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The Data Operating System
Changing the Digital Trajectory of Healthcare
Dale Sanders
Health Catalyst
May 2017
Selling vs. Not
In these webinars, I never sell Health Catalyst.
• I offer advice from past experience
• Advocate change
In this webinar, I’ll “sell” Health Catalyst, but only as
evidence that we practice what we preach, in this case,
development of the Data Operating System
There is also advice buried in the “selling”… if we’re
building a Data Operating System, maybe other folks and
vendors should, too
The Story of Today’s Meeting
• What’s a Data Operating System?
• Why do we need one now in healthcare?
• How can it be implemented?
• Is it real or just another buzz phrase?
First, Thanks…
• Our entire product development team for their incredible
performance
• I’ve never been associated with as much change and productivity in
18 months
• For the brainstorming, engineering & implementation of the Data
Operating System…
• Bryan Hinton
• Imran Qureshi
• Sean Stohl
• Rus Tabet, one of our UI and graphics experts
• For his illustrations and cartoons in this slide deck. You’ll be able to
tell the difference between his and mine. 
• Many other better artists than me whose work inspired many of the
doodles in these slides
We’re not satisfied with the
current trajectory of digital health
But, at Health Catalyst, we’re not
satisfied with ourselves, either. We
are far from perfect.
Fair Warning to the Executives in the Audience
• Get ready to dive into topics that you need to
understand
• The most expensive capital purchase in the
history of your healthcare system wasn’t a new
hospital… it was your EHR
• Software runs your company, for better or worse
• Case in point: The ransomware impact on the UK National
Health System last week
• The healthcare CEOs who thrive going forward, will
understand their software technology and data. They
will rise to the top.
Sanders Version 1.0 Definition of a
Data Operating System (DOS)
A data operating system combines real-time, granular data; and
domain-specific (e.g. healthcare), reusable analytic and computational
logic about that data, into a single computing ecosystem for
application development. A data operating system can support the
real-time processing and movement of data from point-to-point, as well
as batch-oriented loading and computational analytic processing on
that data.
Health Catalyst Data Operating System
Data Platform
Data Ingest
Real-time
Streaming
Source
Connectors
Catalyst Analytics Platform Core Data Services
Real time
Processing
Fabric
Registries Terminology
& Groupers
Apps
FHIR
Data Quality
Data
Governance
Pattern
Recognition
Hadoop/
Spark
Data Export
3rd Party
Applications
Registry
Builder
Leading
Wisely
Care
Management
SAMD &
SMD
Atlas
Hospital IT
Applications
EHR
Integration
Machine Learning
Models
Patient & Provider
Matching
Real time Data Services
NLP
Lambda
Architecture
CAFÉ
Benchmarks
Choosing
Wisely
Patient
Safety
Measures
Builder
ACO
Financials
Patient
Engagement and more …
HL7
Data Pipelines
ML Pipelines
Security, Identity
& Compliance
Metadata
Data Lake
Apps and Fabric Run on Any Data Platform
Fabric & Machine Learning
Apps
Data to FHIR mapping
Various Data Platforms
HadoopHealth Catalyst
Open APIs (FHIR etc)
Epic CernerTeradata Home grownIBM
3rd Party
Applications
Registry
Builder
Leading
Wisely
Care
Management
Hospital IT
Applications
CAFÉ
Benchmarks
Choosing
Wisely
Patient
Safety
Measures
Builder
ACO
Financials
Patient
Engagement and more …
Registries Terminology
& GroupersFHIR
SAMD &
SMD
EHR
Integration Models
Patient & Provider
Matching ML Pipelines
Security, Identity
& Compliance
Oracle
Seven Attributes of the Healthcare Data Operating System
1. Reusable clinical and business logic: Registries, value sets, and other data logic lies on top of the raw data and
can be accessed, reused, and updated through open APIs, enabling 3rd party application development.
2. Streaming data: Near or real-time data streaming from the source all the way to the expression of that data through
the DOS, that can support transaction-level exchange of data or analytic processing.
3. Integrates structured and unstructured data: Integrates text and structured data in the same environment.
Eventually, incorporates images, too.
4. Closed loop capability: The methods for expressing the knowledge in the DOS include the ability to deliver that
knowledge at the point of decision making, for example back into the workflow of source systems, such as an EHR.
5. Microservices architecture: In addition to abstracted data logic, open microservices APIs exist for DOS operations
such as authorization, identity management, data pipeline management, and DevOps telemetry. These
microservices also enable third party applications to be built on the DOS.
6. Machine Learning: The DOS natively runs machine learning models and enables rapid development and utilization
of ML models, embedded in all applications.
7. Agnostic data lake: Some or all of the DOS can be deployed over the top of any healthcare data lake. The
reusable forms of logic must support different computation engines; e.g. SQL, Spark SQL, SQL on Hadoop, et al.
Why is the DOS
important now in
healthcare?
The Data Operating System: Changing the Digital Trajectory of Healthcare
Content is King, the Network is Kong
• If you look at modern businesses, data content is becoming the
driving force behind their business strategy, e.g., GE, Tesla,
Google, Facebook, Delta Airlines, UnitedHealth, Amazon, etc.
• The network of people around this data content creates value–
think of Metcalfe’s Law– and sticky relationships
“Healthcare CEO, what is your Digitization Index?”
Data Assets x Data Usage x Data Skilled Labor
Healthcare is one of the
least digital sectors, and it
shows in profit-margin
growth.
Source: McKinsey Corporate Performance
Analysis Tool; BEA; McKinsey Global Institute
analysis
C-level Advice for a Digital Healthcare Future
1. Population health, value based care, and precision medicine are all
about DATA
• You need a strategic data acquisition strategy– What data do you need
for population health, risk contracting, and precision medicine? How do
you acquire it?
• You need a Chief Analytics or Chief Data Officer– is that your CIO or
not?
2. Your physicians and nurses are over-measured and under-valued, in
large part because they are slaves to data entry and poor software
• You need to push all vendors to follow modern, open software APIs,
including but not limited to FHIR
3. You need a Data Operating System-- leverage and expand the
capability of your Enterprise Data Warehouse
DOS Need #1: The ”Shark Tank” Story
20+ Healthcare IT startups
Pitching great software
applications and creative
ideas
No solution or appreciation for the underlying healthcare data that they
needed
In my head: “We must give these great ideas and applications the data
they need. They cannot possibly afford to build the data infrastructure
and skills that we have in Health Catalyst. The industry can’t afford it.”
We Haven’t Modernized the Data Content Layer
DOS Need #2: Mergers & Acquisitions
• The new company is not integrated until the data is integrated
• HIE’s are not sufficient for data integration… not even close
• Rip and replacing EHRs with a single, common vendor is not an
affordable strategy
• Besides, hybrid vigor is a good thing… you should not put all of your digital eggs in
one basket
Rip and Replace is Not the Answer for M&A
Hundreds of millions of $$ in additional costs and lost time
Keep the disparate, existing source systems–
Finance, supply chain, registration, scheduling, A/R, EHRs, etc.
Virtually Integrated with the Data Operating System
Share transaction-level data.
Integrate data for common metrics around finance, clinical quality, utilization, etc.
