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David K. Crockett, PhD
Senior Director, Research and Predictive Analytics, Health Catalyst
Assistant Professor, Pathology, University of Utah School of Medicine
BMI Graduate Seminar
8/28/2014
Advances in Clinical Data Warehousing
About Health Catalyst
2
Integrated Delivery Systems
Accountable Care Organizations
Community Hospitals
Children’s Hospitals
Founded 2008
Employees 200
HQ Salt Lake City, UT
PatientsHospitals & Clinics
35M1900
Academic Medical Centers
More corporate information is available on our website.
3
Overview
Topics covered today:
Data Modeling Review Vocabulary,
Terminology, Standards
Data Warehouse
EnvironmentCurrent
Research
Ships will sail around the world, but the Flat Earth Society will flourish...Warren Buffet
4
What is a Data Model?
A conceptual representation of:
- data structures (tables) within a database
- context and relationships of data objects
A way to:
- identify and organize the required data
- visualize logical and physical structure
- communicate business requirements
5
Data Model Approaches
“Early
Binding”
“Late
Binding”
Corporate InformationPopularized by Inmon and Imhoff
I2B2Popularized by Academic Medicine
Star SchemaPopularized by Ralph Kimball
Data BusPopularized by Dale Sanders
File Structure AssociationPopularized by IBM mainframes in 1960s
Reappearing in Hadoop & NoSQL
C#
“assignment of values to variables”
EDW
“mapped to standard vocabularies and bound
to business rules”
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BOM Conceptual Data Model
Product Supplier
Order Customer
Bill of Materials Typical Analyses
• Counts
• Simple aggregations
• By various dimensions
7
Star Schema Conceptual Model
Star Schema Typical Analyses
• Transaction counts
• Simple aggregations
• By various dimensions
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Healthcare Data is Unique
Some characteristics of healthcare data that make it unique:
- The data is complex
- Needed data is often in multiple places
- Data is both structured and unstructured
- Inconsistent and variable definitions abound
- Changing Regulatory Requirements
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Data Relations
The key is to relate data, not model data…
High Value AttributesAbout 20 data points account for
90% of healthcare analytic use cases
Charge Code
CPT Code
Date & Time
DRG code
Drug code
Employee ID
Employer ID
Encounter ID
Gender
Diagnosis Code
Procedure Code
Department ID
Facility ID
Lab code
Patient type
Patient / member ID
Payer / carrier ID
Postal code
Provider ID
Core Data Elements
Highest value area
for standardizing
vocabulary!
It takes time and effort to bind data that might never be needed for any analytic use case...
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When is it Best to Bind Data?
Data Analysis
Source Data Content
Source System Analytics
Customized Data Marts
Visualization& Reporting
Others
HR
Supplies
Financial
Clinical
Academic
State Academic
State
Others
HR
Supplies
Financial
Clinical
QlikView, Tableau
Microsoft Access
Web Applications
Excel
SAS, SPSS
et al.
