Unleashing Data: The Key To Driving Massive Improvements

Unleashing Data: The Key to Driving Massive Improvements

Tom Burton, MBA

Co-Founder & Chief Improvement

Officer, Health Catalyst

© 2017 Health Catalyst

2

Learning Objectives

• Illustrate the importance of investing in analytics training and infrastructure to prepare for massive improvement in healthcare outcomes.

• Demonstrate how to unleash data at your organization with efforts across the improvement spectrum.

• Recognize how to sustain and spread improvements across your entire organization.


3

To unleash the full potential of data,

organizations should adopt a

balanced approach to improvement

across the spectrums of both

effort and value.


Proprietary and Confidential4

Our Mission: Be the Catalyst for Massive Improvement in Healthcare Outcomes

510+Team Members

113+Improvement

Case Studies

22Best Places to

Work Awards

90+ MillionPatients

400+Hospitals

4,000+ Clinics

Our Customers Our Company

Academic Medical CentersCommunity Hospitals

Children’s HospitalsManaged Service Organizations

Integrated Delivery Systems

Accountable Care Organizations

• Stanford – Palo Alto, CA

• Albany Medical – Albany, NY

• Indiana University – Indianapolis, IN

• KUHA – Kansas City, KS

• UTMB – Galveston, TX

• Dartmouth-Hitchcock – Lebanon, NH

• Health Share of Oregon – Portland, OR

• CEPAmerica – Oakland, CA

• USMM – Troy, MI

• North Memorial – Minneapolis, MN

• Gulfport Memorial – Gulfport, MS

• Thibodaux – Thibodaux, LA

• NorthBay – Fairfield, CA

• John Muir – Walnut Creek, CA

• Gunnison Valley – Gunnison, UT

• Texas Children’s – Houston, TX

• Children’s Hospital Wisconsin – Milwaukee, WI

• MultiCare – Tacoma, WA

• Providence – Portland, OR

• Alberta Health Services – Edmonton, CA

• Memorial Care – Fountain Valley, CA

• Cedars-Sinai – Los Angeles, CA

• Kaiser – Denver, CO

• Unity Point – Des Moines, IA

• Queens Health – Honolulu, HI

• Community Health Network – Indianapolis, IN

• Health Quest – Poughkeepsie, NY

• Hawaii Pacific Health – Honolulu, HI

• UPMC – Pittsburg, PA

• King’s Daughters Medical Center – Ashland, KY

• Piedmont – Atlanta, GA

• Mission – Asheville, NC

• Orlando Health – Orlando, FL

• Cone Health – Greensboro, NC

• Christiana Care – Wilmington, DE

• Westchester – Valhalla, NY

• Allina – Minneapolis, MN

• Partners – Boston, MA

• OSF – Peoria, IL

• Springfield Clinic – Springfield, IL

• Crystal Run – Middletown, NY

• OneCare Vermont – Colchester, VT

• Adirondacks ACO – Pittsburg, NY

• Adirondacks Health Institute – Glens Falls, NY

Health Catalyst Clients:



Unleashing Data to Achieve Massive Improvements

Organic Improvement

Let innovation happen - Light Effort

Fast Track Improvement

Medium Effort

Comprehensive Outcomes

High Effort

Value Across the spectrum of improvement effort, the value may be light, medium, or high value.

Enablers Highly trained and engaged team members and a robust analytics infrastructure (both platform & applications)

Volume1,000s of day-to-day, better, data-driven

decisions100s of quick win improvements using data

10s of deep changes, eliminating unwarranted

clinical, operational and/or financial variation

Examples

• 2-hour ad-hoc analysis by senior analyst

reveals insight that expanding clinic hours,

versus building an observation wing, will save

$3M in capital expense.

• Automated dashboard saves 4 hours of

manual data collection/reporting per week.

• Data helps clinicians identify high maternal

hypertension rates; insights + interventions

results in 15% improvement in hypertension

rates.

• Dashboard helps identify missing

documentation on high dollar accounts,

improving AR days by 10%.

• Deep process redesign, leveraging

predictive models, reduces sepsis mortality

rate by 15%, saves 125 lives per year, and

reduces costs by $1.6 M.

• Redesigning care management workflow

using mobile technology increases care plan

effectiveness by 28% and saves $3.4 M.

Sample Results

Measures

Technology utilization, number of lives impacted/saved, intervention rates, number/percent improvement, additional revenue, cost savings, cost

avoidance…

Sample

CommunicationsVignettes, improvement snapshots, case study briefs, case studies, webinars, publications…



Financial Value

Clinical Value

Experience Value

XEffort

High

Light High

Value

Improvement Type

The Improvement Spectrum Matrix – Value and Effort



Effort

High

Light High

Financial Value

Clinical Value

Experience Value

Improvement Type

Overemphasis on Deep Improvement Projects



Financial Value

Clinical Value

Experience Value

Effort

High

Light High

Value

Improvement Type

Overemphasis on Light Effort Projects



Financial Value

Clinical Value

Experience Value

Effort

High

Light High

Value

Improvement Type

Overemphasis on One Value Type



Effort

Financial Value

Clinical Value

Experience Value

High

Light High

Value

Improvement Type

IDEAL: Even Spread Across the Improvement Spectrum Matrix


Which type of improvements does your organization focus on? 147

respondents

1. Clinical – 20%

2. Financial – 13%

3. Patient Experience – 10%

4. Balanced mix – 58%

Poll Question #1

12


Which quadrant does your organization tend to gravitate toward? 137

respondents

1. High Effort and High Value – 24%

2. High Effort and Light Value – 9%

3. Light Effort and High Value – 24%

4. Light Effort and Light Value – 6%

5. Balanced approach across both spectrums avoiding high effort,

light value – 37%

Poll Question #2

13



Analogy Is the Key to Understanding



Introducing the Game: SPECTRUM

• Adapted by Health Catalyst from the popular strategy game, 7 Wonders.

