Organizing for Analytics Success - HAS Session 7

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Session #7 – Organizing For Analytics Success

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Poll Question

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Hotel Wi-Fi• HASummit14• PW: analytics

App Questions?• 3 app helpers• Raise hand with

mobile device• Walk to back

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Holly RimmaschChief Clinical Officer, Health Catalyst

Session #7 Organizing for Analytics Success

Ms. Holly Rimmasch is Chief Clinical Officer at Health Catalyst. Prior to joining Health Catalyst, Ms. Rimmasch was an Assistant Vice President at Intermountain Healthcare and was integral in promoting integration of Clinical Operations across hospitals, ambulatory settings and managed care plans. Holly has spent the last 17 years dedicated to improving clinical care including implementation of operational best practices. Ms. Rimmasch holds a Master of Science in Adult Physiology from the University of Utah and a Bachelor of Science in Nursing from Brigham Young University.

Mr. Barlow is a co-founder of Health Catalyst. He oversees all technical client operations. Mr. Barlow is a founding member and former chair of the Healthcare Data Warehousing Association. He began his career in healthcare over 22 years ago at Intermountain Healthcare and acted as a member of the team that led Intermountain’s nationally recognized improvements in quality care and reductions in cost. Mr. Barlow holds a BS from the University of Utah in health education and promotion.

Steve Barlow Co-Founder and Senior Vice President of Client Operations, Health Catalyst

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Where Do We Start?

Cumulative %

% of Total Resources Consumed for each clinical work process

50%

7 CPFs Number of Care Process Families (e.g., ischemic heart disease, pregnancy, bowel disorders, spine, heart failure)

21 CPFs

80%

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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

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Internal Variation vs Resource ConsumptionY

- Axi

s =

Int

ern

al V

aria

tion

in R

esou

rces

Con

sum

ed

Bubble Size = Resources Consumed

Bubble Color = Clinical DomainX Axis = Resources Consumed

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Three Systems of Care Delivery Overview

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Analytic System

Content System

Deployment System

Evidence gathering & evaluating

Knowledge assets (e.g. Order Sets)

Starter sets

Value stream maps

Patient safety protocols

Standard “Knowledge” Work

Team Structures

Roles

Fingerprinting

Implementation

Standard “Organizational” Work

Data driven prioritization

Calculations

Definitions

Enterprise Data Warehouse

Data visualization

Standard “Measurement” Work

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Analytic System Core Activities

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Analytic System

Content System

Deployment System

Unlocking Data to Drive Measurements

Automating the Broad Distribution of

Information

Discovering Patterns in Data

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Strong Analytic System

Non value-add Value-add

Understanding the question

Hunting for data

Interpreting dataData distribution

Gather, compiling or running

Weak Analytic System

Strong Analytic SystemThe majority of time is spent analyzing and interpreting data

Understanding the questionHunting for data

Interpreting data

Data distribution

Gather, compiling or running

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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 Model (Technology Vendors)

FINANCIAL SOURCES

ADMINISTRATIVE SOURCES

EMR SOURCES

DEPARTMENTAL SOURCES

Pt. SATISFACTIONSOURCES

EDW

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EMR SOURCES

Oncology

DiabetesHeart

Failure

Regulatory

Pregnancy Asthma

Labor Productivity

Revenue Cycle

Census



FINANCIAL SOURCES


Redundant Data Extracts

Independent Data Marts (Healthcare Point Solutions, EMRs)

EDW

Less TransformationMore Transformation

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Metadata (EDW Atlas), Security and Auditing

Diabetes

Sepsis

Readmissions

Common, linkable vocabulary

FinancialSource Marts

AdministrativeSource Marts

DepartmentalSource Marts

EMR Source Marts

Patient Satisfaction Source Mart

FINANCIAL SOURCES


EMR SOURCEs



Adaptive Data Model


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Analytic System Exercise

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The Enterprise Shopping Model

