real-time data warehouse
TRANSCRIPT
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Vice President, Digital Solutions & Enterprise Analytics
Cra ig Schwabl, MBA
DISCLAIMER: The views and opinions expressed in this presentation are solely those of the author/presenter and do not necessarily represent any policy or position of HIMSS.
Director, Enterprise Data Services
Jes s e Pres ton, MBA
Real-time Data Warehouse: Oxymoron or Clinical Transformation? Ses s ion # 202, Augus t 12, 2021, 1:15-2:15
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Welcome
Director, Enterprise Data ServicesJes s e Pres ton, MBA
Vice President, Digital Solutions & Enterprise AnalyticsCra ig Schwabl, MBA
#HIMSS21
Conflict of Interest
Craig Schwabl, MBA
Has no real or apparent conflicts of interest to report.
Jesse Preston, MBA
Has no real or apparent conflicts of interest to report.
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Agenda
• Background & Challenges
• Use Cases
• How we got there
• Technical Solution
• Results
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Learning Objectives
• Explore how critical business decisions can leverage real-time data
• Discover solutions in overcoming challenges to cloud analytics migration
• Modify one methodology of cloud migration with real-time data to suit the
needs of your own organization
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Background• In 2016, University Hospitals of Cleveland established a formal Enterprise
Business Intelligence Program
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Business IntelligenceProgram Pillars
Platform
- Data Warehouse- Self Service Reporting- Data Mining- Predictive Analytics- Master Data Mgmt.- ETL Processes- Data validation
Portfolio
- Standard “Packages”- Business Requirements- Request validation- Intake assessment- Prioritization- Kaizen / focus groups
Data Governance
- Data Domains- Data Definitions- Business owners (aka
Data Stewards)- Data use policies- Data access (who)
Program Governance
- BI Strategy- Program Oversight
- Program Prioritization- Business Alignment
- Program Sponsorship- Key Decisions
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Challenges
• Covid-19 put enormous strain on the Health System• Equipment shortages• Supply shortages• Staffing shortages
• Lack of real-time visibility of emerging Covid hotspots• Outbreaks were occurring quickly• Difficult to predict hospital bed utilization
• Covid-19 introduced critical needs for real-time decision-making
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Use Cases
• Need real-time visibility into ventilator usage & allocation
• Need real-time geospatial analysis of Covid cases
• Need real-time allocation/re-allocation of staff
• Need standards-based real-time integration with partners
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How we got there
• Phase 1 – Modify current environment for near-real-time data• High-frequency Extract, Transform, Load (ETL) process
• Advantages• Leverage existing data flow pipelines• Near real-time data for some data types• Straightforward implementation
• Disadvantages• Additional strain on source systems• Required hardware upgrades to our Enterprise Data Warehouse (EDW) and ETL
environments• Wasn’t able to satisfy all use cases
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How we got there• Phase 2 – Design and implement a real-time, cloud-based HL7/FHIR platform
• Advantages• True real-time data• Standards-based• Leverages existing messaging infrastructure• Scalable cloud-based infrastructure
• Disadvantages• More complex environment• Requires new development of HL7 -> FHIR mapping• Relies on newer, nascent technology (FHIR APIs)
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Technical Solution
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Corporate Data Center
Rhapsody Interface Engine Real-time
Data Mart
HL7
Applications
Data MiningSQL
Source Systems
Azure Private Cloud
FHIRAPI
REST
Reporting
Enterprise Data
Warehouse
HL7 -> FHIR Conversion
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Technical SolutionHL7 -> FHIR Mapping
source: fhir.org
Auth, POST -> Response to FHIR API* GET from FHIR API*
* All patient names are fictitious
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Technical Solution
Implementation Considerations:
• Scalability:• University Hospitals generates ~10M HL7 messages per day (~300M monthly)
• Throughput:• Need to ensure sufficient bandwidth
• Code Optimization• Real-time “line speed” mapping to avoid congestion & engine backup
• Platform Evolution• APIs & mapping files
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Results
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“With the ability to quickly see all vital information, UH will also be able to monitor and quickly respond to the need of patients on ventilators”
“Can monitor the patient’s vital information minute-by-minute from anywhere on the floor or even remotely outside the hospital”
wkyc.com
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Results
• Near real-time access to ventilator usage and utilization
• Enabled decisions for reallocation of devices across facilities
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Results • Real-time analysis of cluster outbreaks
• Predictive analysis of communal living outbreak risk
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Results
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• Real-time staffing utilization
• Predict staffing shortages
• Reallocation of staff to different facilities and/or departments
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Additional Use Cases• Support for non-HL7 partner platforms
• Central operations• Census• Scheduling• Billing• Patient movement
• Resource Forecasting• Extended Capacity Management (e.g. bed availability)
• Value-based care (mobile)
• Real-time Exception reporting & alerts• Physician Alerting (ED / admissions)• Clinical data (e.g. Sepsis temperature)
• Advanced ETL packages -> micro-processing
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Questions• Have any organizations successfully made the leap from on-premise analytics to
a cloud environment? If so, what were the business drivers for this transition?
• For organizations that have migrated to the cloud, what were some of the success and challenges that you faced?
• For organizations that have implemented real-time data warehouse capabilities, what were the business problems that were being addressed?
• What source data feeds your real-time data warehouse environment?
• How is your real-time data being used (e.g. self-service analytics, feed machine learning models, AI platforms, geo-spatial analysis, data interoperability, etc)?
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Thank you!(*Reminder to complete the online evaluation of this session)
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Director, Enterprise Data ServicesJes s e Pres ton, MBA
VP, Digital Solutions & Enterprise AnalyticsCraig Schwabl, MBA