Just over three years ago, Health Catalyst publicly announced the development of the Data Operating System (DOSTM). Conceptually, DOS goes back more than 20 years as a single platform that could support what Dale Sanders calls the “Three Missions of Data”—analytics, data-first application development, and interoperability. “Data platforms are the next evolution of the technology stack,” Sanders says. While the Cloud made infrastructure an easy and scalable platform, modern operating systems and programming languages made software platforms scalable and easy to build. He cautions, however, “Data wrangling, especially in healthcare, is still a giant challenge.” Sanders explains that DOS is therefore an essential strategy for Health Catalyst, as well as an important new concept in the world of platforms. “DOS and its concept is a data platform that makes analytics, app development, and interoperability easy and scalable,” Sanders says. In this webinar, Sanders and Bryan Hinton will review the concept of a data operating system and the vision behind it. Hinton, who leads the DOS team for Health Catalyst, will reflect on lessons learned over the past three years and what he has planned for the future.

1. The Health Catalyst Data Operating System (DOS™):
Lessons Learned and Plans for the Next Evolution
Bryan Hinton, CTO
Dale Sanders, Senior Advisor
September 30, 2020

2. © 2019
Health
Catalyst
• Dale: Looking back
• Review the “why” behind DOS… the concept and vision
• Bryan: Looking forward
• How far have we carried the vision?
• What’s left and will it ever end?
Let’s Dive In…
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3. © 2019
Health
Catalyst
• Change of the guard, old to new…
• DOS was a very ambitious vision and
endeavor that we publicly launched less
than four years ago… Jan 2017
• Here’s the status update
• DOS is about Health Catalyst, but it is also
about new concepts, and thinking
differently about the utilization of data in
healthcare and life sciences, and laying the
foundation for digital health
• Has the concept been validated or not?
Why are we giving this webinar today…
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4. © 2019
Health
Catalyst
Today might sound more “sales-y”
than usual for one of my webinars,
so apologies are offered, and
forgiveness appreciated.
We will do our humanly best to give
you the truth… you can decide
whether it’s a sales pitch or not
Today Might Sound ”Sales-y”
Klaus Voormann
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5. © 2020
Health
Catalyst
Generally speaking, computer
science hasn’t addressed the last
and critically important layer in the
technology stack, especially for the
incredibly complex world of
healthcare and life science data.
Where Does DOS Fit in the Tech Stack?
User Interface
Application Software
Operating System
Infrastructure
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Health
Catalyst
• Applications will come and go, but data
is here forever
• Free your data, free your mind
• There’s a big opportunity to locally innovate at
the application layer, by leveraging the data you
already paid handsomely to collect
• Separate technology infrastructures for
managing the same data is a violation of
common sense
• It’s expensive and leads to all sorts of
inconsistencies in data management
• There are, essentially, three missions for data…
A Few Assertions
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7. © 2020
Health
Catalyst
InteroperabilityData Analytics
& AI
Data-First
Application Dev
Three Missions of Data
The Health Catalyst Data Operating System (DOS™) is a single, cloud-
based, API-based architecture with a common, consistent layer of data
content, to support the Three Missions of Data…
Workflow transactions and
ambient analytics in the same
software user experience
Interoperability, portability,
and source-to-subscriber,
transaction-level
exchange of data
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8. © 2020
Health
Catalyst
• The origin of the Data Operating
System (DOS™) concept is this
cartoon
• The digital world of health is
largely stuck in the lower left
two clouds
• Our digital understanding of the
patient requires the entire data
ecosystem
• Digital health is just beginning,
and DOS is the enabling
platform
The Human Health Data Ecosystem
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9. © 2020
Health
Catalyst
The Healthcare Analytics Adoption Model
Level 8 Direct-to-Patient Analytics & Artificial Intelligence
Analytics and AI tools available to patients to empower their health decisions.
Digital Twin and “Patients Like Me” is a core capability, including social
networking with similar patients as extended members of the care team.
Level 7 Personalized Medicine & Prescriptive Analytics
Patient care informed by analytics; decision support based on data from
SDOD, genomics, and patient activation (willingness x ability) in their care.
Population-based protocols customized per patient.
Level 6 Population Health Management & Predictive Analytics
Using predictive models and clinical suggestive analytics to support risk
