Machine Learning Tools Unlock the Most Critical Insights from Unstructured Health Data

Machine Learning
Tools Unlock the
Most Critical Insights
from Unstructured
Health Data
HEALTH CATALYST EDITORS

© 2019 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Shaun Grannis, MD, MS, FAAFP, FACMI
Director Regenstrief Center for Biomedical Informatics
Assoc. Prof, Dept of Family Medicine,
Indiana University School of Medicine
This report is based on a 2018 Healthcare Analytics Summit presentation given by
Shaun Grannis, MD, MS, FAAFP, FACMI, Director Regenstrief Center for Biomedical
Informatics; Assoc. Prof, Dept of Family Medicine, Indiana University School of
Medicine, entitled “Real-World Examples of Leveraging NLP, Big Data, and Data
Science to Improve Population Health and Individual Care Outcomes.”
Machine Learning Tools Unlock Critical Insights

Many healthcare leaders operate on the premise
that health system caregivers and stakeholders
are more effective and better at what they do with
the aid of thoughtful IT.
This concept drives data analytics and technology
integration in healthcare.
But what does thoughtful IT mean?
Although many health systems leverage some
form of IT, that doesn’t mean it’s the best fit.
Thoughtful IT occurs when health systems use the
right technology to lead to accurate data to deliver
better patient care and improve outcomes.

Thoughtful IT leverages machine learning
to revolutionize the way health systems
use data to determine the best course of
patient care.
Two examples include the following:
Natural language processing (NLP):
the digitally-enabled ability to analyze
large amounts of natural language data
for human users.
Text mining: deriving value through the
analysis of mass amounts of text (e.g.,
word frequency, length of words, etc.).
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With NLP and text mining, healthcare
organizations are starting to leverage
technology to access the plethora of
unstructured patient data available in
the EMR (e.g., nursing notes or patient-
reported text such as, “my stomach hurts”).
NLP and text mining can process data
traditional analytics cannot, opening up
richer, more complex data sources.

Traditional analytics typically uses structured
data, consisting mainly of claims data and only
accounting for approximately 20 percent of all
available data.
Structured data exists in a specific, consistent
format and includes basic information, such as
patient demographics, labs, ICD-9 codes, and
medications.
While this data is easy to access, it is often
delayed due to lab processing time and limited
because the information is basic in nature,
revealing surface level details about a patient
but not valuable details such as socioeconomic
information.
Untouched, Unstructured Data Is Key to
Comprehensive Care

To access and understand the remaining
80 percent of unstructured data—including
free text data, physician order data,
nursing notes, and dictation notes—health
systems must rely on NLP.
NLP allows organizations to access the
complex, richer data sets that are harder to
reach because they require sophisticated
technology to derive value from the
massive amounts of everyday language
sitting in the EHR.
Comprehensive Care

Unstructured data, residing in EHRs and
elsewhere, contains the deeper, more
complex information—such as a patient’s
own words to describe symptoms or
another provider’s notes.
It can provide contextual information (e.g.,
living conditions, how the patient perceives
their illness, information about the patient’s
family, etc.), also known as social
determinants of health (SDoH).
Comprehensive Care

Clinicians do their best to capture patient
anecdotes and comments in their notes, but
these records rarely go beyond the EHR.
Free-text inputs (e.g., “I don’t feel good,”
“my right side feels numb,” etc.) are
commonplace in healthcare encounters
and can provide crucial context for
clinicians trying to deliver the best care.
Text Mining Provides Context Around Patient Care—A
Critical Resource for Healthcare Machine Learning Success

Manually collecting and categorizing free-text
notes, however, proves challenging and time-
consuming for healthcare teams, who already
experience burnout related to data collection
and input.
Machine learning can alleviate the
burden of manually organizing free-text
notes in the form of text mining.

Text mining is a sophisticated algorithm that
swiftly and accurately classifies free-text
comments—based on the organization’s choice
of words to search for—into the right categories
(e.g., high priority, low priority, etc.).
Clinicians can refer to these valuable
insights throughout the care process
without having to manually sift through
mass amounts of free-text data.

Even though algorithms don’t deliver results
with 100 percent accuracy, they are still more
accurate than humans.
Machine learning algorithms can aid health
systems in areas in which the staff simply lack
bandwidth.
For example, in 2005 Indiana University Health
(IU Health), implemented a machine learning
early-warning system to identify unusual trends
in the emergency department (ED).
Machine Learning Delivers Real Value in the Real World

Shortly after IU implemented the system, it sent
an alert to the Indiana state health officials
(who have access to all state health system
data) because it flagged abnormally high levels
of patients coming to the ED complaining of the
same symptoms—including dizziness,
confusion, nausea.
A health official responded to the alert by
notifying the hospitals.