Inte
rnal
Exte
rna
l
1 2 3 4 5 6
Research Registries
Operational Events
Clinical Events
Compliance Measures
Materials Management
Disease Registries
Business Rule and Vocabulary Binding Points
Low volatility = Early binding High volatility = Late binding
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Provider
Patient
Bad Debt
Diagnosis Procedure
Facility
EncounterCost
Charge
Employee
Survey
House
Keeping
Catha Lab
Provider
Census
Time
Keeping
Enforced Referential Integrity
ENTERPRISE DATA MODEL
EDW
Less Transformation More Transformation
Enterprise Data Model
FINANCIAL(e.g. Peoplesoft)
ADMINISTRATIVE(e.g. Time Tracking)
EMR SOURCE (e.g. Cerner)
DEPARTMENTAL(e.g. Apollo)
Pt. SATISFACTION(e.g. Press Ganey)
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ENTERPRISE DATA MODEL
Enterprise Shopping Analogy
Produce
Meat
Dairy
__ Apples__ Pears__ Tomatoes__ Carrots
__ Beef__ Ham__ Chicken__ Pork
__ Milk __ Eggs__ Cheese__ Cream
__ Pasta__ Flour__ Sugar__ Soup
Enterprise Store List
Dry Goods
__ Rice__ Beans__ Baking soda
__ Sausage__ Bacon
Your Shopping List
apples
beans
flour
milk
noodles
brown sugar
tomato soup
hotdogs
bananas
lettuce
yogurt
Bank deposit
Get tires rotated
Pick up dry cleaning
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Oncology
DiabetesHeart
Failure
Regulatory
Pregnancy Asthma
Labor Productivity
Revenue Cycle
Census
Redundant Data
Extracts
Less Transformation More Transformation
Dimensional Data Model
FINANCIAL(e.g. Peoplesoft)
ADMINISTRATIVE(e.g. Time Tracking)
EMR SOURCE (e.g. Cerner)
DEPARTMENTAL(e.g. Apollo)
Pt. SATISFACTION(e.g. Press Ganey)
DIMENSIONAL DATA MODEL
Trip #1 to the Store
Dairy Dry Goods
__ 4 eggs __ 2 c shortening__ 1 c butter
__ 1 c sugar__ 2 c brown sugar__ 2 t baking soda__ 2 t vanilla extract__ 1 t salt__ 4 c flour__ 4 c chocolate chips
Dimensional Model - Cookies
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Dimensional Shopping Analogy
DIMENSIONAL DATA MODEL
Dairy Dry Goods
__ ½ c butter
__ ½ c milk
__ 2 eggs
__ 1 c sugar
__ 2 c flour
__ 2 t vanilla extract__ 1 t baking powder
Dimensional Model - Cake
Trip #2 to the Store
How many recipes do we need to
make???
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Dimensional Shopping Analogy
Dimensional Models in Healthcare can be similar to shopping like this …
Play video…
Metadata: Mapping, Security and Auditing
Common, Linkable
Vocabulary
Financial
Source Marts
Administrative
Source Marts
Departmental
Source Marts
Patient
Source Marts
EMR
Source Marts
HR
Source Marts
Diabetes
Sepsis
Readmission
FINANCIAL(e.g. Peoplesoft)
ADMINISTRATIVE(e.g. Time Tracking)
EMR SOURCE (e.g. Cerner)
DEPARTMENTAL(e.g. Apollo)
Pt. SATISFACTION(e.g. Press Ganey)
Human Resources(e.g. PeopleSoft)
Less Transformation More Transformation
Adaptive Data Model
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Adaptive Shopping Analogy
ADAPTIVE DATA MODEL
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Shopping List Revisited
Additional Items Get eggs
Bank deposit
Get tires rotated
Pick up dry cleaning
Once you’re back home, can you make these recipes?
Cake:1 cup white sugar1 ½ cups all-purpose flour2 teaspoons vanilla extract1 ¾ teaspoon baking powder½ cup of butter½ cup milk2 eggs
Cookies:1 cup shortening4 large eggs1 cup sugar2 cups brown sugar2 t vanilla 1 t salt2 t baking soda4 cups all-purpose flour2 cups chocolate chips
Baking Powder
•Vanilla extract
•Chocolate Chips
•Get oil change
•Buy a new couch
•Buy yarn and knitting supplies
And Even MoreInitial List•Apples
•Tomato Soup
•Flour
•Milk
•Turkey
•Lettuce
Sugar
Beans
Hotdogs
Banana
Noodles
Yogurt
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When Best to Bind Data?
One differentiator between these models is how early/late they require you to bind data.
- Early-binding models work well in industries such as retail or banking where business rules/vocabularies are stable.
- Business rules and vocabulary standards in healthcare are more complex and change constantly.
- Binding data too tightly, too early may volatile rules and standards, putting the long-term viability of EDW at risk.