intervention. Risk reduction models are informed by economic models that
reward prevention over treatment.
Level 5 Waste & Care Variability Reduction
Reducing variability in care processes. Focusing on internal optimization and
waste reduction; strong focus on reducing low-value care and adverse events.
Level 4 Automated External Reporting
Efficient, consistent production of reports required for reimbursement,
compliance, accreditation, etc.; adaptability to changing requirements.
Level 3 Automated Internal Reporting
Efficient, consistent production of basic financial and clinical operations
reports; widespread availability in the organization.
Level 2 Standardized Vocabulary & Patient Registries
Curating, organizing, and standardizing core data content. Reusability of data
models and analytics logic balanced with late binding.
Level 1 Enterprise Data Operating System Collecting and integrating the core data content.
Level 0 Fragmented Point Solutions
Tolerating inefficient, inconsistent versions of the truth; cumbersome internal
and external reporting.
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10. © 2020
Health
Catalyst
Our Solutions Use Data, Analytics, & Expertise
to Catalyze Improvement
Analytics Applications: A robust set of applications, built on top of DOS, that generate meaningful insights for improvement
Professional Services
2
The Data Operating System (DOSTM
): A healthcare-specific, open, scalable platform for analytics, application development, and
interoperability
1
Activity-
based
Costing
(CORUSÔ
Suite)
Patient Safety
(Patient Safety
MonitorÔ Suite)
Foundational Software Applications Domain-Specific Software Suites
Services Expertise: Analytical, clinical, financial and operational experts facilitate and accelerate measurable improvement3
Clinical, Financial, and Operational Domain Experts Analysts, Data Scientists, and Data Engineers
Strategic Consulting ž Readiness Assessment ž Opportunity Analysis ž Governance ž Outcomes Improvement ž Population Health ž Technical Support ž
Training
Terminology
Services
Text
Processing
Real-time
Streaming &
Interoperability
Machine
Learning
Big DataClosed-Loop
EHR IntegrationData Warehouse Reusable
Data Logic
Cloud-based
Source
Connectors
Tailored Analytics Accelerators
Care
Management
Benchmarking
(TouchstoneÔ
Suite)
Registries
& Measures
Authoring
(Population
Builder)
Financial & ACO
(Payment Model Analyzer,
Financial Management,
Revenue Cycle,
Hierarchical Condition
Categories, etc.)
Operational
(Supply Chain, Patient
Flow, Surgical Services,
Labor Management,
Practice Management,
etc.)
Population
Health
Foundatio
ns
Clinical & Patient
Safety
(Sepsis, Readmissions,
Heart Failure, Joint
Replacement,
CLABSI,COPD, etc.)
Quality and
Regulatory
Measures
Dashboards
& Reporting
(Leading WiselyÔ)
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11. © 2020
Health
Catalyst
AI Algorithms are Commodities,
Data Platforms are Not
“…it is dangerous to think of
these quick wins as coming for
free. Using the software
engineering framework of
technical debt, we find it is
common to incur massive
ongoing maintenance costs in
real-world ML systems.”
Neural Information Processing Systems (NIPS)
Advances in Neural Information Processing Systems 28 (NIPS 2015)
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12. © 2020
Health
Catalyst
The Machine Learning Code, in the Black Box, is a
Small Fraction of the Machine Learning Investment
and Ecosystem
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13. AI/Data
Science
Commoditize
access to AI
models
IDEA
Add missing data
DOS
Operations
Console
Schedule, define,
monitor, and
troubleshoot ETL
processes
Atlas
Data
Governance
Catalog, manage,
and explore
existing analytics,
data, and building
blocks
Metadata & Task
Management
Health Catalyst DOS Ecosystem
Touchstone
(Benchmarking)
Leading
Wisely
(Visualization)
Data to the Edges:
Rapid Response Analytics
Closed Loop
EHR Decision
Support
Data
published
back to DOS
Population
Builder
Client-
developed
analytics &
apps
3rd-party apps
Data
published
back to DOS
Patient Safety
Monitor
Population Health
Foundations
CORUS Suite
(Activity-Based Costing)
Able Health
(Regulatory Measures)
45+ Analytic
Accelerators
Data published
back to DOS
Data to the Domains
Subsets of data for
specific analytic use
cases & standardized
terminology
Level 1: DOS Marts
Clinical, cost,
claims, etc.
Level 2:
Population SAMs
Sepsis, diabetes, CHF,
COPD, etc.
Customized SAMs
D O S H o m e p a g e i n A t l a s t i e s i t a l l t o g e t h e r
Measures
Manager
View and manage
all measures
in one place
Hundreds of
compulsory
& internal
measures
definitions
Requirements drive
data content needs
Data Acquisition, Movement, Curation, Governing, and Monitoring
>300 data
sources Text
Processing
Integrate text &
discrete data
DOS Data
Lake
Data Processing
Engine
Subject Area
Mart Designer
Aggregate &
manage data
Data Processing
Engine
Source Mart
Designer
Enable real-time
data ingestion
Raw
text
Data
of all
types
HC
Interoperability
Community-based
data integration
Community
data
Precision Medicine
Suite

