Population Health Analytics: Improving Care One Patient at a Time

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www.healthcatalyst.comProprietary and Confidential
Improving Care One Patient at a Time
February 4, 2015
Population Health Analytics

The Buzzword:
Population Health Management
What does it really mean?
• Managing the health outcomes of a population of
patients with a similar condition?
• Going at risk with payers for the outcomes of a
population of patients (Fee-for-Value)?
• Using care management to improve outcomes for
high-risk, high-cost patients?
• Engaging patients and communities for better
health outcomes?

Common Thread: Outcomes
Provide the highest quality care
with an optimal care experience
for a population of patients
at the lowest appropriate cost
3
Quality Outcomes
Experience Outcomes
Cost Outcomes
The key population health management question:
How do we systematically improve outcomes for a
population of patients, one patient at a time?

3 Ingredients of Fire
4
Fire
Fuel

3 Ingredients of Fire
5
Fire
Fuel
What should we be doing?
How are we
doing?
How do we
transform?
Content System
Outcomes
Improvement
3 Ingredients of Outcomes Improvement

A Tautology…
6
"Every system is
perfectly designed to
get the results it
gets.”
- Dr. Paul Batalden
... so re-design your
system to get better
results.

How systematic are we at Outcomes Improvement?
7
Fire
Fuel

Content System
Outcomes
Improvement
3 Systems for Outcomes Improvement
8
How are we
doing?
How do we
transform?

Proprietary and Confidential9
Content System Overview
9
Map the Process
Care improvement map – Includes workflow & clinician's decision-flow across care continuum
Identify Common Problems - Potential Improvements
Specific AIM Statements for outcomes and process to measures for focused improvement
Scope the problem – Define Precise Patient Registries
Specific clinical inclusion and exclusion criteria for the sub-cohort of patients for the AIM
Adopt Standardization Aids
Checklists, order sets, and protocols to make it easy for clinicians to choose the best action
Produce Actionable Visualizations
Scorecards and dashboards that promote best practice behaviors and invite action

Infrastructure:Hosting/Hardware Analytics System Overview
How are we doing?
10
e.g. EPSi,
Peoplesoft,
Lawson
e.g. Lawson,
Peoplesoft,
Ultipro
Subject Area Mart Designer
Source Mart Designer
EMR
EMR Financial
Patient
Sat. HR Administrative Claims
Financial
Patient
Sat.
HR Administrative Claims
e.g. Epic, Cerner
NextGen
e.g. Press Ganey,
NRC Picker
e.g. API Time
Tracking
e.g. Medicare
Private Payers
Shared Frameworks & Tools for improvement
Comorbidity Analyzer, Registry Repository, Attribution Modeler, Common
Definition Repository, Hierarchies, CAFE, Atlas, IDEA, Eventalytics,
Geospatial, Risk & Severity Profiling, etc
Metadata Driven ETL Engine
Enterprise Data Warehouse Platform
Analyze and Interpret Data
• Show correlation and causation
• Integrate clinical, financial, and
patient experience data
• Predict outcomes and prescribe
actions
Shared Reoccurring Data Tasks
• Cohort Definitions
• Patient/Provider Attribution
• Severity/Comorbidity Analysis
• Calculation/Term Definition
• Comparative Repositories
Source Data Integration
• Automatically co-locate data from
different source transactional
systems (EMR, Claims, Financial,
Patient Satisfaction)
• Automatically connect data
together with key identifiers
(Patient, Location, Provider)
Infrastructure
• Security and Auditing capabilities
• Metadata Repository

Deployment System Overview
How do we transform?
11
Improvement Capacity Assessment
Evaluation of organizational capacity for change, current capabilities, and gaps
GovernanceData Governance/Data Stewardship and Advanced Organizational Governance & Prioritization
Improvement MethodologySystematic improvement incorporating LEAN / PDSA principles, AGILE software development, etc.
Accelerated Practices TrainingSystematic training of Adaptive Leadership, Quality Improvement/LEAN skills, and Technology
11

Content System
Outcomes
Improvement
12
How are we
doing?
How do we
transform?