- Flexibility in terms of when to bind data can determine the success of your EDW.
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What does Google do?
Google’s most recent data warehouse (Mesa) also leverages “late-binding”
- “When designing Mesa's predecessor system, we made an assumption that schema changes would be very rare. This assumption turned out to be wrong.”
- “Due to the constantly evolving nature of live enterprise, products, services, and applications are in constant flux.”
- “New applications come on board either organically or due to acquisitions of other companies that need to be supported.”
- “In summary, the design should be as general as possible with minimal assumptions about current and future applications."
http://research.google.com/pubs/pub42851.html
Metadata: Mapping, Security and Auditing
Common, Linkable
Vocabulary
Adaptive Data Model
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Data Source Mapping Library
Library of meta-map files
• Collection of nearly 100 different vendor data types and sources.
• First time can be quite labor intensive, but…
• Subsequent work allows for very rapid deployment and integration of that data feed.
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Common Linkable Vocabulary
Mapped code sets to Common Clinical Hierarchy
Care Process Familyi.e. Heart Failure
92
Care Processi.e. CHF
455
CodesAPRDRG, ICD PX, ICD DX, CPT
Tens ofthousands
12Clinical Programi.e. Cardiovascular
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Heart
Rhythm
Disorders
Vascular
Disorders
Ischemic
Heart
Disease
Heart
Failure
CARDIOVASCULAR
Care Process Families
Clinical Program
CPT-4 Code Groupings
ICD9 ProcedureCode Groupings
ICD9 Diagnosis Code Groupings
Care Processes
Valve
DisordersCHF
Cardio-
myopathy
Pulmonary
Heart
Disease
ICD9 Volumes I-II
17,674
Diagnosis Codes
ICD9 Volume III
3,903
Procedure Codes
2013 CPT®
Code Set
9706 Codes
Common Linkable Vocabulary
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Terminology Mapping
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ICD-based Patient Registries
Big $’s
Small $’s
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Workflow (Dept) RegistriesClinical Support Service Division Department
Diagnostic Clinical Support Service Anatomic Pathology General Anatomic Pathology
Diagnostic Clinical Support Service Anatomic Pathology Histology
Diagnostic Clinical Support Service Anatomic Pathology Electron Microscopy
Diagnostic Clinical Support Service Anatomic Pathology Cytology/cytopathology
Diagnostic Clinical Support Service Anatomic Pathology Molecular Biology
Diagnostic Clinical Support Service Anatomic Pathology Other Anatomic Pathology
Diagnostic Clinical Support Service Clinical Pathology Core Laboratory
Diagnostic Clinical Support Service Clinical Pathology Hematology
Diagnostic Clinical Support Service Clinical Pathology Chemistry
Diagnostic Clinical Support Service Clinical Pathology Bacteriology
Diagnostic Clinical Support Service Diagnostic Imaging General diagnostic radiology
Diagnostic Clinical Support Service Diagnostic Imaging MRI
Diagnostic Clinical Support Service Diagnostic Imaging CT
Diagnostic Clinical Support Service Diagnostic Imaging Ultrasound
Diagnostic Clinical Support Service Diagnostic Imaging Nuclear
Diagnostic Clinical Support Service Diagnostic Imaging Mammography
Diagnostic Clinical Support Service Diagnostic Centers Medical diagnostic center
Diagnostic Clinical Support Service Diagnostic Centers Audiology and Speech Pathology
Diagnostic Clinical Support Service Diagnostic Centers Cardiac diagnostics
Diagnostic Clinical Support Service Diagnostic Centers Pulmonary function lab
Diagnostic Clinical Support Service Diagnostic Centers Perinatal
