Three Keys to Improving Hospital Patient Flow with Machine Learning

Three Keys to Improving
Hospital Patient Flow
with Machine Learning
HEALTH CATALYST EDITORS

© 2020 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Michael Thompson
Executive Director
Enterprise Data Intelligence
Cedars-Sinai Medical Center
This report is based on a 2019 Healthcare Analytics Summit presentation
given by Michael Thompson, MS Predictive Analytics, Executive Director of
the Enterprise Data Intelligence at Cedars-Sinai Medical Center, entitled,
“New Ways to Improve Hospital Flow with Predictive Analytics.”
Improving Hospital Patient Flow

One of the never-ending challenges
healthcare systems face is managing
hospital patient flow—the movement of
patients through the hospital from entry to
discharge.
If not managed effectively, patient flow can
have negative ripple effects throughout the
health system.

Patients are “boarded” in
the emergency department
(ED), waiting to be admitted
to a hospital bed, delaying
patients from receiving
proper care and taking up
ED space with the wrong
patients.

ED crowding increases
left-without-being-seen
patients and wait times.

Patients have overnight
stays in the post-operative
recovery rooms when it
is unnecessary.

ICU readmits within
24- hours.

Surgery delays or
cancellations.

Physicians, nurses, and
staff are overloaded,
resulting in increased
burnout.

Delays in transferring
patients to appropriate
units based on their
clinical conditions and in
discharging patients,
decreasing throughput.

Efficient hospital patient flow allows:
• Newly admitted patients to get to the right
place as soon as they enter the hospital
• Current patients to seamlessly transition to
the right unit
• Patients who are ready for discharge to leave
the hospital with as little delay as possible.

When hospitals manage hospital patient
flow effectively, the health system and the
patients win—hospitals don’t keep patients
longer than necessary.
Patients spend the minimum amount of
time at the hospital, making room for new
patients who need care.

Improve Hospital Patient Flow with Machine Learning
One way for health systems to improve
hospital patient flow is through machine
learning (ML).
Because hospital patient flow is so
complex and full of moving parts, ML
offers predictive models to assist
decision makers with hospital patient
flow information based on near real-
time data.

Improve Hospital Patient Flow with Machine Learning
For example, an academic medical center
created an ML pipeline that leveraged all its
data: patient data, EHR data, clinical and claims
data—to predict length of stay, emergency
department (ED) arrival, ED admissions,
aggregate discharges, and total bed census.
The predictive models proved effective as the
medical center reduced patient wait times and
staff overtime and improved patient outcomes
as well as patient and clinician satisfaction.

ML Targets Ineffective Hospital Patient Flow Problems
ML can be an effective tool to improve hospital patient flow and alleviate
capacity strain burdens. The goal of ML is not only to create predictive
models, but to ultimately improve, and in some cases fix, the challenges that
arise from poor hospital patient flow:
Reduce the need for
regular surge plans.
Eliminate high wait
times and other
delays.
Improve staff
schedules to
match demand.
Increase the number
of patients admitted to
the appropriate
inpatient unit based
on a patient’s
clinical condition.Prevent diversions and
overcrowding in ED.
Use effective case
management
strategies.
Improve discharge
and capacity
management planning.

Three Keys to ML Success
Key 1: Build a data science team.
Introducing data science requires strong
leadership support at the highest level.
Introducing the value of data science to
executive leaders and taking a centralized
approach to all data analytics within the
health system fosters an environment for
data science to succeed.
With C-suite support and a data science
team, data scientists are ready engage other
departments and turn data into intelligence
that drives better decision making.

Key 2: Create a ML pipeline to aggregate all data sources.
To leverage all the data available to a health
system, the data science team should create
an end-to-end ML pipeline (Figure 1)
aggregating the data.
The pipeline should include all data sources,
storage, transformation and modeling, and
visualization components.
It is vital that the ML pipeline include every
data source because if the data isn’t accurate
or doesn’t provide a complete picture, the
predictive models won’t identify the right areas
for opportunity, resulting in wasted effort.

Data Sources Data Storage
Data
Transformation
and Modeling
Data
Visualization
• EHR
• Medical device
monitors
• Consumer
Devices
• Online Surveys
• Enterprise data
warehouse
• Storage of real-
time rectangular
data
• Open-source
programming
language for
statistical
computing and
graphics
• Julia, Python,
and R.
• Digital storage
solution
• Digital
visualization
software
• Interactive
visualization
software
Figure 1. An End-to-End Machine Learning Pipeline.

To make the ML pipeline more friendly and
less intimidating to other team members,
data science leaders should consider
calling the data pipeline by a human name
like “Alex” instead of “machine learning”.
Using a common name helps team
members focus on the insight from the
predictive models and not that the insight
was derived from a machine.

Key 3: Form a comprehensive leadership team to govern data.
Another important piece for ML success is
to include leaders from other departments.
This has two benefits:
1. It ensures multiple viewpoints when
discussing the data science strategy
within the health system.
2. It helps garner support for data science
from a variety of departments throughout
the organization.

For example, a comprehensive leadership
team could include leaders from departments
like operations, nursing, patient satisfaction,
case management, and providers/clinicians
so that the data science team can develop
champions for data science across other
departments.

Creating data science champions who are
not members of the data science team makes
data science implementation more likely to
succeed and helps team members trust it
more when they see their leaders—whom
they already trust—trust and support it.

Back-testing Models Is Key to Garner ML Support
Another important step in achieving long-
lasting institutional support for ML is the
importance of back-testing models—
meeting with team members to show them
how well the model performed compared to
what actually happened.
Back-testing models increases
transparency and sets realistic team
member expectations about predictive
models—that they aren’t perfect.