DOS Need #3: Enable a Personal Health Record
Updated, integrated, shareable, downloadable, transportable
Healthcare data is currently locked in
the cage of the health system and the
technology of the EHR
DOS
DOS Need #4: Scaling Existing, Home Grown Data
Warehouses
• Home grown data warehouses are easy to start and build, but
expensive to evolve and maintain
• There are many of these in healthcare
• But they are also hard to retire… what do you do?
• Rip and replace with a vendor solution? Not attractive.
• That was the only answer Health Catalyst had to these scenarios, and that
answer does not sell
• Not good for Health Catalyst, not good for the industry. We both
need better options.
Selfishly Speaking, Health Catalyst Had to Solve This
But the industry will benefit, too. That’s the beauty of capitalism. 
The DOS Fabric and our new applications addresses this need
DOS Need #5: The Human
Health Data Ecosystem
And, by the way, we don’t
have much of any data on
healthy patients
Precision medicine &
population health need more
data than we currently collect
in the ecosystem… WAY
more data
Only 8% of the data we need
for precision medicine and
population health resides in
today’s EHRs
Healthcare Data
• Ingesting healthcare data into a data lake or data
warehouse is now essentially a commodity, thanks to
open source technology and a late binding, schema-on-
read approach to data models
• What’s not a commodity?
• Understanding the data content, data models, and insanely
complicated nuances of healthcare data
• The analytic logic or “data bindings” to apply to that data
• The technology and skills to deliver this data to the right
person, at the right time, in the right modality
• Keeping up with the changes in the source system data, aka,
change data capture
• Data quality management and governance
• Scaling all of this for a single healthcare system
For dramatic impact, let me share with you the data
content sources in the Health Catalyst library…
EMR Data Sources
26
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
Finance/Costing Data Sources
27
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
Billing Data Sources
28
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
HR/ERP Data Sources
29
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
Claims Data Sources
30
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.QNXT - Claims
29.UMR Claims Source
30.Wisconsin Health Information Organization
(WHIO) - Claims
Clinical Specialty Data Sources
31
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.QNXT/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
Health Information Exchange (HIE) Data
32
1. Adirondack ACO Clinical Data from HIXNY (HIE)
2. ADT HIE Patient Programs
3. Vermont HIE
Patient Satisfaction Data Sources
33
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
Other Sources of Healthcare-Related Data
34
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.QNXT/Cactus - Provider
33.SMS Legacy - Other
34.Truven Quality
35.University HealthSystem
Consortium - Clinical and
Operational Resource
Database
36.University HealthSystem
Consortium - Regulatory
Master Reference & Terminology Data Content
35
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
That’s the data we have in the US healthcare ecosystem,
today; but we are barely getting started on the digitization of
the industry, so imagine what the future data ecosystem
looks like.
DOS Need #6: Providers becoming payers
• The insurance industry is the tail wagging the
healthcare dog
• Does anyone, other than those in the insurance
industry, seriously believe that the current
payer/insurance economic model is working?
• Critical to the improvement of this situation is the
ability for providers to model and assume financial
risk, and compete with, or completely disintermediate,
insurance companies.
• With a Data Operating System, providers have
more and better data to model and manage risk
than the insurers.
DOS Need #7: Extend the life and value of current
EHR investments
Good News, Bad News
Healthcare is using “information technology from the last century.”
• Dr. Robert Pearl, CEO, Permanente Medical Group; CNBC Interview, 16 May 2017
• 9,000 physicians, 34,000 staffers
• Given that we’ve invested $30B in tax money, plus billions more
out-of-pocket, on that information technology, what do we do
now?
• Replace? Not a good idea to spend tens or hundreds of millions of
dollars on incrementally better products, at best
• We can make what we have, better, while new products emerge
We are more digitized in healthcare than ever before, but…
The inevitable curve for technology products is stretched or compressed by market
demand and the pace of technological commoditization associated with the product
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 of
EHRs with DOS and
open APIs, e.g. FHIR.
Role Model Vendors in Silicon Valley
• Google, Facebook, Amazon, Microsoft, Twitter
• Not Apple, by the way
• Apache, W3C, Internet Engineering Task Force, Open Compute
Project, et al
• How do healthcare vendors stack up? Terribly. The evidence is
clear.
• Even some of the vendor “app stores” that appear to support open
APIs, like FHIR, are contractually worded to take your IP and profit
from it, if you contribute to the app store
Collaborate on standardization, compete on innovation
Moving so Fast, Already Outdated…
These are the
tools available
for modern
software
development.
We are at the
beginning of a
software
technology
renaissance.
Most of these
tools are, in one
form or another,
open source.
With Open, Standard Software APIs…
“EHRs would become commodity components in a larger platform that
would include other transactional systems and data warehouses
running myriad apps, and apps could have access to diverse sources
of shared data beyond a single health system’s records.”
“A 21st-Century Health IT System — Creating a Real-World Information Economy”, Kenneth D. Mandl, MD,
MPH; Isaac S. Kohane, MD, MPH; NEJM, 18 May 2017.
Why we can do this,
technically, like never
before
A Partial History of my Experience with
Open Systems Standards
At the risk of jinxing myself, I think I know the major patterns of success and failure
At Northwestern Memorial Healthcare, 2005-2009
We didn’t call it a
DOS, but we had what
amounts to an early
version of it, over 10
years ago.
Supported analytics
and near-real time
exchange of single
records, before HIEs.
Technology options
are much better now.
Hybrid Big Data-SQL Architectures
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.”
The Hadoop, Big
Data ecosystem
gives us all sorts of
options that we never
had before,
technically and
financially
Note of thanks to Ben Stopford
at Confluent
New Technology, New Data Capabilities, at a
Fraction of Past Cost
Lambda Architecture: Two Streams of Data
One stream for batch computations, one for real time transactions and computations
Two different code sets
Kappa Architecture: One Stream of Data
One stream for batch and real-time computations in the serving layer
One code set
Both 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
Health Catalyst Data Operating System
Data Platform
Data Ingest
Real-time
Streaming
Source
Connectors
Catalyst Analytics Platform Core Data Services
Real time
Processing
Fabric
Registries Terminology
& Groupers
Apps
FHIR
Data Quality
Data
Governance
Pattern
Recognition
Hadoop/
Spark
Data Export
3rd Party
Applications
Registry
Builder
Leading
Wisely
Care
Management
SAMD &
SMD
Atlas
Hospital IT
Applications
EHR
Integration
Machine Learning
Models
Patient & Provider
Matching
Real time Data Services
NLP
Lambda
Architecture
CAFÉ
Benchmarks
Choosing
Wisely
Patient
Safety
Measures
Builder
ACO
Financials
Patient
Engagement and more …
HL7
Data Pipelines
ML Pipelines
Security, Identity
& Compliance
Metadata
Data Lake
Apps and Fabric Run on any Data Platform
Fabric & Machine Learning
Apps
Data to FHIR mapping
Various Data Platforms
HadoopHealth Catalyst
Open APIs (FHIR etc)
Epic CernerTeradata Home grownIBM
3rd Party
Applications
Registry
Builder
Leading
Wisely
Care
Management
Hospital IT
Applications
CAFÉ
Benchmarks
Choosing
Wisely
Patient
Safety
Measures
Builder
ACO
Financials
Patient
Engagement and more …
Registries Terminology
& GroupersFHIR
SAMD &
SMD
EHR
Integration Models
Patient & Provider
Matching ML Pipelines
Security, Identity
& Compliance
Oracle
Seven Attributes of the Healthcare Data Operating System
1. Reusable clinical and business logic: Registries, value sets, and other data logic lies on top of the raw data and
can be accessed, reused, and updated through open APIs, enabling 3rd party application development.