Produce

Meat

Dairy

Dry Goods

__ Apples__ Pears__ Tomatoes__ Carrots

__ Beef__ Ham__ Chicken__ Pork

__ Milk __ Eggs__ Cheese__ Cream

__ Pasta__ Flour__ Sugar__ Soup

__ Celery__ Banana__ Melon__ Grapes

__ Turkey__ Sausage

__ Lamb__ Bacon

__ 2% Milk __ Half & Half__ Yogurt__ Margarine

__ Baking soda__ Rice__ Beans__ B. Sugar

E n t e r p r i s e S h o p p i n g M o d e l

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Apples

Tomato Soup

Flour

Milk

Turkey

Lettuce

Sugar

Beans

Hot dogs

Banana

Noodles

Yogurt

Your Shopping List

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Get eggs

Buy flowers

Get tires rotated

Pick up dry cleaning

Additional Items

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Less Transformation

Provider

Patient

Bad Debt

Diagnosis Procedure

Facility

EncounterCost

Charge

Employee

Survey

House Keeping

Cath Lab

Provider

Census

Time Keeping

More Transformation Enforced Referential Integrity

Enterprise Data Model (Technology Vendors)

FINANCIAL SOURCES


EMR SOURCES



EDW

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Using a Independent Mart Shopping Model

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https://dl.dropboxusercontent.com/u/355034/CATALYST%2090%20Second.mp4.zip



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The Independent Mart Shopping Model

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Dairy Dry Goods

__ ½ cup of butter__ ½ cup milk__ 2 eggs

__ 1 cup white sugar__ 1 ½ cups all-purpose flour__ 2 teaspoons vanilla extract__ 1 ¾ teaspoon baking powder

Independent Mart Shopping Model

Cake

Trip #2 to the Store

How many recipes do you need to make?

Trip #1 to the Store

Dairy Dry Goods

__ 4 eggs __ 2 c shortening

__ 1 c sugar__ 2 c brown sugar__ 2 t baking soda__ 2 t vanilla__ 1 t salt__ 4-5 c all-purpose flour __ 4 cups chocolate chips

Independent Mart Shopping Model

Chocolate Chip Cookies

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EMR SOURCES

Oncology

DiabetesHeart

Failure

Regulatory

Pregnancy Asthma

Labor Productivity

Revenue Cycle

Census



FINANCIAL SOURCES


Redundant Data Extracts

Independent Data Marts (Healthcare Point Solutions, EMRs)

EDW


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The Adaptive Shopping Model

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A d a p t i v e S h o p p i n g M o d e l

__ ________________________ ________________________ ________________________ ________________________ ________________________ ________________________ ________________________ ________________________ ________________________ ________________________ ______________________

__ ________________________ ________________________ ________________________ ________________________ ________________________ ________________________ ________________________ ________________________ ________________________ ________________________ ______________________

Store: __________________________________________________

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Shopping List Revisited

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Additional Items Get eggs

Buy flowers

Get tires rotated

Pick up dry cleaning

Once you are home, can you make these recipes?

Cake: 1 cup white sugar 1 ½ cups all-purpose flour 2 teaspoons vanilla extract 1 ¾ teaspoon baking powder ½ cup of butter ½ cup milk 2 eggs

Cookies: 2 cups shortening 4 large eggs 1 cup sugar 2 cups brown sugar 2 t vanilla 1 t salt 2 t baking soda 4 cups all-purpose flour 4-5 cups chocolate chips

Baking Powder•Baking Soda •Buy a new couch •Get oil change•Chocolate Chips•Buy yarn and knitting supplies •Vanilla extract

And Even MoreInitial List•Apples•Tomato Soup•Flour•Milk•Turkey•Lettuce

Sugar

Beans

Hot dogs

Banana

Noodles

Yogurt

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Metadata (EDW Atlas), Security and Auditing

