The Data Operating System. Changing the Digital Trajectory of Healthcare. Why do we need to change the current digital trajectory? What’s the business case for a Data Operating System? What is a Data Operating System and how did we get here? What difference will DOS make? What should we do with it and what should we expect?

1. SESSION #00
The Data Operating System
Dale Sanders
Health Catalyst
Changing the Digital Trajectory of Healthcare

2. For the record…
• I will frequently refer to Health
Catalyst’s strategy and products in
this lecture, but only by necessity
to emphasize the concepts
• I’m not selling products
• I’m advocating concepts and
principles that everyone-- all
vendors-- should follow
• Just like these fine young
men…


3. The story in today’s discussion
• Why do we need to change the current
digital trajectory?
• What’s the business case for a Data
Operating System
• What is a Data Operating System and how
did we get here?
• What difference will DOS make?
• What should we do with it and what
should we expect?

5. But the military has changed, adapted and transformed itself 

6. Advice to C-levels about a digital health future
1. For better or worse, faster or slower, your company runs at the
speed of software now
2. Everything you want and need to do is either helped or hindered
by software and data
3. All C-levels now need to be a little bit Chief Information
Officer and Chief Digital Officer
Thank you, Russel Reynolds Associates for the graphic

7. Raise your digital IQ, or else…
If you choose not to be informed, good luck

8. “Healthcare CEO, what is
your organization’s
Digital Quotient?”
Healthcare is one of the
least digital sectors, and it
shows in profit margin
growth.
Source: McKinsey Corporate Performance Analysis Tool
DQ = Data Assets x Data Usage x Data Skilled Labor

9. Digital disruption is already happening
The world’s largest taxi company… owns no taxis
The world’s largest voice/video communications companies… own no telco
The most popular media company… owns no content
The largest lodging company… owns no property
The world’s most valuable retailer… owns no inventory
The world’s largest software vendors… don’t write the apps
Thanks for the inspiration, Ron Kalifa of Worldpay; and IBM

10. The world’s largest and most successful
healthcare and health management companies,
will own no hospitals
In the digital future of healthcare…
10

11. Digitization of the patient and healthcare process
• We’re collecting data on the backs of physicians and nurses, and that won’t scale
• Lots of great ideas for applications and machine learning, but where’s the data?
• We need to invest significantly in diagnostic and process of care sensors
PwC, 2015

12. The parallels between health and car maintenance
• Every 10 hours, Tesla collects 1 million miles of
driving data
• 25Gbytes per car per hour
• We collect 100Mbytes per patient per year, on
average
• “We can fix problems in your car and make it safer,
long before you know you need it.”
• ”10,000 fatalities and 500,000 injuries per year will
be prevented.”
• Ram Ramachander, Chief Commercial Officer, Social
Innovation Business at Hitachi
An ounce of prevention is worth a pound of cure

13. 80% of Factors Affecting Health Outcomes Fall
Outside Traditional Healthcare Delivery

14. But we have an EMR…
• Only 8% of data required for a population health and precision
medicine initiative resides in today’s EMR/EHR
 Alberta Innovates Health Solutions, Secondary Data Use Project,
March 2016

15. This is my life This is healthcare’s
digital view of my life

16. Humans gravitate towards freedom of choice
6 billion smart phones by 2020 in a world population of 8 billion

17. Evolution of healthcare IT

18. In the meantime, this is what’s possible…
• 103 applications on my iPhone
• 89 different vendors
• They build on a common platform with open
software standards
• I wouldn’t expect nor hope for a single
vendor to meet all my needs

19. Modern software concepts & designs
• Analytics are embedded… it’s ambient to the user interface experience
From a blog I wrote in 2010
“What if Facebook built an EHR?”
• Patient’s evolving health story at the
center of the record
• Embedded video and images
• Integrated text and discrete data
• Secure messaging
• Social support from family & friends
• Flexible security, defined by the
patient

20. In traditional healthcare data
warehousing, this curve
takes too long—hours, days
Silicon Valley gives us a
good role model for
compressing the curve
20