2. Streaming data: Near or real-time data streaming from the source all the way to the expression of that data through
the DOS, that can support transaction-level exchange of data or analytic processing.
3. Integrates structured and unstructured data: Integrates text and structured data in the same environment.
Eventually, incorporates images, too.
4. Closed loop capability: The methods for expressing the knowledge in the DOS include the ability to deliver that
knowledge at the point of decision making, including back into the workflow of source systems, such as an EHR.
5. Microservices architecture: In addition to abstracted data logic, open microservices APIs exist for DOS operations
such as authorization, identity management, data pipeline management, and DevOps telemetry. These
microservices also enable third party applications to be built on the DOS.
6. Machine Learning: The DOS natively runs machine learning models and enables rapid development and utilization
of ML models, embedded in all applications.
7. Agnostic data lake: Some or all of the DOS can be deployed over the top of any healthcare data lake. The
reusable forms of logic must support different computation engines; e.g. SQL, Spark SQL, SQL on Hadoop, et al.
Health Catalyst Initial Fabric Services
Fabric.Identity & Fabric.Authorization microservices
• Fabric.Identity provides authentication i.e., verifying the user is who he/she is claiming to be. Fabric.Authorization stores permissions for various user groups
and then given a user returns the effective permissions for that user.
Fabric.MachineLearning microservice
• A micro-service that plugs into a data pipeline (like ours) and runs machine learning models written in R, Python and TensorFlow. It encapsulates all the ML
tools inside so all you need to do is supply a ML model.
Fabric.EHR set of microservices
• Enables SQL bindings, ML models and application code to show data and insights inside the EHR workspace using SMART on FHIR.
Fabric.PHR set of microservices
• Provides the ability to download, share, and update a Personal Health Record. Integrates data from all available EMRs in a patient’s health ecosystem.
Fabric.Terminology set of microservices
• Provides the ability for application developers to leverage local and national terminology mapping and update services.
Fabric.FHIR microservice
• A data service that sits on top of any data platform (HC EDW, Data Lake, Hadoop etc). Applications using this data service become portable to any other
data platform. It uses data to FHIR mappings (written in Sql, HiveSql etc) to map data and implements an Analytics on FHIR API using a cache based on
Elastic Search.
Fabric.Telemetry
• Provides infrastructure to web and mobile applications to send telemetry data to our Azure cloud and provides tools to analyze it using ElasticSearch.
Default: Build in the FHIR framework, unless it’s not possible
FHIR Mappings (SQL version)
<DataSource><Sql>
SELECT PatientID AS EDWPatientID, CASE GenderCD WHEN 'Female' THEN 'female' WHEN 'Male'
THEN 'male' ELSE 'unknown' END AS gender,BirthDTS as birthDate
FROM [Person].[SourcePatientBASE]
</Sql></DataSource>
<DataSource Path="condition.code" type="object"><Sql>
SELECT PatientID AS EDWPatientID, CONCAT(DiagnosisSourceID,'-',RowSourceDSC,'-
',DiagnosisTypeDSC) as KeyLevel1, CONCAT(DiagnosisSourceID,'-',RowSourceDSC,'-',DiagnosisTypeDSC)
as KeyLevel2, CASE CodeTypeCD WHEN 'ICD9DX' THEN 'http://hl7.org/fhir/sid/icd-9-cm' WHEN
'ICD10DX' THEN 'http://hl7.org/fhir/sid/icd-10-cm' ELSE NULL END AS system, DiagnosisCD as code,
DiagnosisDSC as text
FROM [Clinical].[DiagnosisBASE]
</Sql></DataSource>
56
This is a real world example of how we are converting our relational data models into FHIR information models
{
"EDWPatientID": "Z100069",
"gender": "male",
"birthDate": "1958-01-05T00:00:00",
"condition": [ {
"clinicalStatus": "active",
"verificationStatus": "confirmed",
"category": [ {
"coding": "problem-list-item",
"text": "ICD Problem List Code"
} ],
"code": [ {
"system": "http://hl7.org/fhir/sid/icd-9-
cm",
"code": "185",
"text": "Malignant neoplasm of prostate
(HCC)"
} ] } ] }
FHIR Output From the Previous Slide
57
Sampling of the 200+ Health Catalyst Reusable Value Sets
These, along with the CMS/NQF/MACRA values sets are being ported to the Measures Builder Library (MBL)
content management system, for reuse in Health Catalyst and 3rd party applications.
Acute Coronary Syndrome (ACS)
Blood Utilization Dashboard
Breast Milk Feeding
Catheter Associated Urinary Tract Infection (CAUTI)
Prevention
Central Line Associated Blood Stream Infections (CLABSI)
Prevention
Colorectal Surgery
Early Mobility in the ICU
Glycemic Control in the Hospital
Heart Failure
Joint Replacement - Hip & Knee
Labor and Delivery
Patient Flight Path - Diabetes
Patient Safety Explorer
Pediatric Appendectomy Pediatric Asthma
Pediatric Explorer
Pediatric Sepsis Pneumonia
Population Explorer
Readmission Explorer
Sepsis Prevention
Spine Surgery
Stroke (Acute Ischemic & TIA)
Surgical Site Infection Prevention
Venous Thrombo-Embolism (VTE) Prevention
Coronary Artery Bypass Graft Surgery
Diabetes - Adult
Chronic Obstructive Pulmonary Disease (COPD)
Central line-associated bloodstream infection (CLABSI) Risk – Clinical Analytics and Decision Support
Congestive Heart Failure, Readmissions Risk – Clinical Analytics and Decision Support
COPD, Readmissions Risk – Clinical Analytics and Decision Support
Respiratory (COPD, Asthma, Pneumonia, & Resp. Failure), Readmission Risk – Clinical Analytics and Decision Support
Forecast IBNR claims/year-end expenditures – Financial Decision Support
Predictive appointment no shows – Operations and Performance Management
Pre-surgical risk (Bowel) – Clinical Analytics and Decision Support and client request
Propensity to pay – Financial Decision Support
Patient Flight Path, Diabetes Future Risk – Clinical Analytics and Decision Support
Patient Flight Path, Diabetes Future Cost– Clinical Analytics and Decision Support
Patient Flight Path, Diabetes Top Treatments – Clinical Analytics and Decision Support
Patient Flight Path, Diabetes Next Likely Complications (Glaucoma) – Clinical Analytics and Decision Support
Patient Flight Path, Diabetes Next Likely Complications (Retinopathy) – Clinical Analytics and Decision Support
Patient Flight Path, Diabetes Next Likely Complications (ESRD) – Clinical Analytics and Decision Support
Plus several more… (Nephropathy, Cataracts, CHF, CAD, Ketoacidosis, Erectile Dysfunction, Foot Ulcers)
Machine Learning Models in DOS
In Development
Built
Planned
Patients Like This – Clinical Analytics and Decision Support
Sepsis Risk – Clinical Analytics and Decision Support
Readmission Risk – Clinical Analytics and Decision Support
Post-surgical risk (Hips and Knees) – Clinical Analytics and Decision Support
INSIGHT socio-economic based risk – Clinical Analytics and Decision Support and client request
Native SQL/R predictive framework and standard package - Platform
Feature selection, Parallel Models, Rank and Impact of Input Variables – Platform
Predictive ETL batch load times – Platform
Composite Health Risk – Clinical Analytics and Decision Support
Composite All Cause Harm Risk – Clinical Analytics and Decision Support
Early detection of CLABSI, CAUTI, Clostridium difficile (c. diff) hospital infections – Clinical Analytics and Decision Support
Early detection of Sepsis/Septicemia (Blood Infection) – Clinical Analytics and Decision Support
Hospital Census Prediction - Operations and Performance Management
Hospital Length of Stay Prediction – Operations and Performance Management
Public data sets, benchmarks, “Catalyst Risk”, expected mortality, length of stay – CAFÉ collaboration
Clusters of population risk (near term risk/cost) – Population Health and Accountable Care
Managing and Reusing the Explosion of Measures
and Value Sets
Measures Builder Library (MBL) is a content management system and set of APIs that allows registries, value sets, and other
measures to be consistently managed, verified, governed, and reused for application development
Role Model Software Development for the Fabric
1. Open Source & Collaborative Development: All code is available on
github.com/HealthCatalyst. External developers can submit enhancements.