Content System Overview
13
Map the Process
Care improvement map – Includes workflow & clinician's decision flow across care continuum
Identify Common Problems - Potential Improvements
Specific AIM Statements for outcomes and process to measures for focused improvement
Scope the problem – Define Precise Patient Registries
Specific clinical inclusion and exclusion criteria for the sub-cohort of patients for the AIM
Checklists, order sets, and protocols to make it easy for clinicians to choose the best action
Produce Actionable Visualizations
Scorecards and dashboards that promote best practice behaviors and invite action

Care Improvement Map
Sepsis and septic shock

Identify Potential Improvements
Process AIMs and Outcome Goals
Transformation Process
Starter Set
Content
Implement
Intervention
Measure &
Sustain
Review &
Select AIM Define Cohort
Iterate on
Metrics
Heart Failure: AIM #1
Starter Set
Content
Implement
Intervention
Measure &
Sustain
Review &
Select AIM Define Cohort
Iterate on
Metrics
Heart Failure: AIM #2
Process Improvement AIM:
Improve Follow-up Visit Scheduling
From 43% to 90% by October 31, 2015
Process Improvement AIM:
Improve Medication Reconciliation
From 58% to 80% by June 30, 2015
Heart Failure Outcome Improvement Goal:
Maintain and Improve Cardiac Function = Increase % of HF population with
adequate cardiac function from 64% to 80% by December 31, 2015
2-4 Process Improvement AIMS should produce a significant outcome improvement

Standard Patient Registry
Start with administrative codes

Supplemental
ICD9 (38,250)
Medications
(72,581)
Problem
List
(22,955)
ICD9
493.XX
(29,805)
Additional
Potential Rules
(101,389)
17
Total Count of Distinct Patients = 106,714
Precise patient registry
Move to clinically defined cohorts
Standard Registry
Precise Patient Registry

18
or Knowledge Assets
Admits/1000 members
IP days/1000 members
OP visits/1000 members
Procedures/1000 members
ED visits/1000 members
Readmissions/1000 members
Utilization
Who should
get the care?
Cost/case
Cost/procedure
OR minutes
L&D minutes
Other LOS
Order Sets
Workflow
Cost per case
Nursing hours by unit
OR minutes
L&D minutes
Cycle times
Cost per ancillary test
Environmental services
What care
should be
included?
How can care
be delivered
efficiently ?
Indications for Intervention
Diagnostic algorithms
Indications for Referral
Triage Criteria
Treatment and Monitoring
Algorithms
Health Maintenance and
Preventive Guidelines
Standardized Follow-up Checklist
Post-acute care order sets
IP (SNF, IRF)
Home health, Hospice
Clinical Ops Procedure Guidelines
Knowledge
Asset Type
Substance Selection Clinical Supply Chain
Management
Admission Order Sets Supplementary Order Sets
Pre-Procedure Order Sets
Post-procedure Order Sets
Bedside Care Practice Guidelines
Discharge Checklist
Patient Injury Prevention Protocol
Risk Assessment
Transfer Checklist
Question to
ask
Examples Possible Measures

Home
(Patient Portal)
* To Invasive
Care Processes
Clinic Care
Non-recurrent
Clinic Care
Chronic
Acute Medical
IP Med-Surg
Acute Medical
IP ICU
Invasive
Medical
Invasive
Surgical
Diagnostic Work-up
Bedside care
Triage to Treatment Venue
Substance
Preparation
Invasive*
Subspecialist
Chronic
Disease
Subspecialist
Screening & Preventive Symptoms
Procedure
Indications for Intervention
Diagnostic algorithms
Triage Criteria
Preventive, Diagnostic, Triage
and Clinic Care, Algorithms;
Referral & Intervention
Indications (scientific flow)
Utilization
Treatment and
Monitoring
Algorithms
Treatment and Monitoring Algorithms
Health Maintenance and
Preventive Guidelines
Substance Selection
Substance Selection
Clinical Supply Chain Management
Admission Order SetsAdmission Order Sets
Supplementary Order Sets
Pre-Procedure Order Sets
Post-
procedure
Order Sets
Order sets and indications for
selection of substances and
clinical supplies (scientific-flow
focus)
Order Sets
Post-procedure Care
Discharge
Bedside care practice guidelines, risk
assessment and patient injury prevention
protocols, bedside care procedures,
transfer and discharge protocols
Standardized
Follow-up
Post-acute
care order sets
IP (SNF, IRF)
Home health
Hospice
Clinical ops procedure guidelines and
patient injury prevention
Implementation of protocols
based on MD orders and clinical
operations-initiated activities
(Lean/TPS workflow focus)
Workflow
Care
Process
Models
Value
Stream
Maps
MD Population Knowledge Assets

= Negative Impact = Positive or Negative = Positive Impact
Knowledge Asset
Type
Discounted
FFS
Per Diem
Per Case Bundled Per Case
Condition
Capitation
Full
Capitation
CMS Commercial CMS Commercial
Workflow
Diagnostic Variation
Standing Orders
MedicationSelection
Triage
Patient Safety
Ambulatory Treatment
and Monitoring
Indications for
Intervention
Payment structure considerations

