1. Enterprise Data Warehousing in Healthcare
Build vs Buy considerations
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2. Context for this topic Assess Design Implement Test Deploy 1 Decision
Should we build this EDW from scratch, or partner with a vendor to deliver this capability for the organization. Are those my only options?
Assess
Design
Implement
Test
Deploy
“Do we have the tools to use data to transform care delivery and streamline operations?”
“Assessment of our current state indicates we need a different future state. What should that look like?”
“Key to our future state will be an Enterprise Data Warehouse (EDW) to integrate data from multiple sources and facilitate Triple Aim reporting. Make it so.”
“We will test our EDW to ensure it is meeting our expectations.”
“We will roll out our EDW and analytic applications to a broad group of users, including clinicians, leaders, and administrators.”
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3. Agenda Examine the options Other Considerations About Health Catalyst®
Building an EDW
Buying an EDW
Hybrid solution
Other Considerations
About Health Catalyst®
Questions
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4. Poll Question
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5. Building an EDW
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6. What is an EDW? Metadata, Security, and Auditing
Common, Linkable Identifiers
FINANCIAL SOURCES
(e.g. EPSi, Peoplesoft, Lawson)
DEPARTMENTAL SOURCES
(e.g. Apollo)
Financial Source Marts
Departmental Source Marts
Asthma
Administrative Source Marts
Patient Source Marts
Hospital Operations
PATIENT SATISFACTION SOURCES
(e.g. NRC Picker, Press Ganey)
ADMINISTRATIVE SOURCES
(e.g. API Time Tracking)
Primary Care
EMR
Source Marts HR
Source Mart
EMR SOURCE
(e.g. Epic, Cerner)
Human Resources (e.g. PeopleSoft)
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7. Why choose to build an EDW?
Benefits of a data warehouse:
Performance
Integration
Ease of use
Historically
Dearth of proven solutions for healthcare
Pioneers had to demonstrate value prior to funding
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8. Considerations: building an EDW
Worst case scenario: project failure
50% of data warehouse projects fail
Common among unsuccessful EDW builds:
A solid business imperative is missing
Executive sponsorship and engagement is missing
Frontline users are not involved from start to finish
Ongoing success is elusive
Most healthcare IT groups not prepared to function like a
software company
Limited opportunities to learn lessons before funding is cut
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9. Pros/Cons: Building an EDW
Building it yourself means infinite customization
Getting “something” built yourself may be “good enough”
(for now)
Pride of authorship
Cons
Staffing
Shortage of qualified resources
Re-learning lessons
“Skunkworks” projects can be culturally divisive
Project management challenges
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10. Buying an EDW
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11. Why might someone buy an EDW?
Developing well-engineered, custom software may
not be your core competency
Market offerings for healthcare data warehousing solutions are better than 10 years ago
Support and maintenance agreement help ensure
long term success
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12. Considerations: Buying an EDW
Many of the same risks with any vendor relationship
Evaluating an analytics vendor
Completeness of vision
Culture and values
Total cost
Ability to execute
Where else have they had success?
Will their approach work for my organization?
Do they have expert resources?
More information:
How to evaluate a clinical analytics vendor
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13. Pros/Cons: Buying an EDW
Shortest time to value
Real world experience: what works best in practice
An infusion of help
Lower total cost of ownership
Cons
Initial cost
Knowledge transfer
Tradeoff between “perfect” and “extremely good”
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14. Poll Question
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15. Hybrid Build and Buy
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16. Buying AND Building an EDW
In practice, more like “Buy and Extend”
Leverage a vendor partner for some components
Develop, using internal resources, others
Examples:
Select a vendor to:
Implement the data warehouse
Integrate several high priority data sources
Leverage internal resources to:
Integrate additional data sources
Develop additional analytic applications
Integrate data from the warehouse into operational processes
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17. Why choose to build AND buy?
