Data Science Reveals Patients at Risk for Adverse Outcomes Due to COVID-19 Care Disruptions

Data Science Reveals
Patients at Risk for
Adverse Outcomes
Due to COVID-19
Care Disruptions

© 2021 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
This article is based on a 2020 Healthcare Analytics Summit
(HAS 20 Virtual) presentation by Imran Qureshi, Chief Information
Officer and Chief Data Science Officer, b.well Connected Health,
titled, “Navigating the Post-COVID World Through Data Science.”
Patients at Risk for Adverse Outcomes
Imran Qureshi
Chief Data Science Officer,
b.well Connected Health

As of January 2020-21, the pandemic has
resulted in 76 million COVID-19 cases and
over 1.6 million deaths, many of those
healthcare personnel.
Healthcare organizations face additional
challenges from the pandemic’s major
disruption in routine care. COVID-19 has
impacted every area of healthcare:
Ambulatory practice
visits have declined
60 percent while
telehealth visits
have increased.
Diagnostic testing
has decreased as
well as patients
seeking care in a
hospital setting.
Hospitals nationwide
have lost $60.1
billion a month,
largely due to an
unexpected stop in
elective procedures.

These significant care disruptions have far-
reaching effects on patients’ health, leaving
organizations guessing about how to reach
patients and deliver care when routine
checkups stopped.
Without these regular touchpoints, health
systems struggle to identify and treat patients
at a higher risk for adverse outcomes.
While some people have stopped going to
the doctor during the pandemic, patients
with serious conditions, such as comorbidities
and chronic diseases, can’t afford to miss
routine visits.

Conditions like diabetes and heart disease
negatively impact a person’s immune
system, putting them at higher risk for
contracting COVID-19, other serious
illnesses, and worse long-term health.
The danger in delayed care can also lead
to irreversible conditions, which providers
and patients could have prevented with
earlier involvement, and in some cases,
an early death.

With so many unknown variables—vaccine
development, adherence to face mask mandates,
and social distancing guidelines—health systems
can rely on healthcare data science to identify
their most vulnerable patients and predict the
effects of care disruptions on those patients.
Predictive modeling, based on accurate data,
provides visibility into at-risk patient groups,
possible patient outcomes, and the best- and
worst-case scenarios, helping health systems
plan accordingly and prevent declining health.

Healthcare Data Science Helps Health
Systems Identify High-Risk Patients
In the new, fluid care landscape, health systems
can tackle care disruption with data science-
driven methods (e.g., machine learning and
predictive models) to identify patients most
affected by abrupt care delivery changes.
With predictive models providing information
about patients at the highest risk for worse
outcomes due to care disruptions, health
systems can apply early interventions and
expend resources on patients with more
complex care needs.

Healthcare Data Science Helps Health
Systems Identify High-Risk Patients
To identify patients at the highest risk for
adverse outcomes from care disruptions,
health systems can first identify care
disruption in the past.
Although not on the same scale as COVID-
19, historical care disruption information
(e.g., reduced primary care provider (PCP)
visits or increased uninsured patients) is a
starting point.
Once health systems have gathered
enough data sets to identify patients who
have experienced care disruptions, they
are ready to create predictive models.

Care Disruption in Predictive Models
Forecasts Future Outcomes
To account for disruptions in care in
predictive models, healthcare data science
practitioners can include a specific care
disruption as a feature in the model, such as
listing a disruption in PCP visits (Figure 1).
Including disruptions as features in the
predictive model allows the algorithm to
predict the impact of care disruption on
future outcomes (e.g., total cost).

Age Gender Diabetes CHF
Had PCP Visit
Disruption
Total Cost
(Target Outcome)
79 M Y N Y $100,000
79 M Y N N $20,000
65 F N Y Y $30,000
Figure 1: A predictive model with PCP disruption as a feature.

While the predictive models are only
estimates, they are still powerful tools in
helping health systems identify their most
vulnerable populations.
Focusing on the information a health system
can access, such as the effects of the care
disruption (e.g., stopped PCP visits), instead
of focusing on the information a health
system does not have (e.g., the impact of
the cause of COVID-19 on outcomes), will
help providers identify the patients at highest
risk of adverse outcomes.

Data science experts can also implement
analysis techniques—such as oversampling,
class balancing, zero inflation, and others—to
account for having fewer positive samples in
the past data compared to today.

Quantifying Care Disruption Is Critical for
Accuracy in Healthcare Data Science
After identifying and selecting a care
disruption to include as a feature in the
model, the next step is to convert the care
disruption into a numeral to insert into the
predictive models.
To do so, data science teams must quantify
each care disruption. It is up to each health
system how to quantify each one.

For example, one hospital could calculate the
proportion of telehealth visits compared to
normal visits or estimate the number of visits
per month for emergency room, PCP, and
specialist visits compared to last year’s visits.

Once a data scientist quantifies the care
disruption, she must derive a number that
reflects how much it has actually happened.
The simplest way to think of this is to create
a moving average.
For example, a health system can create a
three-month moving average to eliminate
small variations, such as whether a patient
went to the doctor on June 29 or July 2,
but still accurately identify any changes
in routine care.

As data scientists adjust this moving three-month
average, they can identify when the change in the
moving average was more significant than the
threshold, resulting in care disruption (Figure 2).

Figure 2: A graph using a moving average to quantify care disruption.