2. Open & Modular: All APIs will be publicly published. Customers can pick and choose from the
Health Catalyst components or replace any component with their own or from a third party
3. Secure by Design: Security services make it easy to build security into any application
4. Microservices architecture: REST-based services that can be called from web, mobile or BI
tools
5. Big Data: Leverages Big Data technologies to provide high-speed and reliable platform
6. Easy Install & Updates: All services install via Docker
7. Scalable: All services are designed to run in multiple nodes and cluster themselves automatically
Why can’t healthcare be the role model, instead of Silicon Valley?
Should we aspire to something less? Is that acceptable?
How Will We Know if We are a Role Model?
1. Successfully implementing the Data Operating System
2. Fast, simple releases every 2 weeks. Constant improvement of our apps.
3. Analytics driven UI and applications—intelligent user interfaces, driven by situational awareness of the
physician, nurse, patient, etc.
4. Constantly consuming and expanding the ecosystem of data as the enabler to great apps, not apps as the
enabler of data
5. Machine learning and pattern recognition that clearly amazes all of us with its value to humanity
6. Economic scalability-- we're so efficient with our products, which work across multiple OS and data
topologies, that it's economically efficient to constantly deploy
7. Auto-fill analytics—a play on words, but how do we, through pattern recognition and machine learning,
anticipate next steps in our clients’ decision making?
8. When Google, Facebook, Amazon, and Microsoft come to us for advice about software success and value
These are Health Catalyst’s software development vital signs
For Health Catalyst Clients
63
Join and explore the Health Catalyst Community
to learn more and engage with our team
community.healthcatalyst.com
Health Catalyst Platform Community
64
Ask Questions about DOS
Request Features
Review Roadmaps and
Release Notes
Contact our Community Manager,
Kate Weaver, to request access
kate.weaver@healthcatalyst.com
Summary Thoughts
There will be people who hope we fail.
There will be people who expect us to fail.
There are many more people who hope we don’t.
That’s who we’re working for.
Healthcare Analytics Summit 17
ERIC J. TOPOL
Author, The Patient Will
See You Now and The
Creative Destruction of
Medicine. Director,
Scripps Translational
Science Institute
DAVID B. NASH,
MD. MBA
Dean, Jefferson
School of
Population
Health
JOHN MOORE
Founder and Managing
Partner, Chilmark Research
ROBERT A. DEMICHIEI
Executive Vice President and
Chief Financial Officer, University
of Pittsburgh Medical Center
THOMAS D.
BURTON
Co-Founder, Chief
Improvement Officer,
and Chief Fun Officer,
Health Catalyst
DALE SANDERS
Executive Vice
President, Product
Development,
Health Catalyst
THOMAS DAVENPORT
Author , Consultant
Competing on Analytics*, ,
Analyitcs at Work, Big Data at
Work, Only Humans Need
Apply:Winners and Losers in the
Age of Smart Machines.
*Recognized by Harvard
Business Review editors as one
the most important management
ideas of the past decade, one of
HBR’s ten must-read articles in
that magazine’s 90-year history.
Summit highlights
Industry Leading Keynote Speakers
We’ll hear from well-known healthcare visionaries. We’ll also
hear from two C-level executives leading large healthcare
organizations.
CME Accreditation For Clinicians
HAS 17 will again qualify as a continuing medical education
(CME) activity.
30 Educational, Case Study, and Technical
Sessions
We have the most comprehensive set of breakout sessions of
any analytics summit. Our primary breakout session focus is
giving you detailed, practical “how to” learning examples
combined with question and opportunities.
The Analytics Walkabout
Back by popular demand, the Analytics Walkabout will feature
24 new projects highlighting a variety of additional clinical,
financial, operational, and workflow analytics and outcomes
improvement successes.
Analytics-driven, Hands-on Engagement for
Teams and Individuals
Analytics will continue to flow through the three-day summit
touching every aspect of the agenda.
Networking and Fun
We’ll provide some new innovative analytics-driven
opportunities to network while keeping our popular fun run and
walk opportunities and dinner on the down.
Early Bird
PricingSINGLE ENTRY
1 Pass -
$595
Save $300
BEST VALUE
3 PACK
3 Passes -
$545/each
Save
$1,000+5 PACK
5 Passes -
$495/each
Save
$2,000+
Sept. 12-14, 2017
Grand America Hotel
Salt Lake City, UT

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The Data Operating System: Changing the Digital Trajectory of Healthcare

  • 1. The Data Operating System Changing the Digital Trajectory of Healthcare Dale Sanders Health Catalyst May 2017
  • 2. Selling vs. Not In these webinars, I never sell Health Catalyst. • I offer advice from past experience • Advocate change In this webinar, I’ll “sell” Health Catalyst, but only as evidence that we practice what we preach, in this case, development of the Data Operating System There is also advice buried in the “selling”… if we’re building a Data Operating System, maybe other folks and vendors should, too
  • 3. The Story of Today’s Meeting • What’s a Data Operating System? • Why do we need one now in healthcare? • How can it be implemented? • Is it real or just another buzz phrase?
  • 4. First, Thanks… • Our entire product development team for their incredible performance • I’ve never been associated with as much change and productivity in 18 months • For the brainstorming, engineering & implementation of the Data Operating System… • Bryan Hinton • Imran Qureshi • Sean Stohl • Rus Tabet, one of our UI and graphics experts • For his illustrations and cartoons in this slide deck. You’ll be able to tell the difference between his and mine.  • Many other better artists than me whose work inspired many of the doodles in these slides
  • 5. We’re not satisfied with the current trajectory of digital health But, at Health Catalyst, we’re not satisfied with ourselves, either. We are far from perfect.
