data operating system: what it really means and why … 5.66 ehr w/dr 17.nextgen - ambulatory...
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Data Operating System:What It Really Means and Why You Will Need It
Imran QureshiChief Software Development OfficerHealth Catalyst
Sean StohlSVP - Platform Operations Product DevelopmentHealth Catalyst
Pete HessVP Platform Engineering – DOSHealth Catalyst
Session #22
Learning Objectives• Describe the major problems health systems are facing today.
• Demonstrate why current analytics solutions aren’t good enough.
• Analyze why we need to evolve from data warehouses.
• Show how a data operating system addresses common use cases.
The State of Healthcare Today
Hospital Margins Are at RiskHospital Profit Margins 6%
2011
0.5%Annual growth/cost reduction needed to maintain margins
“Revenue growth will also temper amid declining reimbursement from both private and governmental payers.”
“..uncertainty over federal health care policy also poses a strong headwind (for hospitals' financial performance).”
CBO Publication 2016 – Projecting Hospitals’ Profit Margins
Moody's: Preliminary FY 2016 US NFP
Hospitals with negative margins
3.3%2025 (Projected)
41%2025 (Projected)
Clinicians Are Over-Worked and Over-Measured
“…the emphasis is really on data
collection, but what physicians ought to be
doing is data synthesis.”
50% Physicians penalized 1% of
payments by Meaningful Use
Steven J. Stack, MD, President, AMA
Gary Botstein, MD, DeKalb Medical
“Today’s search engines are better at
helping clinicians diagnose disease than
our EMRs.”
Lloyd B. Minor, MD, Dean, Stanford School of Medicine
Risk Management and Population Health Require More Data
Source: Chilmark Research – 2017 Healthcare Analytics Market Trends Report
EMRs1. Affinity - ADT/Registration2. Allscripts - Ambulatory EMR
Clinicals3. Allscripts
Enterprise/Touchworks -Ambulatory EMR
4. Allscripts Sunrise - Acute EMR Clinicals
5. Aprima ERM6. Cerner - Acute EMR Clinicals7. Cerner - PowerWorks
Ambulatory EMR8. Cerner HomeWorks - Other9. CPSI - Acute EMR Clinicals10. eClinicalWorks - Ambulatory
EMR Clinicals11. Epic - Acute EMR Clinicals12. Epic - Ambulatory EMR
Clinicals13. GE (IDX) Centricity -
Ambulatory EMR Clinicals14. McKesson Horizon - Acute
EMR Clinicals15. McKesson Horizon Enterprise
Visibility16. Meditech 5.66 EHR w/DR17. NextGen - Ambulatory Practice
Management18. Quality Systems (Next Gen) -
Ambulatory EMR Clinicals19. Siemens Sorian Clinicals -
Inpatient EMR
Finance/Costing1. Affinity - Costing2. Allscripts (EPSi) - Budget3. Allscripts (EPSi) - Costing4. Allscripts (TSI) - Costing5. BOXI - GL6. Cost Flex - Costing7. Digimax Materials
Management - Inventory Management
8. IOS ENVI - Costing9. Kaufman Hall Budget Advisor
- Other10. Lawson - Accounts Payable11. Lawson - Accounts
Receivable12. Lawson - GL13. Lawson - Supply Chain14. McKesson - Accounts
Payable15. McKesson Enterprise
Materials Management16. McKesson HPM - Costing17. McKesson HPM - GL18. McKesson PFM - Accounts
Payable19. McKesson PFM - GL20. McKesson Series - Accounts
Receivable21. Meditech - GL22. Microsoft Great Plains - GL23. Oracle (Hyperion) - Costing24. Oracle (PeopleSoft) - GL25. Oracle (PeopleSoft) - Supply
Chain26. PARExpress27. PPM - Costing28. Smartstream - GL29. StrataJazz - Costing
Billing1. Affinity - Hospital Billing2. CHMB 360+ RCM - Hospital
Billing3. CPSI - Hospital Billing4. Epic - Hospital Billing5. GE (IDX) Centricity - Hospital
Billing6. GE (IDX) Centricity -
Professional Billing7. HealthQuest - Patient
Accounting8. Keane - Hospital Billing9. McKesson Series - Patient
Billing10. McKesson STAR - Hospital
Billing11. MD Associates - Professional
Billing12. Siemens Sorian Financials -
