JOE CORKERY: I'm Joe Corkery, and I
lead the Healthcare and Life Sciences product
team at Google Cloud.
When we first started planning for Next,
we intended for this talk to be about how health care
organizations are transforming their businesses using
AI and ML.
However, since that time, the world--
and especially as it relates to health care--
has seen a completely different type of transformation
as a result of COVID-19.
With that in mind, we decided to focus this talk more
about how Google Cloud is helping organizations respond
to COVID-19, leveraging AI and ML,
Google Cloud's Healthcare and Life Sciences products,
as well as some of the work that our partners are doing.
I want to start by talking through Google Cloud
Healthcare and Life Sciences team's mission, which
is to create solutions to empower provider, payer,
and pharma organizations to transform their business
and the delivery of health care.
Our development efforts to deliver on that mission
are primarily organized around four main pillars.
Organize-- we want to make it easy to ingest, normalize,
and join data to unlock the potential of your data.
Accessible-- we want to support open standards, APIs,
interoperability and discovery, to ensure that the data can
be made accessible for use.
Secure-- we lead the industry in security, privacy,
and compliance, from endpoint to data center.
Everything we do here is core to our mission
and at the heart of our work.
Useful-- with secure, accessible,
and well-organized data, we aim to help organizations generate
insights and new capabilities that advance the Quadruple
Aim of health care.
The Quadruple Aim of health care is a framework
of goals designed to improve the patient experience of care,
improve the health of populations,
reduce the per capita cost of health care,
and improve the work experience of clinicians and staff.
This useful pillar is especially critical today
during the COVID19 pandemic, as organizations
seek to understand and take action on a wide range of data
available to them.
Here's our overall agenda for the session,
which we've divided into three distinct parts.
In the first session, we will cover the application of AI
to COVID forecasting, and we'll be
joined by Dr. Thomas Tsai, a surgeon and health policy
researcher in the Department of Surgery
at Brigham and Women's Hospital and in the Department of Health
Policy and Management at the Harvard T.H. Chan School
of Public Health.
He's affiliated with the Harvard Global Health Institute,
with whom Google Cloud is partnered
on the development of the COVID-19 public forecasts.
In the second session, we'll dive a bit deeper
into how Google Cloud's health care-specific product
offerings are being used to address COVID-19.
Finally, in our last session, we'll
highlight work being done by one of our partners,
Schrodinger, and how it's being used to combat COVID-19.
Let's get started.
Now I'd like to welcome Dr. Thomas Tsai to share
his thoughts on our collaboration to deliver
the COVID-19 public forecasts.
THOMAS TSAI: Thank you, Joe, for that introduction.
As Joe said, my name is Tom Tsai.
I'm a surgeon and assistant professor in health policy
and management at the Harvard School of Public Health
and a faculty member of the Harvard Global Health
Institute.
The goal of the Harvard Global Health Institute
is to provide actionable data to guide policymaking
to improve health in both the United
States and the global context.
During COVID-19, our efforts have focused around three core
areas--
developing models of US hospital capacity
to help plan for storage capacity
to meet the demand of COVID-19 cases, supporting
federal and state policymakers in developing, testing targets,
and a contact tracing strategy, and, three,
providing scientific communication
to combat misinformation.
We are public health researchers, health policy
experts and clinicians.
We try to bring all those perspectives to bear
when informing policy response to urgent health care needs,
such as the COVID-19 pandemic.
Over the last few months, we have
embarked on a national conversation in conjunction
with the Harvard Safra Center for Ethics
and multiple academic groups, think tanks, foundations,
and policy groups to create convergence
around a key set of metrics for suppression of COVID-19.
This has resulted in our COVID-19 risk level dashboard
and test target dashboard, globalepidemics.org.
Our goal has been to not just show the underlying risk,
but more importantly tie the community level
risk to a corresponding set of public health
actions around testing and non-pharmacological
interventions, such as social distancing and even shutdowns.
While the dashboard shows us where we are,
what it doesn't do is predict what will happen in the future.
And because of this, states and even counties
are unable to fully prepare for what is to come.
If hospitals policymakers, and public health officials
could predict with more reliability future COVID-19 19
outbreaks, then they could be better prepared.
This is where artificial intelligence and machine
learning come into play.
Through AI and machine learning, we
can create forecast models that are
able to predict cases, deaths, and other important metrics
over the next 14 days, at both the US state and county level.
And this is where Google Cloud comes in.
Cloud AI, in partnership with the Harvard Global Health
Institute, has developed a novel COVID-19 forecasting
model that is able to predict cases, deaths,
and other important metrics such as hospitalizations
in the 14-day period at the US state and county level.
These forecasts are powered by machine
learning informed epidemiological models that
continuously learn from underlying public data sets.
The forecasts are available free to the public.
To generate the COVID-19 public forecasts,
Google Cloud researchers developed a novel time series
machine learning approach that combines
artificial intelligence with a robust epidemiological
foundation.
By design, this new model is trained on public data
and leverages an architecture that allows researchers
to dive into the different relationships
that the model has learned to better interpret
why it makes certain forecasts.
