Healthcare leaders today are faced with increasingly complex and unprecedented challenges. With COVID-19 taking the world by storm, the need for an intelligent system of insights that can proactively deliver actionable and real-time knowledge on patient populations is imminent to providing better care.

2. Rapid Response to Hospital
Operations using Data and AI during
COVID-19
Rohan D’Souza
Head of Product, KenSci
Tony Pastorino
VP, IS, Indiana University Health

3. Agenda
Leveraging Data & AI investments
How IU health leveraged the cloud and data to build self-
service offerings for COVID-19 response
Improving Patient Flow with ML
Operationalizing Machine Learning at IU Health to
improve hospital operations
KenSci Realtime Command Center
Learning and experience with deployment of the
Realtime COVID-19 Command Center at multiple large
health systems

4. Indiana University Health is
a non-profit Health System,
and the largest healthcare
provider in the state of
Indiana.
PATIENT ADMISSIONS
AVAILABLE BEDS
TEAM MEMBERS
INDIANA RESIDENTS SERVED
~120,000
~3,000
30,000+
1M+

5. Our Journey with Data & AI
ZettabytesGigabytes
2000’s 2020’s
Azure Data Platform
Social
Graph IoT
Image
LOB
CRM
ERP
SQL Server
LOB
ERM
ERP
2010’s
Analytics Platform System APS
Terabytes Petabytes

6. Our Investments in Data & AI
Self-Service analytics
for COVID-19
Advanced analytics & ML
for improving hospital ops
SQL
Modern data
warehousing
“We require self-service
dashboards to uncover
insights and take action”
“We’re trying to
predict when our
patients churn LOS ”
“We want to integrate all our
data—including Big Data—
with our data warehouse”
1
/
2/ 3/

7. Leveraging Data & AI Investments for
COVID-19 Response

8. Our Ecosystem & Process
Azure Data
Lake Storage
STORE
Power BI
VISUALIZE
Azure SQL
Data Warehouse
MODEL & SERVE
Informatica
Cloud
Azure
Databricks
INGEST & PREP

9. Data Layers
REPORTING LAYER
Team Workspaces
Intra-departmental, Securable Reporting Layer
Supporting 27 different IUH Analytics Teams
INTEGRATED LAYER
Harmonized Layer
Rebuilt every 24 hours
3+ Years currently
SOURCEMART LAYER
31 Sources
1403 Tables
Loaded every 24 hours
10+ Years of Raw Data
2 Types
Secured By Datatype
Secured by Business
Owner
CUBES
Built from Integrated Layer
Rebuilt every 24 hours
3+ Years currently
ML ANALYTICS *
Built from Integrated Layer
Deidentified

10. Empowering Self-service analytics during COVID-19
• Over 250 BI Analysts and Citizen
Data Scientists across the system
• Strong Self Service Motivation
• Centralized and Decentralized
Analytics
• Data Gathering vs. Data Insight /
Analysis
• Valuable time gathering,
massaging, reconciling, and
validating data
• More time for data insights
EMPOWER INSIGHT DECISION MAKING

11. Improving Patient Flow with AI/ML

12. Understanding Healthcare as a complex system
Interdependent
Cascading effect of bottlenecks
leading to downstream patient
leakage/loss & crowding
Volatile
Difficult to forecast variables across
patient volumes, acuity, census, and
staffing requirements
Noisy
Finding meaning and opportunities
within the vast data ecosystem is a
massive undertaking
ED
Elective
Surgery
Admit
Transfer/
Direct Admit
Inpatient
OR
Outpatients
Discharge
ARRIVALS INTERHOSPITAL TRANSPORT CAPACITY LENGTH OF STAY STAFFING READMISSION

13. DischargeAdmi
t Inpatients
Length of Stay
Risk of Readmission
Risk of Obs. Failure
Discharge Disposition
Inpatient Predictions
Enable clinicians to prioritize the neediest patients first, and identify opportunity areas for
systematic improvement.
Optimize availability of
inpatient beds
Improve readmission
rates & penalties
Correct patient status
assignment
Outcomes

14. Length of Stay
Risk of Readmission
Risk of Obs. Failure
Discharge Disposition
Inpatient Predictions
Enable clinicians to prioritize the neediest patients first, and identify opportunity areas for
systematic improvement.
Optimize availability of
inpatient beds
Improve readmission
rates & penalties
Correct patient status
assignment
Outcomes

15. © 2019 KENSCI CONFIDENTIAL
Solution
generating scores
in minutes from
data delivery
85% of encounters predicted
within 1.65 days of actual LOS
at the time of admission –
establishing one of the best
documented AI solution in a
production setting
Summary: Solution Performance

16. KenSci Realtime Command Center

17. Bringing Together Data & AI for COVID-19 Response
Risk Stratification Realtime Hospital CensusSurge Planning
IDENTIFYING PATIENTS AT HIGH-RISK PLANNING FOR PATIENT VOLUMES TRACKING PATIENT CAPACITY
KENSCI SMART ON FHIR BASED DATA & AI ACCELERATOR
Data Ingestion Pipeline Real-time data feed & InsightsRapid Secure Deployment Built for the Future

18. Unified Data Analytics Architecture with Azure Databricks and KenS
Data Ingestion Agent
Runtime ML engine
Data Preparation Model Development Model Production
ML OpsPipeline
Monitoring
Key VaultActive Directory RelationalBlob
Data Ingress
Integration
EMR Claims IOMT Streaming
Data
?
Azure Cloud
Services
Model
Experimentation
Model
Explainability
Single
Sign-on
Role-based
Security
Visualization &
Analytics
Variation
Analysis
Feature
Bank
KenSci Analytics Portal

19. Learnings & Takeaways
For other health
systems
For other Data &
AI Teams

20. Feedback
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Don’t forget to rate and
review the sessions.