Inpatient Registration and Billing
HR/ERP1. API Healthcare - Time and
Attendance2. iCIMS3. Kronos - HR4. Kronos - Time and
Attendance5. Lawson - HR6. Lawson - Payroll7. Lawson - Time and
Attendance8. Maestro9. MD People10. Now Solutions Empath - HR11. Oracle (PeopleSoft) - HR12. PeopleStrategy/Genesys -
HR13. PeopleStrategy/Genesys -
Payroll14. Ultimate Software Ultipro -
HR15. WorkDay
1. 835 – Denials2. Adirondack ACO Medicare3. Aetna - Claims4. Anthem - Claims5. Aon Hewitt - Claims6. BCBS Illinois7. BCBS Vermont8. Children's Community Health Plan
(CCHP) - Payer9. Cigna - Claims10. CIT Custom - Claims11. Cone Health Employee Plan (United
Medicare) - Claims12. Discharge Abstract Data (DAD)13. Hawaii Medical Service Association
(HMSA) - Claims14. HealthNet - Claims15. Healthscope16. Humana (PPO) - Claims17. Humana MA - Claims18. Kentucky Hospital Association (KHA) -
Claims19. Medicaid - Claims20. Medicaid - Claims - CCO21. Merit Cigna - Claims22. Merit SelectHealth - Claims23. MSSP (CMS) - Claims24. NextGen (CMS) - Claims25. Ohio Hospital Association (OHA) -
Claims26. ProHealth - Claims27. PWHP Custom - Claims28. QXNT - Claims29. UMR Claims Source30. Wisconsin Health Information
Organization (WHIO) - Claims
Claims1. Allscripts - Case Management2. Apollo - Lumed X Surgical System3. Aspire - Cardiovascular Registry4. Carestream - Other5. Cerner - Laboratory6. eClinicalWorks - Mountain Kidney Data
Extracts7. GE (IDX) Centricity Muse - Cardiology8. HST Pathways - Other9. ImageTrend10. ImmTrac11. Lancet Trauma Registry12. MacLab (CathLab)13. MIDAS - Infection Surveillance14. MIDAS - Other15. MIDAS - Risk Management16. Navitus - Pharmacy17. NHSN18. NSQIPFlatFile19. OBIX - Perinatal20. OnCore CTMS21. Orchard Software Harvest - Pathology22. PACSHealth - Radiology23. Pharmacy Benefits Manager24. PICIS (OPTUM) Perioperative Suite25. Provation26. Quadramed Patient Acuity Classification
System - Other27. QXNT/Vital - Member28. RLSolutions29. SafeTrace30. Siemens RIS - Radiology31. SIS Surgical Services32. StatusScope - Clinical Decisions33. Sunquest - Laboratory34. Sunrise Clinical Manager35. Surgical Information System36. TheraDoc37. TransChart - Other38. Varian Aria - Oncology39. Vigilanz - Infection Control
Clinical Specialty
1. Fazzi - Patient Satisfaction2. HealthStream - Patient
Satisfaction3. NRC Picker - Patient Satisfaction4. PRC - Patient Satisfaction5. Press Ganey - Patient
Satisfaction6. Sullivan Luallin - Patient
Satisfaction
Pat. Sat1. AHRQ Clinical Classification
Software (CCS)2. Charlson Deyo and Elixhauser
Comorbidity3. Clinical Improvement Grouper
(Care Process Hierarchy)4. CMS Hierarchical Condition
Category5. CMS Place Of Service6. LOINC7. National Drug Codes (NDC)8. NPI Registry9. Provider Taxonomy10. Rx Norm11. CMS/NQF Value Set Authority
Center
Terminology
1. Adirondack ACO Clinical Data from HIXNY (HIE)
2. ADT HIE Patient Programs 3. Vermont HIE
HIE
200+ Current Data Sources
Machine Learning Has Potential, but It’s Not Scaling
• Every machine learning project is a one-off requiring a lot of work.
• Hospitals can’t find enough data scientists.
• Clinicians don’t trust “black box” machine learning.
2xReadmissions predicted by UNC machine learning model vs LACE
Advisory Board 2017 - The Rise of Machine Learning.
So, machine learning feels out of reach…
20% Decrease in CLABSI rate
Indiana University System – Scottsdale Institute, Inside Edge (2016)
50+ Health Catalyst Machine Learning Models
What Is the Solution?
First, Let Me Tell You a StoryA decade ago, best-of-breed applications ruled healthcare.• Each fit the individual workflows well.• But data remained in silos and apps did not
interoperate.
Today, we have one monolithic application (EHR).• Clinical data is in one place.