  • 6. Fair Warning to the Executives in the Audience • Get ready to dive into topics that you need to understand • The most expensive capital purchase in the history of your healthcare system wasn’t a new hospital… it was your EHR • Software runs your company, for better or worse • Case in point: The ransomware impact on the UK National Health System last week • The healthcare CEOs who thrive going forward, will understand their software technology and data. They will rise to the top.
  • 7. Sanders Version 1.0 Definition of a Data Operating System (DOS) A data operating system combines real-time, granular data; and domain-specific (e.g. healthcare), reusable analytic and computational logic about that data, into a single computing ecosystem for application development. A data operating system can support the real-time processing and movement of data from point-to-point, as well as batch-oriented loading and computational analytic processing on that data.
  • 8. Health Catalyst Data Operating System Data Platform Data Ingest Real-time Streaming Source Connectors Catalyst Analytics Platform Core Data Services Real time Processing Fabric Registries Terminology & Groupers Apps FHIR Data Quality Data Governance Pattern Recognition Hadoop/ Spark Data Export 3rd Party Applications Registry Builder Leading Wisely Care Management SAMD & SMD Atlas Hospital IT Applications EHR Integration Machine Learning Models Patient & Provider Matching Real time Data Services NLP Lambda Architecture CAFÉ Benchmarks Choosing Wisely Patient Safety Measures Builder ACO Financials Patient Engagement and more … HL7 Data Pipelines ML Pipelines Security, Identity & Compliance Metadata Data Lake
  • 9. Apps and Fabric Run on Any Data Platform Fabric & Machine Learning Apps Data to FHIR mapping Various Data Platforms HadoopHealth Catalyst Open APIs (FHIR etc) Epic CernerTeradata Home grownIBM 3rd Party Applications Registry Builder Leading Wisely Care Management Hospital IT Applications CAFÉ Benchmarks Choosing Wisely Patient Safety Measures Builder ACO Financials Patient Engagement and more … Registries Terminology & GroupersFHIR SAMD & SMD EHR Integration Models Patient & Provider Matching ML Pipelines Security, Identity & Compliance Oracle
  • 10. Seven Attributes of the Healthcare Data Operating System 1. Reusable clinical and business logic: Registries, value sets, and other data logic lies on top of the raw data and can be accessed, reused, and updated through open APIs, enabling 3rd party application development. 2. Streaming data: Near or real-time data streaming from the source all the way to the expression of that data through the DOS, that can support transaction-level exchange of data or analytic processing. 3. Integrates structured and unstructured data: Integrates text and structured data in the same environment. Eventually, incorporates images, too. 4. Closed loop capability: The methods for expressing the knowledge in the DOS include the ability to deliver that knowledge at the point of decision making, for example back into the workflow of source systems, such as an EHR. 5. Microservices architecture: In addition to abstracted data logic, open microservices APIs exist for DOS operations such as authorization, identity management, data pipeline management, and DevOps telemetry. These microservices also enable third party applications to be built on the DOS. 6. Machine Learning: The DOS natively runs machine learning models and enables rapid development and utilization of ML models, embedded in all applications. 7. Agnostic data lake: Some or all of the DOS can be deployed over the top of any healthcare data lake. The reusable forms of logic must support different computation engines; e.g. SQL, Spark SQL, SQL on Hadoop, et al.
  • 11. Why is the DOS important now in healthcare?
  • 13. Content is King, the Network is Kong • If you look at modern businesses, data content is becoming the driving force behind their business strategy, e.g., GE, Tesla, Google, Facebook, Delta Airlines, UnitedHealth, Amazon, etc. • The network of people around this data content creates value– think of Metcalfe’s Law– and sticky relationships
  • 14. “Healthcare CEO, what is your Digitization Index?” Data Assets x Data Usage x Data Skilled Labor Healthcare is one of the least digital sectors, and it shows in profit-margin growth. Source: McKinsey Corporate Performance Analysis Tool; BEA; McKinsey Global Institute analysis
  • 15. C-level Advice for a Digital Healthcare Future 1. Population health, value based care, and precision medicine are all about DATA • You need a strategic data acquisition strategy– What data do you need for population health, risk contracting, and precision medicine? How do you acquire it? • You need a Chief Analytics or Chief Data Officer– is that your CIO or not? 2. Your physicians and nurses are over-measured and under-valued, in large part because they are slaves to data entry and poor software • You need to push all vendors to follow modern, open software APIs, including but not limited to FHIR 3. You need a Data Operating System-- leverage and expand the capability of your Enterprise Data Warehouse
  • 16. DOS Need #1: The ”Shark Tank” Story 20+ Healthcare IT startups Pitching great software applications and creative ideas No solution or appreciation for the underlying healthcare data that they needed In my head: “We must give these great ideas and applications the data they need. They cannot possibly afford to build the data infrastructure and skills that we have in Health Catalyst. The industry can’t afford it.”
  • 17. We Haven’t Modernized the Data Content Layer
  • 18. DOS Need #2: Mergers & Acquisitions • The new company is not integrated until the data is integrated • HIE’s are not sufficient for data integration… not even close • Rip and replacing EHRs with a single, common vendor is not an affordable strategy • Besides, hybrid vigor is a good thing… you should not put all of your digital eggs in one basket
  • 19. Rip and Replace is Not the Answer for M&A Hundreds of millions of $$ in additional costs and lost time Keep the disparate, existing source systems– Finance, supply chain, registration, scheduling, A/R, EHRs, etc. Virtually Integrated with the Data Operating System Share transaction-level data. Integrate data for common metrics around finance, clinical quality, utilization, etc.
  • 20. DOS Need #3: Enable a Personal Health Record Updated, integrated, shareable, downloadable, transportable Healthcare data is currently locked in the cage of the health system and the technology of the EHR DOS
  • 21. DOS Need #4: Scaling Existing, Home Grown Data Warehouses • Home grown data warehouses are easy to start and build, but expensive to evolve and maintain • There are many of these in healthcare • But they are also hard to retire… what do you do? • Rip and replace with a vendor solution? Not attractive. • That was the only answer Health Catalyst had to these scenarios, and that answer does not sell • Not good for Health Catalyst, not good for the industry. We both need better options.
