Hospital Capacity Management: How to Prepare for COVID-19 Patient Surges

Hospital Capacity Management:
How to Prepare for COVID-19 Patient Surges
Jason Jones, John Hansmann, Monica Horvath

© 2020 Health Catalyst
Proprietary. Feel free to share but we would appreciate a Health Catalyst citation.
Hospital Capacity Management
Health systems use capacity planning
methods to balance the availability of beds,
staff, and other resources with patient demand.
Planning for annual surges in volume
(e.g., flu season) is difficult, given uncertainty
about timing and duration, but the unprece-
dented scale of the COVID-19 pandemic has
far exceeded the worst surges.
COVID-19 spread, and severity have proven
variable over time and place, leading to
demand spikes and empty beds.

A variety of factors, including population
demographics, governmental policies, local
culture, and the physical environment, have
shaped COVID-19 demand for healthcare
resources.
As regions around the world have been
struggling to obtain sufficient beds, staff, and
equipment during the pandemic, advanced
analytics-driven capacity planning has
emerged as a healthcare must-have.

Capacity forecasting provides critical insight
into when a patient surge will occur and
if/when demand will exhaust resources.
This foresight allows planners to design and
implement mitigation strategies that can
prevent disastrous pandemic resource
strain, like those in Italy and New York City
in the spring of 2020 and Florida, Texas, and
Arizona in the summer of 2020.

Meanwhile, overestimating demand can be
dangerous to the financial viability of these
same care delivery organizations.
Although national and regional outbreak
forecasts exist, hospital leaders need
projections that incorporate local dynamics,
their expert knowledge, and risk tolerance.

The Value of a Reliable Capacity Planning Model
In choosing a capacity planning model,
organizations can’t afford to waste
valuable discussion time debating model
methodology, which is likely with black
box or proprietary models.
Using a transparent capacity planning tool
from an established analytics partner,
such as Health Catalyst, promises more
efficient, accurate outcomes.

The Value of a Reliable Capacity Planning Model
Health Catalyst initially built its COVID-19 Capacity
Planning Tool on the well-regarded, open-source
COVID-19 Hospital Impact Model for Epidemics
(CHIME) application from Penn Medicine.

Known COVID-19 Case Data Boosts Accuracy
At the beginning of the pandemic,
health systems forecasted the need
for patient beds, staff, and supplies
using the standard SIR model for
infectious disease.
Users could adjust the model at a
single point in time to account for
the impact of mitigation policies,
such as social distancing.

But the required assumptions for
accurate adjustment were often
unknowable or more dynamic.
Even the most sophisticated models
predicted patient volumes and deaths
that disease spread rapidly proved
wrong.

As more data became available from
sources such as the New York Times
and the U.S. Census, Health Catalyst
added the ability to leverage empirical
forecasting with well-documented
methods from the fields of microbiology
and epidemiology and implemented the
open-source statistical computing
package R.

As a result, Health Catalyst makes increasing
data and model sophistication available
through a simple, transparent interface
healthcare planners can easily leverage.
These design decisions have led to increased
credibility in the broader healthcare community
and kept conversations focused on decisions
and action.

COVID-19 New Case Forecasting Over Time
COVID-19 spread rates have progressed
differently over place and time.
In the five sample regions in the animation
in the next slide (Figure 1), black points
indicate data used in generating the
forecast, grey points indicate actual counts
held out from the forecast, and the yellow
rectangle shows the next 30 days.

COVID-19 New Case Forecasting Over Time
The colored lines show the forecast
using the measures of spread
(reproduction number versus
doubling time) and forecast
techniques (e.g., linear versus spline)
that worked best for each region.
(Figure 1: COVID-19 disease rate progression.)

Access to Multiple Scenarios Guides Hospital
Capacity Management
Rapidly changing conditions that place
limits on social interaction, travel, and
business operations can hamper the
accuracy of outbreak forecasts.
A capacity planning tool aims to buy
leadership time to act.
Organizations should adjust model
inputs to reflect different pandemic
scenarios, given the high level of
uncertainty in how the population will
react to the virus.