• Objective of the game: Gain the most improvement points.

• There are multiple ways to earn improvement points .

• 3 years, 7 cards per year.

• Play a card and pass the rest to your neighbor.



Each Table Represents a Care Delivery SystemEach Player Represents a Department in that System



• Competition/conflict Between Departments(+ or - Points Calculated at the End of Each Year)

• Budget for Improvement Efforts (1 point / 3 coins)

• Data-Driven Culture (on game board)

• Light Effort Improvements

• Collaboration Between Departments

• System-Wide Adoption

• High Effort Clinical, Financial, and Patient Experience Improvements

Scoring: Improvement Point Categories

Improvement

point symbol

At the end of Year III, once the budget disputes have been resolved, the players total

their improvement points on the scoring sheet in each of the following categories:



Game Board Layout

Key



Principles – Spectrum Year 1 – Invest for Success• Invest in Data Infrastructure (The Brown Cards)

– Acquire Data

– Grant Access to Data

– Build Actionable Metrics

– Find Insights in Data

• Invest in People - Train Key Roles and Skills (The Gray Cards)

– Analytics Engineer

– Change Agent

– Key Stakeholder

• Progress on your journey toward a Data-Driven Culture (Game Board)

– From Scorecards to Embedded Analytics

– Data-driven Culture Core Capabilities



Only 8% of data required

for the population health

and precision medicine

strategy resides in

today’s EMR/EHR.Source: Alberta Innovates Health Solutions, Secondary

Data Use Project, March 2016

http://www.aihealthsolutions.ca/initiatives-

partnerships/secondary-use-data-project/presentations/

Acquire Data . . .

http://www.aihealthsolutions.ca/initiatives-partnerships/secondary-use-data-project/presentations/



Just Beginning: Digitization of Health

The Growing Ecosystem of Human Health Data

Healthcare

Encounter

Data

7x24

Biometric

Data

Consumer

Data

Genomic

&

Familial

Data

Social

Data

Outcomes

Data



Imagine the Richness of the Picture

22

The Growing Ecosystem of Human Health Data

Healthcare

Encounter

Data

7x24

Biometric

Data

Consumer

Data

Genomic

&

Familial

Data

Social

Data

Outcomes

Data

What is your 1, 3, and 5 year strategic data acquisition plan?



Acquire DataGartner: Health Data Convergence Hub

“Definition: The health data convergence hub is the

orchestration platform that brings together data from across the

consumer/citizen/patient health and wellness continuum and

prepares the data for delivery to downstream consumption

platforms, applications, analytics and "things." It automates the

ingestion of data — both structured and unstructured — from all

identified and permissioned sources; provides tracking and

traceability; and manages identity, compliance and security. It

may process algorithms and deliver the output to the correct

modality.”

- Laura Craft, Vi Shaffer, “Gartner: Hype Cycle for Healthcare Providers, 2017”



From Data Warehouse to Data Operating System

Traditional Data WarehouseHealth Data Convergence Hub

HC: Data Operating System

1. Collects data from EHR & Claims

2. Enables creating static reports

3. Enables SQL queries

4. Data is updated nightly

5. Not available in the EHR workflow

6. Requires replacing your existing DW

7. Proprietary schemas

8. Deals with tables and columns

1. Collects data from many sources

2. Enables creating static reports and web/mobile apps

3. Enables SQL, R, Python, Deep Learning queries

4. Data is always up-to-date

5. Insights are easily available in the EHR workflow

6. Works with your existing DW (or use our DW)

7. Industry standard schemas e.g., FHIR

8. Deals with tables, columns and clinical entities like

registries, measures

9. Provides centralized security at app and data levels

10. Machine Learning is as easy to use as SQL

11. Content Marketplace to share executable content with

other health systems



Acquire DataKey Concepts To Accelerate Data Acquisition

Late-binding – don’t early bind complex data and waste valuable time during

data acquisitions by binding raw data into strict definitions, unless there is

persistent and global agreement, rather co-locate data into Source Data Marts or

Data Lakes with minimal transformation, allowing for flexibility in multiple future

use-cases.

Automation Tools – Use tools which automate the tedious and predictable

steps of building data marts. (e.g. Source Connectors, SMD, SAMD)

Leverage Big Data Capabilities – Make sure your data platform can leverage

silicon valley technologies like Hadoop/Spark, Machine Learning, Natural

Language Processing etc.



Grant Access

One of the most challenging

polarities for organizations to

balance is how to grant

access to data.



Data Access Polarity: Data Protection AND Data Sharing

Symptoms of Extreme:• Legitimate data request denied.

• IT controls all final signoff for

data access.

• 6 month process to get access.

Symptoms of Extreme:• Data breach.

• Inappropriate data use.

Evidence of Balance:

• Streamlined access

approval process.

