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Population Stratification Made Easy,
Quick, and Transparent for Anyone
Eric Just
SVP and General Manager, Application Suite Business
(Former Software Developer, Architect, Analyst, Analytics Team Manager)
Half of the $3.5 trillion spent
on healthcare annually in the
U.S. can be attributed to five
percent of the population,
who are often ideal
candidates for risk-stratified
care management.
Introduction:
Why Stratification for
Population Health is so
Challenging
© 2019
Health
Catalyst
Estimates for the time analysts spend doing non-analytics tasks like:
• Preparing data (SQL)
• Repeating tasks
• Inefficient communications
• Simple tasks
4
© 2019
Health
Catalyst
• Integrating diagnoses from multiple data
sources.
• Identifying which encounters were inpatient
encounters.
• Calculating LOS.
• Identifying heart failure diagnosis codes.
Reinventing the wheel wastes analysts
time (AND compute resources)!
Repetitive Tasks
5
Patents filed for new wheel designs
throughout the 20th century.
© 2019
Health
Catalyst
Inefficient Communication Cycles
6
Analyst
Subject Matter
Expert (SME)
Population Health Business User
© 2019
Health
Catalyst
7
© 2019
Health
Catalyst
Percentage of analysts/data scientists who agree with the following
statement:
Analytic results in my organization are consistent: if you ask
different analysts to do the same analysis, you will get the same
results.
8
© 2019
Health
Catalyst
Polychronic Patients Who Were Recently
Discharged Who are High Risk for Readmission
9
Claims data, clinical data (integrated)
Population definitions for chronic conditions
Population definitions for polychronic (which ones, how many?)
Recently discharged (from where, how long ago?)
Inputs to Machine Learning model
Machine Learning model
© 2019
Health
Catalyst
Polychronic Patients Who Were Recently
Discharged Who are High Risk for Readmission
10
Claims data, clinical data (integrated)
Population definitions for chronic conditions
Population definitions for polychronic (which ones, how many?)
Recently discharged (from where, how long ago?)
Inputs to Machine Learning model
Machine Learning model
© 2019
Health
Catalyst
1. Analysts are valuable, specialized resources who are often used
inefficiently because re-use is limited in most analytic environments.
2. Analysts are typically able to provide little visibility to Subject Matter
Experts (or other analysts). The lack of visibility creates variation and
more inefficiency.
3. Stratification use cases often rely on complex definitions. With no
agreement on the building blocks, little visibility (black boxes),
project timelines become untenably long.
Why Stratification for Population Health
is So Challenging
11
AGENDA
© 2019
Health
Catalyst
1. Analysts are valuable, specialized resources who are often used
inefficiently because re-use is limited in most analytic environments.
2. Analysts are typically able to provide little visibility to Subject Matter
Experts (or other analysts). The lack of visibility creates variation and
more inefficiency.
3. Stratification use cases often rely on complex definitions. With no
agreement on the building blocks, little visibility (black boxes),
project timelines become untenably long.
Why Stratification for Population Health
is So Challenging
12
A Platform that Supports
Reusable Content
© 2019
Health
Catalyst
The Health Catalyst DOS™ Mart Suite
The next generation of data marts delivering content into the Data Operating System
14
Reusable, curated content leveraging
decades of experience.
Analysts no longer need to reinvent the wheel.
Results are more consistent.
Analysts can focus on creating value-add
business analytics versus complex SQL across
multiple data sources.
Patents filed for new wheel designs
throughout the 20th century.