Diabetes

Sepsis

Readmissions

Common, linkable vocabulary

FinancialSource Marts

AdministrativeSource Marts

DepartmentalSource Marts

EMR Source Marts

Patient Satisfaction Source Mart

FINANCIAL SOURCES


EMR SOURCEs



Adaptive Data Model


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Poll Question #1 - Analytic

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How would you describe your analytics and enterprise data warehousing approach? (choose the best answer that applies)

a. We do not currently have a centralized analytics data repository (e.g., enterprise data warehouse-EDW)

b. We have an EDW based on the enterprise data model approach

c. We have an EDW based on the independent data mart approach

d. We have an EDW based on the adaptive or late-binding architecture approach

e. Unsure or not applicable

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Content System Core Activities

Defining a Clinically Driven Patient Cohort

Using Evidence to Identify Three Types

of Waste

Standardizing Care Delivery through

Shared Baselines.

Analytic System

Content System

Deployment System

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Strong Content System

Time

Measured in Weeks

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Habit of allFront-line Clinicians

at Every Facility

New Clinical or Operational Best Practice

Knowledge Discovered

Measured in Years

Strong Content System

Weak Content System

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Clinical Content System Components

How do we accelerate Evidence Integration into Care Delivery?

Evidence Based Population Management Content: Outcome, process and balanced metrics related to improvement AIM statements, intervention indications, triage criteria, order sets, indications for referral, patient and provider education materials, predictive algorithms, care guidelines and protocolsEvidence Based Patient Safety Content: Outcome, process and balanced metrics related to improvement AIM statements, At risk screening criteria, safety protocols, near miss and incident tracking

What Types of Waste are created without standard work? Ordering Waste: Populations (Heart Failure, Diabetes, etc.)Workflow Waste: Departmental Patient Injury Waste: Patient Safety

How can data accelerate Waste Elimination?Value Stream Maps, A3s, Standard Work starter sets, Outcome, process and balanced metrics related to improvement AIM statements

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Content System Exercise

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Find as many numbers sequentially from 1 to 50 in 20 seconds.

On your mark…Get set…GO!

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2425

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ROUND #1

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3 8

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50

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Find as many numbers sequentially from 1 to 50 in 20 seconds.On your mark…Get set…GO!

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ROUND #2

25 1 29 5 41 35 45 33 15 49 9 23 31 13 3 19 27 3917 21 7 53 37 43 47

14 50 4 36 8 28 24 18 2 26 38 16 46 20 6 34 48 1022 32 12 42 30 40 44

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Find as many numbers sequentially from 1 to 50 in 20 seconds.On your mark…Get set…GO!

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1 2 4 5 7 8 10 12 14 153 6 9 11 13 16

17181920212223242534 33 32 31 30 29 28 27 26

50494746444341393736 484542403835

START

END

ROUND #3

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Poll Question #2 - Content

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Rate the level of content standardization (choose the answer that best applies)

a. No standardization. Our clinicians use their best judgment based on their individual training

b. 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. High degree of standardization, including standardized content for ambulatory and inpatient care management and utilization criteria. The same workflow and care delivery content is followed and measured regardless of what unit or facility a patient enters

d. Unsure or not applicable

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Deployment System Core Activities

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Analytic System

Content System

Deployment System

Organizing for Scalable Improvement

Applying Agile Principles to Care Improvement

Accelerate Root Cause Analysis by Combining

Analytics and Lean Principles

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Strong Deployment System

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Baseline Performance

Improvement with focused project

team

Inability to sustain gains

over time

Weak Deployment System

Baseline Performance

Improvement with permanent

integrated teams

Gains sustained over

time

Strong Deployment System

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Population Health Hierarchy

“Ordering of Care”