• Consistent regular

auditing.

• Appropriate use of

data.

• Data stewards

grant access.



Grant Access#1 Polarity: Data Sharing AND Data Protection

Key Concepts in Granting Access effectively

Data Steward Ownership - Shift access decision away from IT into

clinical/business owners hands – IT usually overemphasizes data protection.

Trust but Verify – Assume good intent and grant access to data more liberally

but increase your frequency and depth of your auditing capability.

Create Team- or Role-based data access policies – Streamline granting

access to entire teams or role groups to access data for improvement purposes.

When a new individual joins a group or gets a new role, access is automatically

granted.



Build Actionable Metrics

The most sophisticated and accurate

predictive model is worthless

unless it promotes an action

that would otherwise

not have happened.



The 5 Rights of Information Delivery

The How:

We believe if you get the

• Right Information, to the

• Right Audience, at the

• Right Granularity, at the

• Right Time, in the

• Right Visualization/Modality

… you produce the Right Action,

to Improve Outcomes

The Why:



Discover Insight

Data becomes valuable

when an insight is discovered,

such as a trend, pattern, correlation, or

causation.



Average Before=8 hours delayAverage After=3 hours delay

Is This Result Good?

Adapted from The Health Care Data Guide, p. 16-17

Poor sample size,

looks like

improvement but no

improvement

occurred in reality

Trend occurred

naturally,

Intervention didn’t

cause improvement

Change wasn’t

permanent,

numbers are

slipping

Improvement

happened before

intervention, not

because of

intervention

Outlier caused

process to look like

it needed

improvement

Real

improvement

caused by

intervention



Why Traditional Scorecards Lack Insight

Metric Region 1 Region 2 Overall Score

Financial Metric XX.X XX.X XX.X

Quality Metric YY.Y YY.Y YY.Y

Experience Metric ZZ.Z ZZ.Z ZZ.Z

Scorecard Shows:

Current Measurement vs.

Target.

Does not show:

Trend, Variation, Noise vs.

Signal, Drill-down detail.

Static Report Shows:

Current Measurement

compared with Historic.

Does not show:

Variation, Noise vs. Signal,

Trend (very well), drill-down

detail.

Metric Last Year Current Year

LY Current Month LY Year To Date Current Month Year to Date

Financial Metric XX.X XX.X XX.X XX.X

Quality Metric YY.Y YY.Y YY.Y YY.Y

Experience Metric ZZ.Z ZZ.Z ZZ.Z ZZ.Z

Scorecard

Static Report



Insight Trap: Rush to Judgment and Punish the Outliers

Current Condition

• Significant Volume.

• Significant Variation.

Option 1: “Punish the Outliers” or

“Cut Off the Tail”

Strategy

• Set a minimum standard of quality.

• Focus improvement effort on those people not meeting the

minimum standard.

# of

Cases

Mean

Excellent OutcomesPoor Outcomes

# of

Cases

Focus on

Minimum

Standard

Metric




Leveraging InsightLearning Approach: Focus on Better Process

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 process variation by

following the “Shared Baseline.”

• Often those performing the best make the greatest improvements.

Excellent Outcomes

# of

Cases

Focus on Best

Practice Care

Process Model

Poor Outcomes

Current Condition

• Significant Volume.

• Significant Variation.

# of

Cases

Mean




Variation Over Time

Mean

Upper Control Limit

Lower Control Limit

Adapted from R.C. Lloyd & Associates

Current Condition



Mean

Upper Control Limit

Lower Control Limit

Adapted from R.C. Lloyd & Associates

Variation Over Time

Variation Reduction



New Mean

Reduce Variation and Improve Outcomes

Intervention



Using Control Charts to Discover Insights

Adapted from The Health Care Data Guide, p. 116




– Acquire Data

– Grant Access to data


– Find Insights in data



– Change Agent

– Key Stakeholder

• Progress on your Journey Toward a Data-Driven Culture (Game Board)





“I told you I wasn’t a hunter gather. I’m an analyst!”

28

Train Analytics Engineer



Once Better Infrastructure Is in Place Analytics Engineers Have More Time to Interpret Data

Non value-add work Value-add work

Understanding the question

Hunting for data

Interpreting dataDistributing data

Gathering, compiling or running

Weak Analytic System Strong Analytic System

Understanding the question

Hunting for data

Interpreting data

Distributing data

Gather, compiling or running

*poll of Analyst customer pre Catalyst Analytics Platform install

75%* of analyst time is spent in hunting & gathering in a weak analytics system



Core

Create insights

Present insights in a

compelling way

Understanding of

healthcare data

Core Technical

PL/SQL

Data modeling

Visualization &

reporting tools

Needed for the Future

• Stats, predictive, machine learning, AI, etc. • Visualization principles (e.g. Tufte, Few)

• Quality improvement (e.g. Lean, 6 Sigma) • Project management

The Skills Needed in the Analytics Space



You must have both the technical skill AND the clinical or

operational context (this is usually best achieved by partnering

with a change agent who has deep domain knowledge).

Otherwise, you might jump to the wrong conclusions …

Insights Require Technical Skill AND Context

Higher ice

cream sales

We taste

better

More shark

attacks

- -

Stop selling ice cream!