For example, in the Figure 2 graph, something
happened between March and April that
decreased a patient’s moving average of four
PCP visits per month to one visit per month.
Healthcare data science teams can use this
number to measure, or quantify, the change in
this patient’s care, and then insert that number
into the predictive model.

Care Disruption Combined with Case Mix
Information Leads to Better Results
Measuring disruptions in care is the best
place to start identifying high-risk patients,
but it is not sufficient on its own.
Another important tool in identifying
patients at increased risk for adverse
outcomes from COVID-19-induced
changes to healthcare is case mix.

Case mix includes health information such as
chronic diseases and changes in cost or care
over the past few years.
Adding care disruptions and case mix as
features in the predictive models allows for
a more accurate prediction because the
model can learn why two patients who look
the same have different costs.

For example, if a health system has two
patients, who both have the same reduction
in PCP visits, data scientists should include
each patient’s case mix in addition to the
care disruption (i.e., reduced PCP visits).
In this example, the case mix reveals that
the first patient has diabetes and congestive
heart failure, both chronic conditions that
put him at risk for worse outcomes.

The case mix would also show that the
second patient is healthy, and the hospital
does not need to apply interventions or
include additional monitoring.
With the case mix information, a health
system can avoid wasting its limited
resources, staffing, and supplies monitoring
patients who do not need additional care.

Predictive Model Results Reveal
Which Care Disruption to Prioritize
With so many disruptions in care from
the pandemic, data scientists need to
understand which care disruption (e.g.,
a drop in PCP visits, loss of insurance,
or an increase in ER visits) to address
for each patient.
Data science teams can use feature
contribution, a concept in healthcare
data science that takes a prediction
and distributes that prediction to each
feature, allowing teams to understand
how much each feature contributes to
the prediction.

In the model below (Figure 3), care teams
need to prioritize addressing the disruption
in PCP visits because it is the greatest
contributor to the outcome (in this case,
total cost).

PCP Visit
Disruption
Insurance
Disruption
Total Cost
(Target
Outcome)
79 M Y N -3 -4 $100,000
Figure 3: A data science model that includes feature contribution.
PCP Visit
Disruption
Insurance
Disruption
Total Cost
(Target
Outcome)
$40,000 $5,000 $30,000 $0 $30,000 $5,000 $100,000
Feature Contributions
PCP Visit Disruption
should be addressed
for this patient

Care Disruptions Impact Physician
Performance, Affect Populations
In addition to the impact of care disruption
on patients, health systems can also use
machine learning to understand how
changes in care affect provider perform-
ance including safety, cost, and quality.
Predictive models reveal how provider
performance variations impact patients
at a higher risk for adverse outcomes.

For example, Dr. Jones’s patients cost
an average of $100,000, while Dr.
Smith’s patients cost an average of
$80,000 for the same procedure.
At first, the data shows that Dr. Jones
costs the health system more money,
but additional data, such as the episode
date, reveal that Dr. Jones’s patients
received care before the pandemic
and Dr. Smith’s patients after.

The pandemic caused significant care
disruptions, such as a halt in primary care
visits and non-essential care (e.g., physical
therapy), explaining the cost differential
between the doctors instead of assuming
that the difference is related to provider
performance.

Because non-essential services stopped
due to COVID-19, patients might have
cost the health system more money
because they received a full breadth of
services before they were unavailable.
With this information, leadership can use
cost to learn which patients have not
received complete services and if they
are at higher risk for worse outcomes
because of it.

Healthcare Data Science Can Prevent
Adverse Outcomes for At-Risk Patients
With the novel coronavirus, health systems
can no longer rely on the fundamental
assumption of data science—that the
future looks like the past.
However, because care disruption has
always been present in healthcare, even
before COVID-19, health systems can use
that information in predictive models to
forecast future outcomes.

Care disruptions during the ongoing pandemic
directly increase the risk of adverse outcomes
for some patients.
Health systems must identify these at-risk
patients to implement the right care
interventions before conditions worsen.

Predictive modeling with varied data sets
allows leaders to identify the patients at the
highest risk of adverse outcomes from care
disruptions and plan for different what-if
scenarios.
With data science, providers and their care
teams can proactively intervene and avoid
waiting until these high-risk patients come
to the hospital.

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

More about this topic
Link to original article for a more in-depth discussion.
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Imran is the Chief Data Science Officer at Clarify Health where he oversees the data
acquisition, data engineering and data science teams. Previously Imran was the Chief
Software Development Officer at Health Catalyst where he was responsible for the
software development in the company including leading the engineering team building
the Data Operating System (DOS). Before Health Catalyst, he was the Chief
Technology Officer at Acupera where he led the team that built the care management
platform that was successfully implemented in Ascension, Montefiore, Kaiser, and
other health systems. Prior to that, Imran was VP of Engineering at CareAnyware, where he led
development of the largest cloud-based EHR for Home Health and Hospice. He also spent 12 years
at Microsoft, including building the slideshow part of PowerPoint and building the email experience for
Hotmail. He holds several patents and has a Computer Science degree from Stanford University.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Imran Qureshi

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
Millenials, and a “Best Perks for Women.”

Data Science Reveals Patients at Risk for Adverse Outcomes Due to COVID-19 Care Disruptions

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Data Science Reveals Patients at Risk for Adverse Outcomes Due to COVID-19 Care Disruptions