21. Find
The Truth
Tell
The Truth
Face
The Truth
• Nebulous in healthcare
• Relative to point of view
• Tribalism trumps facts
• Presentation matters
• Diplomacy, sensitivity
• Mastery, Autonomy, Purpose
• Humility
• Openness
• Community vs Individual
If you miss the soft side of humanity on the data and digital
journey, it’s going to backfire on you
The Nebraska Cornhuskers story with Brent Musberger

22. Poll Question #1
At $35B in US government incentives and $105B from the budgets of healthcare systems
and physician offices, how would you describe the return on investment in Electronic
Medical Records, in terms of their total benefits to US healthcare?
a) Terrible
b) Poor
c) Mediocre
d) Positive
e) Superb

23. So, what’s a Data
Operating System?

24. Using related concepts…
DOS is a combination of the following,
but enabled by modern technology,
designs, and software…
1. HIE
2. Clinical Data Repository
3. Enterprise Data Warehouse
DOS is a platform of constantly updated raw and organized
data, within a domain such as healthcare, that enables
rapid development and changes to the software
applications built upon it.

25. DOS is a Hybrid Architecture
Gartner: Hybrid Transactional/Analytical Processing (HTAP)
“Because traditional data warehouse practices will be outdated by the end of 2018,
data warehouse solution architects must evolve toward a broader data management
solution for analytics.”
Reservation
dogs, aka,
“rez dogs”
are the best
25

26. As computer scientists, we overlooked the last and
critically important layer in the technology stack…
The Data Layer

27. Three precipitating events led to the DOS concept

28. 1. Our Enterprise Data Warehouse at Northwestern
University Medicine, 2005-2009

29. Fast forward to 2016: New technology and designs
Kappa architecture from Silicon Valley
• One data stream for batch and real-time computations in the serving layer
• One code set
Kappa architectures can be
implemented with a combination
of open source tools like Apache
Kafka, Apache HBase, Apache
Hadoop (HDFS, MapReduce),
Apache Spark, Apache Drill,
Spark Streaming, Apache Storm,
and Apache Samza.
Note of thanks to Julian Forgeat of Google

30. 2. Realizing the breadth of the human health
data ecosystem
• What data do we need for research, personalized care, and community health?
And, by the way, we have barely
any data on healthy patients
30

31. 3. The ”Shark Tank” story
20+ Healthcare IT startups
Pitching great software
applications and creative
ideas
In my head: “We must give these great ideas and applications the
data they need. They cannot possibly afford to build the data data
infrastructure and skills that we have in Health Catalyst. They and
the industry can’t afford it.”

32. For dramatic impact, let me quickly share with you
the data content & sources in the Health Catalyst
library… and it’s growing everyday

33. EMR Data Sources
1. Affinity - ADT/Registration
2. Allscripts - Ambulatory EMR Clinicals
3. Allscripts Enterprise/Touchworks - Ambulatory EMR
4. Allscripts Sunrise - Acute EMR Clinicals
5. Aprima ERM
6. Cerner - Acute EMR Clinicals
7. Cerner - PowerWorks Ambulatory EMR
8. Cerner HomeWorks - Other
9. CPSI - Acute EMR Clinicals
10.eClinicalWorks - Ambulatory EMR Clinicals
11.Epic - Acute EMR Clinicals
12.Epic - Ambulatory EMR Clinicals
13.GE (IDX) Centricity - Ambulatory EMR Clinicals
14.McKesson Horizon - Acute EMR Clinicals
15.McKesson Horizon Enterprise Visibility
16.Meditech 5.66 EHR w/DR
17.NextGen - Ambulatory Practice Management
18.Quality Systems (Next Gen) - Ambulatory EMR Clinicals
19.Siemens Sorian Clinicals - Inpatient EMR