Diagnostic Clinical Support Service Diagnostic Centers Vascular Lab
Therapeutic Clinical Support Service Substances Pharmacy
Therapeutic Clinical Support Service Substances Blood Bank
Therapeutic Clinical Support Service Substances Parenteral Nutrition
Therapeutic Clinical Support Service Substances Infusion Therapy
Therapeutic Clinical Support Service Oxygenation Services Respiratory Therapy
Therapeutic Clinical Support Service Oxygenation Services Hyperbaric Oxygen Therapy
Therapeutic Clinical Support Service Oxygenation Services ECMO
Therapeutic Clinical Support Service Rehabilitation Services Physical Therapy
Therapeutic Clinical Support Service Rehabilitation Services Occupational Therapy
Therapeutic Clinical Support Service Rehabilitation Services Speech Therapy
Therapeutic Clinical Support Service Rehabilitation Services Cardiopulmonary Rehabilitation
Therapeutic Clinical Support Service Rehabilitation Services Other Rehab Services
Therapeutic Clinical Support Service Dialysis Acute Dialysis
Therapeutic Clinical Support Service Dialysis Chronic Dialysis
Clinical Support Service Division Department
Acute Medical Clinical Support Service Emergency Care Emergency Care Units
Acute Medical Clinical Support Service Emergency Care Air Transport
Acute Medical Clinical Support Service Emergency Care Ground Transport - Ambulance
Acute Medical Clinical Support Service Critical Care Med-Surg ICU
Acute Medical Clinical Support Service Critical Care Trauma ICU
Acute Medical Clinical Support Service Critical Care Burn Unit ICU
Acute Medical Clinical Support Service Critical Care CCU
Acute Medical Clinical Support Service Critical Care NICU
Acute Medical Clinical Support Service Critical Care PICU
Acute Medical Clinical Support Service General Med-Surg Acute Care Pediatric Acute
Acute Medical Clinical Support Service General Med-Surg Acute Care Psychiatric Acute
Acute Medical Clinical Support Service General Med-Surg Acute Care Adult Acute
Acute Medical Clinical Support Service General Med-Surg Subacute Care Observation Care Unit
Acute Medical Clinical Support Service General Med-Surg Subacute Care Sleep Lab
Acute Medical Clinical Support Service General Med-Surg Subacute Care Newborn Nursery
Invasive Clinical Support Service Surgical Services Inpatient Surgery and Recovery
Invasive Clinical Support Service Surgical Services Same Day Surgery and Recovery
Invasive Clinical Support Service Surgical Services Outpatient Surgery (ASC) and Recovery
Invasive Clinical Support Service Interventional Medical Services Cath Lab
Invasive Clinical Support Service Interventional Medical Services Interventional Radiology
Invasive Clinical Support Service Interventional Medical Services GI lab
Invasive Clinical Support Service Interventional Medical Services Labor and Delivery
Invasive Clinical Support Service Interventional Medical Services Radiation Oncology
Invasive Clinical Support Service Interventional Medical Services Bronchoscopy
Invasive Clinical Support Service Anesthesia Services Surgical anesthesia
Invasive Clinical Support Service Anesthesia Services Obstetrical anesthesia
Invasive Clinical Support Service Clinical Equipment Supplies and Services Medical Supplies Sold to Patients
Invasive Clinical Support Service Clinical Equipment Supplies and Services DME
Invasive Clinical Support Service Clinical Equipment Supplies and Services Central Services
Notes:
Gen Med-Surg Acute Care includes progressive care units (PCUs) such as intermediate care units, direct observation units, step-
down units, telemetry units or transitional care units (Source: American Association of Critical Care Nurses fact sheet)
Clinical Support Service Division Department
Clinic Care Clinical Support Service Primary Care Clinics Family Medicine