14. © 2020
Health
Catalyst
From a January 2017 lecture
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15. © 2020
Health
Catalyst
One of My Observations in Life
The greatest leaders, companies, and organizations in
history exhibit chronic, constructive dissatisfaction.
Bryan Hinton personifies this trait.
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16. Learnings and Future Plans

17. © 2020
Health
Catalyst
Healthcare Analytics Adoption Model
Variation
Reduction
Improve
Health
Efficiency
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18. © 2020
Health
Catalyst
The Data Operating System
Data Ingest
Real-time
Streaming
Source
Connectors
Catalyst Analytics Platform Fabric Data Services
Real-time
Processing
Health Catalyst Applications
Data
Quality
Data
Governance
Pattern
Recognition
Hadoop/
Spark
Data Export
Population &
Registry
Builder
Leading
Wisely
Care
Management
Atlas
Client-built
Applications
NLP
Touchstone
Benchmarks
CORUS
Cost
Accounting
Patient
Safety
Measures
Manager
ACO
Financials
Patient
Engagement
HL7
Data Pipelines Metadata
Data Lake
Reusable
Content
AI Models
Third-party
Apps
Artificial
Intelligence
Pipelines
Marketplace
SAMD
& SMD
Fabric Application Services
Terminology
& Groupers
EMR
Integration
Security, Identity
& Compliance
Patient & Provider
Matching
Value Sets
& Measures
Standard,
Extensible
Data Models
RegistriesFHIR
HL7
Analytic
Accelerators
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19. Healthcare Analytics Adoption Model
Data First Application
Platform &
Late-Binding
Capabilities
Terminology
Infrastructure
& Standard Data Models
“Interoperability
Foundation”
Advanced Analytics,
Data Science,
Big Data

20. © 2020
Health
Catalyst
Learning and Evolving with
Documented Results with Improvement
ON THE GROUND
EXPERIENCE
From 2018 to 2020 YTD
• 1,000 documented
financial, operational,
and clinical
improvements
• Estimated Economic
valuation of $407M
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21. © 2020
Health
Catalyst
Learning and Evolving from Industry Analysts
INDUSTRY ANALYSTS
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22. © 2020
Health
Catalyst
Tool Integration and Scaling Challenges
Disparate Systems for:
• Streaming
• Machine Learning
• Traditional ETL
• Data Curation
• In-memory Analytics
Causing:
• Focus on tool integration and
interoperability and difficult to
scale operations rather than
value creation.
Providing:
• Technical capability with limited
healthcare knowledge built-in
Resulting in:
• Large, sustained custom
technology support costs
• With limited business success
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23. © 2020
Health
Catalyst
• Technology focused
• Limited to domain specific content
• Very flexible, difficult to scale
Open Data Platforms and Tools
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24. © 2020
Health
Catalyst
• Tend to be focused around a specific model (large or small)
• Sometimes can ingest multiples sources but are limited for non-
model focused analytics
Examples:
• Source System Analytic Capabilities (EMR, Finance, ERP)
• Population Health Platforms
Closed or Niche Data Systems
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25. © 2020
Health
Catalyst
Learning and Evolving from Other Industries
OTHER INDUSTRIES
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26. © 2020
Health
Catalyst
Many businesses use data to support their decisions instead of drive their
actions. But why? After all, data is really only valuable if you can translate it
into actionable insights. Gaining these insights starts with figuring out what you
want from your data finding its value. Here we talk about the questions you should
ask regarding the context, need, vision and outcome of your data, and we offer a
helpful framework for turning that data into meaningful stories and business
successes.
In 1910, Scottish writer and poet Andrew Lang said, "He uses statistics as a
drunken man uses lampposts—for support rather than illumination." Decades
later, many modern businesses still do just that, using data to support rather
than drive their decisions.
Daniel Waisberg
https://www.thinkwithgoogle.com/marketing-resources/data-
measurement/data-to-insights-blueprint-for-your-business/
From data to insights
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27. © 2020
Health
Catalyst
Learning and Evolving from Other Industries
OTHER INDUSTRIES
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Health
Catalyst
Netflix
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Health
Catalyst
Data Mesh
Data Orchestration Layer
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Health
Catalyst
Software Engineering Data Engineering
Domain Driven Design Data Mesh
Service Mesh Data Orchestration Layer
Declarative programming Declarative Dataflows
Reactive Programming Data Driven Execution
Unit Testing Data Quality and Business Rule Testing
DevOps DataOps
Interfaces Schema
Dynamic Programming Late-Binding and Schema on Read
Static Typing Schema on Write
Code Centric Code Centric
Bytecode Directed Acyclic Graphs (DAGs)
Software Engineering applied to Data Engineering
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31. © 2020
Health
Catalyst
Learning and Evolving
ON THE GROUND
EXPERIENCE
INDUSTRY
ANALYSTS
SOLUTIONS FROM
OTHER INDUSTRIES
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32. © 2020
Health
Catalyst
Data Success Framework
How to turn data into insight-driven decisions at scale
Generate Govern Deliver
Acquisition Analysis &
Engineering
Operations Literacy Quality Access Reporting &
Analytics
Modality Any
Location
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33. © 2020
Health
Catalyst
Data Success Framework Capabilities
Generate Govern Deliver
Acquisition Analysis &
Engineering
Operations Literacy Quality Access Reporting &
Analytics
Modality Any
Location
Structured &
Unstructured
Streaming/IOT
Ambulatory
Master Data
Patient Matching
Terminology
Self-Service
Individual Desktop
Clinical Workflows
EMR
Notification
(mobile, email,
phone)
Industry Standards
(HL7v2, CCD,
FHIR, Direct)
API Support
(FHIR, etc.)
33

