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>> Hi. I'm Doug Seven,
Senior Director at Microsoft Health Next.
I want to welcome you back to the final video
in this series on the Azure API for FHIR.
For this video I'd like to introduce Rashmi Raj,
who's a director at Microsoft Health Next.
>> Hello. My name is Rashmi Raj.
I lead engineering team for health data ingestion.
Today I'm going to demo total knee replacement
using Azure IoT Connector for FHIR,
but before we do the demo,
let's do a quick overview of Azure IoT Connector and FHIR.
FHIR, stands for Fast Healthcare Interoperability Resources.
This is an emerging standard for Health Data Exchange.
We have released FHIR server at open source on GitHub,
and we have also released Azure API for
FHIR at a managed service on Azure.
To enrich the ecosystem of FHIR we have released
services and tools to ingest data from different sources,
persist it in FHIR and make it
available for down-stream conjunctions.
In this demo, we are going to focus on IoMT,
Internet of Medical Things, data,
devices, variables and sensor collecting data,
which can be used to improve health outcomes.
We have released IoMT FHIR Connector
at open source on GitHub,
and we have also released Azure IoT Connector for
FHIR at a managed feature of Azure API for FHIR.
This provides end-to-end data pipeline
from these devices for PHI data,
and bring it to Azure API for FHIR.
It seamlessly integrates with IoT Hub and IoT Central,
and it's scalable and easy to use.
In this demo, we will use two devices,
activity watch and knee brace,
and the data will flow to FHIR using the IoT Connector.
The data from EMR will also flow to FHIR using FHIR APIs.
We will use Power BI Connector for
FHIR to visualize the data in Power BI,
and this dashboard will be embedded in Dynamics 365.
With this, let's go into the demo.
We are looking at Sara,
who's 52-years-old woman, and
she's going through total knee replacement surgery.
Before the surgery she has also opted in pre-op program,
where she has been given an activity watch.
Activity watch is recording her activity level,
think of it something like Garmin Fitbit,
which is recording her step count activity level,
along with her heart rate and sleep data.
In this dashboard, a care team can
see Sara's pre-op activity data,
but along with it, a care team can also see her medication data,
her reported pain level data which is coming from EMR,
and this is where power of FHIR comes in,
which allows you to bring data from different sources.
If they operate, then, aggregate
them for longitudinal view of the patient.
Care team can also go and do deep dive in the data,
and care team can look at heart rate,
sleep quality as well.
Now, Sara has gone through the surgery and care team would
like to make personalized rehab decision for her.
The data that came to this system,
and along with historical and other patient data,
allows to enable data analytics
and proactive precision rehab scenario here.
The system gives the visualization based on
real data for the preferred path for Sara for the rehab.
She's been recommended to use
patient at home rehab with physiotherapy visit.
At this time, care team can also use
clinical judgment or previous experience to slice and dice data,
for example, they can look at other patients
like Sara who have gone through the surgery.
Other patients, who are also female,
similar age rate, similar disease status,
smoking status, and similar pre-op label,
and what was the recommended path for them.
At this time, based on the data and analytics capabilities,
Sara has been recommended for at-home rehab with physiotherapy,
and when she got home she has also been given a knee brace,
which can record her exercise level,
her knee extension and flexion as well.
The same pipeline which was built for activity words
can be used for knee brace data now.
This is where the scalability of the platform comes in,
where you build this end-to-end pipeline
once and scale it for new devices,
or scale it for more devices.
Now, the data starts flowing in real time in the system,
and the system also provides
notification capability if Sara needs attention.
This is an example where care team can get
notification if Sara is not
following her regimen or needs attention.
Care team can go and look at her data,
and care team can see
her extension and flexion data as compared to the goal,
can see her exercise level data,
and indeed, Sara is not following her trajectory path.
This may be an example there,
care team's schedules, home physiotherapy visit,
and since this dashboard is also available on mobile device,
the home team can also take
this data along when they go and visit Sara.
After the physiotherapy visit,
Sara can start following the recommended recovery path.
This is an example where Azure API for FHIR and
IoT Connector can allow
ingesting data from different sources from EMRs,
as well as from divisive real-time data,
and enabled analytics scenarios.
This is just an example.
Same technology and architecture platform
can be expanded to support scenarios
like chronic disease management
and telehealth as well.
In summary, we saw how open FHIR standard,
and tools and services that we are
building is removing barriers of data interpretability,
and allowing you to bring data from different sources to
enable scenarios like remote patient monitoring,
disease rehabilitation, and distributed clinical trials.
Thank you for watching the demo.
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>> Thank you, Rashmi,
and thank you for taking the time to watch
this video series on the Azure API for FHIR.
In this video series we showed you everything from
why FHIR's becoming important in health technology today,
to how to use the Azure API for FHIR when building your solutions.
You can follow the links on the screen to learn more
about how to use the Azure API for FHIR and
everything you saw in this video series.
Thank you very much.
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