SPEAKER: As data science and technology continue to advance,
we're seeing more and more industries investigate how
their data can make an impact.
When it comes to health care, data
is now at the very heart of solutions
that help improve patient outcomes
and allow scientists to develop new therapies.
In today's video, we're going to walk you
through an example of how you might build a data
platform for advancing clinical trials using Looker
and other Google Cloud Services.
And while we'll be focusing on life sciences
research for today's demonstration,
it's important to note that these technologies can
be leveraged for a variety of use cases
throughout the health care industry and beyond.
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In 2020, as many as 5.8 million Americans
were living with Alzheimer's disease.
And this number is projected to nearly triple
to 14 million people by 2060.
So today, we're focusing on building
a data-powered application to help physicians and researchers
analyze huge amounts of information
to discover new Alzheimer's treatments.
Let's dive in.
When it comes to working with health care data,
privacy and data protection are at the very forefront
of your technology considerations.
Lucky for you, Google Cloud is committed
to keeping your patient data safe, with services
that meet fundamental security and privacy
requirements for protected health information.
Now that we have housekeeping out of the way,
let's get back to the fundamental problem.
When it comes to analyzing new treatments,
there are loads of different data sources
we need to consider--
MRIs and PET scans, patient medical history data,
positive and negative side effects
reported from drug treatments, and lab data
collected over time.
And we're not just talking about small volumes here.
Medical imaging alone generates about 2 trillion images
per year, or nearly 4 and 1/2 exabytes of data.
OK, so we have lots of data in all different structures,
how do we make sense of all of this
and give practitioners the insights they need?
Let's start by taking a closer look at Looker.
Looker's architecture is unique among business intelligence
platforms because it's powered by Looker's modeling
layer, LookML, which sits directly
on top of your data warehouse.
Looker is an API-first application,
which provides data as a service to other systems
by leveraging LookML, Looker's modeling layer,
to deliver consistent data across all applications.
In the application we're building today,
BigQuery and Looker are the primary analytical engines
for structured patient data.
But we'll also use Cloud Storage to serve up
unstructured data, like images, and Vision AI and TensorFlow
to add additional insights into our BigQuery data.
Now the question is, how will this architecture empower
health care professionals and life sciences researchers?
Let's find out.
OK, so let's pretend you're a health care
provider who has patients participating
in a clinical trial.
You can navigate to the application landing page
to see a high-level overview of the trial, including
information like the research question,
the trial status, when it started, and the type of study.
Additionally, you can dig into some of the details
for each treatment group, like seeing
the severity level and a breakdown of all
the participants.
Now, aside from simply viewing high-level statistics,
you can interact with this data by adjusting the timeline--
maybe to better understand if the usage of each treatment
has changed throughout the trial.
Now, the super cool part of all this
is that we're using third-party UI components for a custom
experience, but we're still pulling data
from our unified data model, ensuring data governance
and consistency.
OK, so you have a sense of the overall trial,
now let's move over to My Patients.
The first four tabs over here allow
you to see detailed information about patients, ranging
from the basic information, like their sex, age, blood type,
and BMI, to more complex information,
like medical events and family history.
But that's not all.
Like we showed before, interaction is key here.
So you can drill into things like the hippocampal volume
to get more details on what's going on here.
Now you can see a modal pulling in data
through Looker's embedded iframe, which we'll talk
through more in just a minute.
Back on my screen, I can navigate
to the Predictive Insights tab.
And here, we'll be able to see the results
of our artificial intelligence solution,
where we can see things like a description of the recommended
treatment and predicted positive and negative side effects.
It's important to note that Google Cloud toolset allows
us to feed in non-quantitative data, like MRI images,
into a statistical model.
For example, using Vision AI, we can identify and measure
the size of certain neural structures.
These insights help guide physicians
and ease the burden of searching for microscopic structures
in the brain so they can focus on treating and managing
their patient's health.
Now that you know your treatment plan,
you can share details with the patient
or send them directly to the pharmacy to be filled.
So all this data in the demo is stored in BigQuery,
but can be abstracted further using Looker,
and fed into other microservices across Google and beyond,
like the 3D brain studio over here, which uses our technology
to take patient 2D images and present it
in 3D for physicians.
Although custom and hybrid solutions
create an amazing experience for end users,
they can sometimes take more effort to build.
Oftentimes, there's a need to incorporate
standard embedded reports because it enables
organizations to quickly integrate dashboards
into applications.
Let's take a closer look.
Like we showed before in our drill modal,
Looker dashboards can be fully embedded through an iframe
to show the fastest time to value
and allow developers to focus on more custom feature sets.
If we go back to our main application,
we can also navigate to the trial data page.
Here, we'll be able to see a more detailed overview
of all trial data, with full access
to Looker's built-in capabilities,
like scheduling and download functionality, drilling,
and alerts.
Awesome.
So now you've seen how to use Google Cloud to create
a tailored, custom data experience.
By combining intelligent and secure Google Cloud Services,
you can build amazing applications
that leverage artificial intelligence and data analytics
to design, build, and scale the next generation of modern data
applications.
Ready to learn more?
Check out the links below.
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