Terminology DOS Mart
Person DOS Mart
Clinical DOS Mart
Claims DOS Mart
Cost DOS Mart
Surgery DOS Mart
Patient Flow DOS Mart
Future (Roadmap) DOS Marts
Future (Roadmap) DOS Marts
Future (Roadmap) DOS Marts
© 2019
Health
Catalyst
Master Reference & Terminology Data Sources
15
1. AHRQ Clinical Classification Software (CCS)
2. Charlson Deyo and Elixhauser Comorbidity
3. Clinical Improvement Grouper (Care Process Hierarchy)
4. CMS Hierarchical Condition Category
5. CMS Place Of Service
6. CPT®
7. HCPCS
8. ICD
9. LOINC®
10.MS-DRG
11.National Drug Codes (NDC)
12.NPI Registry
13.Reference maps
14.Rx Norm
15.TriCare DRG
© 2019
Health
Catalyst
Value Sets
16
6,175 Distinct Value Sets
1.Value Set Authority Center
• CMS eCQM Value Sets
• HL7 C-CDA Value Sets
2.Chronic Conditions Warehouse
Value Sets (CMS CCW)
3.Bundled Payments for Care
Improvement (BPCI) Value Sets
© 2019
Health
Catalyst
Value Set Authority Center
17
© 2019
Health
Catalyst
Value Set Governance - Atlas
18
© 2019
Health
Catalyst
1. Analysts are valuable, specialized resources who are often used
inefficiently because re-use is limited in most analytic environments.
2. Analysts are typically able to provide little visibility to Subject Matter
Experts (or other analysts). The lack of visibility creates variation and
more inefficiency.
3. Stratification use cases often rely on complex definitions. With no
agreement on the building blocks, little visibility (black boxes),
project timelines become untenably long.
Why Stratification for Population Health
is So Challenging
19
Visibility and Authoring Tools
20
© 2019
Health
Catalyst
Population Builder is an easy-to-use visual analytics tool that:
• Enables analysts to rapidly develop, analyze, and visualize populations
up to 90% faster.
• Brings reusable content
and authoring to non-technical
users.
• Allows centrally defined
populations to be
published and governed.
Health Catalyst Population Builder
The first Health Catalyst analyst tool that surfaces and demonstrates the benefits
of the DOS Mart Suite
21
© 2019
Health
Catalyst
Population Builder
Improve SME and analyst collaboration
Enable the SME and the analyst to ‘co-pilot’.
Population Builder’s easy-to-use interface gives non-technical users unprecedented
access to DOS content authoring.
22
© 2019
Health
Catalyst
1. Analysts are valuable, specialized resources who are often used
inefficiently because re-use is limited in most analytic environments.
2. Analysts are typically able to provide little visibility to Subject Matter
Experts (or other analysts). The lack of visibility creates variation and
more inefficiency.
3. Stratification use cases often rely on complex definitions. With no
agreement on the building blocks, little visibility (black boxes),
project timelines become untenably long.
Why Stratification for Population Health
is So Challenging
23
© 2019
Health
Catalyst
24
Claims data, clinical data (integrated)
Population definitions for chronic conditions
Population definitions for polychronic (which ones, how many?)
Recently discharged (from where, how long ago?)
Inputs to Machine Learning model
Machine Learning model
Polychronic Patients Who Were Recently
Discharged Who are High Risk for Readmission
Pre-defined Content
25
© 2019
Health
Catalyst
What is Population Builder: Stratification Module?
Pre-defined content that provides up to 100% efficiency gains in stratifying patients for
population health
Chronic condition library
Asthma
Alzheimer’s Disease and Related Dementia
Arthritis (Osteoarthritis and Rheumatoid)
Atrial Fibrillation
Autism Spectrum Disorders
Cancer (Breast, Colorectal, Lung, and Prostate)
Chronic Kidney Disease
Chronic Obstructive Pulmonary Disease (COPD)
Coronary Artery Disease (CAD)
Dementia; Cognitive Decline
Depression
Diabetes
Hepatitis (chronic viral B&C)
Heart Failure (CHF)
HIV/AIDS
Hyperlipidemia
Hypertension
Ischemic Heart Disease
Osteoporosis
Schizophrenia and Other Psychotic Disorders
Stroke
Comorbidity population: for patients with 2 or more of these
chronic conditions
Standard risk models
LACE
Charlson Deyo
Elixhauser
Transitions of Care
Currently admitted as inpatient or had at least one obs
or inpatient discharge event in the last 72 hours
High ED Utilization
HCUP definition
ML-based Predicted Readmission
Model based on 23 input variables from a variety of
clinical and claims data
26
© 2019
Health
Catalyst
What is Population Builder: Stratification Module?