- Although non-clinical data remains separate.• But cited as:
- Biggest cause of physician burnout.- Forces one-size-fits-all workflows.- Stifles innovation.
Siloed apps don’t work.
App (EHR)
Data
One app for everything doesn’t work.
Data Data Data
App (EHR) App
Best of breed apps working together.
Data Operating System
AppApp
Data
App
Data
App
Data
Single Monolithic App Model Isn’t Working
We Already Have a Model for This
Your iPhone has best-of-breed apps that all work together and share your data (e.g., your contacts).
Imagine if you were forced to use your email application to order Uber.
Precision Software
“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.
Best-of-breed apps
Many software vendors
Data operating
system
Centralized security
Machine learning
In clinical workflow
Health Catalyst Workflow Apps
Health Catalyst Data Operating System (DOS™)
Ope
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Patie
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Fina
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Com
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Anal
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Res
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Precise Patient Registries
Patient Safety
Clin
ical
Impr
ovem
ent
Leading Wisely
MACRA Measures & Insights
Measures Business Library
CAFÉ
Care Management
CORUS
HCC Insights
Uncompensated Care Suite
Machine Learning Text Analytics
70+ Health Catalyst Analytics Accelerators
Acute Coronary Syndrome (ACS)Blood Utilization DashboardBreast Milk FeedingCatheter Associated Urinary TractInfection (CAUTI) PreventionCentral Line Associated Blood StreamInfections (CLABSI) PreventionColorectal SurgeryEarly Mobility in the ICUGlycemic Control in the HospitalHeart FailureJoint Replacement - Hip & KneeKey Process Analysis (KPA)Labor and DeliveryPatient flight Path - DiabetesPatient Safety ExplorerPediatric AppendectomyPediatric AsthmaPediatric ExplorerPediatric SepsisPneumoniaPopulation ExplorerReadmission ExplorerSepsis PreventionSpine SurgeryStroke (Acute Ischemic & TIA)Surgical Site Infection Prevention
Clinical Analytics & Decision SupportDepartment Explorer: Emergency ServicesDepartment Explorer: Surgical ServicesLabor Management ExplorerLeading WiselyPatient Experience ExplorerPatient Flow ExplorerPractice Management: Patient AccessProvider ProductivitySupply Chain ExplorerMACRA Measures & InsightsCommunity Care
Operations & Performance Management
CORUS (Clinical & Operational Resource Utilization System)Advanced Billing MonitoringFinancial Management ExplorerGeneral Ledger ExplorerHIM Documentation Workflow AnalyzerRevenue Cycle Advisor: HospitalRevenue Cycle Advisor: ProfessionalRevenue Gap Finder
Financial Decision Support
ACO ExplorerACO MSSP MeasuresAttribution ModelerBundled PaymentsHCC InsightsPMPM AnalyzerRisk Model Analyzer
Accountable Care
Patient StratificationPatient IntakeCare CoordinationCare CompanionCare Team InsightsCatalyst 4 Health
Care Management & Patient Relationships
Cohort Definition Framework
Research Informatics
Collective Analytics for Excellence (CAFÉ)
Comparative Analytics
The Evolution from Data Warehouse
to a Data Operating System
Evolution from a Data Warehouse to a Data Operating System
Data Warehouse Data Operating System
1 Collects data from EHR and claims. Collects data from many sources.
2 Enables creating reports. Enables creating reports and web/mobile apps.
3 Enables SQL queries. Enables SQL, machine learning (R/Python) queries.
4 Data is updated nightly. Data is updated in real-time.
5 Not available in the EHR workflow. Insights are easily available in the EHR workflow.
6 Requires replacing your existing EDW. Works with your existing EDW (or use our EDW).
7 Proprietary schemas. Industry standard schemas (e.g., FHIR).
8 Text analytics is a separate process. Text analytics is built-in.
9 Works with rows and columns. Works with rows, columns and reusable healthcare logic like registries, measures, risk, insights.
