The Analytic System: Finding Patterns in the Data

© 2014 Health Catalyst
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www.healthcatalyst.comProprietary and ConfidentialFollow Us on Twitter #TimeforAnalytics
John L. Haughom, MD
June 2014
The Analytic System:
Finding Patterns in the Data

Healthcare: The Way It Should Be
Part One – Forces Driving Transformation
• Chapter One – Forces Defining and Shaping
the Current State of U.S. Healthcare
• Chapter Two – Present and Future
Challenges Facing U.S. Healthcare
Part Two – Laying the Foundation for
Improvement and Sustainable Change
• What will it take to successfully ride the
transformational wave?
Part Three – Looking into the Future
• What will it take to successfully ride the
transformational wave?
2
Available for FREE download at: http://www.healthcatalyst.com/ebooks/healthcare-transformation-healthcare-a-better-way/

Implementing an Effective System of
Production in Healthcare
Analytic
system
Content
system
Deployment
system
Scalable
and
sustainable
outcomes

Analytic System Components
4

Metadata: EDW Atlas security and auditing
Common, linkable
Vocabulary
Financial
Source Marts
Administrative
Source Marts
Departmental
Source Marts
Patient
Source Marts
EMR
Source Marts
HR
Source Mart
Diabetes
Sepsis
Readmissions
Less transformationMore transformation
FINANCIAL SOURCES
(e.g. EPSi, Peoplesoft,
Lawson)
ADMINISTRATIVE
SOURCES
(e.g. API Time Tracking)
EMR SOURCE
DEPARTMENTAL
SOURCES
(e.g. Apollo)
PATIENT SATISFACTION
SOURCES
(e.g. NRC Picker, Press
Ganey)
Human Resources
(e.g. PeopleSoft)
Late-Binding™ Data Warehouse

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Population Health Management
Clinical Integration hierarchy - care process families
Hyperlipidemia
Acute
Myocardial
Infarction
(AMI)
Percutaneous
Intervention
(PCI)
Coronary
Artery Bypass
Graft (CABG)
Cardiac
Rehab
Ischemic Heart Disease
care process family
Home OutpatientClinic Care Inpatient SNF Home Health Hospice
Coronary
Atherosclerosis

Population Health Management
Clinical Integration hierarchy - clinical programs
Vascular Disorders
care process family
Heart Rhythm Disorders
care process family
Heart Failure
care process family
Ischemic Heart Disease
care process family
Cardiovascular clinical program
Home OutpatientClinic Care Inpatient SNF Home Health Hospice

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Clinical Integration hierarchy
Clinical programs – ordering of care
Primary
Care
care
process
families
e.g.,
Diabetes
CV
care
process
families
e.g.,
Heart
Failure
W&C
care
process
families
e.g.,
Pregnancy
GI
care
process
families
e.g.,
Lower GI
Disorders
Resp-
iratory
care
process
families
e.g.,
Obstructive
Lung
Disorders
Neuro
Sciences
care
process
families
e.g.,
Spine
Disorders
Musculo-
skeletal
care
process
families
e.g.,
Joint
Replace-
ment
Surgery
care
process
families
e.g.,
Urologic
Disorders
General
Med
care
process
families
e.g.,
Infectious
Disease
Oncology
care
process
families
e.g.,
Breast
Cancer
Peds
Spec
care
process
families
e.g.,
Peds
CV Surg
Mental
Health
care
process
families
e.g.,
Depression

Linking the three systems
Clinical Integration hierarchy

Top 10 care process
families account for
over 40% of the
opportunity
Top 32 care process
families account for
80% of the opportunity
Care process families by resources consumed (high to low)
Percentoftotalresourcesconsumed
Inpatient per case KPA

Frequency distribution with control limits
0.5% 0.5%
99%
2.33 std. devs. 2.33 std. devs.
Numberoftimesobserved
(Number,rate,percentage,proportion)
Value observed
Defect Defect
Within
specifications
Upper control
limit
Lower control
limit
Centerline
Spread