Clinic Care Clinical Support Service Primary Care Clinics Pediatrics
Clinic Care Clinical Support Service Primary Care Clinics Internal Medicine
Clinic Care Clinical Support Service Primary Care Clinics OB-Gyn
Clinic Care Clinical Support Service Medical Subspecialty/Chronic Disease Clinics Allergy
Clinic Care Clinical Support Service Medical Subspecialty/Chronic Disease Clinics Dermatology
Clinic Care Clinical Support Service Medical Subspecialty/Chronic Disease Clinics Pain Centers
Clinic Care Clinical Support Service Medical Subspecialty/Chronic Disease Clinics Behavioral health
Clinic Care Clinical Support Service Medical Subspecialty/Chronic Disease Clinics Peds Neurology
Clinic Care Clinical Support Service Medical Subspecialty/Chronic Disease Clinics Rheumatology
Clinic Care Clinical Support Service Medical Subspecialty/Chronic Disease Clinics Oncology
Clinic Care Clinical Support Service Medical Subspecialty/Chronic Disease Clinics Non-invasive cardiology
Clinic Care Clinical Support Service Medical Subspecialty/Chronic Disease Clinics Anticoagulation
Clinic Care Clinical Support Service Medical Subspecialty/Chronic Disease Clinics Eating disorders
Clinic Care Clinical Support Service Medical Subspecialty/Chronic Disease Clinics Endocrinology
Clinic Care Clinical Support Service Medical Subspecialty/Chronic Disease Clinics Gastroenterology
Clinic Care Clinical Support Service Medical Subspecialty/Chronic Disease Clinics Nephrology
Clinic Care Clinical Support Service Medical Subspecialty/Chronic Disease Clinics MFM
Clinic Care Clinical Support Service Medical Subspecialty/Chronic Disease Clinics Pulmonary
Clinic Care Clinical Support Service Medical Subspecialty/Chronic Disease Clinics Other Clinic
Clinic Care Clinical Support Service Surgical Subspecialty Clinics Ophthalmology
Clinic Care Clinical Support Service Surgical Subspecialty Clinics Bariatrics
Clinic Care Clinical Support Service Surgical Subspecialty Clinics Vein Center
Clinic Care Clinical Support Service Surgical Subspecialty Clinics ENT
Clinic Care Clinical Support Service Surgical Subspecialty Clinics Burn
Clinic Care Clinical Support Service Surgical Subspecialty Clinics Orthopedics
Clinic Care Clinical Support Service Surgical Subspecialty Clinics Plastic Surgery
Clinic Care Clinical Support Service Surgical Subspecialty Clinics Urology
28
Subject Area Mart (SAM)
Subject Area Mart (SAM) Designer:
Important component of the Health Catalyst infrastructure.
A wizard-type application for rapid extraction of specific data from various sources for a given use case (e.g., a diabetes SAM)
Includes templates for guided development of content knowledge assets, such as:
• Order sets
• Substance selection
• Indications for intervention
• Clinical supply chain management
• Bedside and invasive care clinical operations protocols
• Triage criteria
• Diagnostic algorithms
• Indications for referral
• Treatment and monitoring algorithms
• Health maintenance and preventive guidelines
29 29
Current Research Topics
‘Continuum of Care’ Network Analysis
Geo-Spatial Analytics (GIS)
Machine Learning (prediction)
Genomics (NGS)
Natural Language (NLP)
Imaging
Continuum of care network analysis
Continuum of Care
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75% of U.S. total
Clinic Care Outpatient Inpatient SNF LTCH/IRF Home Health Hospice
31
(Service Area Definition)
Population Density (boundaries based on
2010 Census data at zip code level)
Dartmouth AtlasHealth Referral Regions (boundaries based on
cardiac and neuro surgery)
Central Place Theory (boundaries based on distribution of medical
specialties)
Dartmouth HRR Central Place Theory Population density
Geo-Spatial Analytics
32 32
Geo-Spatial Analytics
(Geography and Coverage of Health Network)
33 33
(Dartmouth HRR overlay)