34. Finance/Costing Data Sources
1. Affinity - Costing
2. Allscripts (EPSi) - Budget
3. Allscripts (EPSi) - Costing
4. Allscripts (TSI) - Costing
5. BOXI - GL
6. Cost Flex - Costing
7. Digimax Materials Management - Inventory
Management
8. IOS ENVI - Costing
9. Kaufman Hall Budget Advisor - Other
10.Lawson - Accounts Payable
11.Lawson - Accounts Receivable
12.Lawson - GL
13.Lawson - Supply Chain
14.McKesson - Accounts Payable
15.McKesson Enterprise Materials Management
16.McKesson HPM - Costing
17.McKesson HPM - GL
18.McKesson PFM - Accounts Payable
19.McKesson PFM - GL
20.McKesson Series - Accounts Receivable
21.Meditech - GL
22.Microsoft Great Plains - GL
23.Oracle (Hyperion) - Costing
24.Oracle (PeopleSoft) - GL
25.Oracle (PeopleSoft) - Supply Chain
26.PARExpress
27.PPM - Costing
28.Smartstream - GL
29.StrataJazz - Costing

35. Billing Data Sources
1. Affinity - Hospital Billing
2. CHMB 360+ RCM - Hospital Billing
3. CPSI - Hospital Billing
4. Epic - Hospital Billing
5. GE (IDX) Centricity - Hospital Billing
6. GE (IDX) Centricity - Professional Billing
7. HealthQuest - Patient Accounting
8. Keane - Hospital Billing
9. McKesson Series - Patient Billing
10.McKesson STAR - Hospital Billing
11.MD Associates - Professional Billing
12.Siemens Sorian Financials - Inpatient
Registration and Billing

36. HR/ERP Data Sources
1. API Healthcare - Time and Attendance
2. iCIMS
3. Kronos - HR
4. Kronos - Time and Attendance
5. Lawson - HR
6. Lawson - Payroll
7. Lawson - Time and Attendance
8. Maestro
9. MD People
10.Now Solutions Empath - HR
11.Oracle (PeopleSoft) - HR
12.PeopleStrategy/Genesys - HR
13.PeopleStrategy/Genesys - Payroll
14.Ultimate Software Ultipro - HR
15.WorkDay

37. Claims Data Sources
1. 835 – Denials
2. Adirondack ACO Medicare
3. Aetna - Claims
4. Anthem - Claims
5. Aon Hewitt - Claims
6. BCBS Illinois
7. BCBS Vermont
8. Children's Community Health Plan (CCHP) -
Payer
9. Cigna - Claims
10.CIT Custom - Claims
11.Cone Health Employee Plan (United
Medicare) - Claims
12.Discharge Abstract Data (DAD)
13.Hawaii Medical Service Association (HMSA) -
Claims
14.HealthNet - Claims
15.Healthscope
16.Humana (PPO) - Claims
17.Humana MA - Claims
18.Kentucky Hospital Association (KHA) -
Claims
19.Medicaid - Claims
20.Medicaid - Claims - CCO
21.Merit Cigna - Claims
22.Merit SelectHealth - Claims
23.MSSP (CMS) - Claims
24.NextGen (CMS) - Claims
25.Ohio Hospital Association (OHA) - Claims
26.ProHealth - Claims
27.PWHP Custom - Claims
28.QXNT - Claims
29.UMR Claims Source
30.Wisconsin Health Information Organization
(WHIO) - Claims

38. Clinical Specialty Data Sources
1. Allscripts - Case
Management
2. Apollo - Lumed X Surgical
System
3. Aspire - Cardiovascular
Registry
4. Carestream - Other
5. Cerner - Laboratory
6. eClinicalWorks - Mountain
Kidney Data Extracts
7. GE (IDX) Centricity Muse -
Cardiology
8. HST Pathways - Other
9. ImageTrend
10.ImmTrac
11.Lancet Trauma Registry
12.MacLab (CathLab)
13.MIDAS - Infection
Surveillance
14.MIDAS - Other
15.MIDAS - Risk Management
16.Navitus - Pharmacy
17.NHSN
18.NSQIPFlatFile
19.OBIX - Perinatal
20.OnCore CTMS
21.Orchard Software Harvest -
Pathology
22.PACSHealth - Radiology
23.Pharmacy Benefits Manager
24.PICIS (OPTUM)
Perioperative Suite
25.Provation
26.Quadramed Patient Acuity
Classification System - Other
27.QXNT/Vital - Member
28.RLSolutions
29.SafeTrace
30.Siemens RIS - Radiology
31.SIS Surgical Services
32.StatusScope - Clinical
Decisions
33.Sunquest - Laboratory
34.Sunrise Clinical Manager
35.Surgical Information System
36.TheraDoc
37.TransChart - Other
38.Varian Aria - Oncology
39.Vigilanz - Infection Control