Actionable Visualizations

Poll Question #1
Content System
What types of standardized content have you implemented to
support Population Health Management? 192 respondents
A. Just Starting – 42%
‒ We have not standardized content to support Population Health Management.
Our clinicians use their best judgment based on their individual training.
B. Mid-Journey – 49%
‒ We have begun to standardize some content (e.g. CPOE to implement
standardized order sets – provided by our EMR vendor). We have not yet created
standard content for both workflow and clinical domains across the continuum of
care.
C. Mature – 9%
‒ We have implemented standardized content to manage ambulatory and inpatient
care management (e.g., ambulatory treatment algorithms, order sets, bedside
care protocols) and utilization criteria (e.g., diagnostic algorithms, triage criteria,
indications for referral and intervention) regardless of what unit or facility a patient
enters the same workflow and care delivery content is followed and measured.

Content System
Outcomes
Improvement
23
How are we
doing?
How do we
transform?

Infrastructure:Hosting/Hardware Analytics System Overview
How are we doing?
24
e.g. EPSi,
Peoplesoft,
Lawson
e.g. Lawson,
Peoplesoft,
Ultipro
Subject Area Mart Designer
Source Mart Designer
EMR
EMR Financial
Patient
Sat. HR Administrative Claims
Financial
Patient
Sat.
HR Administrative Claims
e.g. Epic, Cerner
NextGen
e.g. Press Ganey,
NRC Picker
e.g. API Time
Tracking
e.g. Medicare
Private Payers
Shared Frameworks & Tools for improvement
Comorbidity Analyzer, Registry Repository, Attribution Modeler, Common
Definition Repository, Hierarchies, CAFE, Atlas, IDEA, Eventalytics,
Geospatial, Risk & Severity Profiling, etc
Metadata Driven ETL Engine
Enterprise Data Warehouse Platform
Analyze and Interpret Data
• Show correlation and causation
• Integrate clinical, financial and
patient experience data
• Predict outcomes and prescribe
actions
Shared Reoccurring Data Tasks
• Cohort Definitions
• Patient/Provider Attribution
• Severity/Comorbidity Analysis
• Calculation/Term Definition
• Comparative Repositories
Source Data Integration
• Automatically co-locate data from
different source transactional
systems (EMR, Claims, Financial,
Patient Satisfaction)
• Automatically connect data
together with key identifiers
(Patient, Location, Provider)
Infrastructure
• Security and Auditing capabilities
• Metadata Repository

Strong Analytic System
Non value-add Value-add
Understanding the question
Hunting for data
Interpreting data
Data distribution
Gather, compiling or running
Weak Analytic System
Strong Analytic System
The majority of time is spent
analyzing and interpreting data
Understanding the question
Hunting for data
Interpreting data
Data distribution
Gather, compiling or running
25

Less Transformation
Provider
Patient
Bad Debt
Diagnosis Procedure
Facility
EncounterCost
Charge
Employee
Survey
House
Keeping
Catha Lab
Provider
Census
Time
Keeping
More Transformation Enforced Referential Integrity
Enterprise Data Modeling
(Many Technology Vendors)
FINANCIAL SOURCES
(e.g. EPSi, Lawson,
PeopleSoft)
ADMINISTRATIVE
SOURCES
(e.g. API Time Tracking,
Lawson HR)
EMR SOURCES
(e.g. Cerner, Epic,
NextGen)
DEPARTMENTAL
SOURCES
(e.g. Apollo)
Pt. SATISFACTION
SOURCES
(e.g. NRC Picker, Press
Ganey)
EDW

EMR SOURCES
(e.g. Cerner, Epic,
NextGen)
Oncology
Diabetes
Heart
Failure
Regulatory
Pregnancy Asthma
Labor
Productivity
Revenue
Cycle
Census
Pt. SATISFACTION
SOURCES
Ganey)
DEPARTMENTAL
SOURCES
(e.g. Apollo)
FINANCIAL SOURCES
(e.g. EPSi, Lawson,
PeopleSoft)
ADMINISTRATIVE
SOURCES
(e.g. API Time Tracking,
Lawson HR)
Redundant
Data Extracts
Dimensional Data Modeling
(EMRs & Healthcare Point Solutions)
EDW
Less TransformationMore Transformation