  • 22. Selfishly Speaking, Health Catalyst Had to Solve This But the industry will benefit, too. That’s the beauty of capitalism.  The DOS Fabric and our new applications addresses this need
  • 23. DOS Need #5: The Human Health Data Ecosystem And, by the way, we don’t have much of any data on healthy patients Precision medicine & population health need more data than we currently collect in the ecosystem… WAY more data Only 8% of the data we need for precision medicine and population health resides in today’s EHRs
  • 24. Healthcare Data • Ingesting healthcare data into a data lake or data warehouse is now essentially a commodity, thanks to open source technology and a late binding, schema-on- read approach to data models • What’s not a commodity? • Understanding the data content, data models, and insanely complicated nuances of healthcare data • The analytic logic or “data bindings” to apply to that data • The technology and skills to deliver this data to the right person, at the right time, in the right modality • Keeping up with the changes in the source system data, aka, change data capture • Data quality management and governance • Scaling all of this for a single healthcare system
  • 25. For dramatic impact, let me share with you the data content sources in the Health Catalyst library…
  • 26. EMR Data Sources 26 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
  • 27. Finance/Costing Data Sources 27 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
  • 28. Billing Data Sources 28 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
  • 29. HR/ERP Data Sources 29 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
  • 30. Claims Data Sources 30 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.QNXT - Claims 29.UMR Claims Source 30.Wisconsin Health Information Organization (WHIO) - Claims
  • 31. Clinical Specialty Data Sources 31 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.QNXT/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
  • 32. Health Information Exchange (HIE) Data 32 1. Adirondack ACO Clinical Data from HIXNY (HIE) 2. ADT HIE Patient Programs 3. Vermont HIE
  • 33. Patient Satisfaction Data Sources 33 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
  • 34. Other Sources of Healthcare-Related Data 34 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.QNXT/Cactus - Provider 33.SMS Legacy - Other 34.Truven Quality 35.University HealthSystem Consortium - Clinical and Operational Resource Database 36.University HealthSystem Consortium - Regulatory
  • 35. Master Reference & Terminology Data Content 35 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
  • 36. That’s the data we have in the US healthcare ecosystem, today; but we are barely getting started on the digitization of the industry, so imagine what the future data ecosystem looks like.
  • 37. DOS Need #6: Providers becoming payers • The insurance industry is the tail wagging the healthcare dog • Does anyone, other than those in the insurance industry, seriously believe that the current payer/insurance economic model is working? • Critical to the improvement of this situation is the ability for providers to model and assume financial risk, and compete with, or completely disintermediate, insurance companies. • With a Data Operating System, providers have more and better data to model and manage risk than the insurers.
  • 38. DOS Need #7: Extend the life and value of current EHR investments
  • 39. Good News, Bad News Healthcare is using “information technology from the last century.” • Dr. Robert Pearl, CEO, Permanente Medical Group; CNBC Interview, 16 May 2017 • 9,000 physicians, 34,000 staffers • Given that we’ve invested $30B in tax money, plus billions more out-of-pocket, on that information technology, what do we do now? • Replace? Not a good idea to spend tens or hundreds of millions of dollars on incrementally better products, at best • We can make what we have, better, while new products emerge We are more digitized in healthcare than ever before, but…
  • 40. The inevitable curve for technology products is stretched or compressed by market demand and the pace of technological commoditization associated with the product 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 of EHRs with DOS and open APIs, e.g. FHIR.
  • 41. Role Model Vendors in Silicon Valley • Google, Facebook, Amazon, Microsoft, Twitter • Not Apple, by the way • Apache, W3C, Internet Engineering Task Force, Open Compute Project, et al • How do healthcare vendors stack up? Terribly. The evidence is clear. • Even some of the vendor “app stores” that appear to support open APIs, like FHIR, are contractually worded to take your IP and profit from it, if you contribute to the app store Collaborate on standardization, compete on innovation
  • 42. Moving so Fast, Already Outdated…
  • 43. These are the tools available for modern software development. We are at the beginning of a software technology renaissance. Most of these tools are, in one form or another, open source.
  • 44. With Open, Standard Software APIs… “EHRs would become commodity components in a larger platform that would include other transactional systems and data warehouses running myriad apps, and apps could have access to diverse sources of shared data beyond a single health system’s records.” “A 21st-Century Health IT System — Creating a Real-World Information Economy”, Kenneth D. Mandl, MD, MPH; Isaac S. Kohane, MD, MPH; NEJM, 18 May 2017.
  • 45. Why we can do this, technically, like never before
  • 46. A Partial History of my Experience with Open Systems Standards At the risk of jinxing myself, I think I know the major patterns of success and failure
  • 47. At Northwestern Memorial Healthcare, 2005-2009 We didn’t call it a DOS, but we had what amounts to an early version of it, over 10 years ago. Supported analytics and near-real time exchange of single records, before HIEs. Technology options are much better now.
  • 48. Hybrid Big Data-SQL Architectures 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.”
  • 49. The Hadoop, Big Data ecosystem gives us all sorts of options that we never had before, technically and financially Note of thanks to Ben Stopford at Confluent New Technology, New Data Capabilities, at a Fraction of Past Cost
  • 50. Lambda Architecture: Two Streams of Data One stream for batch computations, one for real time transactions and computations Two different code sets
  • 51. Kappa Architecture: One Stream of Data One stream for batch and real-time computations in the serving layer One code set Both 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
  • 52. Health Catalyst Data Operating System Data Platform Data Ingest Real-time Streaming Source Connectors Catalyst Analytics Platform Core Data Services Real time Processing Fabric Registries Terminology & Groupers Apps FHIR Data Quality Data Governance Pattern Recognition Hadoop/ Spark Data Export 3rd Party Applications Registry Builder Leading Wisely Care Management SAMD & SMD Atlas Hospital IT Applications EHR Integration Machine Learning Models Patient & Provider Matching Real time Data Services NLP Lambda Architecture CAFÉ Benchmarks Choosing Wisely Patient Safety Measures Builder ACO Financials Patient Engagement and more … HL7 Data Pipelines ML Pipelines Security, Identity & Compliance Metadata Data Lake
  • 53. Apps and Fabric Run on any Data Platform Fabric & Machine Learning Apps Data to FHIR mapping Various Data Platforms HadoopHealth Catalyst Open APIs (FHIR etc) Epic CernerTeradata Home grownIBM 3rd Party Applications Registry Builder Leading Wisely Care Management Hospital IT Applications CAFÉ Benchmarks Choosing Wisely Patient Safety Measures Builder ACO Financials Patient Engagement and more … Registries Terminology & GroupersFHIR SAMD & SMD EHR Integration Models Patient & Provider Matching ML Pipelines Security, Identity & Compliance Oracle
  • 54. Seven Attributes of the Healthcare Data Operating System 1. Reusable clinical and business logic: Registries, value sets, and other data logic lies on top of the raw data and can be accessed, reused, and updated through open APIs, enabling 3rd party application development. 2. Streaming data: Near or real-time data streaming from the source all the way to the expression of that data through the DOS, that can support transaction-level exchange of data or analytic processing. 3. Integrates structured and unstructured data: Integrates text and structured data in the same environment. Eventually, incorporates images, too. 4. Closed loop capability: The methods for expressing the knowledge in the DOS include the ability to deliver that knowledge at the point of decision making, including back into the workflow of source systems, such as an EHR. 5. Microservices architecture: In addition to abstracted data logic, open microservices APIs exist for DOS operations such as authorization, identity management, data pipeline management, and DevOps telemetry. These microservices also enable third party applications to be built on the DOS. 6. Machine Learning: The DOS natively runs machine learning models and enables rapid development and utilization of ML models, embedded in all applications. 7. Agnostic data lake: Some or all of the DOS can be deployed over the top of any healthcare data lake. The reusable forms of logic must support different computation engines; e.g. SQL, Spark SQL, SQL on Hadoop, et al.