39. Health Information Exchange (HIE) Data
1. Adirondack ACO Clinical Data from HIXNY (HIE)
2. ADT HIE Patient Programs
3. Vermont HIE

40. Patient Satisfaction Data Sources
1. Fazzi - Patient Satisfaction
2. HealthStream - Patient Satisfaction
3. NRC Picker - Patient Satisfaction
4. PRC - Patient Satisfaction
5. Press Ganey - Patient Satisfaction
6. Sullivan Luallin - Patient Satisfaction
40

41. Master Reference & Terminology Data Content
1. AHRQ Clinical Classification Software (CCS)
2. Charlson Deyo and Elixhauser Comorbidity
3. Clinical Improvement Grouper (Care Process Hierarchy)
4. CMS Hierarchical Condition Category
5. CMS Place Of Service
6. LOINC
7. National Drug Codes (NDC)
8. NPI Registry
9. Provider Taxonomy
10.Rx Norm
11.CMS/NQF Value Set Authority Center

42. Wearables and Home Monitoring
1. FitBit
2. Garmin
3. Apple
4. Microsoft
5. Precor
6. Misfit
7. Adidas
8. Nokia
9. Omron
10.Polar
11.TomTom
12.LifeFitness
13.And
14. iHealth
15.Bayer
16.BodyTrace
17.AccuCheck
18.Abbott
19.Qardio
20.ReliOn
21.Trividia
22.Nonin
23.Strava
24.myfitnesspal
25.fatsecret
26.Sony
27.HealthKit
28.HiGi

43. Other Sources of Healthcare-Related Data
1. 2010 US Census Detail for
State of Colorado
2. Affiliate Provider Database
3. All Payer All Claims (certain
States) ---In process UT, CO,
MA
4. Alliance Decision Support
5. Allscripts - Ambulatory
Practice Management
6. Allscripts - Patient Flow
7. Allscripts EHRQIS - Quality
8. Avaya
9. Axis (MDX)
10.Bed Ready - Other
11.Cerner Signature
12.CMS Standard Analytical Files
13.Daptiv
14.Echo Credentialing - Provider
Management
15.ePIMS
16.First Click-Wellness
17.FlightLink
18.GE (IDX) Centricity - Practice
Management
19.HCUP (NRD, NIS, NED
Sample sets)
20.Health Trac
21.HealtheIntent
22.Hyperion
23.InitiateEMPI
24.Innotas
25.IVR Outreach Detail
26.MIDAS - Credentialing Module
27.Morrisey Medical Staff Office
for Web (MSOW)
28.National Ambulatory Care
Reporting System (NACRS)
29.Nextgate EMPI
30.Onbase
31.PHC Legacy EDW
32.QXNT/Cactus - Provider
33.SMS Legacy - Other
34.Truven Quality
35.University HealthSystem
Consortium - Clinical and
Operational Resource
Database
36.University HealthSystem
Consortium - Regulatory

44. • That’s the data that Health Catalyst manages and understands
in the US healthcare ecosystem today, and the list is growing.
• Re-creating the technology and intellectual property is not
scalable for the industry. We have to open it up and reuse it.
• We are barely getting started on the digitization of the industry.