Metadata (EDW Atlas), Security and Auditing
Diabetes
Sepsis
Readmissions
Common, linkable
vocabulary
Financial
Source Marts
Administrative
Source Marts
Departmental
Source Marts
EMR
Source Marts
Patient
Satisfaction
Source Mart
FINANCIAL SOURCES
(e.g. EPSi, Peoplesoft,
Lawson)
ADMINISTRATIVE
SOURCES
(e.g. API Time Tracking)
EMR SOURCEs
(e.g. Cerner, Epic,
NextGen)
DEPARTMENTAL
SOURCES
(e.g. Apollo)
Pt. SATISFACTION
SOURCES
Ganey)
Adaptive Data Modeling
Less TransformationMore Transformation

Information Management
2929
DATA CAPTURE
• Acquire key data elements
• Assure data quality
• Integrate data capture into operational
workflow
DATA ANALYSIS
• Interpret data
• Discover new information in the data
(data mining)
• Evaluate data quality
DATA PROVISIONING
• Move data from transactional systems into
the Data Warehouse
• Build visualizations for use by clinicians
• Generate external reports (e.g., CMS)
Knowledge Managers (Data
quality, data stewardship and
data interpretation)
Application Administrators
(optimization of source systems)
Data Architects
(Infrastructure, visualization, analysis, reporting)
= Subject Matter Expert
= Data Capture
= Data Provisioning
= Data Analysis

Less Effective Approach
“Punish the Outliers”
# of
Cases
Current Condition
• Significant Volume
• Significant Variation
# of
Cases
Option 1: “Punish the Outliers” or
“Cut Off the Tail”
Strategy
• Set a minimum standard of quality
• Focus improvement effort on those
not meeting the minimum standard
Mean
Focus on
Minimum
Standard
Metric
Excellent OutcomesPoor Outcomes Excellent OutcomesPoor Outcomes
1 box = 100 cases in a year

Effective Approach to improvement:
Focus on “Better Care”
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”
Strategy
• Identify evidenced based “Shared Baseline”
• Focus improvement effort on reducing
variation by following the “Shared Baseline”
• Often those performing the best make the
greatest improvements
Mean
Focus on
Best Practice
Care Process
Model
Poor Outcomes
1 box = 100 cases in a year

# 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
32

Internal Variation versus Resource Consumption
Y-Axis=InternalVariationinResourcesConsumed
Bubble Size = Resources
Consumed
Bubble Color = Clinical DomainX Axis = Resources Consumed
1
2
3
4

Prioritize: Pareto Analysis App
34

0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
% of Total Cumulative %
35
X-Axis = Care Processes by resources consumed (High to Low)
Y-Axis=Percentoftotalresourcesconsumed
Pareto Analysis >> Prioritization
Top 85 Care Processes account
for 80% of the opportunity (+45)
for 62% of the opportunity (+27)
for 34% of the opportunity

Poll Question #2
Analytics System
How is data from disparate transactional systems
integrated? (e.g. EMR, Cost, Patient Satisfaction) 215
respondents
‒ Analyst manually integrate data into spreadsheets.
‒ We use one of our transactional systems (e.g. EMR or Financial) to
integrate a limited subset of data for some of our transactional systems
for key operational reports.
C. Mature – 13%
‒ We have implemented an Enterprise Data Warehouse Platform, fully
automated load from all of our transactional systems runs at least daily
which integrates data based on common linkable identifiers (e.g. patient
and provider IDs), with near-real time loads for selected data.

Poll Question #3
Analytics System
What technical tools do you use to move your
organization away from reactionary, emotional decisions
toward data-driven decisions? 193 respondents
‒ We don't use any technical tools to help us with data driven
prioritization, although we have some reports.
‒ We use some spreadsheet analysis and reports to evaluate options but
opportunities are still typically selected based on politics, a crisis or the
most vocal advocate.
C. Mature – 17%
‒ We have robust applications which provide our centralized clinical and
operational governance team with objective criteria for use in prioritizing
improvement initiatives, including identifying our key processes based
on size and variability.

Content System
Outcomes
Improvement
38
How are we
doing?
How do we
transform?

Deployment System Overview
How do we transform?
39
Improvement Capacity Assessment
Evaluation of organizational capacity for change, current capabilities, and gaps
GovernanceData Governance/Data Stewardship and Advanced Organizational Governance & Prioritization
Improvement MethodologySystematic improvement incorporating LEAN / PDSA principles, AGILE software development, etc.
Accelerated Practices TrainingSystematic training of Adaptive Leadership, Quality Improvement/LEAN skills, and Technology
39