34. Level 9 Direct-to-patient
Analytics & Artificial
Intelligence
User Generated
Content
Streaming/IoT
API Support
De-Identification Intelligent
Execution
Mobile
Level 8 Personalized
Medicine &
Predictive Analytics
SQL, R, Python,
Spark Support
Records API
Level 7 Clinical Risk
Prevention &
Predictive Analytics
NLP & Data
Science
Extensibility
Automated
Optimization
Recommendations
Profiling Personalized &
Automatic Viz
Level 6 Population Health
Mngt & Suggestive
Analytics
Ambulatory Executable
Content
Declarative
Dataflows
Measure Catalog
Analytics Catalog
Population
Catalog
Business Rules Scalable Access
Management
Guided Analytics
Self Service
Notifications
Level 5 Waste & Care
Variability Reduction
Integrated Data
Quality
Declarative
Dataflows
Adaptive Data
Modeling
Measures
Framework
Alerting
24x7 Support
Data Lineage
Training
Data Platform
Integration
Data Quality
Reporting
Stewardship Analytic
Applications
Limited Viz Layer
Business Logic
Business
Workflows
Clinical
Workflows
Level 4 Automated External
Reporting
3rd Party
Organizations
Level 3 Automated Internal
Reporting
DataOps Support Scriptable Column Level
Security
Burstable Reporting Web
Desktop Apps
Conference
Room
EMR
Level 2 Standardized
Vocabulary & Patient
Registries
Structured &
Unstructured
Source System
Connectors
Patient Matching
Pragmatic Data
Binding
Business Glossary Terminology
Level 1 Enterprise Data
Operating System
ELT
RBDMS & Data
Lake Support
Master Data Common
Operations Console
Data Catalog Row Level Security
Auditing
Dashboards Industry
Standards
Individual
Desktop
Level 0 Fragmented Point
Solutions
Excel Support Flat File Extracts

35. © 2020
Health
Catalyst
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Health
Catalyst
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37. © 2020
Health
Catalyst
Anatomy of a Data Pipeline
Business
Logic
Data
Persistence
Code
Persistence
Optimization
Schema
Creation
Data Validation
Framework
Integration
Incremental
Loading
Management
Full Load Path
Dependency
Management
Logging
Schema
Evolution
Index
Management
Execution
Optimization
Notifications
Restartability
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38. © 2020
Health
Catalyst
Declarative nature of DOS Bindings
Business Logic
SQL, R, XQuery,
Delimited
Python, Spark
Data
Persistence
Code
Persistence
Optimization
Schema
Creation
Data Validation
Framework
Integration
Incremental
Loading
Management
Dynamic
Switching
between
Full/Incremental
Dependency
Management
Logging
Schema
Evolution
Index
Management
Execution
Optimization
Notifications
Restartability
What users do
What DOS does
Significant code reduction for both development and maintenance
and improved SLA due to optimized execution
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Health
Catalyst
Dependency Management
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Health
Catalyst
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Health
Catalyst
320+ Pre-mapped Data Sources
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Health
Catalyst
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Health
Catalyst
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44. Q&A

45. Thank you!