10 Provides centralized security at app and data levels.
11 Makes machine learning as easy to use as SQL.
12 Content Marketplace to share executable content with other health systems.
The Health Catalyst Data Operating System (DOS)
Data Ingest Real-time Streaming
Source Connectors
Catalyst Analytics Platform Fabric Data Services
Real time Processing
Health Catalyst Applications
Data Quality
Data Governance
Pattern Recognition
Hadoop/ Spark
Data Export
RegistryBuilder
Leading Wisely
Care Management
Atlas
Client BuiltApplications
Fabric Real Time Services
NLP
CAFÉBenchmarks
Choosing Wisely
Patient Safety
MeasureLibrary
ACO Financials
Patient Engagement
and more …
HL7
Data Pipelines Metadata
Data Lake
Reusable Content ML Models
3rd Party Apps
Machine Learning Pipelines
Marketplace
SAMD & SMD
Fabric Application Services
Registries Terminology & Groupers
FHIREHR Integration
Security, Identity& Compliance
Patient & Provider Matching
Measures
Standard Data Models
Monolithicsystem
Fabric - Building Blocks for Healthcare• Microservices.• Open APIs.• FHIR-based.• Open-source.• Reusable clinical and financial logic.• Fast access to ALL aggregated data.• Interoperable and portable apps.• Install only the components you want.• Replace any component with your own.
Who Needs a Data Operating System?
Clinicians Hospital IT Hospital Leaders
Data Administrators
Software Vendors Patients Data
Scientists
Clinicians Need a Data Operating System
Mergers & Acquisitions have left health systems with fragmented sources of data.
“I have to login to multiple EHRs just to see bits of patient data. How can I make a good decision about this patient in 10 minutes?”
What if you could access the data in a single place from all the fragmented sources of data?
Data Sources
Data Aggregation
Clients
Reusable logic
EHRs Claims Care Mgmt
Social Determinants of HealthDevice
G/L Cost (ERP)
Retail
Notes
Patient Generated
Patient Satisfaction
Reports Web Apps Mobile Apps
Data Warehouse
Registries Measures Cost Accounting Insights Risk Trends Predictions
Machine Learning Models
Data Aggregation + Reusable Logic + Open APIs
EHR Apps
Clinicians Need a Data Operating System (cont.)
Extend life and value of current EHR investments.
“Our clinicians are over-worked and over-measured, but under-informed”
What if the EHR was not just a place to record data but a place where clinicians see synthesized data and are informed?
How Can We Be Non-intrusive?
No more “black box” yes or no recommendationsFocus on data synthesis and let clinicians choose the right path
Refer to Pain Clinic
Send Summary to PCP15x
How likely are clinicians to follow care recommendations if they are in an EHR vs reports.
Evolution from a Data Warehouse to Data Operating System for Clinicians
EHR Data Operating System
1 Contains EHR data. Contains aggregated data from all sources.
2 Designed for data entry. Designed for data synthesis.
3 Each EHR is an island. Works with all the EHRs in your health system.
4 Apps are not portable. Apps are portable: standard APIs and data models.
5 No access to machine learning. Has built-in machine learning.
6 No reusable logic. Has reusable logic like registries, measures, risk.
7 Each integration requires making changes in the EHR.
Integrate once into your EHR. Insights from many apps can show up without reconfiguring the EHR.
8 Information overload for clinicians. Hospital IT can centrally define rules to control what is shown in EHR workspace.
Fabric.EHR, the GPS for Clinicians
Analyst(SQL)
Apps(FHIR)
Data Scientists(REST)
Fabric.EHR Hospital IT(Rules)
Target by Patient, User, and Encounter
Epic Cerner Allscripts Other
Poll Question #1
How many EHRs does your health system use?
1) Zero2) One3) Two4) Three5) Four or more6) Unsure or not applicable
Hospital IT Needs a Data Operating System
Scaling existing data warehouses
“We have the data, but it is impossible for us to scale to more than a few reports or apps with the team we have.”
What if you could enable lots of reports and apps without having to hire more people?
SQL or Big Data?
SQL is great for most cases Simpler. Existing skills. Existing code.
You need big data Volume (typically > 5TB). Velocity (typically > 1/day). Variety (typically 10+ sources).
A data operating system allows you to use the same tools with SQL or big data.
False choice: You don’t need to choose!
Evolution from Data Warehouse to Data Operating System for IT
Data Warehouse Data Operating System
1 Source Mart Designer (SMD) creates source marts.
Source Mart Designer (SMD) creates source marts and data lakes.
2 Subject Area Mart Designer (SAMD) creates SQL bindings.
Subject Area Mart Designer (SAMD) creates SQL bindings and Hadoop/Spark bindings.
3 SAM Engine runs pipelines via SQL Server.
SAM Engine runs pipelines via SQL Server or via Hadoop.
4 Atlas tracks meta-data for SQL data.
Atlas tracks metadata for SQL data and Hadoop data with smart search across both.
5 Machine learning is a separate process.
Create machine learning (R/Python) bindings and SAM engine runs them at scale.
6 Only deals with data. myCatalyst portal to access all your analytical assets: data, reports, apps, re-usable logic, permissions with smart search across all of them.