Warmer weather

Higher ice

cream sales

More shark

attacks



Recruit Change Agent

"In times of change, learners inherit the

future, while the learned find

themselves beautifully equipped to deal

with a world that no longer exists."

- Eric Hoffer



Diffusion of InnovationChange Agents Are Typically Early Adopter SMEs

Innovators

earlyadopters

earlymajority

laggards(never adopters)

* Adapted from Rogers, E. Diffusion of Innovations. New York, NY: 1995.

latemajority

Innovators. Recruit

innovators to re-design

care delivery

processes

Th

e C

ha

sm

N = number of individuals in group

N

N = number needed to influence group

(but they must be the right individuals)

Early adopters. Recruit early adopters to

chair improvement and to lead

implementation at each site.

(key individuals who can rally support)



Select Early Adopters Leaders

• You need both willing and able leaders.

• Identify those wanting to lead permanent improvement efforts –throw their hat in the ring (willingness).

• Allow those not wishing to lead to participate in the selection process (recommend top 3 picks – those with natural leadership = ability ).

• Executive leadership can select from top recommendations the most open minded leaders and give them decision rights.

• Involvement in the selection process leads to much, much better adoption later (“Onboard for the take-off not just the crash-landing.” – Dr. David Burton).



Change Agent Role: Preparing People for Change

Awareness

Desire

Knowledge

& Ability

Actively

Sustaining

Time

Pro

ductivity &

Perf

orm

ance

High

Low

Keep the chaos period as

short as possible

Minimize the loss of

Productivity & Engagement

Change!

Ensure managers/leaders are informed and preparedto lead their teams through the change – available and active.

Warn people that the process may be uncomfortable, but that they will survive.

Identify champions to represent key player groups in the design and implementation process.

Leadership defines the why the change is needed, sets the vision, then defines the sandbox for those impacted to participate in designing the future.



Engage Key Stakeholders

“Things get done only if the data we

gather can inform and inspire those in

a position to make a difference.”

–Mike Schmoker



Four Levels of Stakeholder Information Needs

Stakeholder

Group

Key Data Need Group Role

Executive Prioritization & Visibility Controls resources and funding allocations.

Domain

Leadership

Prioritization & Visibility Understands domain interactions and

tradeoffs (clinical, operational or financial).

Adoption Best Practice Tracking &

Actionable Metrics

Influences others and encourages change

(adoption of new processes).

Innovation Process Design &

Outcomes Prediction

Identifies root cause of poor outcomes and

designs better processes to produce better

outcomes.



Example Stakeholder Analysis

STAKEHOLDER IMPACT IMPORTANCE MATRIX AREA (see Stakeholder Matrix)

Current HEATProjected

HEATProjected HEAT

Name of functional role/group

affected by the change

Degree of impact on

this stakeholder

Level of

stakeholder's

influence on the

success of the

change

Where do they land on

the stakeholder matrix?Today

After CEO Email

goes out

After the details

of the role changes

are shared

SVPs (SEL) significant medium a. KEY PLAYER productive zone productive zone productive zone

SVPs (IL) significant high a. KEY PLAYER overwhelmed overwhelmed overwhelmed

EL significant medium a. KEY PLAYER underwhelmed productive zone productive zone

STDs significant high a. KEY PLAYER underwhelmed overwhelmed overwhelmed

TDs significant high a. KEY PLAYER underwhelmed productive zone productive zone

SDAs / DAs

(Tech Ops Pool)significant high a. KEY PLAYER underwhelmed underwhelmed overwhelmed

Domain Experts (IL) significant high a. KEY PLAYER underwhelmed overwhelmed overwhelmed

Analytic Dirs (IL) significant a. KEY PLAYER underwhelmed overwhelmed overwhelmed

SDAs / DAs (IL) significant high a. KEY PLAYER underwhelmed underwhelmed overwhelmed

Analysts (Prod Dev) significant a. KEY PLAYER underwhelmed underwhelmed overwhelmed

Leadership Team moderate high a. KEY PLAYER overwhelmed overwhelmed overwhelmed

HR minor or none medium c. keep informed productive zone productive zone productive zone

Finance - FPA moderate medium a. KEY PLAYER underwhelmed productive zone productive zone

Accounting moderate Low c. keep informed underwhelmed productive zone productive zone

Marketing minor or none Low c. keep informed underwhelmed productive zone productive zone

Customers moderate Low d. Keep satisfied productive zone underwhelmed productive zone

Identify Champions

to represent large

groups.

Keep Satisfied

Meet Their Needs

Key Player

Manage Closely

Monitor

Minimum Effort

Keep Informed

Show Consideration

Low High

High

Low

Interest of Stakeholders

Po

we

r / In

flu

en

ce

of

Sta

ke

ho

lde

rs



Provide Visibility to All StakeholdersShared Accountability Date: 10/1/2015 Overall Status

Risks and Uncertainties

Recruit /Train Kickoff AIM Intervention Rollout plan Results

Aug 2015 Aug 2015 Sep 2015 -- Sep 2015 Oct 2015

Guidance teamMission, charter,

roles confirmed

Review draft

cohort and dataFinalize cohort

Define rollout

plan

Review initial

results

Content and

Analytics Lead

Review AIM

options

Data quality

issues identified

Identify

intervention(s)

Guidance team

validation

Implementation

plan adjusted

Implementation

team

Best practice

gathering

Direct

observation

Direct

observation

Solicit front-line

plan input

Review lessons

learned

Workgroup

team

Profile prelim.