Pre-defined content that provides up to 100% efficiency gains in stratifying patients for
population health
Chronic condition library
Asthma
Alzheimer’s Disease and Related Dementia
Arthritis (Osteoarthritis and Rheumatoid)
Atrial Fibrillation
Autism Spectrum Disorders
Cancer (Breast, Colorectal, Lung, and Prostate)
Chronic Kidney Disease
Chronic Obstructive Pulmonary Disease (COPD)
Coronary Artery Disease (CAD)
Dementia; Cognitive Decline
Depression
Diabetes
Hepatitis (chronic viral B&C)
Heart Failure (CHF)
HIV/AIDS
Hyperlipidemia
Hypertension
Ischemic Heart Disease
Osteoporosis
Schizophrenia and Other Psychotic Disorders
Stroke
Comorbidity population: for patients with 2 or more of these
chronic conditions
Standard risk models
LACE
Charlson Deyo
Elixhauser
Transitions of Care
Currently admitted as inpatient or had at least one obs
or inpatient discharge event in the last 72 hours
High ED Utilization
HCUP definition
ML-based Predicted Readmission
Model based on 23 input variables from a variety of
clinical and claims dataa
Claims data, clinical data (integrated)
27
Conclusion
28
© 2019
Health
Catalyst
1. Analysts are valuable, specialized resources who are often used
inefficiently because re-use is limited in most analytic environments.
A platform that supports reusable content (DOS)
2. Analysts are typically able to provide little visibility to Subject Matter
Experts (or other analysts). The lack of visibility creates variation and
more inefficiency.
Visibility and authoring tools (Population Builder)
3. Stratification use cases often rely on complex definitions. With no
agreement on the building blocks, little visibility (black boxes),
project timelines become untenably long.
Pre-defined content (Population Builder: Stratification Module)
Why Stratification for Population Health
is So Challenging
29
© 2019
Health
Catalyst
30
© 2019
Health
Catalyst
Let us know after the webinar.
We are looking for pilot customers who want to use our Stratification
Module with to feed Epic’s Care Management tool. If you are interested
in that opportunity, please indicate in the poll question that follows the
Webinar.
If You are Interested…
31
© 2019
Health
Catalyst
Elizabeth Coudare for providing
the SQL example.
J
Thank You
32
Q&A
33

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Population Stratification Made Easy, Quick, and Transparent for Anyone

  • 1. Population Stratification Made Easy, Quick, and Transparent for Anyone Eric Just SVP and General Manager, Application Suite Business (Former Software Developer, Architect, Analyst, Analytics Team Manager)
  • 2. Half of the $3.5 trillion spent on healthcare annually in the U.S. can be attributed to five percent of the population, who are often ideal candidates for risk-stratified care management.
  • 4. © 2019 Health Catalyst Estimates for the time analysts spend doing non-analytics tasks like: • Preparing data (SQL) • Repeating tasks • Inefficient communications • Simple tasks 4
  • 5. © 2019 Health Catalyst • Integrating diagnoses from multiple data sources. • Identifying which encounters were inpatient encounters. • Calculating LOS. • Identifying heart failure diagnosis codes. Reinventing the wheel wastes analysts time (AND compute resources)! Repetitive Tasks 5 Patents filed for new wheel designs throughout the 20th century.