Based on the existing data the health system
used, nothing triggered attention outside of the
early-warning system.
But, after further inquiry into the complaints,
IU Health discovered that all the people who
complained of these symptoms had something
in common—they all lived in the same
apartment complex.
Later, it was revealed that the heater in the
apartment complex was malfunctioning and
releasing carbon monoxide into the
apartments, causing tenants to get sick.

With machine learning’s ability to dissect,
organize, and analyze massive amounts of
data at a rapid rate, health systems can
focus on responding to alerts and outliers
in data (Figure 1), intervene in the
prevention stage, and immediately take
action to address gaps in care—versus
providing care after a patient’s condition
has worsened.

Figure 1. Outliers in ED visits.

Machine learning can also help
organizations alleviate the burden of
reporting and increase accuracy.
With countless measures to report and
constant changes in reporting figures,
health systems and clinicians struggle to
report accurate information consistently
for many reasons:
They are overburdened with other tasks.
They don’t know what they’re supposed to
report, due to frequent reporting changes.
They assume someone else is doing it.
Automated Reporting Increases Accuracy, Alleviates
Provider Burden
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Machine learning can automate communication
systems, reducing the manual burden of
reporting and, in some cases, completely
removing it.
In one experience, IU Health treated many
cancer patients in the Kentucky region.
The State of Kentucky requires reporting of
cancer diagnoses to the state health
department, and the number of cancer
incidences at the time of the study seemed
too low.
Provider Burden

To address the communication issue of
inadequate reporting of cancer diagnoses
between the Kentucky State Health Department
and IU Health, IU Health used machine learning,
instead of traditional ICD-9 codes, to identify
cancer incidence from free text reports.
The machine learning algorithms flagged words
that IU Health selected (e.g., “tumor,”
“malignant,” etc.).
The algorithm proved more accurate than the
human flagging system.
Provider Burden

Predictive models, such as NLP and text mining,
also help providers take a holistic approach to care.
For example, a patient might receive care in a local
clinic for hypertension but may have other unmet
needs (e.g., food, housing, and employment).
Often these SDoH can influence a patient’s well-
being as much, or more, than medical care.
Because the average patient spends less than
one percent of their life in a clinic, clinicians
must understand what goes on in a patient’s
life outside of the clinic doors.
Predictive Models Account for Social
Determinants of Health

To effectively capture SDoH, IU Health built a custom
prediction model using environmental data, crime
statistics, housing statistics, and socioeconomic status
of zip code, combined with clinical data.
The predictive model provided the community care
nurses in primary care clinics with information about
patients’ challenges outside the healthcare setting;
the nurses could then identify referrals for nutrition
and financial counseling, and more.
With machine learning algorithms specific to a
healthcare organization’s unique population,
providers are empowered to treat patients more
comprehensively.
Predictive Models Account for Social
Determinants of Health

As health data only stands to grow, health
systems need tools to tap into this
expanding resource to extract better data.
Although machine learning isn’t perfect with
an inevitable margin of error, and the
industry still has more questions than
answers, machine learning is starting to
offer true value in the complex world of
healthcare.
Machine Learning Tools Take Healthcare Delivery
to the Next Step

As health systems continue to provide
quality care on shrinking budgets and
reimbursements, data and analytics become
critical for success.
However, the industry needs more, high-
quality data to develop better algorithms that
answer the right questions.
These insights will allow clinicians to focus
on patients while relying on the data to make
the most informed decisions.
to the Next Step

Machine learning will never replace the human factor
in healthcare, but it can relieve care teams of time-
consuming tasks so they can focus on what matters
most—delivering care to patients how, when, and
where they need it.
to the Next Step

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
Dr. Shaun Grannis, Director Regenstrief Center for Biomedical Informatics and Assoc.
Prof, Dept of Family Medicine, Indiana University School of Medicine, collaborates
closely with national and international public and population health stakeholders to
advance the technical infrastructure and data-sharing capabilities in varying settings.
His research is focused on improving discovery and decision support in a variety of
contexts by developing, testing, and implementing innovative approaches for data
integration, patient matching, predictive modeling and other novel data science use
cases, including developing novel population health data frameworks supporting
fusion of community and social determinants of health with clinical data, as well as leveraging
machine learning-based models to improve discovery and decision support in a variety of contexts.
Dr. Grannis has been a part of Regenstrief Institute since 2001 when he began a National Library of
Medicine sponsored medical informatics fellowship. He became the Associate Director of the Clem
McDonald Center for Biomedical Informatics at Regenstrief in 2015 and was appointed Director in
2017. He assumed the role of Vice President in 2019.
Shaun Grannis, MD, MS, FAAFP, FACMI, FAMIA

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
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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
Millenials, and a “Best Perks for Women.”

Machine Learning Tools Unlock the Most Critical Insights from Unstructured Health Data

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Machine Learning Tools Unlock the Most Critical Insights from Unstructured Health Data