data and cohort

Prioritize, select

AIM

Solicit front line

input

Finalize rollout

plan

Create AIM

statement #2

Workgroup

training

Guidance team

validation

Refine cohort and

metrics

Analytics dev &

test

Guidance team

validationRepeat process

Guidance team

validation

Improvement Initiative Progress

Keynot

startedIn

processdone well

someconcerns

strongconcerns

Accomplishments Next Steps Issues / Help needed

• Feedback sessions with SMEs • Consolidate app versions

• Define rollout plan

• QV access

Long-term

AIM Goal

To realize X% in shared savings in the JCL ACO by the end of 2015. Going forward, achieve and sustain a

X% reduction in the PMPM, a X% reduction in patient leakage, and a X% decrease in out-of-network referrals

by February 2016.

AIM Performance

Goal #1:

Source CMS Claims data into the EDW by September 15th, 2015. Wire up the Shared Accountability application to

claims data, and show incremental value via the ability to determine JCL ACO network leakage as well as PMPM costs,

by October 1st, 2015.

Lau

nc

h/R

oll

ou

t d

ate

: 1

0/1

/20

15




– Acquire Data






– Change Agent

– Key Stakeholder






Developing a Data Driven Culture

“We’ve got to use every piece of data and

piece of information, and hopefully that will

help us be accurate with our player

evaluation. For us, that’s our life blood.”

- Billy Beane, General Manager Oakland A’s



The Journey Towards a Data-Driven Culture

Spreadsheet

Silos

• Silos or pockets of analysis.

• Conflicting spreadsheet reports and

interpretations of data.

• Battles over data ownership.

• Most time spent on hunting for and

gathering data.

• Focus is on is the data “right.”

Centralized

Reporting

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, Allscripts, NextGen)

DEPARTMENTAL SOURCES

(e.g. Apollo)

Pt. SATISFACTION

SOURCES

(e.g. NRC Picker, Press Ganey)

• Centralized single source of truth

established in EDW.

• Significant time spent on

standardizing definitions.

• Data begins to be trusted.

• Report queue begins to build.

• Focus is on requirements for

dashboard applications and

reports.

Data-Driven

Improvement Culture

• Improvement teams use analytics to

accelerate best practice adoption.

• Data drives decisions and actions.

• Focus is on growing and sustaining

outcomes improvement through

elimination of waste and variation

leveraging analytics.



Analytics Embedded in WorkflowImagine: Facebook as an EHR

From a blog Dale Sanders wrote in 2010

• Patient’s evolving health story at the

center of the record, not the encounter.

• Embedded video and images.

• Text and discrete data.

• Secure messaging.

• Social support from family & friends

• Flexible security, defined by the patient.

They expand our sense of connectedness.

Analytics is embedded…it’s ambient to the experience.



Analytics Embedded in WorkflowImagine: Amazon as a Clinical Order Entry System

• Drug and device availability

• Pricing

• Home delivery

• Automatic refills

• Patient reported outcomes

From a blog Dale Sanders wrote in 2010



Kawamoto et al, University of Utah, BMJ, 2005

Physicians are 15x more likely to change their ordering and

treatment protocols if presented with substantiating data at the

point of care vs. presented with the same data in a clinical

process improvement meeting.




Info

rmation C

ost

Information Benefit

High

Low High

Bubble Size = Count of Information Type

Lower the cost of

building reports through

more automated data

acquisition tools.

Analytics Embedded in

Workflow Software

Predictive Models/

Machine Learning

Dynamic Visualizations

(e.g. Qlik Tableau)

Parameter Based

Reports

Static Reports &

Score Cards




Analytics Embedded in

Workflow Software

Predictive Models/

Machine Learning


(e.g. Qlik Tableau)

Parameter Based

Reports

Static Reports &

Score Cards

Info

rmation C

ost

Information Benefit

High

Low High


Lower the cost of

building more advanced

analytics by leveraging

better design tools and

well trained Analytics

Engineers.




Info

rmation C

ost

Information Benefit

High

Low High


Replace & consolidate static

reports and score cards with more

advanced analytics over time. Analytics Embedded in

Workflow Software

Predictive Models/

Machine Learning


(e.g. Qlik Tableau)

Parameter Based

Reports

Static Reports &

Score Cards




Info

rmation C

ost

Information Benefit

High

Low High


Dramatically increase analytics

embedded in workflow as they are

15 X more likely to be adopted. Analytics Embedded in

Workflow Software

Predictive Models/

Machine Learning


(e.g. Qlik Tableau)

Parameter Based

Reports

Static Reports &

Score Cards



Capabilities to SCALE Outcomes Improvement

Leadership, Culture, and Governance

Financial Alignment

Where do we focus?

How are we compensated?

What should we

be doing?

How are we doing?

How do we change?

Clinical Outcomes

Cost Outcomes

Experience Outcomes




– Acquire Data






– Change Agent

– Key Stakeholder





Which infrastructure component does your organization struggle with?

154 respondents

1. Acquiring data – 29%

2. Granting access to data – 10%

3. Building actionable metrics – 32%

4. Finding insights in data – 29%

Poll Question #3

65


Which role is the most scarce in your organization? 154 respondents

1. Analytics engineer – 51%

2. Change agent – 43%

3. Key stakeholder – 6%

Poll Question #4

66


Which capability/question does your organization struggle with most?

