The Digitization of Healthcare: Why the Right Approach Matters and Five Steps to Get There

The Digitization of Healthcare:
Why the Right Approach Matters
and Five Steps to Get There

© 2018 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
The Digitization of Healthcare
This report is based on a Dale Sander’s webinar, “Raising the Digital
Trajectory of Healthcare,” presented August 15, 2018
Dale Sanders
President of Technology

With ever-evolving technology and analytics
capabilities, healthcare could be on the brink
of a data-driven transformation.
While many other industries are capturing the
vast potential of artificial intelligence (AI) to
revolutionize their work, healthcare hasn’t yet
reached the level of digitization to allow it to
leverage these next-generation tools and
capabilities.
To capitalize on the opportunities of this
advanced analytics era, healthcare must
raise its digital trajectory.

Global management consulting firm McKinsey
ranks industries based on their Digital Quotient
(DQ), which it derives from a cross product of
three areas:
• Data assets
• Data skills
• Data utilization

The healthcare sector’s DQ comes in at
below almost all other industries, leaving a
lot of room increase digitization.
There’s a specific way, however, in which
healthcare must approach digitization that’s
mindful of the human side of healthcare
decision making while prioritizing data and
the technical infrastructure for a true data-
analytics-driven industry.

The right way to increase digitization will
also increase the sense of humanity
around healthcare to better address
patient and clinician needs.
Healthcare may have largely ignored the
human, or softer side, of a digital strategy.
Clinicians today feel the brunt of a less
human-centered digital environment and
suffer from the emotional burdens of
burnout, including the highest rate of
suicide of any profession.
Increasing Digitization for More Humanity
Around Healthcare

Healthcare’s current data-driven strategy is
ineffective, and even harmful to the
industry it’s meant to serve.
It’s nonhuman-centered approach is
robbing clinicians of their sense of
mastery, autonomy, and purpose and
holding them to performance measures
that don’t adequately represent the
quality of their work.
Around Healthcare

A 2018 article documented physician burnout,
attributing much of it to the EHR era and the
poor way the U.S. healthcare system has
implemented a data-driven strategy.
In a survey, the Medical Group Management
Association (MGMA) identified that 84 percent
of physicians are now participating in the Merit-
based Incentive Payment System (MIPS).
Around Healthcare

Of them, 82 percent consider the Medicare
Access and CHIP Reauthorization Act (MACRA)
Quality Payment Program very or extremely
burdensome, and 73 percent said MIPS does not
support their practice’s clinical quality priorities.
These data points indicate how far healthcare’s
data-driven strategy has alienated physicians, as
compulsory measures that have little to do with
the quality of the outcomes or patient
relationships are increasingly burdensome.
Around Healthcare

Most of the data that’s generated in healthcare is
about the administrative overhead of healthcare
(e.g., claims data), not about the current state of
patients’ health and well-being.
On average, healthcare collects data from
patients three times per year, leaving
another 362 days of the year without
tracked health information.
To optimize diagnoses and treatments,
predict health risks, and develop long-term
care plans, clinicians need whole-patient
data that provides a comprehensive picture
of daily health beyond the acute-care setting.
Healthcare Must Fill Big Data Gaps

At best, EHRs hold only 8 percent of the data full
healthcare digitization and advanced analytics require.
Only 20 percent of the factors affecting health outcomes
fall inside a traditional healthcare delivery system,
as most of what affects health outcomes falls
outside of the four walls of healthcare delivery.
This model also leaves no data on healthy
patients, as they have no clinical encounters,
who represent the ideal artificial intelligence
training set.
This leaves no data to train AI algorithms
about how to achieve more healthy
patients, not treat sick patients.
Healthcare Must Fill Big Data Gaps

While healthcare’s digital strategy has largely
ignored the digitization of the patient’s whole
state of health, innovations are coming to
market over the next several years that will
capture more daily health data.
Health tracking innovations, such as
biosensors individuals can wear at all times,
will eventually give patients more health data
and AI-driven insights than health systems
will have, empowering patients in the
decision-making process.
Innovations Will Put Patients at the
Center of the Data Ecosystem

For example, John Rogers, the founder and
executive director of the Center for Bio-
Integrated Electronics at Northwestern
University, is producing bio-integrated
electronics in the form of microns-thin,
one-inch squared, skin-pliable sensors.
The tiny wafers have a Bluetooth antenna,
a CPU, physiologic monitors, and a wireless
power system. Some professional sports
teams are already starting to wear these
during competition. Rogers and team aim
to eventually print the sensors on skin as
a dissolvable tattoo.

