Health Catalyst, Inc · NASDAQ:HCAT
Health Catalyst — Technical Presentation (July 2014)
21 slides from Health Catalyst's technical presentation, July 2014
Slide 1: Late Binding in Data Warehouses: Designing for Analytic Agility
Late Binding in Data Warehouses: Designing for Analytic Agility Dale Sanders, July 2014 © 2014 Health Catalyst Follow Us on Twitter #TimeforAnalytics Proprietary and Confidential www.healthcatalyst.com © 2014 Health Catalyst Follow Us on Twitter #TimeforAnalytics www.healthcatalyst.com
Slide 2: Overview
Overview • The concept of “binding” in software and data engineering • Examples of data binding in healthcare • The two tests for early binding • Comprehensive & persistent agreement • The six points of binding in data warehouse design • Data Modeling vs. Late Binding • The importance of binding in
Slide 3: Late Binding in Software Engineering
Late Binding in Software Engineering 1980s: Object Oriented Programming ● Alan Kay Universities of Colorado & Utah, Xerox/PARC ● Small objects of code, reflecting the real world ● Compiled individually, linked at runtime, only as needed ● Major agility and adaptability to address new use cases Steve
Slide 4: Late Binding in Data Engineering
Late Binding in Data Engineering Atomic data must be “bound” to business rules about that data and to vocabularies related to that data in order to create information Vocabulary binding in healthcare is pretty obvious ● Unique patient and provider identifiers ● Standard facility, department, and rev
Slide 5: Data Binding
Data Binding Software Programming Pieces of Vocabulary meaningless data Binds “systolic & diastolic data to blood pressure” 112 Rules 60 “normal” What’s the rule for declaring and managing a “hypertensive patient”? © 2014 Health Catalyst Follow Us on Twitter #TimeforAnalytics www.healthcatalyst.com
Slide 6: HHS/HRSA HTN Definition
HHS/HRSA HTN Definition Joint National Committee on Prevention, Detection, Evaluation, and Treatment of High Blood Pressure in its Seventh Report (JNC VII 2003 ) • The classification of blood pressure, which is the average of two or more readings each taken at two or more visits after initial screen
Slide 7: Why Is This Concept Important?
Why Is This Concept Important? Knowing when to bind data, and how tightly, to vocabularies and rules is THE KEY to analytic success and agility Comprehensive Persistent Agreement Agreement Is the rule or vocabulary widely Is the rule or vocabulary stable accepted as true and accurate in and rarely c
Slide 8: Six Binding Points in a Data Warehouse
Six Binding Points in a Data Warehouse SOURCE SOURCE SYSTEM CUSTOMIZED DATA DATA CONTENT ANALYTICS DATA MARTS ANALYSIS SUPPLIES SUPPLIES MATERIALS MANAGEMENT QlikView CLINICAL CLINICAL INTERNAL DISEASE REGISTRIES Microsoft Access/ FINANCIAL FINANCIAL ODBC COMPLIANCE AND PAYER MEASURES Web applicatio
Slide 9: Data Modeling for Analytics
Data Modeling for Analytics Five Basic Methodologies ● Corporate Information Model Early binding ‒ Popularized by Bill Inmon and Claudia Imhoff ● I2B2 ‒ Popularized by Academic Medicine ● Star Schema ‒ Popularized by Ralph Kimball ● Data Bus Architecture ‒ Popularized by Dale Sanders ● File Structur
Slide 10: Binding to Analytic Relations
In data Binding warehousing, to Analytic Relations the key is to relate data, not model data Core Data Elements Charge code In today’s environment, about 20 data elements CPT code represent 80-90% of analytic use cases. This will Date & Time grow over time, but right now, it’s fairly simple. DRG cod
Slide 11: The Bus Architecture
The Bus Architecture Client Third Party Ad Hoc Vendor Apps Developed Apps Apps Query Tools Late Binding Bus Architecture ICD diagnosis code Payer/carrier ID Department ID Encounter ID Employee ID Employer ID Date & Time Patient type Provider ID Member ID DRG code Drug code CPT code Facility ID Lab c
Slide 12: Healthcare Analytics Adoption Model
Healthcare Analytics Adoption Model Personalized Medicine Tailoring patient care based on population outcomes and Level 8 & Prescriptive Analytics genetic data. Fee-for-quality rewards health maintenance. Organizational processes for intervention are supported Clinical Risk Intervention Level 7 & Pr
Slide 13: Poll Question: Analytic Maturity
Poll Question: Analytic Maturity • At what Level of the Model does your organization consistently operate? 82 respondents • Level 0 – 11% • Level 1-2 – 35% • Level 3-4 – 41% • Level 5-6 – 12% • Level 7-8 – 1% © 2014 Health Catalyst Follow Us on Twitter #TimeforAnalytics www.healthcatalyst.com
Slide 14: Progression in the Model
Progression in the Model The patterns at each level • Data content expands • Adding new sources of data to expand our understanding of care delivery and the patient • Data timeliness increases • To support faster decision cycles and lower “Mean Time To Improvement” • Data governance expands • Advoca
Slide 15: The Expanding Ecosystem of Data Content
The Expanding Ecosystem of Data Content • Real time 7x24 biometric monitoring data for all patients in the ACO • Genomic data • Long term care facility data 2-4 years • Patient reported outcomes data* • Home monitoring data • Familial data • External pharmacy data • Bedside monitoring data 1-2 years
Slide 16: Six Phases of Data Governance
Six Phases of Data Governance You need to move through these phases in no more 2-4 years than two years • Phase 6: Acquisition of Data 1-2 years • Phase 5: Utilization of Data • Phase 4: Quality of Data • Phase 3: Stewardship of Data 3-12 months • Phase 2: Access to Data • Phase 1: Cultural Tone of
Slide 17: One Page Self Inspection Guide
One Page Self Inspection Guide © 2014 Health Catalyst Follow Us on Twitter #TimeforAnalytics www.healthcatalyst.com
Slide 18: In Conclusion
In Conclusion • Late Binding is not complicated; don’t overthink it • It’s more simple than what we’ve been doing. It’s just contrary to current thinking, which makes it seem complicated. • Early binding is fine… do it whenever you can • As long as you’ve reached Comprehensive and Persistent agreeme
Slide 19: I hope this has been helpful…
I hope this has been helpful… • Questions? © 2014 Health Catalyst Follow Us on Twitter #TimeforAnalytics 19 www.healthcatalyst.com
Slide 20: Thank You
Thank You Upcoming Educational Opportunities The Deployment System: Creating the Organizational Infrastructure to Support Sustainable Change Date: July 23, 1-2pm, EST Presenter: Dr. John Haughom, Senior Advisor, Health Catalyst Register at http://healthcatalyst.com/ Healthcare Analytics Summit Join
Slide 21: Healthcare Analytics Summit objective and mobile app
OBJECTIVE Obtain unbiased, practical, educational advice on proven analytics solutions that really work in healthcare. The future of healthcare requires transformative thinking by committed leadership willing to forge and adopt new data-driven processes. If you count yourself among this group, then