151 respondents

1. Analytics: How are we doing? – 14%

2. Best Practice: What should we be doing? – 7%

3. Adoption: How do we change? – 51%

4. Financial Alignment: How are we compensated? – 10%

5. Governance: Where do we focus? – 18%

Poll Question #5

67



Principles – Spectrum Year 2 – Leverage the Entire Spectrum• On-Going Opportunity Analysis (What card should I play?)

– Return on Luck

– Build an Opportunity Pipeline (at least 3 X your capacity)

– Evaluate Each Opportunity (Effort, Value, Capability, Capacity, and Willingness)

• Light Effort Improvements (The Blue Cards)

– The Prerequisites of Organic Improvement

– Be Opportunistic – TCH – Chest X-ray Story

• Deep Continuous Improvement (The Green Cards)

– Avoiding the Tower of Babel – Pick ONE Improvement Methodology

– Organize Consistent On-going Interdisciplinary Teams

• No Margin, No Mission (The Coins)

– Involve the Finance Team Early and Establish Baselines for ROI Calculations

– Fund Clinical Outcomes with Financial Results in Other Domains

– Overhead Value Analysis



On-Going Opportunity Analysis

“Luck is not the key.

How you handle good or bad luck

is what matters.”

- Jim Collins



Return on Luck – Jim Collins

Return on Luck Keys

• Do both great and mediocre companies encounter the same amount of luck, good and bad? -YES

• What can you do to capitalize on your luck?

• Have you turned your bad-luck events into a big part of what makes your company great?

• Are you squandering your good luck events?

Good Luck• Governor with healthcare background is elected.

• Payer agrees to shared savings upside opportunity.

• Local provider wants to join healthcare system and open an institute.

Bad Luck

• Joint Commission visits at a bad time.

• New regulatory reporting required.

• Major philanthropy contributor stops giving.

• Competitor opens new facility.

• PCP refers out of network.

Common “Luck” Events in Healthcare



Build a Pipeline of Opportunities

One of the Analytics Engineer’s primary roles is to fill the pipeline of insights

and potential improvement opportunities.

Pipeline should exceed the execution capacity

of the organization.

• Motivates an increase in capacity.

• Teaches the organization to prioritize based on highest value.

• Allows for back-up opportunities to be advanced should a high priority

initiative stall indefinitely.

Do not limit pipeline to high-effort opportunities. Many organic improvements

can occur simply by exposing the opportunity.



Value

Type

Financial

(Hard $)

Clinical /

OperationalExperience

Light< 1 FTE or

$XXX

Process

Metric

Improved

Save

Clinician or

Patient Time

Medium > 1 < 3 FTE

Patient

Outcome

Improved

Overall

Satisfaction

score

improves

High > 3 FTE Life Saved

Evaluate Each Opportunity

Return on Investment

Effort to Achieve

• Light: < 1 FTE investment

• Medium: > 1 but < 3 FTE

• High: > 3 FTE investment

Value of Opportunity

Capability

• Do we have the skills required? What would it take to gain skills?

• Do we have the equipment/tools required?

Capacity

• Do the stakeholders required to make the change have the time to focus on this improvement? What can we take off their plate?

Willingness

• Is there an acceptance of the need for change? Do we have front-line buy-in?

• How much resistance will be encountered? From whom? (mini stakeholder analysis)

Organizational Readiness



Readiness Assessment• Quickly asses readiness with on-line surveys. (e.g. use something like survey monkey or

Health Catalyst provides a free on-line Outcomes Improvement Readiness Assessment

at https://oira.healthcatalyst.com .

• As you focus in on specific initiatives spend the time to interview key stakeholders of the

most important improvement initiatives and assess capability, capacity and willingness.

https://oira.healthcatalyst.com/




– Return on Luck















Light Effort Organic Improvements

To promote organic improvements organize Analytic Resources in a Hub and Spoke model

Centralized (Hub)

• Analytics Infrastructure.

• Analytic Engineer Training.

– Tool Training.

– Visualization standards.

– Statistical Analysis.

– Machine learning.

• Data Governance.

– Data Steward Training.

– Common Metric definitions.

– Data Quality Standards.

– Standardized Visualization Look & Feel to promote ease of use.

De-centralized (Spoke)

• Responding to departmental

questions using Ad-hoc queries

or analysis.

• Custom dashboard

development.

• Interpretation of data, based on

local context and knowledge.

76

• Access to content

enabled through a

security model

endorsed by senior

leadership.

• Provisioning process

well defined and

operationalized.

Broadly Accessible Data

• Analytic tool

capabilities support

what end users are

trying to do.

• Analytic community

has the ability to share

and distribute content.

Analytic Toolset Alignment

• Teams are provided

education on the core

capabilities to support

their use of the data.

• Support function

available to answer

and direct questions.

Training & Support

• Continuants

understand what is

available, what is

changing, and what is

coming.

• Value being delivered

by the platform is

consistently and

broadly being

messaged.

Communication

• Individual or group is on point to grow analytics capabilities.

• Ensure evolving roadmap aligns with business/clinical priorities.

Analytics Leadership

The Prerequisites of Organic Improvement



Organic Improvements – Be Opportunistic

Texas Children’s Example

Context

• Working on Asthma Action Plan Initiative.

