In this webinar, Phil Rowell, M.J., Vice President of Clinical and Business Intelligence at Carle Health, will describe how Carle Health became an early adopter of AI and leveraged AI-powered analytics to tackle the complexities of COVID-19, improve sepsis management, and accurately forecast patient outcomes and associated costs based on historical and current data.

1. 1
Webinar: Leveraging AI to Accelerate
Systemwide Improvements at Carle Health

2. 2
Agenda
o Introductions
o Understanding Augmented Intelligence
o Examining AI Use Cases
o Q & A

3. 3
Guest Speaker
Phillip Rowell
Vice President,
Clinical and Business Intelligence
Carle Health

4. 4
Our Health System
Carle Health provides a broad spectrum of healthcare services to a large and predominantly rural
area across all 28 counties in east-central and southern Illinois

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Augmented Intelligence (AI)
o Continues to evolve and assists humans in
making accurate, insightful decisions
o Extracts the most relevant insights from millions
of datasets to solve business-critical issues
o Requires a strong data foundation to support
effective AI and advanced analytics
o Is key to achieving sustainable
improvements—now and in the future

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AI Use Cases from the Frontlines

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Three AI Use Cases
A roadmap addressing pitfalls to avoid and best practices
1 2 3
Forecast
COVID-19 patient numbers
and staffing needs
Identify
inflection points within data
for sepsis mortality
Stratify & Predict
patient outcomes and
associated costs

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Use Case 1: Forecasting Pandemic Needs
o Pandemic pressures required novel solutions to support
sustained operations in a challenging operating model
o Needed a way to predict the number of COVID-19 patients
that would need care during the pandemic
o COVID-19 analytic insights allowed Carle to
staff appropriately based on New York Times published
infection rates integrated into hospitals admission, discharge,
and transfer (ADT) data
Lacked reporting capabilities to support insights into COVID-19-related admissions

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Use Case 1: Forecasting Approach
o COVID-19 Patient and Staff Tracker Tool –
patient and staff exposures
o COVID-19 Forecasting Analysis – 5-day forecast
for inpatient admissions and daily census
o Forecasted across levels of care
(e.g., non-critical care versus ICU)
o Forecasts (and downstream calculations)
include confidence limits that represent “best”
and “worst” case scenarios
o Included calculations for estimating staffing
and PPE needs
Deployed a suite of predictive capabilities to address pandemic needs

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Use Case 1: Forecasting Results
o Promoted AI models to production in Q3 2020
o Forecasted bed utilization and census has
matched >4X
o Adopted and utilized by senior leaders
Combining data and AI fueled real-world clinical and operational improvements
“COVID-19 continues to raise awareness
about the importance of data in
operational decision making... through
the thoughtful application of our AI
capabilities, we were able to avoid over
1,000 hours of manual work.”
Chief Medical Officer, Robert Healy, M.D.

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Use Case 2: Identify Inflection Points to Reduce Sepsis Mortality
o Needed to understand sepsis performance at a deeper level:
– Have we reduced sepsis rates overtime?
– Are the sepsis rates better at one hospital compared to another?
– If we set a sepsis improvement goal, is it statistically different
from current performance?
– What will our sepsis performance be in a year?
– What will we change to see expected sepsis results?
o Increasing amounts of data made it difficult to understand
actual sepsis improvement opportunities
Difficulty identifying sepsis improvement signals from noise

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Use Case 2: Identifying a Shift in Sepsis Mortality
o AI model identified sepsis outcomes
overtime and compared compliance
by location
o Expected mortality indicates expected
mortality has increased approximately
at the same time discharges have
decreased and unadjusted mortality
has increased
o AI revealed that a likely reason for the
increased, unadjusted mortality rate is
the increased patient severity as
opposed to alternatives such as care
worsening
Timeframe: 10/01/2019 - 08/31/2021
EXCLUSIONS: Hospice, COVID Positive, and Outside Hospital Transfers

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Use Case 3: Stratify and Predict Patient Outcomes and Costs
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Risk
Score
Overall
Disease
Burden (ACGs)
Age, Gender
Medication
Patterns
Disease
Markers
(EDCs)
Resource Use
Population
Markers
o Needed a way to surface and prioritize high-
and risking-risk patients
o Used the Johns Hopkins Adjusted Clinical
Groups (ACG) Model to stratify patients
based on risk:
– Disease burden (morbidity and resource
use) ACGs
– Hospital admission (6,12 mo; ICU; long-LOS
(12+d) and injury–related admission)
– 30-day unplanned readmission
– Cost range (health care, pharmacy)
Health Alliance couldn’t predict patient health status and associated cost

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Use Case 3: ACG Model Opportunities
Analytic Efficiency
Create population analytics across
any patients (all ages and plans)
Pharmacy
Chronic condition medication
adherence, polypharmacy
review and reduction
Care Management
Cohort-based and risk-adjusted
pre-post spend and utilization
Care Coordination
Patients engaged with primary care,
reduced low value care or
duplicative testing and improved
medication management
Cost Forecasting
Actual to expected spend accuracy
and intervention-driven change
Proactive Care
Patients with elevated blood
pressure and CV risk factors or
prediabetes that progress to
hypertension or diabetes
Health Alliance leveraged ACG to reveal AI-informed opportunities

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What’s Next for AI at Carle Health?
o Continue investment in a strong data
foundation to support effective AI and
advanced analytics for future success
o Identify new opportunities to apply AI
and customize algorithms to our specific
healthcare dynamics
o Support usage and adoption across our
healthcare system

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Thank you