In the current healthcare data ecosystem,
patients are embedded within a largely
disintegrated healthcare delivery system,
allowing for problems with handoffs between
the bubbles surrounding the patients.
These different bubbles are generating very
little data about the patient, and, as noted
earlier, the data is mostly administrative
versus telemetry about the patient’s health.

A data ecosystem with the patient at the
center shares biosensor-enabled patient-
generated data back to the healthcare system.
The process constantly updates and uploads
data to a cloud-based AI platform, where
algorithms diagnose the patient’s condition,
calculate composite and specific health risk
scores, and recommend options for
treatment or maintaining health.

Figure 1 shows a recommendation
for a data assets roadmap for
healthcare to guide data acquisition
and data government strategies to
truly understand the patient at the
center of healthcare.
Much of healthcare is stuck in the
lower left of this diagram (healthcare
encounter and claims data).
A Data Assets Roadmap for Healthcare
Figure 1: A data assets roadmap for healthcare

As data becomes more available in other
areas (e.g., socioeconomic and genomics
data) and healthcare prepares the
resources to use it, the industry stands
to broaden the scope of its data and
leverage that data effectively in work-flow
decision making.
Guidelines can help health systems
understand what the need to accomplish
and have in place to achieve digitization.
A Data Assets Roadmap for Healthcare

To digitize in the right, sustainable way, health systems must
follow these five guidelines:
1. Achieve and Maintain Clinician and Patient Engagement
2. Adopt a Modern Commercial Digital Platform Versus
a Homegrown Solution
3. Digitize the Assets (the Patients) and the Processes
4. Understand the Importance of Data to Drive AI Insights
5. Prioritize Data Volume over AI Model Sophistication
Five Must-Haves for Healthcare
Digitization the Right Way

Healthcare must ensure that its data-driven
and digital strategies are adding to clinicians’
sense of mastery, autonomy, and purpose.
Data must support their passion for their skill
and sense of mission, rather than making them
feel constantly monitored.
These same principles must eventually apply
to patients, as data-driven and digital strategies
need to help patients feel like they’re mastering
their own health and are autonomous.

Patient and clinician engagement require
sensitivity to the human receiving the message,
allowing people to face the truth without feeling
threatened or over-measured.
This is the human side of the data-driven
strategy in healthcare, and, for successful
digitization, it’s just as critical, or more so, than
technology and advanced analytics.

2. Adopt a Modern Commercial Digital Platform Versus a Homegrown Solution
Healthcare needs a modern, data-integrated
platform that can handle the digitization.
It’s still very difficult to manage the data within
a domain, especially a complex domain like
healthcare.
The application developers working at the
data layer have to wrangle and deal with
data on their own.

No one has pre-processed the data and made
the data layer easy to access and utilize. In a
data operating system (e.g., the Health
Catalyst® Data Operating System [DOSÔ],
however, data is the last layer in the stack,
making it easier for application developers to
take advantage of complex software.

A modern digital platform has seven attributes:
1. Reusable clinical and business logic
2. A single data stream to feed analytics
and workflow applications
3. Structured and unstructured data integration
4. Closed-loop capability
5. Microservices architecture
6. AI/machine learning
7. Agnostic data lake

Figure 2 (next slide) shows an example of a
modern data operating system architecture
(Health Catalyst’s DOS).
The diagram shows curated data content in the
upper-third of the diagram.
This contains the intermediate data models that
have comprehensive and persistent agreement
about logic, making it easier for application
developers in the upper-right (the DOS
marketplace) to develop apps and take
advantage of the infrastructure underneath it.

Figure 2: A modern data operating system architecture
Data Operating System Architecture

With AI algorithms as the commodities,
healthcare must understand the significance of
the infrastructure required to pull off machine
learning in AI.
Even though the public cloud makes platform
infrastructure accessible and affordable,
organizations that choose homegrown over
commercial solutions likely lack the scalability
and capability advanced analytics require.

Any industry requires digitization of the assets
it’s trying to manage and optimize, followed by
digitization of the production system for
managing those assets.
Using the aircraft/airline industry as an example,
airplanes are the digital assets.
The processes that the industry has digitized—
including air traffic control, baggage handling,
ticketing, and maintenance—shows the ability to
manage both the people as well as the aircraft.

For healthcare to understand its assets (patients),
it needs to make patients more digital.
Healthcare has digitized registration,
scheduling, encounters, diagnosis, orders,
billings, and claims.
Healthcare must now digitize beyond the
clinical encounter to capture the whole
picture of patient health and healthcare
optimization.