  • 55. Health Catalyst Initial Fabric Services Fabric.Identity & Fabric.Authorization microservices • Fabric.Identity provides authentication i.e., verifying the user is who he/she is claiming to be. Fabric.Authorization stores permissions for various user groups and then given a user returns the effective permissions for that user. Fabric.MachineLearning microservice • A micro-service that plugs into a data pipeline (like ours) and runs machine learning models written in R, Python and TensorFlow. It encapsulates all the ML tools inside so all you need to do is supply a ML model. Fabric.EHR set of microservices • Enables SQL bindings, ML models and application code to show data and insights inside the EHR workspace using SMART on FHIR. Fabric.PHR set of microservices • Provides the ability to download, share, and update a Personal Health Record. Integrates data from all available EMRs in a patient’s health ecosystem. Fabric.Terminology set of microservices • Provides the ability for application developers to leverage local and national terminology mapping and update services. Fabric.FHIR microservice • A data service that sits on top of any data platform (HC EDW, Data Lake, Hadoop etc). Applications using this data service become portable to any other data platform. It uses data to FHIR mappings (written in Sql, HiveSql etc) to map data and implements an Analytics on FHIR API using a cache based on Elastic Search. Fabric.Telemetry • Provides infrastructure to web and mobile applications to send telemetry data to our Azure cloud and provides tools to analyze it using ElasticSearch. Default: Build in the FHIR framework, unless it’s not possible
  • 56. FHIR Mappings (SQL version) <DataSource><Sql> SELECT PatientID AS EDWPatientID, CASE GenderCD WHEN 'Female' THEN 'female' WHEN 'Male' THEN 'male' ELSE 'unknown' END AS gender,BirthDTS as birthDate FROM [Person].[SourcePatientBASE] </Sql></DataSource> <DataSource Path="condition.code" type="object"><Sql> SELECT PatientID AS EDWPatientID, CONCAT(DiagnosisSourceID,'-',RowSourceDSC,'- ',DiagnosisTypeDSC) as KeyLevel1, CONCAT(DiagnosisSourceID,'-',RowSourceDSC,'-',DiagnosisTypeDSC) as KeyLevel2, CASE CodeTypeCD WHEN 'ICD9DX' THEN 'http://hl7.org/fhir/sid/icd-9-cm' WHEN 'ICD10DX' THEN 'http://hl7.org/fhir/sid/icd-10-cm' ELSE NULL END AS system, DiagnosisCD as code, DiagnosisDSC as text FROM [Clinical].[DiagnosisBASE] </Sql></DataSource> 56 This is a real world example of how we are converting our relational data models into FHIR information models
  • 57. { "EDWPatientID": "Z100069", "gender": "male", "birthDate": "1958-01-05T00:00:00", "condition": [ { "clinicalStatus": "active", "verificationStatus": "confirmed", "category": [ { "coding": "problem-list-item", "text": "ICD Problem List Code" } ], "code": [ { "system": "http://hl7.org/fhir/sid/icd-9- cm", "code": "185", "text": "Malignant neoplasm of prostate (HCC)" } ] } ] } FHIR Output From the Previous Slide 57
  • 58. Sampling of the 200+ Health Catalyst Reusable Value Sets These, along with the CMS/NQF/MACRA values sets are being ported to the Measures Builder Library (MBL) content management system, for reuse in Health Catalyst and 3rd party applications. Acute Coronary Syndrome (ACS) Blood Utilization Dashboard Breast Milk Feeding Catheter Associated Urinary Tract Infection (CAUTI) Prevention Central Line Associated Blood Stream Infections (CLABSI) Prevention Colorectal Surgery Early Mobility in the ICU Glycemic Control in the Hospital Heart Failure Joint Replacement - Hip & Knee Labor and Delivery Patient Flight Path - Diabetes Patient Safety Explorer Pediatric Appendectomy Pediatric Asthma Pediatric Explorer Pediatric Sepsis Pneumonia Population Explorer Readmission Explorer Sepsis Prevention Spine Surgery Stroke (Acute Ischemic & TIA) Surgical Site Infection Prevention Venous Thrombo-Embolism (VTE) Prevention Coronary Artery Bypass Graft Surgery Diabetes - Adult Chronic Obstructive Pulmonary Disease (COPD)
  • 59. Central line-associated bloodstream infection (CLABSI) Risk – Clinical Analytics and Decision Support Congestive Heart Failure, Readmissions Risk – Clinical Analytics and Decision Support COPD, Readmissions Risk – Clinical Analytics and Decision Support Respiratory (COPD, Asthma, Pneumonia, & Resp. Failure), Readmission Risk – Clinical Analytics and Decision Support Forecast IBNR claims/year-end expenditures – Financial Decision Support Predictive appointment no shows – Operations and Performance Management Pre-surgical risk (Bowel) – Clinical Analytics and Decision Support and client request Propensity to pay – Financial Decision Support Patient Flight Path, Diabetes Future Risk – Clinical Analytics and Decision Support Patient Flight Path, Diabetes Future Cost– Clinical Analytics and Decision Support Patient Flight Path, Diabetes Top Treatments – Clinical Analytics and Decision Support Patient Flight Path, Diabetes Next Likely Complications (Glaucoma) – Clinical Analytics and Decision Support Patient Flight Path, Diabetes Next Likely Complications (Retinopathy) – Clinical Analytics and Decision Support Patient Flight Path, Diabetes Next Likely Complications (ESRD) – Clinical Analytics and Decision Support Plus several more… (Nephropathy, Cataracts, CHF, CAD, Ketoacidosis, Erectile Dysfunction, Foot Ulcers) Machine Learning Models in DOS In Development Built Planned Patients Like This – Clinical Analytics and Decision Support Sepsis Risk – Clinical Analytics and Decision Support Readmission Risk – Clinical Analytics and Decision Support Post-surgical risk (Hips and Knees) – Clinical Analytics and Decision Support INSIGHT socio-economic based risk – Clinical Analytics and Decision Support and client request Native SQL/R predictive framework and standard package - Platform Feature selection, Parallel Models, Rank and Impact of Input Variables – Platform Predictive ETL batch load times – Platform Composite Health Risk – Clinical Analytics and Decision Support Composite All Cause Harm Risk – Clinical Analytics and Decision Support Early detection of CLABSI, CAUTI, Clostridium difficile (c. diff) hospital infections – Clinical Analytics and Decision Support Early detection of Sepsis/Septicemia (Blood Infection) – Clinical Analytics and Decision Support Hospital Census Prediction - Operations and Performance Management Hospital Length of Stay Prediction – Operations and Performance Management Public data sets, benchmarks, “Catalyst Risk”, expected mortality, length of stay – CAFÉ collaboration Clusters of population risk (near term risk/cost) – Population Health and Accountable Care
  • 60. Managing and Reusing the Explosion of Measures and Value Sets Measures Builder Library (MBL) is a content management system and set of APIs that allows registries, value sets, and other measures to be consistently managed, verified, governed, and reused for application development
  • 61. Role Model Software Development for the Fabric 1. Open Source & Collaborative Development: All code is available on github.com/HealthCatalyst. External developers can submit enhancements. 2. Open & Modular: All APIs will be publicly published. Customers can pick and choose from the Health Catalyst components or replace any component with their own or from a third party 3. Secure by Design: Security services make it easy to build security into any application 4. Microservices architecture: REST-based services that can be called from web, mobile or BI tools 5. Big Data: Leverages Big Data technologies to provide high-speed and reliable platform 6. Easy Install & Updates: All services install via Docker 7. Scalable: All services are designed to run in multiple nodes and cluster themselves automatically Why can’t healthcare be the role model, instead of Silicon Valley? Should we aspire to something less? Is that acceptable?
