shows the basic components of using Microsoft cloud services to enable Power BI on top of Internet of Medical Things (IoMT) and Fast Healthcare Interoperability Resources (FHIR®) data.
+The reference architecture below shows the basic components of using the Microsoft cloud services to enable Power BI on top of Internet of Medical Things (IoMT) and Fast Healthcare Interoperability Resources (FHIR®) data.
 
 You can even embed Power BI dashboards inside the Microsoft Teams client to further enhance care team coordination. For more information on embedding Power BI in Teams, visit [here](/power-bi/collaborate-share/service-embed-report-microsoft-teams).
 
 :::image type="content" source="media/iot-concepts/iot-connector-power-bi.png" alt-text="Screenshot of the MedTech service and Power BI." lightbox="media/iot-concepts/iot-connector-power-bi.png":::
 
-MedTech service can ingest IoT data from most IoT devices or gateways whatever the location, data center, or cloud.
+The MedTech service can ingest IoT data from most IoT devices or gateways whatever the location, data center, or cloud.
 
 We do encourage the use of Azure IoT services to assist with device/gateway connectivity.
 
@@ -40,4 +40,4 @@ In this article, you've learned about the MedTech service and Power BI integrati
 >[!div class="nextstepaction"]
 >[MedTech service overview](iot-connector-overview.md)
 
-(FHIR®) is a registered trademark of [HL7](https://hl7.org/fhir/) and is used with the permission of HL7.
+FHIR® is a registered trademark of Health Level Seven International, registered in the U.S. Trademark Office and is used with their permission.

articles/healthcare-apis/iot/iot-connector-teams.md

@@ -5,7 +5,7 @@ author: msjasteppe
 ms.service: healthcare-apis
 ms.subservice: fhir
 ms.topic: conceptual
-ms.date: 03/25/2022
+ms.date: 08/16/2022
 ms.author: jasteppe
 ---
 
@@ -15,9 +15,9 @@ In this article, we'll explore using the MedTech service and Microsoft Teams for
 
 ## MedTech service and Teams notifications reference architecture
 
-When combining MedTech service, a Fast Healthcare Interoperability Resources (FHIR®) service, and Teams, you can enable multiple care solutions.
+When combining the MedTech service, a Fast Healthcare Interoperability Resources (FHIR®) service, and Teams, you can enable multiple care solutions.
 
-Below is the MedTech service to Teams notifications conceptual architecture for enabling the MedTech service, FHIR, and Teams Patient App.
+Below is the MedTech service to Teams notifications conceptual architecture for enabling the MedTech service, the FHIR service, and the Teams Patient App.
 
 You can even embed Power BI Dashboards inside the Microsoft Teams client. For more information on embedding Power BI in Microsoft Team visit [here](/power-bi/collaborate-share/service-embed-report-microsoft-teams).
 
@@ -42,4 +42,4 @@ In this article, you've learned about the MedTech service and Teams notification
 >[!div class="nextstepaction"]
 >[MedTech service overview](iot-connector-overview.md)
 
-(FHIR®) is a registered trademark of [HL7](https://hl7.org/fhir/) and is used with the permission of HL7.
+FHIR® is a registered trademark of Health Level Seven International, registered in the U.S. Trademark Office and is used with their permission.

articles/healthcare-apis/iot/iot-data-flow.md

@@ -6,15 +6,15 @@ author: msjasteppe
 ms.service: healthcare-apis
 ms.subservice: iomt
 ms.topic: conceptual
-ms.date: 07/22/2022
+ms.date: 08/16/2022
 ms.author: jasteppe
 ---
 
 # MedTech service data flow
 
 This article provides an overview of the MedTech service data flow. You'll learn about the different data processing stages within the MedTech service that transforms device data into Fast Healthcare Interoperability Resources (FHIR®)-based [Observation](https://www.hl7.org/fhir/observation.html) resources.
 
-Data from health-related devices or medical devices flows through a path in which the MedTech service transforms data into FHIR, and then data is stored on and accessed from the FHIR service. The health data path follows these steps in this order: ingest, normalize, group, transform, and persist. Health data is retrieved from the device in the first step of ingestion. After the data is received, it's processed, or normalized per a user-selected/user-created schema template called the device mapping. Normalized health data is simpler to process and can be grouped. In the next step, health data is grouped into three Operate parameters. After the health data is normalized and grouped, it can be processed or transformed through FHIR destination mappings, and then saved or persisted on the FHIR service.
+Data from health-related devices or medical devices flows through a path in which the MedTech service transforms data into FHIR, and then data is stored on and accessed from the FHIR service. The health data path follows these steps in this order: ingest, normalize, group, transform, and persist. Health data is retrieved from the device in the first step of ingestion. After the data is received, it's processed, or normalized per a user-selected/user-created schema template called the device mapping. Normalized health data is simpler to process and can be grouped. In the next step, health data is grouped into three Operate parameters. After the health data is normalized and grouped, it can be processed or transformed through a FHIR destination mapping, and then saved or persisted on the FHIR service.
 
 This article goes into more depth about each step in the data flow. The next steps are [Deploy the MedTech service using the Azure portal](deploy-iot-connector-in-azure.md) by using a device mapping (the normalization step) and a FHIR destination mapping (the transformation step).
 
@@ -26,6 +26,7 @@ This next section of the article describes the stages that IoMT (Internet of Med
 Ingest is the first stage where device data is received into the MedTech service. The ingestion endpoint for device data is hosted on an [Azure Event Hubs](../../event-hubs/index.yml). The Azure Event Hubs platform supports high scale and throughput with ability to receive and process millions of messages per second. It also enables the MedTech service to consume messages asynchronously, removing the need for devices to wait while device data gets processed.
 
 > [!NOTE]
+>
 > JSON is the only supported format at this time for device data.
 
 ## Normalize
@@ -43,6 +44,7 @@ Group is the next stage where the normalized messages available from the previou
 Device identity and measurement type grouping enable use of [SampledData](https://www.hl7.org/fhir/datatypes.html#SampledData) measurement type. This type provides a concise way to represent a time-based series of measurements from a device in FHIR. And time period controls the latency at which Observation resources generated by the MedTech service are written to FHIR service.
 
 > [!NOTE]
+>
 > The time period value is defaulted to 15 minutes and cannot be configured for preview.
 
 ## Transform
@@ -51,6 +53,7 @@ In the Transform stage, grouped-normalized messages are processed through FHIR d
 At this point, [Device](https://www.hl7.org/fhir/device.html) resource, along with its associated [Patient](https://www.hl7.org/fhir/patient.html) resource, is also retrieved from the FHIR service using the device identifier present in the message. These resources are added as a reference to the Observation resource being created.
 
 > [!NOTE]
+>
 > All identity look ups are cached once resolved to decrease load on the FHIR service. If you plan on reusing devices with multiple patients it is advised you create a virtual device resource that is specific to the patient and send virtual device identifier in the message payload. The virtual device can be linked to the actual device resource as a parent.
 
 If no Device resource for a given device identifier exists in the FHIR service, the outcome depends upon the value of `Resolution Type` set at the time of creation. When set to `Lookup`, the specific message is ignored, and the pipeline will continue to process other incoming messages. If set to `Create`, the MedTech service will create a bare-bones Device and Patient resources on the FHIR service.  
@@ -60,7 +63,7 @@ Once the Observation FHIR resource is generated in the Transform stage, the reso
 
 ## Next steps
 
-To learn how to create Device and FHIR destination mappings, see
+To learn how to create device and FHIR destination mappings, see
 
 > [!div class="nextstepaction"]
 > [Device mappings](how-to-use-device-mappings.md)