But I Already Have a Data Warehouse
The Health Catalyst DOSRuns on Any Data Warehouse
Real-time Streaming
Data Warehouses Fabric Data Services
Real time Processing
Health Catalyst Applications
Data Quality
Data Governance
Pattern Recognition
RegistryBuilder
Leading Wisely
Care Management
Atlas
Client BuiltApplications
Fabric Real Time Services
NLP
CAFÉBenchmarks
Choosing Wisely
Patient Safety
MeasureLibrary
ACO Financials
Patient Engagement
and more …
HL7
Metadata
Reusable Content ML Models
3rd Party Apps
Machine Learning Pipelines
Marketplace
SAMD & SMD
Fabric Application Services
Registries Terminology & Groupers
FHIREHR Integration
Security, Identity& Compliance
Patient & Provider Matching
Measures
Standard Data Models
HC EDW
Epic Cerner
Teradata
Home grown
IBMOracle
Hadoop/ Spark
Allscripts
Hospital Leaders Need a Data Operating System
Machine learning is too hard.
“I would love to use machine learning, but I would have to build a huge infrastructure and
hire a bunch of expensive data scientists.”
What would a data platform look like if it was designed to make machine learning as easy
as SQL?
Providers need to manage risk.
“How can I manage risk if I don’t have all the data and can’t easily calculate risk?”
What if it was easy to calculate risk and trend it over time both at population level
and patient level?
Machine Learning as Easy as SQL
Model
Real DataThe data operating system takes care of running your machine learning algorithms at scale in production.
Predictions
Predictions can be routed to EHR workflow via the data operating system.
Feature Extraction
Tools for analysts to create “features” using SQL (or R/ Python).
Feature library for re-use.
Train and Model
Selection
Data operating system tools make it easy to train models.
Training Data
Access data as longitudinal patient records.
Access registries, measures, terminology hierarchies, cost accounting, etc.
Model Creation
Point-and-click wizards for common machine learning algorithms.
For advanced scenarios, include R, Python and deep learning scripts.
Poll Question #2
How does your health system calculate per-member, per-month costs for an insurance contract?
a) Machine learning modelsb) Extrapolating from last yearc) We negotiate up from what the insurance company offersd) Rough calculationse) We need to but can’t todayf) Unsure or not applicable
Data Administrators Need a Data Operating System
Security that is secure, but allows access to the data.
“I need to be HIPAA compliant so I can’t allow anyone to access the data.”
What if you could easily implement common healthcare security models in a central place?
Are you protecting the right thing?
We Don’t Need More Locks
We need intelligent security…
Imagine your hospital having these on every door and passing out keys
Security Should Not Be an Afterthought
Secure by design• At app level: Fabric.Identity and Authorization
- Integrates with existing identity providers like AD.- Centralized permissions.
• At data level: Fabric.FHIR data service- Implements permissions at the data layer.- At conceptual layer (patient, medications, diagnosis,
claims) instead of table and column level.
Centralized Security Console• Set permissions for all your applications in one
place.
Implements HL7 Security Reference Model
Built-in support for common healthcare use cases:• By affiliates.• By role.• By care program.• By sensitivity of data.• By insurance company.• Define your own security
template.
Lessons Learned• Healthcare industry faces significant challenges
• Hospital margins are at risk.• Clinicians are over-worked and over-measured.• One monolithic app (EHR) model is not working.• Machine learning has potential, but is not scaling.
• The future of healthcare is an app ecosystem• Best-of-breed applications from many companies all working together.
• The data operating system is an evolution of the data warehouse• Enables best-of-breed applications.• Integrates into the EHR workflow.• Leverages big data, machine learning, and text analytics.
ScalableAll services are designed to run in multiple nodes and cluster themselves automatically.
Easy Install and UpdatesAll services install via Docker containers.
Microservices ArchitectureREST-based services that can be called from web, mobile, or BI tools.
Secure by DesignSecurity services make it easy to build security into any application.
Open and 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.
Can Health Catalyst Be a Role Model?Open Source and Collaborative Development• All code is available on
github.com/HealthCatalyst.• External developers can submit enhancements.
Join Us
• Help us evolve the data operating system concept.
• Start using the available Health Catalyst DOS components.
• Use the Health Catalyst DOS to start putting insights into EHR workflow for your clinicians.
• Build new components for the Health Catalyst DOS or build better versions of existing components.
Together we can make software be a catalyst not an obstacle [email protected]
Thank You