Variation in a process
is due to
Random causes (common
causes)
Assignable causes (special
causes)
The Causes of Variation

Condition Acceptable INR range
DVT/Pulmonary Embolus 2.0-3.0
Atrial Fibrillation 2.0-3.0
Anterior Myocardial Infarction (AMI) 2.5-3.5
Valve Replacement 2.5-3.5
A Clinical Example
Frequency distributions and control limits are common in healthcare

Statistical process control chart
(How a process behaves over time)
ValuesObserved
Time
The further a point moves off the center line the higher the
probability it is not random variation and the greater the
probability you can identify an assignable cause.
Centerline
Clinical process XYZ
Lower control
limit
Assignable (special cause)
variation
Random (common cause)
variation
Upper control
limit
Title

Process improvement
Uses of control charts
Quality
Time
Unstable process
worsebetter
Control limits
Assignable
variation
suggesting an
unstable process
Stable process
Process
capability
Random variation
suggesting a stable
process
Improvement

Approach to improvement
# of
cases
# of
Cases
Option 1: Punish the outliers
Mean
Focus on
minimum
standard
metric
Excellent outcomesPoor outcomes Excellent outcomesPoor outcomes
1 box = 100 cases in a year
Current condition
• Significant volume
• Significant variation
Punish the outliers

Approach to improvement
Excellent outcomesPoor outcomes
# of
Cases
Current condition
• Significant volume
• Significant variation
Excellent outcomes
# of
Cases
Option 2: Identify best practice
“Narrow the curve and shift it to the right”
Mean
Focus on best
practice care
process
model
Poor outcomes
1 box = 100 cases in a year
Focus on better care

# of
Cases
# of
Cases
Excellent outcomes
# of
Cases
Poor outcomes
Excellent outcomes
# of
Cases
Poor outcomes
1
2
3
4
Variability
High
Low
Resource consumptionLow High
Improvement Approach - Prioritization
18

A Demonstration
Demonstrating the power of modern analytics…
…Finding Meaningful Patterns in your data
19

What Does Health Catalyst Do?
● Enterprise Data Warehouse
“single source of truth”
● Library of data acquisition
adapters
● Metadata repository
● Auditing and access control
● Supports a variety of
analytic applications
‒ Health Catalyst
‒ Client developed
20
Platform

● Reports & Dashboards
● Ad-hoc query
● Registries
● Quality measures
● Population health
● Data mining
● Clinical improvement
● Workflow analysis
● Modeling and predictive
analytics
21
Applications
Platform

Installation
● Configuration
● Data Architecture
Improvement
● Project Management
● Clinical Improvement
● “Lean” Process Improvement
22
Applications
Services
Platform

Application Families
23
Foundational
Applications
Discovery Applications Advanced Applications
Provide deep insights
into evidence-based
metrics that drive
improvement in
quality and cost
reduction through
managing populations,
workflows, and patient
injury prevention.
Encourage broad use
of the data warehouse
by presenting
dashboards, reports,
and basic registries
across clinical and
departmental areas.
Allow users to discover
patterns and trends
within the data that
inform prioritization,
inspire new
hypotheses, and
define populations
for management.

Demos
24
Discovery ApplicationsFoundational Applications Advanced Applications`
Population Suites
e.g., Ischemic Heart Disease
Workflow / Operational Suites
e.g., Acute Medical
Patient Injury Prevention Suites
e.g., Infection Prevention
Patient Injury Prevention Modules
e.g., CAUTI, CLABSI, SSI
Workflow/Operational Modules
e.g., ICU, MedSurg, Emergency
Population Modules
e.g., CABG, Stent, AMI
Labor Management Explorer
Rev Cycle Explorer
Patient Satisfaction Explorer
General Ledger Explorer
Readmission Explorer
Population Explorer
Patient Flow Explorer
Practice Management Explorer Suite
Financial Management Explorer
CAFE—Comparative Analytics Framework and Exchange—across Healthcare Systems and National Benchmarks
EDIT—Executive Dashboard Integration Tool (Key Performance Indicator editable collage from all app categories)
Key Process Analysis (KPA)
Cohort Builder
Comorbidity Analyzer
Payment Model Analyzer
Readmission Predictor
Patient Flight Plan Predictor
ACO Explorer Suite
Metric Correlation Analyzer
Regulatory Explorer
Attribution Modeler