  • 6. © 2019 Health Catalyst Inefficient Communication Cycles 6 Analyst Subject Matter Expert (SME) Population Health Business User
  • 8. © 2019 Health Catalyst Percentage of analysts/data scientists who agree with the following statement: Analytic results in my organization are consistent: if you ask different analysts to do the same analysis, you will get the same results. 8
  • 9. © 2019 Health Catalyst Polychronic Patients Who Were Recently Discharged Who are High Risk for Readmission 9 Claims data, clinical data (integrated) Population definitions for chronic conditions Population definitions for polychronic (which ones, how many?) Recently discharged (from where, how long ago?) Inputs to Machine Learning model Machine Learning model
  • 10. © 2019 Health Catalyst Polychronic Patients Who Were Recently Discharged Who are High Risk for Readmission 10 Claims data, clinical data (integrated) Population definitions for chronic conditions Population definitions for polychronic (which ones, how many?) Recently discharged (from where, how long ago?) Inputs to Machine Learning model Machine Learning model
  • 11. © 2019 Health Catalyst 1. Analysts are valuable, specialized resources who are often used inefficiently because re-use is limited in most analytic environments. 2. Analysts are typically able to provide little visibility to Subject Matter Experts (or other analysts). The lack of visibility creates variation and more inefficiency. 3. Stratification use cases often rely on complex definitions. With no agreement on the building blocks, little visibility (black boxes), project timelines become untenably long. Why Stratification for Population Health is So Challenging 11 AGENDA
  • 12. © 2019 Health Catalyst 1. Analysts are valuable, specialized resources who are often used inefficiently because re-use is limited in most analytic environments. 2. Analysts are typically able to provide little visibility to Subject Matter Experts (or other analysts). The lack of visibility creates variation and more inefficiency. 3. Stratification use cases often rely on complex definitions. With no agreement on the building blocks, little visibility (black boxes), project timelines become untenably long. Why Stratification for Population Health is So Challenging 12
  • 13. A Platform that Supports Reusable Content
  • 14. © 2019 Health Catalyst The Health Catalyst DOS™ Mart Suite The next generation of data marts delivering content into the Data Operating System 14 Reusable, curated content leveraging decades of experience. Analysts no longer need to reinvent the wheel. Results are more consistent. Analysts can focus on creating value-add business analytics versus complex SQL across multiple data sources. Patents filed for new wheel designs throughout the 20th century. Terminology DOS Mart Person DOS Mart Clinical DOS Mart Claims DOS Mart Cost DOS Mart Surgery DOS Mart Patient Flow DOS Mart Future (Roadmap) DOS Marts Future (Roadmap) DOS Marts Future (Roadmap) DOS Marts
  • 15. © 2019 Health Catalyst Master Reference & Terminology Data Sources 15 1. AHRQ Clinical Classification Software (CCS) 2. Charlson Deyo and Elixhauser Comorbidity 3. Clinical Improvement Grouper (Care Process Hierarchy) 4. CMS Hierarchical Condition Category 5. CMS Place Of Service 6. CPT® 7. HCPCS 8. ICD 9. LOINC® 10.MS-DRG 11.National Drug Codes (NDC) 12.NPI Registry 13.Reference maps 14.Rx Norm 15.TriCare DRG
  • 16. © 2019 Health Catalyst Value Sets 16 6,175 Distinct Value Sets 1.Value Set Authority Center • CMS eCQM Value Sets • HL7 C-CDA Value Sets 2.Chronic Conditions Warehouse Value Sets (CMS CCW) 3.Bundled Payments for Care Improvement (BPCI) Value Sets
  • 17. © 2019 Health Catalyst Value Set Authority Center 17
  • 18. © 2019 Health Catalyst Value Set Governance - Atlas 18
  • 19. © 2019 Health Catalyst 1. Analysts are valuable, specialized resources who are often used inefficiently because re-use is limited in most analytic environments. 2. Analysts are typically able to provide little visibility to Subject Matter Experts (or other analysts). The lack of visibility creates variation and more inefficiency. 3. Stratification use cases often rely on complex definitions. With no agreement on the building blocks, little visibility (black boxes), project timelines become untenably long. Why Stratification for Population Health is So Challenging 19
  • 21. © 2019 Health Catalyst Population Builder is an easy-to-use visual analytics tool that: • Enables analysts to rapidly develop, analyze, and visualize populations up to 90% faster. • Brings reusable content and authoring to non-technical users. • Allows centrally defined populations to be published and governed. Health Catalyst Population Builder The first Health Catalyst analyst tool that surfaces and demonstrates the benefits of the DOS Mart Suite 21