Capacity Management
For example, quick adoption of travel restrictions
and social distancing policies initially slowed viral
spread and evaded a dramatic patient surge in
some areas.
As restrictions have been differentially
enforced or lifted, however, spread
keeps changing.

Capacity Management
An accurate capacity planning tool supports
timely action with its ability to manage, use,
and save scenarios.
The ability to easily compare a variety
of situations gives a better picture of
uncertainty around resource availability
and provides more support to mitigation
efforts.

Preparing for Fundamental Changes in
Hospital Utilization Patterns
Capacity planning usually occurs at a
regional level based on the hospital’s market
share and utilization patterns.
Given differences in population densities and
changing social distancing restrictions
between regions, suitable mitigation
strategies will vary between hospitals.

Some organizations have been surprised to
find they cannot reliably estimate market
share and thus alter this parameter during
scenario modeling.
Cultural factors can shape market share,
such as when a patient feels more
comfortable presenting at an academic
medical center over a community hospital
(even if they lack objective evidence to
support this preference).

As well, hospital utilization patterns have changed.
Examples include pausing non-emergent services
and shifting labor and delivery services to
ambulatory surgical centers.
Some hospitals are working with local and more
distant skilled nursing facilities as a place for
COVID-19 patients to convalesce once out of
intensive care.

Celebrating Executive Adoption of Data-Driven
Decision Making and the Ongoing Need
One unexpected silver lining of the COVID-19
crisis has been the willingness of healthcare
executive culture to embrace data science to
support its decisions.
Many data science applications exist at the
point of care or service to support individual
providers and patients, but engaging health
system leadership in data science has been
more challenging.

The deployment of advanced analytics for
capacity planning is a rare opportunity to
directly influence executives and strategic
planners to likewise work in a data-driven way.

While governmental policies initially slowed
the spread of COVID-19 to prevent dramatic
patient surges in many areas, as regions have
reopened during the summer months,
infection rates have again risen.
Hospital leadership will need to monitor
forecasts for some time, particularly as fall
and winter approach.

An effective capacity planning tool
must leverage robust data and
advanced analytics in the face of
shifting circumstances.
Such a solution will provide valuable
insights into several possible futures
and gift healthcare leaders and
managers with the time to anticipate
the necessary resources to keep
communities healthy.

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

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currently serves as Chief Data Scientist at Health Catalyst. Previously, he served at
Kaiser Permanente (KP) in various roles including Research Scientist and VP,
Information Support for Care Transformation. Prior to KP, Jones was a Medical
Informaticist for Intermountain Healthcare. Other roles have included analytic and
marketing leadership positions at Bayer HealthCare, data and information product
development at UnitedHealth Group, and various academic adjunct faculty positions.
Jones received his PhD in Biostatistics from the University of Southern California in
2001. His mission is to leverage data to achieve the Quadruple Aim.
Other Clinical Quality Improvement Resources
Click to read additional information at www.healthcatalyst.com
Jason Jones, PhD

…currently serves as Chief Data Scientist at Health Catalyst. Previously, he served at
Kaiser Permanente (KP) in various roles including Research Scientist and VP,
Information Support for Care Transformation. Prior to KP, Jones was a Medical
Informaticist for Intermountain Healthcare. Other roles have included analytic and
marketing leadership positions at Bayer HealthCare, data and information product
development at UnitedHealth Group, and various academic adjunct faculty positions.
Jones received his PhD in Biostatistics from the University of Southern California in
2001. His mission is to leverage data to achieve the Quadruple Aim.
John Hansmann, MSIE, LFHIMSS, DSHS

Population Health and Quality analytics solution designer with deep experience serving
healthcare organizations. 19+ years experience in the design and execution of research
methodology, informatics, and analytics. My mission is to design and deploy products
and services that meaningfully improve the value, quality, safety, and experience
patients receive when getting healthcare.
Monica Horvath

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.”

Hospital Capacity Management: How to Prepare for COVID-19 Patient Surges

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