One of the challenges of a novel pandemic like COVID-19
is that traditional epidemiological modeling
depends on assumptions of certain parameters,
such as the transmission rate, the incubation period,
and the risk of mortality.
What the AI machine learning approach enables
is an opportunity to learn from the data in real time
as you're progressing through this pandemic.
We hope that these measures not only
help the public understand how the model works, but more
importantly, can enable further innovations
in infectious disease modeling.
We're calling on public health officials
to prioritize data quality and transparency,
as these underlying data are what informs and enables the AI
models to make accurate predictions of health care
professionals.
As these data are public, we hope
users will use this data to create
their own dashboards to guide and inform
local decision making.
There's a saying that all models are wrong,
but some models are useful.
The COVID-19 public forecast model is uniquely positioned
to provide not just useful information,
but more importantly, actionable insights
that can be used to help suppress the COVID-19 pandemic.
It's been our privilege at the Harvard Global Health Institute
to partner with Google Cloud on this very important effort.
We look forward to continue this ongoing collaboration.
JOE CORKERY: Thank, you Thomas.
I really appreciate you participating in this session.
The COVID-19 public forecast model
generates a 14-day forecast of the development of COVID-19
in each US state and county.
It's intended to be used as one of many signals
to help first responders in health care, the public sector,
and other impacted organizations to be better prepared for what
lies ahead.
The data is available in BigQuery,
where users can interact with it directly and without cost,
as it is included in the COVID-19 public data set
program that makes a large number of COVID-related data
sets freely searchable on BigQuery.
the data can also be downloaded directly as a CSV
for your own analysis and your own tools.
Finally, we also created a high level dashboard view
of the data using Google Data Studio, which
is what you're seeing here.
Let's take a closer look at that dashboard
now to get a better understanding of the results.
In the COVID-19 public forecast data studio dashboard,
you will see data projections over the next 14 days
for the United States.
The forecasts are generated roughly every day
and are available for all US states and counties.
This dashboard shows historical data side
by side with these forecasts.
The map view allows you to see which states are expected
to have the highest increase in the number of deaths
and confirmed cases.
You can also quickly navigate between states
to view the forecast for each one.
Let's pick Arizona.
Additional metrics such as hospitalizations, ICU,
and ventilator forecasts are also available.
For US counties, historical and forecast data
is available for cases, deaths, and hospitalizations.
You can use the dropdown menu to select one or more counties
and view a time series visualization for that county.
Let's pick Yuma.
Additional data at the county and state level
is available in the BigQuery table and CSVs.
In addition to our partnership with the Harvard Global Health
Institute, we work closely with SADA
and HCA Healthcare care to make the COVID-19 public forecasts
available in the national response portal,
with the goal of ensuring as many people can have access
to the data as possible.
Many organizations have found value from these types of data,
including HCA, as highlighted here by Dr. Edmond Jackson.
Our hope is that other organizations will also
be able to take this data and use it to plan accordingly
to address COVID-19 needs at the hospital level.
Let's change gears now to focus on Google Cloud's health care
specific products and how they're
being used to help with the COVID-19 crisis.
We just talked about how organizations
like HCA Healthcare are benefiting from AI/ML
from COVID forecasting.
Data is what powers these ML models.
However, the data plumbing required in health care
to build those models is challenging and time consuming.
The Google Cloud health care API is designed
to make the data plumbing easier, cheaper, and faster
to accelerate health data interoperability and enable you
to unlock the power of your data to create and/or consume
AI models, to run large-scale cross-modality analytics,
and to build and deploy novel health applications.
Let's continue with the COVID theme
and walk through an example of how a large provider system was
able to quickly gain insights into COVID admissions using
the health care API.
When COVID hit, it exposed a real problem
in many provider systems.
The disease was moving faster than the data
that they were using to make their decisions.
Some of the challenges they faced
included data latency, where their existing data analytics
ran off Epic Clarity exports, which can often
be more than 24 hours old.
Data continuity-- regional hospitals and clinics each
had their own siloed view of just their own data.
And finally, available IT skills--
a real lack of local IT staff made
rollout of a new technical solution impossible.
What they needed was a way to tap into real-time events,
and in particular those that are broadcast on the HL7v2 message
stream.
The solution that they developed leverage the real time
feed by consuming the HL7v2 message feed directly
into the health care API, transforming that data
into FHIR, and then projecting that data
into BigQuery for analytics.
The data feeds were then combined
across regional hospitals and clinics
to create a single aggregated data set for the entire system.
They were then able to use pre-built Looker dashboards
that combine national data with their local data,
as well as their own data, to gain better insights.
Let's dive a bit deeper into the specifics of the solution.
The Google Cloud health care API was
the key to moving from sometime to real-time analytics.
We were able to plug into the Rhapsody interface engine,
and land the HL7 messages directly into the health care
API message store.
We then leveraged our data harmonization suite
to transform the HL7v2 messages into a FHIR representation
in real time.