Organizational Assessment I October 15, 2014 I 40
Readiness Assessment Example
1) Data Access
Process
2) Registry
Definition Process
3) Data
Governance &
Data Quality
Process
4) Sustained Care
Improvement
Process
5) Standardized Criteria
for Treatment & Venue
6) Cost Allocation
Methodology
12) Data Integration
Infrastructure
11) Missing Data
Element Capture
10) Data-driven
Prioritization
9) Prescriptive
Modeling
8) Standardized
Calculations & Definitions
for Internal Reporting
7) Standardized
Protocols for Population Health
Deployment
Content
Analytics

Small Teams (Designs Innovation)
• Meet weekly in iteration planning meeting
• Build DRAFT processes, metrics, interventions
• Present DRAFT work to Broader TeamsOB
Innovators
Guidance Team (Prioritizes Innovations)
• Meet quarterly to prioritize allocation of technical staff
• Approves improvement AIMs
• Reviews progress and removes road blocksOB Newborn GYN
W&N
W&N
Innovators
Innovators
Early Adopters
Broad Teams (Implements Innovation)
• Broad RN and MD representation across system
• Meet monthly to review, adjust and approve DRAFTs
• Lead rollout of new process and measurementOB
W&N
W&N
W&N
Innovators
Early Adopters
Early Adopters
Executive Leadership Team
• Prioritizes sequence of formation of Guidance Teams
• Approves Board Level Outcomes Goals
• Reviews progress and removes road blocks

Improvement Types
42
Outcomes
Improvement
Examples: Reduction in Mortality Rate; Hard
Cost Savings; Time Savings (Soft Cost);
Improved Health Function
DifficultytoAchieve
Process
Improvement
Examples: Process Step: % of Patients with
scheduled follow-up visit at discharge; Data
Quality: % of Heart Failure Patients with
Ejection Fraction captured in EMR
Opportunity
Identification
Improvement
Examples: Potential $ Savings from Variation
Reduction (Key Process Analysis) ; Potential
$ Leakage reduction by encouraging
providers to refer patients in network

Requirements Gathering
Project Plan/ Estimation
Use Cases/
Functional Specs
Design
Specifications
Code
Test
Fix / Integrate
High Level
Stories
Vision
Release 1
Release 2
Release 3
Release 4
$
$
$
$
$$
$$
$$$$$
Documentation
Customer
sees the
product
Value to
the
Customer
Traditional
“Waterfall”
Agile
Sources: Adapted from various ideas taught by Alistair Cockburn
and Martin Fowler – see alistair.cockburn.us and www.thoughtworks.com
Traditional Approach vs. Agile Approach

Accelerated Practices Program
PREPARING HEALTHCARE TEAMS
TO ACCELERATE OUTCOMES IMPROVEMENT
Immersive Quality Improvement Training
• 8 Session Course - taught over 4-6 months, 2 ½ days per month
• Train the trainers – required for coaches and team leaders
• Quality Improvement Theory applied on actual project with 2-4 person
team
Executive Training
• 2 day executive course taught quarterly
• Provides leadership visibility into training and high level principles
Just-in-time Training
• Library of 10-15 minute modules used as needed by permanent teams
• Readily available to clinical, technical and operational team members

Poll Question #4
Deployment System
How are teams organized to improve the quality of care and
sustain improvements? 237 respondents
‒ We have ad-hoc improvement teams organized on a project basis in a reactive
mode (e.g., to respond to a TJC sentinel event). After a project ends, many of the
gains achieved may be lost because limited organizational infrastructure remains
to sustain the gains.
‒ Our Quality Resources Department provides support to Service Lines and
Departments apply quality improvement and workflow principles to improvement
initiatives. Some individual units or facilities may focus on quality but dispersion of
improvements to all units or all facilities is limited. Improvement is still project
based.
C. Mature – 11%
‒ We have organized permanent interdisciplinary cross facility teams, which include
clinical and technical subject matter experts with process improvement skills;
these teams permanently own the quality, cost, safety and satisfaction of their
care delivery domain. Senior executive leadership and Board meetings spend the
majority of their time reviewing the goals and progress of these permanent
improvement teams.

Outcomes
Improvement
Content System
46
Science Project Centric
Pockets of excellence, Limited
roll-out of improvement across
all facilities
Research Centric
Academic ideas with no
practical application. Lots
of published papers.
Information System
Centric
“If we build it they will
come.” Focus on reducing
information request queue.
Automation Centric
“Paved Cow Paths”
(Process is automated but not
improved – many EMR
deployments)
Organization Centric
Management “Flavor of the month”
Clinicians disengage if evidence
and measurement are both
missing
LEAN Centric
Un-sustainable Improvements.
Can’t manually measure
after 2 or 3 projects.
Ignite Change
Scalable & Sustainable
Outcomes Improvement in
Population Health

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Population Health Analytics: Improving Care One Patient at a Time

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