45. The Data Operating System
Health Catalyst Applications
Client-Built
Applications
Registry
Builder
Leading
Wisely
Care
Management
CAFÉ
Benchmarks
ACO
Financials
Choosing
Wisely
Patient
Safety
Measure
Library
Patient
Engagement
Catalyst Analytics Platform
Data Ingest Data ExportData Pipelines
Source
Connectors
Hadoop/
Spark
Data Lake
Fabric Real-Time Services
Real-Time
Processing
HL7
Real-Time
Streaming
Machine
Learning
Pipelines
Marketplace
Atlas and more …SAMD & SMD
Fabric Application Services
Registries Terminology
& Groupers
FHIREHR
Integration
Security, Identity
& Compliance
Patient & Provider
Matching
Measures
Fabric Data Services
Data
Governance
Pattern
Recognition
NLP
Data QualityMetadata
Standard
Data Models
ML Models
3rd Party Apps
Reusable
Content

46. What difference will DOS
make?
46

48. Enable unprecedented software development
• 10x – 100x faster software application development
• 100x more options for software applications by enabling the start-up
brains in Matter Chicago, for example

49. Related impact at Amazon
Between 2001 and 2009, they implemented similar concepts to DOS as well
as a “DevOps” culture in software development.
The results were dramatic…

50. Rapidly accelerate the value of Mergers,
Acquisitions, and Partnerships
• Your new company is not integrated until your data is
integrated
• HIE’s are not sufficient for data integration… not even close
• Ripping and replacing EMRs and ERP systems with a single,
common vendor is not an affordable or timely strategy
Thanks to Russ Tabet for the illustration
50

51. Enable a Personal Health Record
Updated, integrated, shareable, downloadable, transportable
• A Personal Health Record is more than what’s in an EHR
• Think of Mint.com and what it is to personal financial data

52. Scale home grown data warehouses
• There are many of these in healthcare
• Home grown data warehouses are easy
to start and build, but expensive to evolve
and maintain
• But they are also hard to retire… what
do you do?
• Rip and replace with a new vendor
solution? Not attractive.

53. Enable Providers to become Payers
• Current actuarial techniques inherently
inflate risk to the benefit of payers
• With DOS, providers are in a strong position
to manage Total Cost of Care, risk, and
margins
• Use the applications on top of DOS to further
understand and lower risk, and manage
margins
• With DOS, negotiate contracts from a
position of data-strength
We Sagittarians could have
turned out very differently
53

54. • The demand for EHRs
was stretched by
federal incentives.
That’s over.
• The underlying
software and
database technology
of EHRs was
commoditized a long
time ago.
• We can stretch the
lifecycle and value of
EHRs with DOS and
open APIs, e.g. FHIR.
Extend the life and value of current EHR investments

55. Enable community health
• The BUILD Health Challenge in the US
• Bold, Upstream, Integrated, Local, Data-driven
• Blue Zone Projects
• National Geographic inspired
Community health is fundamentally a digital initiative BUILD Communities
Blue Zone Communities

56. The Chargemaster of Evil
• Our digital journey of the future must include cost
accounting and price transparency.
One last plea since I have the stage and you’re hostages… 
• Let’s commit to making the chargemaster what
it should be…True costs plus a reasonable,
sustainable bottom line margin
56

57. Poll Question #2
a) Yes
b) No
c) What’s a chargemaster?
Do you believe chargemasters are the root of many US healthcare evils and that it
is within the scope of influence of healthcare executives to dramatically improve
that situation?

58. Wrapping up
• Why we need to change the digital trajectory
• We have a moral obligation to the country
to raise our Digital Quotient in healthcare
• What is a Data Operating System and how did
we get here?
• Making the data layer easier for application
development to give us more choices
• What difference will DOS make?
• Better, faster, cheaper software
for EHRs, M&A, PHRs, and more
58

59. For more on this general topic…
• Rob DeMichiei, CFO at UPMC
• Activity Based Cost accounting with 20% of the effort
• Health City Cayman Islands
• A role model in healthcare quality, affordability, and access
• Imran Qureshi, Health Catalyst
• Deeper dive on the Data Operating System
• John Moore, Chilmark
• Convergence of payers and providers
• Jim Adams, The Advisory Board, annual IT National Meetings
• September 13-14 in Marina del Rey
• September 27-28 in Chicago
• November 14-15 in Washington DC
59