Discovery

• While exploring the data MD Leader and Analytics

Engineer find anomalies in data around chest x-rays

unrelated to the asthma action plan initiative.

• Appears high percentage of chest x-rays from ED are

unwarranted.

• Analytics Engineer, performs deeper analysis within a

few hours and discovers highly utilized order set used

by Resident MDs in ED.

Intervention

• New default orders set using best practice

intervention criteria for x-ray designed to

replaces old order set.

• Resident MDs in ED instructed to use new

order set for children presenting with asthma.

Result

• Achieved and sustained a 49 % decrease in unnecessary chest x-ray orders.

– Better Care for Patients

– Elimination of Unnecessary Cost

– No Extra X-ray Exposure to Kids

• Value = High

• Effort = Light




– Return on Luck















Rosetta Stone: Translation between different

improvement methodologies.

Key Principle: Pick ONE Methodology and use it

consistently across your organization

Avoiding the Tower of Babel –Pick ONE Improvement Methodology



1 2 3 4 5 6 7

Analyze the Opportunity

and Define the Problem.

Scope the Opportunity

and Set Goals.

Explore Root Causes and Set Process

Aims.

Design Interventions

and Plan Initial Implementation

Implement Interventions and Measure

Results.

Monitor, Adjust, and Continually

Learn.

Diffuse and Sustain.

Is it an adoption problem?

Are data valid?

Do we need to adjust our interventions?

Do we need to reevaluate root cause?

Start with a directive from executive leadership based on high-level opportunity analysis and readiness assessment

The Seven Essential Elements of Improvement



Organization of TeamsClinical and Technical

Prioritization

Adoption

Innovation

etc.

Outcomes Improvement

Executive Leadership Team

Content & Analytics Team(s)

Data Governance

CommitteeDomain Guidance Team

Provides domain oversight

and drives priorities

Outcomes

Improvement Team(s)Drives innovation & adoption

Workgroup(s)

as neededWorkgroup(s)

as neededInnovates

Domain 1

Domain 2



Women & Newborn Guidance Team - Prioritization

Key

Key Stakeholder

Change Agent/SME

Analytics Engineer

Structure Typically Needed for Deep Effort Improvements

• Meet quarterly to prioritize allocation of

technical staff.

• Approves improvement AIMs

• Reviews progress and removes road

blocks.

OB NewbornGYN

Women & Newborn Guidance Leadership Dyad:

1) MD Clinical Program Director 2) Administrative Director

Domain Leadership Dyads:

1) MD Lead & 2) RN Lead.

SME

Data Steward

Analytics

Engineer

Analytics Team covers

entire guidance team.Financial

Analyst

Small Teams - Innovation • Integrates Data from all relevant sources.

• Meet weekly in iteration planning meeting to identify improvement opportunity and insights.

• Build DRAFT processes, metrics, interventions & presents DRAFT work to Broader Teams.

• Grants access of analytic assets to broader team.

Domain Leadership Dyad

+ Analytics Team

OB Workgroup

Broad Teams – Adoption

• Broad RN and MD representation across system.

• Meet monthly to review, adjust and approve DRAFTs.

• Act as change agents to lead rollout of new process and measurement.

Guidance Leadership Dyad.

+ Domain Leadership Dyad.

+ Analytics Team.

+ Clinical representation from across system.

*All resources serve in these improvement roles part time ranging from

5% (MDs) to 50% (Analytics Engineer) of their time.



Process Aim: By X date increase the number of patients who have

follow-up completed within 7 days from X% to Y%.

Outcome Goal:By X date decrease readmits from 22.1% to 17.7%.

Track follow-up compliance

(hospitalists, advanced practitioner,

cardiology nurse) discharge and

post results weekly.

Develop, educate, and implement

on a discharge scheduling protocol

to facilitate improved appointment

follow-up 7 days per week.

Intervention #1 Intervention #2

Heart Failure Example, Focus on Transitions




– Return on Luck















Funding Improvement Work

“No Margin, No Mission”»Sister Irene Kraus

Founding Chief Executive of the Daughters of Charity National Health System

American Hospital Association Chair



Funding Improvement WorkInvolve the Finance Team Early in the ProcessWorking with CFO sanctioned financial analyst or other key stakeholders:

Set baseline costs for current process

Calculate improvement value:

- Hard Cost Savings = $ will be removed from the budget next year

- Soft Cost Efficiency Gain = Improvement efficiency will allow for employee to work on higher priority tasks

- Cost Avoidance = Project the value of reversing a trend such as an upward cost trend that becomes flat due to improvement efforts

Negotiate with Payers on shared savings opportunities



The Right Granularity: Best Practice Compliance

Admits/1000 members

IP days/1000 members

OP visits/1000 members

Procedures/1000 members

ED visits/1000 members

Readmissions/1000 members

UtilizationWho should

get the care?

Cost/case

Cost/procedure

OR minutes

L&D minutes

Other LOSOrder Sets

Clinical

Support

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

Granularity

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 Examples of Best Practice Standard Possible Measures

Administrative

Support

Workflow

How can

administrative

operations be

performed

efficiently ?