Demo 1: Key Process Analysis (KPA). Identify areas of
greatest opportunity for quality improvement and savings
Demo 2: Population Explorer. Identify potential risk by
understanding relative size of disease populations and risk
profiles
Demo 3: Heart Failure. Achieving quality improvement and
cost reductions by directing targeted interventions to high-risk
patients
Demo 4: Community Care. Monitoring high-risk patients in
primary care to prevent expensive acute treatment
Demos: How Analytics Drive Improvement & Savings

Dr. J.
15 Cases
$15,000 Avg. Cost Per Case
Mean Cost per Case = $10,000
$5,000 x 15 cases =
$75,000 opportunity
Total Opportunity = $75,000Total Opportunity = $175,000
$4,000 x 25 cases =
$100,000 opportunity
Total Opportunity = $500,000Total Opportunity = $1,200,000
Cost Per Case, Vascular Procedures
KPA: Measuring Opportunity
Using provider variation to calculate the potential financial impact of
improving and standardizing care processes

Poll Questions
2. Does your organization effectively engage front line
clinicians in improvement projects where they
routinely analyze care processes to eliminate
inappropriate variation and improve processes over
time?
91 Respondents
a. 5 – Definitely – 19%
b. 4 – 22%
c. 3 – 26%
d. 2 – 25%
e. 1 – Not at all – 8%
27

Demo 1: Key Process Analysis (KPA). Identify areas of
greatest opportunity for quality improvement and savings
Demo 2: Population Explorer. Identify potential risk by
understanding relative size of disease populations and risk
profiles
Demo 3: Heart Failure. Achieving quality improvement and
cost reductions by directing targeted interventions to high-risk
patients
Demo 4: Community Care. Monitoring high-risk patients in
primary care to prevent expensive acute treatment
Demos: How Analytics Drive Shared Savings

In Summary…
• A good Analytic System that unlocks your data, automates its distribution
and makes it easy to see important patterns in the data is necessary to
support meaningful and sustainable improvement.
• The data model on which your EDW is based matters.
• A Clinical Integration Hierarchy can help you organize how you think
about and manage health care delivery.
• Differentiating random variation from assignable or “special cause”
variation is important in healthcare and in improvement.
• Good use of your data can help guide you in an effort to maximize
improvement and value for the investment.
29

Poll Question
3. Using our discussion of an Analytic System as a
guide, on a Scale of 1-5, how effective is your
organization’s analytical strategy and capability?
78 Respondents
a. 5 – Very Effective – 9%
b. 4 – 15%
c. 3 – 35%
d. 2 – 25%
e. 1 – Very Limited – 15%
30

Thank You
Upcoming Educational Opportunities
Late-Binding Data Warehousing: An Update on the Fastest Growing Trend in
Healthcare Analytics
Date: July 10th
Presenter: Dale Sanders, Senior Vice President, Health Catalyst
Register at http://healthcatalyst.com/
Healthcare Analytics Summit
Join top healthcare professionals for a high-powered analytics summit using analytics to drive
an engaging experience with renowned leaders who are on the cutting edge of healthcare
using data-driven methods to improve care and reduce costs.
Date: September 24th-25th
Location: Salt Lake City, Utah
Save the Date: http://www.healthcatalyst.com/news/healthcare-analytics-summit-2014
For Information Contact:
John.Haughom@healthcatalyst.com

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