The health care API FHIR store can automatically
project the data through to BigQuery
and can be visualized using a Looker dashboard.
Traditionally, standing up this kind
of a solution within the health care system would take months,
but we needed to act at the speed of COVID.
And by using the health care API, we were able to do this.
The result was that the C-suite had visibility
into real-time data about the number of cases, resources,
and utilization.
And all of this was delivered from start
to finish in just three weeks.
Before we move on, I want to take a slightly closer look
at the architecture behind the solution.
The first step in delivering the solution
was setting up the cloud environment
using the data protection toolkit, which
automates the deployment of cloud projects and resources
following HIPAA best practices, automating permissions
using the principle of least privilege,
and setting up log retention.
Once patients are admitted, transferred, or discharged,
the EHR, or the electronic health record,
sends out an HL7 message, which our Pub/Sub adapter listens
for, captures, and delivers to the health care API HL7v2
message store.
The HL7v2 data was converted to FHIR
via transformation pipeline run in Cloud Data Fusion.
However, that wasn't the only source
of data brought into the solution.
We also brought in data from operational systems
in CSV format.
We worked with the system to create a Whistle Mapping config
to harmonize the data into FHIR from these different sources,
which were in different schemas, terminologies, and value sets.
As a result, both operational and clinical data
are harmonized in real time to the FHIR store, which
then streams data directly into BigQuery so that the Looker
dashboards reflect up to date information
for decision-making.
As we discussed on the previous slide,
there's a lot of value in the structured data contained
in the HL7 messages and the operational data.
However, there is a significant amount
of value locked up in unstructured data contained
in the clinical narrative of the progress notes.
Extracting this value requires a different approach.
Let's dive a little bit deeper into clinical entity
extraction.
What you're seeing here is a small part
of a typical ICU note.
It's very dense.
There are lots of unintuitive abbreviations.
There's weird punctuation, because large sections
are often generated by the EHR.
And there are two languages here, both English and Latin.
But there is often more detail and richness of context
buried in the clinical note.
And that is important to understand,
and it is not available in the structured EHR data.
So what can we do about this?
The Healthcare Natural Language API,
which is coming soon in beta, will
help you find, assess, and link the knowledge in your data.
In particular, it allows you to extract clinically relevant
entities from a text to understand
the relationship between those entities
to understand the context in which those entities appear
in the text, as well as to associate
those with known ontologies.
In addition to the capabilities offered by the Healthcare
Natural Language API, the need for custom information
extraction models that have high fidelity for health care
and life sciences applications is significant.
AutoML entity extraction for health care,
which is also coming soon, provides you
with the ability to generate high quality custom
models for health applications with no coding skills required.
Just upload and label the training data,
train the model with the simple click,
and then deploy that model at scale.
A major US health center was seeing patients
presenting to them with a wide variety of different symptoms
through multiple clinical entry points, including their call
centers, chatbots, telemedicine visits, and of course
in-person care.
The data that was being collected
at the different entry points was not connected together,
and there was not an easy way to pull all this data together
to identify common patterns in their population.
In response, they decided to kick off
a pilot to automate the extraction
of clinical information from these unstructured data sources
to better make more accurate predictions and better
triage their COVID-19 patients.
The early results of this effort were promising.
They pooled all the disparate data sets together
into a single store and also enhanced their chatbots
to ask COVID-19 specific questions as well.
As a result of their ability to better extract information
from multiple sources, they've seen
many improvements in the accuracy of their diagnoses,
as well as a reduction in their time to treat.
They're also exploring how this can be used in the future
to better identify chronic conditions related to COVID.
To close, I want to highlight one of our partners
that's working on finding new treatments for COVID-19.
Schrodinger is one of those partners.
Schrodinger is a drug and material discovery company
that has built a platform that uses physics and chemistry
principles to create computational models that
predict how different substances will interact
at the molecular level.
These simulations can be run at an enormous scale
to assess billions of molecules in silico
before selecting candidates to take into the lab.
We announced our initial partnership in May
when Google Cloud was providing Schrodinger with access
to powerful computing capacity to accelerate drug discovery
for its commercial partners and its own internal pipeline.
However, more recently, and in response to the COVID-19
pandemic, Google Cloud is providing Schrodinger
with 16 million hours of GPU time
through our researcher credit program.
And this is to enable computational drug discovery
for COVID-19, which if used consecutively,
would equate to 1,826 years of around the clock computing.
To accelerate this drug discovery process,
Schrodinger has teamed up with Takeda, Novartis, Gilead
Sciences, and WuXi AppTech in a philanthropic initiative
to share ideas, resources, and data,
with the goal of developing novel and viral therapeutics
for COVID-19.
We're very excited to be able to support this initiative
and we wish them the very best of luck.
We hope that you've found this useful and informative
and that this will be helpful for your organization
as you combat the COVID-19 crisis.
We're now in the final month of Next.
We have several Healthcare and Life Science sessions
that you might find useful.
You can easily find this information link
from the Google Cloud Healthcare and Life Sciences website,
or from our Next home page.
Thank you very much.