12 Clinical Programs Cardiovascular

133 Care Process Families Heart Failure

1610 Care Processes Acute Myocardial Infarction

Primary CareCare

ProcessFamilies

e.g.,Diabetes

CV

CareProcessFamilies

e.g.,Heart

Failure

W&C

CareProcessFamilies

e.g., Pregnancy

GI

CareProcessFamilies

e.g., Lower GIDisorders

Resp-iratory

CareProcessFamilies

e.g., Obstructive Lung

Disorders

Neuro Sciences

CareProcessFamilies

e.g.,Spine

Disorders

Musculo-skeletal

CareProcessFamilies

e.g., Joint

Replace-ment

Surgery

CareProcessFamilies

e.g.,Urologic

Disorders

GeneralMed

CareProcessFamilies

e.g.,Infectious Disease

Oncology

CareProcessFamilies

e.g., BreastCancer

Peds SpecCare

ProcessFamilies

e.g.,Peds

CV Surg

Mental Health

CareProcessFamilies

e.g., Depressio

n

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Organization of teamsClinical and technical

Provides steady state domain governance and oversight

GUIDANCE TEAM

Refines Work Group output and leads implementation

CLINICALIMPLEMENTATIO

NTEAM

Provides a forum to develop and/or refine clinical content and analytics feedback

WORKGROUP

Oversees data governanceSupports developmentof clinical content and

analytics feedback

CONTENT AND

ANALYTICSTEAM

Provides overall governance and prioritization of initiatives

SENIOR EXECUTIVE

LEADERSHIP TEAM

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Care Process Family

Case Count Rank

LOS Hours (Capacity)

Rank

Total Charges

Rank

Total Direct Cost Rank

Total Direct Cost

Opportunity Rank

Organizational Readiness

(1 to 10)1 = most ready

Trauma 9 2 2 3 3

Ischemic Heart Disease

3 7 1 2 2

Infectious Disease 6 3 3 1 1

Pregnancy 1 1 7 4 8

Heart Failure 10 8 4 5 5

Joints 11 13 8 6 16

Normal Newborn 2 6 20 24 32

GI Disorders 4 4 6 7 4

Lower Respiratory 5 5 5 8 6

Ranking Comparison

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Organizational Teams

Women & Children’s Clinical Program Guidance Team

Pregnancy SAM

PregnancyMD LeadRN SME

Knowledge Manager

DataArchitect

Application Administrator

Guidance Team Leads

= Subject Matter Expert= Data Capture

= Data Provisioning & Visualization

= Data Analysis

Normal Newborn SAM

Normal Newborn MD LeadRN SME

GynecologySAM

GynecologyMD LeadRN SME

• Permanent Teams• Integrated Clinical and Technical members• Supports Multiple Care Process Families

MD Lead

Nurse Lead

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Information Management

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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

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Standard “Organizational” Work OverviewKickoff AIM Statement

Implementation Design Launch Approval Results Review

• Mission• Cohort Discover• Data Analysis and

Review• Best Practices• Building Multiple

Potential AIM statements

• Supplement content

• Refine Cohort• Refine Metrics• Develop Draft

Visualizations• Develop

Recommended AIM statement #1

• Cluster Reps Obtain Front Line Input

• Finalize Cohort• Develop Additional

metrics based on feedback

• Develop Additional Visualizations to support

• PDSA cycle

• Cluster Reps Obtain Front Line Input

• Improvement Plan • Implementation Plan• Develop cluster rep

assignments, and deliverables

• Collect cluster rep feedback

• Prepare Initial Results from AIM statement #1

• Summarized report for historical review

• Refine, recommend AIM statement #2

MonthlyTasks and

Checkpoints

7 Steps(Work Streams)

1.Gather Knowledge Assets

2.Define Cohort

3.Select AIM Statement

4.Select, Build, Refine Metrics

5.Develop Implementation Planfor Process Improvement

6. Implementation

7. Measure Progress

Select Initial Metric Build and Refine Build and Refine Build and Refine

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Deployment System Exercise

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Round 1

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Only the Clinician can talk

The Architect cannot look at the drawing (no mind reading)

The Architect can’t start drawing

Only the Architect can draw

The Clinician can only watch – no talking

1 minute to describe 1 minute to draw

1M59585756555453525150494847464544434241403938373635343332313029282726252423222120191817161514131211109876543210 1M59585756555453525150494847464544434241403938373635343332313029282726252423222120191817161514131211109876543210