Geo-Spatial Analytics
34 34
(Central Place Theory overlay)
Geo-Spatial Analytics
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Facility level groupings
Level 1 – primary care clinics, pharmacy
Level 2 – urgent care facilities
Level 3 – facilities with 24-hour service (e.g., emergency)
Drive time isochrones per facility grouping
Level 1 – 15 minutes
Level 2 – 30 minutes
Level 3 – 60 minutes
35
Geo-Spatial Analytics
(Facility Levels/Drive Times)
36 36
(Drive Time Isochrones)
Geo-Spatial Analytics
Machine Learning
(Feature Selection, Classification, Disease Cohorts)
Machine Learning
Microsoft Azure ML Group
Experian Consumer Data Group
FAA/NASA Air Traffic Control
Prediction work in:
Hospital Readmissions
Chronic Disease Management
Population/Employer Group Risk
Patient Flight Path/Profilier
37
Hospital Readmissions
38
Patient Name MRN Admit Type Gender AgeReadmissionsHF
Risk ScorePhone
Perdue, Shawnna Kryslyn MRN209C50C0F49 Urgent Care Female 62 97 (208) 577-7061
Orpen, Iolanda Kashius MRNCCBEC693622 Urgent Care Male 73 94 (208) 458-4255
Hurrion, Marguetta Quiniyah MRNDEA639A42A4 Urgent Care Female 40 91 (208) 552-6755
Oddy, Didra Domnique MRN6DF69BFC148 Urgent Care Male 79 89 (208) 663-0623
Pimblett, Kerrin Makel MRNBCD6EDF89C8 Urgent Care Female 39 84 (208) 559-4857
Ruffli, Jerrie Ailie MRNCC8263FC964 Urgent Care Female 86 81 (208) 604-3937
Yuryev, Arpi Cheila MRN171F5D0C377 Urgent Care Male 72 80 (208) 604-8735
Vanner, Arezo Kobe MRN99EC7398F76 Urgent Care Male 67 79 (208) 279-7121
Kirton, Gaudencio Chatherine MRN23F833761BE Urgent Care Female 62 77 (208) 649-5242
Scrivin, Elcid Darelle MRNC2BD5EE1B32 Urgent Care Female 60 74 (208) 375-2942
Yakunikov, Pauljoseph Sieara MRNF4BCC2F10AE Urgent Care Male 72 74 (208) 553-1328
Caddens, Helina Tanyanika MRND156E6895BC Urgent Care Male 37 73 (208) 713-7560
Jedryka, Ronelle Aleni MRN80B04E0C287 Urgent Care Female 41 70 (208) 668-2676
Specific Heart Failure Cohort Regression based, pre-calculated Current PPV (precision) is 92% Integrated in current workflow
39
Disease density analysisUse ICD patient registry groupings to analyze third-party payer populations to determine the “density” of disease by Clinical Program, Care Process Family and Care Process.
Risk (severity level) analysisApply a risk stratification framework (e.g., Charlson-Deyo comorbidity, CMS-HCC) to disease registry populations highlighted in the claims-based disease density analysis.
Historical CohortsCompare a statistical sample of historical data from delivery system data sets (inpatient facility, outpatient facility, clinic care) to the claims-based disease registry data (drill down) to forecast cost of care.
Population Risk
Other Research Topics
Natural Language (NLP)Parsing and sentiment analysis for large blocks of industry blogs (HIStalk) and benchmarks (KLAS).
StandardsCapture evidence based/best practice clinical protocols in current electronic formatting such as ONC’s S&I Framework.
GenomicsIndustry leader collaboration for reporting clinical NGS results out of single warehouse environment.
ImagingIndustry leader collaboration for live image annotation, meta-layer indexing and exchange service access in warehouse environment.
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Final Thoughts
41
The EDW is the “ROI of the EMR” (the EMR is a MEANS, not an END)
Appropriate timing of binding (early binding, late binding, adaptive binding) will increase success of EDW efforts
The thickness of a requirements document is inversely related to the success of a given EDW project
It’s the “intervention that matters”
42
Thank you!
Questions?
Contact information:David K. Crockett, PhD - HealthCatalystSenior Director, Research and Predictive Analytics
www.linkedin.com/in/davidkcrockett/