Healthcare aspires towards making health
optimization recommendations for patients
that are informed not only by the latest clinical
trials but also by local and regional data about
similar patients and their real-world health
outcomes over time.
From a data perspective, this requires
outcomes in cost data, predictive analytics,
machine learning, social determinants of
health, and recommendation engines.

To succeed, AI needs breadth and depth of
data within the domain.
This doesn’t mean only the number of
records an organization has, but rather the
number of facts about those patients.
To round out that digital ecosystem,
healthcare needs to collect more facts
about each of those patients.

AI-enabled registries can provide more accurate
patient information than rules-based registries.
A Swedish study evaluated the ability of k-
means clustering (a popular unsupervised
algorithm) and hierarchical clustering (clusters
with predetermined ordering from top to bottom)
in AI to identify previously unidentified sub-
groups of patients with diabetes.
Current diabetes definitions tend to use a
rules-based registry defined according to
ICD codes (e.g., a lab test).

Going forward, however, these registries will
be more defined by the patterns that data
show, not in the rules that registry users apply
to and impose on the data.
Increasing the density of patient data with
more digitization will further enable these
advanced analytics insights.

Figure 3 represents the analogy between a human brain and AI pattern recognition.
Cerebral cortex: the
data base and
algorithms for
classification &
clustering
Retina: The data collection system
for feature extraction.
The more times you pass through this
loop with different “data”, the faster
and better you become at feature
extraction and classifying “people”
Figure 3: The analogy between a human brain and pattern recognition

Somebody looks at a crowd of people and using the retina as the data
collection system for feature extraction. As this individual is looking at
people, she’s starting to extract features about the people—their height,
weight, skin color, hair color, age, etc.
The more the viewer passes
through this loop repeatedly with
different data, the faster and better
she becomes at feature extraction
and classifying people by whatever
it is that she’s looking for.

Using a neural network framework, AI mimics
the human pattern recognition and classification
process (e.g., telling the viewer she sees people).
Generative adversarial networks (GANs) mimic
the opposite human process; they describe what
people look like.
These GANs can start producing images of
people and images of data in general.
From an AI perspective, GANs might produce the
ability to generate training sets like never before.

AI concepts have existing in healthcare for some time,
but their application stands to change, as healthcare
gains more data and AI techniques improve.
A 2018 journal article about the real-time use of
AI for the identification of small polyps during a
colonoscopy followed clinicians using AI-enabled
scopes during the diagnostic process.
The study found 94 percent accuracy in the detection
of small polyps. That’s significant, as these polyps
are hard to identify with the human eye.

There’s a long-existing debate about data
volume versus AI model sophistication—
whether a complex AI model can overcome
a lack of data volume and data features.
There was a school of thought that
sophisticated models could overcome
a lack of data.
A paper, however, from a team at Google
revealed that simple models with a lot of
data trump more elaborate models based
on less data.

The findings suggest that as AI models
increasingly become a commodity, the
data will make the difference.
To achieve personalized, precision
healthcare, the industry needs to invest
in the accumulation of better data about
patients.
AI model sophistication on its own is not
going to overcome the limitations of poor
data and inadequate data size.

The foundational goal of healthcare’s digital strategy must be to enhance
mastery, autonomy, and purpose for clinicians and patients.
Keeping that human perspective first, the industry
must also understand the urgency for more
patient data the modern data platforms
that can fully manage and leverage the
information tell a more complete story
about patient health.
The Digital Trajectory of Healthcare
Starts and Continues with the People

To raise the digital trajectory in the right
people- and data-centered way, organizations
can follow the guidelines for digitization,
including engaging clinicians and
patients, adopting a modern digital
platform, digitizing the whole patient,
understanding the importance of
data for AI, and growing data volume.
The Digital Trajectory of Healthcare
Starts and Continues with the People

For more information:
“This book is a fantastic piece of work”
– Robert Lindeman MD, FAAP, Chief Physician Quality Officer

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Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Health Catalyst is a mission-driven data warehousing, analytics and outcomes-improvement company
that helps healthcare organizations of all sizes improve clinical, financial, and operational outcomes
needed to improve population health and accountable care. Our proven enterprise data warehouse
(EDW) and analytics platform helps improve quality, add efficiency and lower costs in support of more
than 65 million patients for organizations ranging from the largest US health system to forward-thinking
physician practices.
Health Catalyst was recently named as the leader in the enterprise healthcare BI market in
improvement by KLAS, and has received numerous best-place-to work awards including Modern
Healthcare in 2013, 2014, and 2015, as well as other recognitions such as “Best Place to work for
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