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1M59585756555453525150494847464544434241403938373635343332313029282726252423222120191817161514131211109876543210

Round 2

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The Architect still cannot look at the drawing (still no mind reading capabilities )

You can interact as much as you want

You can erase and redraw

2 minutes to describe and draw interactively

1M59585756555453525150494847464544434241403938373635343332313029282726252423222120191817161514131211109876543210

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Poll Question #3 - Deployment

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How are teams organized to improve the quality of care and sustain improvements? (choose the answer that best applies)

a. We have ad hoc, reactive improvement teams organized on a project basis

b. Our quality department supports service lines and departments for quality and workflow improvement initiatives

c. We have organized, permanent, interdisciplinary, process improvement teams. These teams permanently own the quality, cost, safety and satisfaction of their care delivery domain


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Poll Question #4 - Deployment

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How do you align and prioritize improvement priorities across your organization? (choose the answer that best applies)

a. We don’t have alignment of our improvement priorities. We have free form improvement that is prioritized in silos across the organization

b. We have alignment of our improvement priorities within our hospital, but not across our entire enterprise

c. We have a very clear prioritization and governance process for our improvement priorities, tied to our strategic plan


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Problems with Missing SystemsInformation System Centric

If we build it they will come. Focus on reducing information request queue.

Research CentricAcademic ideas with no

practical application. Lots of published papers.

Organization CentricNULL SET

(Clinicians stop coming to meetings if evidence and measurement are both

missing.)

Analytic System

Content System

Deployment System

Science Project CentricPockets of excellence, Limited

roll-out of improvements.

LEAN CentricUn-sustainable Improvements.

Can’t manually measure after 2 or 3 projects.

Automation CentricPaved Cow Paths (Process is automated but not improved –

many EMR deployments.)

Healthcare Analytics Summit 14

Three Systems to Ignite Change

Analytic System

Content System

Deployment System

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Scalable & Sustainable Outcomes

Improved population healthCare delivery is evidenced based, improvements in cost and quality are scalable and sustainable

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In Summary

Don’t boil the ocean!

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Analytic System

Content

System

Deployment

System

Analytic System

• Be agile and adaptive

• Enable knowledge discovery

Content System

• Use best practices to understand and reduce waste

Deployment System

• Leadership is key

• Permanent structures and processes/systemic approach

• Dedicated resources

All 3 systems are needed.

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Analytic Insights

AQuestions &

Answers

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Session Feedback Survey

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1. On a scale of 1-5, how satisfied were you overall with this session?

1) Not at all satisfied2) Somewhat satisfied3) Moderately satisfied4) Very satisfied5) Extremely satisfied

2. What feedback or suggestions do you have? (free form text)

3. On a scale of 1-5, what level of interest would you have for additional learning on this topic (articles, webinars, collaboration, training)

1) No interest2) Some interest3) Moderate interest4) Very interested5) Extremely interested

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Upcoming Breakout Sessions

2:25 PM – 3:25 PM

9. Getting the Most Out of Your Data AnalystJohn Wadsworth, VP, Technical Operations Health Catalyst* This is a hands-on session

10. How to Make Analytics a Strategic, C-Level ImperativeJon Brown, VP and Associate CIO, Mission HealthGene Thomas, VP & CIO, Memorial Hospital Gulfport

11. Creating Physician EngagementBryan Oshiro, MD, CMO, Health CatalystChris D. Spahr, MD, Enterprise Quality Executive, CHW

12. User Group Kickoff & New Product RoadmapThomas D. Burton, SVP, Co-Founder, Health CatalystSteve Barlow, SVP & Co-Founder, Health CatalystHolly Rimmasch, Chief Clinical Officer, Health Catalyst* This is an interactive feedback session

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Location

Grand Ballroom D

Grand Ballroom A

Savoy

Venezia