Best of both worlds
Rapid implementation
As customizable as a purely “build” solution
Higher potential for return on investment
Mitigates risk
Benefit from the lessons your vendor has learned
Infusion of resources to help deploy and train
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18. Other considerations Potential pitfalls for any data warehouse project
“Boil-the-ocean syndrome”
Seeking governance “perfection”
Rigid architecture
Insights
Original article
Comparing data warehouse architectures for Healthcare
Other Resources
Healthcare Data Warehousing Association (HDWA)
The Data Warehousing Institute (TDWI)
Health Catalyst Knowledge Center
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19. About Health Catalyst
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20. Health Catalyst Profile
Integrated Delivery Systems
Patients Impacted
Hospitals
& Clinics
25M 1400
Community Hospitals
Academic Medical Centers
Founded
2008
Employees
140 HQ
Salt Lake City, UT
Children’s Hospitals
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21. What does Health Catalyst offer?
Services
Installation Services, Clinical Improvement Services
Change Process
and Behavior
Analytic Applications
Key Process Analysis, Dashboards, Advanced Analytics
Apply Evidence and Standardize
Late-Binding™
Data Warehouse Platform
Data Warehouse, Architecture, Metadata Management, Security, and Auditing
Integrates Disparate Data
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22. Application Families
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23. Reduce Healthcare Labor Costs by using an EDW and Analytics
“The work our organization is doing with the Health Catalyst team pushes the envelope of what it means to have near real-time data at our fingertips to make operational decisions. Their technology and processes put us on track to understand the potential upstream and downstream financial impact of even the smallest decision.”
~ Director Clinical Planning and Financial Management
Objective
Health Catalyst Solution
Results to date
Manage labor costs by allocating resources appropriately to the demand for services
Eliminate the need to manually pull and consolidate reports on a monthly basis for a 3rd party point solution
Enable business and unit managers to better track and manage performance by providing more timely information
Late-Binding™ Data
Warehouse
Labor Productivity Advanced Application Module
Installation Services
Improvement Services
Estimated 2% reduction in total salaries and benefits to date
66% percent increase in the speed of data availability
Estimated NPV of $425K over a 4-year period from automated data integration
500% increase in the number of managers using data to drive decisions
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24. How to Reduce Heart Failure Readmissions
“Our data is now accessible to stakeholders throughout the organization, including clinical teams who can use it to drive improvements in care processes, care quality and patient outcomes”
~ Director of Clinical & Business Analytics
Objective Health Catalyst Solution Results to date
Define Heart Failure (HF) baseline measures for 30 and 90-day readmissions rates
Implement evidence- based practice interventions to drive HF readmission rate reductions
Establish balance metrics including ED visits, observation days and patient satisfaction
Develop sustaining processes for evaluating readmission rates to ensure continuous process improvement
Late-BindingTM data warehouse that enables faster time-to-value
Integration of clinical, patient satisfaction and financial data to establish baseline, ongoing and balance measures
Discovery, Foundational and Advanced HF applications including cohort finder, registry and evidence- based clinical content
Healthcare analytic visualization including gauges and trend lines for at-a-glance view
Seasonally adjusted rate reduction of 21% in 30- day and 14% in 90-day readmissions
2X increase in the number of phone calls made to patients within 48 hours of discharge
Average of 63 % increase in physician medication reconciliation within 48 hours of discharge
Follow-up appointment intervention baseline and balance measures established
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25. Questions and Answers Additional Information
See us at HIMSS 2014 booth #6076
Call to request a meeting
Listen to two clients speak:
– “Improving Outcomes with an Innovative Approach to Population Health Analytics”
» Stanford Hospital & Clinics
» Yohan Vetteth, Pravene Nath, MD
» Date/Time: Thursday, 2/27, 12 PM
» Location: Room 304A, Session #229
– “Blending Clinical and Financial Data to Drive the Value Equation”
» Texas Children’s Hospital
» Charles Macias, MD
» Date/Time: Wednesday, 2/26, 1 PM
» Location: Room 304A, Session #191
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@healthcatalyst
#HCDatawarehouse
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