Reviewing the accuracy of the model
with teams may also foster brainstorming
discussions that lead to ideas for new
models or other explanations about why
the models were inaccurate.
These alternative explanations can
offer insight to what should be included
in the next iteration of the model, further
increasing accuracy.

Another valuable practice when
discussing ML models with team
members is to use a variety of
models and compare which
models are the most accurate.
Comparisons help team members
understand that different models
have varying degrees of accuracy
and that no models are ever 100
percent accurate.

Asking team members to share their
thoughts about the machine learning
findings (usually displayed in a graph)
gives team members a chance to
interact with the models.
Rather than listening to the data
analyst report on the findings, they
have an opportunity to interact with
the data and participate in the
process that is data science.

In the process of refining the model,
leaders and team members become
invested in the model, offer suggestions
for improvement (like adding new data
variables), and eventually own the model.
This is one strategy for increasing
participation and support for data science
that leads to data democratization.

Alerts Prove More Effective than Daily Emails
It can be tempting for data science teams to
disseminate information from the predictive
models on a daily basis.
The problem with daily information is that
executive leaders develop data fatigue—
seeing the same email with slightly different
numbers every day makes it hard to focus
on what the numbers mean and eventually
tend to ignore the emails.

In order to prevent data fatigue, but still
effectively communicate the predictive
model findings to executive leadership, the
data science team can send information in
the form of alerts (Figure 2).
For example, the alerts are triggered
whenever the alert level for one of the
next three days is either high or very
high—a condition set by a health system’s
leadership team, so they are notified only
when they feel it is necessary.

Alert Level Alert Trigger Descriptive Text
Very High
Lower band
above 1,000
Very high risk of census exceeding
1,000 on Tuesday.
High
Projection line
above 1,000
Census is likely to exceed 1,000 on
Tuesday.
Medium
Upper band
above 1,000
Current conditions suggest there is a
chance census could exceed 1,000 on
Tuesday.
Low
No bands
above 1,000
Census is unlikely to exceed 1,000 on
Tuesday.
Figure 2. Alert Level Table

ML Combined with Expertise Improves
Hospital patient flow challenges are not a
single department problem but a problem
the entire health system should strive to
overcome.
To effectively improve hospital patient
flow, it is imperative for operational and
clinical leaders to be involved from the
start in order to recognize the value of
data science.

The data science team has a responsibility
to democratize data—ensure its availability
to decision makers at every level.
However, access to data doesn’t mean that
the interpretation of data will be uniform.
The data science team should equip
leaders with easy-to-understand models
at first and work closely with them until
they feel comfortable, slowly building
their analytics acumen.

An agile approach to data science
allows leaders to experience the data,
not just review it.
Agility within ML is crucial because
with each model iteration, participation
from a clinician or an administrative
leader increases their understanding
and, as a result, the accuracy of the
predictive model.
At this point, leaders throughout the
organization are referencing data and
then leveraging it to make decisions.

ML models can improve hospital patient
flow, but only do so effectively when
leadership adds valuable perspectives
through suggesting new variables to
consider in the predictive models.
When ML and committed team members
come together, ML is more accurate
because it is sensitive to a health
system’s needs, schedules, insurance
plans, and most importantly, its patients.

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.
Three Keys to Improving Hospital Patient Flow with Machine Learning
Meaningful Machine Learning Visualizations for Clinical Users: A Framework
Valere Lemon, MBA, RN, Senior Subject Matter Expert; Alejo Jumat, User Experience Designer, Sr.
AI in Healthcare: Finding the Right Answers Faster
Health Catalyst Editors
AI-Assisted Decision Making: Healthcare’s Next Frontier
Machine Learning Tools Unlock the Most Critical Insights from Unstructured Health Data
A Data-Driven Systems Approach to Improving Emergency Care
Health Catalyst Success Stories

Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Michael Thompson has over 30 years of using analytics to unleash hidden stories within data. The
quest has led him to use a variety of data warehousing, visualization, statistical, data mining
methodologies, and analytic modeling creations. His team’s work has been shared in the Wall Street
Journal, industry symposiums, and publications over the years. With a personal goal to help others
on their journey to find opportunities hidden in their data, Mike has shared his adventures as a
speaker at industry events (national and international) on computational health topics and as a
lecturer on data and analytic topics at Georgia Tech University, Mercer University, Emory University,
and UCLA. After two and half decades of torturing data with various levels of sophistication, Mike updated his skills
by earning a Master of Predictive Analytics/Data Science degree from Northwestern University. Within the private
sector, Mike has led analytic teams across large financial and healthcare organizations. Currently, Mike leads a multi-
disciplined team (physicians, nurses, statisticians, data scientists, data analysts, data engineers, and data warehouse
developers) to fulfill the insatiable financial, operational, clinical, quality, and population health analytic needs of the
organization as the Executive Director of Enterprise Data Intelligence at Cedars-Sinai in Los Angeles, CA.
Michael Thompson

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.”

Three Keys to Improving Hospital Patient Flow with Machine Learning

Recommended

Recommended

More Related Content

What's hot

What's hot (20)

Similar to Three Keys to Improving Hospital Patient Flow with Machine Learning

Similar to Three Keys to Improving Hospital Patient Flow with Machine Learning (20)

More from Health Catalyst

More from Health Catalyst (20)

Recently uploaded

Recently uploaded (20)

Three Keys to Improving Hospital Patient Flow with Machine Learning