  • 62. How Will We Know if We are a Role Model? 1. Successfully implementing the Data Operating System 2. Fast, simple releases every 2 weeks. Constant improvement of our apps. 3. Analytics driven UI and applications—intelligent user interfaces, driven by situational awareness of the physician, nurse, patient, etc. 4. Constantly consuming and expanding the ecosystem of data as the enabler to great apps, not apps as the enabler of data 5. Machine learning and pattern recognition that clearly amazes all of us with its value to humanity 6. Economic scalability-- we're so efficient with our products, which work across multiple OS and data topologies, that it's economically efficient to constantly deploy 7. Auto-fill analytics—a play on words, but how do we, through pattern recognition and machine learning, anticipate next steps in our clients’ decision making? 8. When Google, Facebook, Amazon, and Microsoft come to us for advice about software success and value These are Health Catalyst’s software development vital signs
  • 63. For Health Catalyst Clients 63 Join and explore the Health Catalyst Community to learn more and engage with our team community.healthcatalyst.com
  • 64. Health Catalyst Platform Community 64 Ask Questions about DOS Request Features Review Roadmaps and Release Notes Contact our Community Manager, Kate Weaver, to request access kate.weaver@healthcatalyst.com
  • 65. Summary Thoughts There will be people who hope we fail. There will be people who expect us to fail. There are many more people who hope we don’t. That’s who we’re working for.
  • 66. Healthcare Analytics Summit 17 ERIC J. TOPOL Author, The Patient Will See You Now and The Creative Destruction of Medicine. Director, Scripps Translational Science Institute DAVID B. NASH, MD. MBA Dean, Jefferson School of Population Health JOHN MOORE Founder and Managing Partner, Chilmark Research ROBERT A. DEMICHIEI Executive Vice President and Chief Financial Officer, University of Pittsburgh Medical Center THOMAS D. BURTON Co-Founder, Chief Improvement Officer, and Chief Fun Officer, Health Catalyst DALE SANDERS Executive Vice President, Product Development, Health Catalyst THOMAS DAVENPORT Author , Consultant Competing on Analytics*, , Analyitcs at Work, Big Data at Work, Only Humans Need Apply:Winners and Losers in the Age of Smart Machines. *Recognized by Harvard Business Review editors as one the most important management ideas of the past decade, one of HBR’s ten must-read articles in that magazine’s 90-year history. Summit highlights Industry Leading Keynote Speakers We’ll hear from well-known healthcare visionaries. We’ll also hear from two C-level executives leading large healthcare organizations. CME Accreditation For Clinicians HAS 17 will again qualify as a continuing medical education (CME) activity. 30 Educational, Case Study, and Technical Sessions We have the most comprehensive set of breakout sessions of any analytics summit. Our primary breakout session focus is giving you detailed, practical “how to” learning examples combined with question and opportunities. The Analytics Walkabout Back by popular demand, the Analytics Walkabout will feature 24 new projects highlighting a variety of additional clinical, financial, operational, and workflow analytics and outcomes improvement successes. Analytics-driven, Hands-on Engagement for Teams and Individuals Analytics will continue to flow through the three-day summit touching every aspect of the agenda. Networking and Fun We’ll provide some new innovative analytics-driven opportunities to network while keeping our popular fun run and walk opportunities and dinner on the down. Early Bird PricingSINGLE ENTRY 1 Pass - $595 Save $300 BEST VALUE 3 PACK 3 Passes - $545/each Save $1,000+5 PACK 5 Passes - $495/each Save $2,000+ Sept. 12-14, 2017 Grand America Hotel Salt Lake City, UT

Editor's Notes

  1. To deliver to this future vision we are announcing a broad expansion of what we have previously called the Catalyst Analytics Platform. The Health Catalyst Data Operating System will include all the data ingest, processing, and distribution capabilities and software services needed to build rich, immersive healthcare applications needed. At the core of the Health Catalyst Data Operating System will be Catalyst’s Metadata driven Analytics Engine. The Analytics engine will add real-time data ingestion and analytics computation to its existing capabilities and provide a significant expansion of its machine learning capabilities. We will provide deep support for NLP as well. This builds on top of the support it provides to connect and ingest to 140+ of the most common data sources in healthcare with many more to come. We will also add a layer of services to the kernel of the data operating system that will allow you to integrate with the Metadata, Data Processing Pipeline, and the raw data in the analytics system. On top of that kernel we will introduce a suite of healthcare specific services that expose healthcare data in a way that has never been done before in the industry. Historically healthcare data has been walled off by vendors for their use only. The Health Catalyst Data Operating System will allow applications to start data rich rather than data poor. Over the next day and half we will be presenting on the various services we are building in this layer. No longer will analytics be relegated to the realm of dashboards and reports. In the app layer these we are taking two approaches to close the usability and information gap and deliver these next generation experiences. First we will do the work to enable you to integrate the information directly into your EHR screens so that information is provided in context to those who need it – where they need it. In addition to that we are building a suite of applications that are built with usability and analytics in mind and at the forefront. Like EMRs should have been from the beginning. Over the next couple of days you will be learning more about these experiences we are building and why we chose them to start with and this is only the beginning of what we will do. We will also be opening up these same services for third parties to leverage.
  2. To deliver to this future vision we are announcing a broad expansion of what we have previously called the Catalyst Analytics Platform. The Health Catalyst Data Operating System will include all the data ingest, processing, and distribution capabilities and software services needed to build rich, immersive healthcare applications needed. At the core of the Health Catalyst Data Operating System will be Catalyst’s Metadata driven Analytics Engine. The Analytics engine will add real-time data ingestion and analytics computation to its existing capabilities and provide a significant expansion of its machine learning capabilities. We will provide deep support for NLP as well. This builds on top of the support it provides to connect and ingest to 140+ of the most common data sources in healthcare with many more to come. We will also add a layer of services to the kernel of the data operating system that will allow you to integrate with the Metadata, Data Processing Pipeline, and the raw data in the analytics system. On top of that kernel we will introduce a suite of healthcare specific services that expose healthcare data in a way that has never been done before in the industry. Historically healthcare data has been walled off by vendors for their use only. The Health Catalyst Data Operating System will allow applications to start data rich rather than data poor. Over the next day and half we will be presenting on the various services we are building in this layer. No longer will analytics be relegated to the realm of dashboards and reports. In the app layer these we are taking two approaches to close the usability and information gap and deliver these next generation experiences. First we will do the work to enable you to integrate the information directly into your EHR screens so that information is provided in context to those who need it – where they need it. In addition to that we are building a suite of applications that are built with usability and analytics in mind and at the forefront. Like EMRs should have been from the beginning. Over the next couple of days you will be learning more about these experiences we are building and why we chose them to start with and this is only the beginning of what we will do. We will also be opening up these same services for third parties to leverage.
  3. I like the polygon graphic in the upper right, but there is a big long horizontal part that I would like to change to be more interesting.
  4. I like the polygon graphic in the upper right, but there is a big long horizontal part that I would like to change to be more interesting.