The Data Operating System: Changing the Digital Trajectory of Healthcare

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 IngestReal-time

Streaming

Source

Connectors

Catalyst Analytics Platform Core Data Services

Real time

Processing

Fabric

Registries Terminology

& Groupers

Apps

FHIR

Data Quality

Data

Governance

Pattern

RecognitionHadoop/

Spark

Data Export

3rd Party Applications

Registry

Builder

Leading

Wisely

Care Management

SAMD &

SMD

Atlas

Hospital ITApplications

EHR Integration

Machine Learning

ModelsPatient & Provider

Matching

Real time Data Services

NLPLambda

Architecture

CAFÉ

Benchmarks

Choosing

WiselyPatient

Safety

Measures

Builder

ACO

Financials

Patient Engagement and more …

HL7

Data Pipelines

ML PipelinesSecurity, 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


Registry

Builder

Leading

Wisely

Care Management


CAFÉ

Benchmarks

Choosing

WiselyPatient

Safety

Measures

Builder

ACO

Financials



& GroupersFHIRSAMD &

SMD

EHR Integration Models

Patient & Provider

MatchingML 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?

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&AHundreds 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 SystemShare 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 StandardsAt 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 IngestReal-time

Streaming

Source

Connectors

Catalyst Analytics Platform Core Data Services

Real time

Processing

Fabric


& Groupers

Apps

FHIR

Data Quality

Data

Governance

Pattern

RecognitionHadoop/

Spark

Data Export


Registry

Builder

Leading

Wisely

Care Management

SAMD &

SMD

Atlas


EHR Integration

Machine Learning

ModelsPatient & Provider

Matching

Real time Data Services

NLPLambda

Architecture

CAFÉ

Benchmarks

Choosing

WiselyPatient

Safety

Measures

Builder

ACO

Financials


HL7

Data Pipelines

ML PipelinesSecurity, 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


Registry

Builder

Leading

Wisely

Care Management


CAFÉ

Benchmarks

Choosing

WiselyPatient

Safety

Measures

Builder

ACO

Financials



& GroupersFHIRSAMD &

SMD

EHR Integration Models

Patient & Provider

MatchingML 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 SetsMeasures 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

[email protected]

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. TOPOLAuthor, The Patient Will

See You Now and The

Creative Destruction of

Medicine. Director,

Scripps Translational

Science Institute

DAVID B. NASH,

MD. MBADean, Jefferson

School of

Population

Health

JOHN MOOREFounder and Managing

Partner, Chilmark Research

ROBERT A. DEMICHIEIExecutive Vice President and

Chief Financial Officer, University

of Pittsburgh Medical Center

THOMAS D.

BURTONCo-Founder, Chief

Improvement Officer,

and Chief Fun Officer,

Health Catalyst

DALE SANDERSExecutive Vice

President, Product

Development,

Health Catalyst

THOMAS DAVENPORTAuthor , 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 SpeakersWe’ll hear from well-known healthcare visionaries. We’ll also

hear from two C-level executives leading large healthcare

organizations.

CME Accreditation For CliniciansHAS 17 will again qualify as a continuing medical education

(CME) activity.

30 Educational, Case Study, and Technical

SessionsWe 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 WalkaboutBack 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 IndividualsAnalytics will continue to flow through the three-day summit

touching every aspect of the agenda.

Networking and FunWe’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 $300BEST 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