  • 22. © 2019 Health Catalyst Population Builder Improve SME and analyst collaboration Enable the SME and the analyst to ‘co-pilot’. Population Builder’s easy-to-use interface gives non-technical users unprecedented access to DOS content authoring. 22
  • 23. © 2019 Health Catalyst 1. Analysts are valuable, specialized resources who are often used inefficiently because re-use is limited in most analytic environments. 2. Analysts are typically able to provide little visibility to Subject Matter Experts (or other analysts). The lack of visibility creates variation and more inefficiency. 3. Stratification use cases often rely on complex definitions. With no agreement on the building blocks, little visibility (black boxes), project timelines become untenably long. Why Stratification for Population Health is So Challenging 23
  • 24. © 2019 Health Catalyst 24 Claims data, clinical data (integrated) Population definitions for chronic conditions Population definitions for polychronic (which ones, how many?) Recently discharged (from where, how long ago?) Inputs to Machine Learning model Machine Learning model Polychronic Patients Who Were Recently Discharged Who are High Risk for Readmission
  • 26. © 2019 Health Catalyst What is Population Builder: Stratification Module? Pre-defined content that provides up to 100% efficiency gains in stratifying patients for population health Chronic condition library Asthma Alzheimer’s Disease and Related Dementia Arthritis (Osteoarthritis and Rheumatoid) Atrial Fibrillation Autism Spectrum Disorders Cancer (Breast, Colorectal, Lung, and Prostate) Chronic Kidney Disease Chronic Obstructive Pulmonary Disease (COPD) Coronary Artery Disease (CAD) Dementia; Cognitive Decline Depression Diabetes Hepatitis (chronic viral B&C) Heart Failure (CHF) HIV/AIDS Hyperlipidemia Hypertension Ischemic Heart Disease Osteoporosis Schizophrenia and Other Psychotic Disorders Stroke Comorbidity population: for patients with 2 or more of these chronic conditions Standard risk models LACE Charlson Deyo Elixhauser Transitions of Care Currently admitted as inpatient or had at least one obs or inpatient discharge event in the last 72 hours High ED Utilization HCUP definition ML-based Predicted Readmission Model based on 23 input variables from a variety of clinical and claims data 26
  • 27. © 2019 Health Catalyst What is Population Builder: Stratification Module? Pre-defined content that provides up to 100% efficiency gains in stratifying patients for population health Chronic condition library Asthma Alzheimer’s Disease and Related Dementia Arthritis (Osteoarthritis and Rheumatoid) Atrial Fibrillation Autism Spectrum Disorders Cancer (Breast, Colorectal, Lung, and Prostate) Chronic Kidney Disease Chronic Obstructive Pulmonary Disease (COPD) Coronary Artery Disease (CAD) Dementia; Cognitive Decline Depression Diabetes Hepatitis (chronic viral B&C) Heart Failure (CHF) HIV/AIDS Hyperlipidemia Hypertension Ischemic Heart Disease Osteoporosis Schizophrenia and Other Psychotic Disorders Stroke Comorbidity population: for patients with 2 or more of these chronic conditions Standard risk models LACE Charlson Deyo Elixhauser Transitions of Care Currently admitted as inpatient or had at least one obs or inpatient discharge event in the last 72 hours High ED Utilization HCUP definition ML-based Predicted Readmission Model based on 23 input variables from a variety of clinical and claims dataa Claims data, clinical data (integrated) 27
  • 29. © 2019 Health Catalyst 1. Analysts are valuable, specialized resources who are often used inefficiently because re-use is limited in most analytic environments. A platform that supports reusable content (DOS) 2. Analysts are typically able to provide little visibility to Subject Matter Experts (or other analysts). The lack of visibility creates variation and more inefficiency. Visibility and authoring tools (Population Builder) 3. Stratification use cases often rely on complex definitions. With no agreement on the building blocks, little visibility (black boxes), project timelines become untenably long. Pre-defined content (Population Builder: Stratification Module) Why Stratification for Population Health is So Challenging 29
  • 31. © 2019 Health Catalyst Let us know after the webinar. We are looking for pilot customers who want to use our Stratification Module with to feed Epic’s Care Management tool. If you are interested in that opportunity, please indicate in the poll question that follows the Webinar. If You are Interested… 31
  • 32. © 2019 Health Catalyst Elizabeth Coudare for providing the SQL example. J Thank You 32