AR Escalation Process

Network Design Process

Recruiting/Onboarding Process

AR Days

% out of network utilization

% Turnover

Team member

satisfaction/engagement

AR Escalation Process

Budgeting Process

Supply Chain Procurement



Involve Finance Early: Payment Model Considerations

= Negative Impact = Positive or Negative = Positive Impact

Improvement TypeDiscounted

FFS Per Diem

Per Case Bundled Per CaseCondition

Capitation

Full

CapitationCMS Commercial CMS Commercial

Workflow

Diagnostic Variation

Standing Orders

Medication Selection

Triage

Patient Safety

Ambulatory Treatment

and Monitoring


Indications for

Intervention

Operational Workflow

Diagnostic Variation

Standing Orders

Substance Selection

Triage Criteria

Patient Safety

Treatment and Monitoring

Algorithms


Indications for Intervention

25

Administrative Workflow

Depending on the

type of

improvement,

the financial impact

could be positive

or negative based

on the payment

model mix.

Therefore,

proactively involve

finance and

negotiate shared

savings with payers

up-front when

possible.



Funding Improvement Work: Balancing Value Mix Helps Fund Clinical & Experience Improvements

High

Light High

Value

IDEAL: Even spread across the Improvement Spectrum Matrix

Effort

Financial Value

Clinical Value

Experience Value

Improvement Type

As your governance team

prioritizes improvement initiative

make sure that the projected

hard $ cost savings can fund

the improvement efforts required

across all value types.



Funding Improvement Work:Overhead Value Analysis

• Frequently an organization has built up a large

inventory of regularly produced reports (with an

associated cost)

• In addition, many vended technical

point-solutions continue to be maintained over

decades (with an associated maintenance fee)

• Overhead Value Analysis is the “spring

cleaning process” of reviewing the cost to

maintain or produce these analytic assets

compared with the benefit each currently is

providing the organization.




– Return on Luck














How does your organization prioritize opportunities for outcomes

improvement? 122 respondents

1. We can’t say no to anything. – 14%

2. We estimate the value to the organization. – 9%

3. We estimate both effort and value. – 20%

4. We estimate effort, value, and readiness. – 25%

5. It’s all about the politics. – 32%

Poll Question #6

92


Which of the prerequisites of organic improvement is weakest in your

organization? 121 respondents

1. Broadly accessible data. – 22%

2. Alignment around an analytic toolset. – 18%

3. Analytic training and support. – 25%

4. Communication of analytic value and roadmap. – 31%

5. All of these components are working well. – 4%

Poll Question #7

93


True or False: My organization has stale analytic reports that are

never used but continue to be produced? 144 respondents

1. True – 73%

2. False – 27%

Poll Question #8

94



Principles – Spectrum Year 3 – Sustain and Spread• Interdepartmental Collaboration Requires Good Data Governance (The Yellow

Cards)

– Improve Data Quality.

– Train for Data Literacy.

– Promote Appropriate Data Access.

• System-Wide Adoption (The Purple Cards)

– Establishing an Analytic Services Working Group (User Group).

– Marketing your Analytics Like a Small Business (The Improvement Vignette).

– Establish Improvement Governance.

• Avoiding Conflict and Contention (The Red Cards)

– The Worst 10 Practices in Healthcare Analytic Interactions.

– The Productive Zone – Helping Everyone Engage in the Work.

– Improving Interdepartmental Communication (Intent and Impact).



Interdepartmental Collaboration

“Coming together is a beginning.

Keeping together is progress.

Working together is success.”

-Henry Ford



Definition of Data Governance

Data governance refers to the plans, processes, and

principles that are proactively applied to ensure that an

organization’s data is managed in such a way to

maximize the value of that data to the organization.



The Triple Aim of Data Governance

1. Ensuring Data Quality

• Data Quality = Completeness x Accuracy x Timeliness.

2. Building Data Literacy

• Train on analytics basics (Data Awareness).

• Technical tools and analytic techniques (Analytics Team).

• Data content (Context provided by Data Stewards).

3. Maximizing Data Utilization

• Promote cross-department appropriate usage of data. Track # of users

per month by dashboard. Measure direct access.

https://www.healthcatalyst.com/demystifying-healthcare-data-governance

Utilization



Data Use Defines Data Quality Requirements

Balance the cost of achieving data quality with benefit.

Data Use Category Improvement Comparison or

Accountability

Research

Aim Outcomes Improvement Comparison, Choice,

Spur Change

Discover New

Knowledge

Test observability Test Observable No Test Test Blinded

Sample size Just Enough Obtain 100% Just in case

Flexibility of hypothesis Flexible, Changes as

Learning Takes Place

No Hypothesis Fixed

Is change an

improvement?Run Charts and

Shewart Charts

No Change Focus T-Test, F-Test, p-value

Cost of data quality Low/Medium High Medium/High

Common challenges Tendency to Apply

More Rigor Than

Needed

Data Often Used for

Punishment

Access to Data

Sometimes

Problematic

Adapted from The Health Care Data Guide, p. 27



Data Literacy - Typical Current State

• Large backlogs of

analytic/report

requests.

• Knowledge workers = clinical

or operational knowledge

AND access to tools and

data.

Knowledge

Workers

Drillers

Viewers

User Distribution



Authors or

Knowledge

Workers

Viewers

Drillers

Knowledge

Workers

Data Literacy - Desired Future State

Increase number of knowledge workers by

doing the following:

• Expand data access.

• Simplify data structures.

• Continue use of naming standards.

• Provide better tools.

Promote shift in culture by rewarding process

knowledge discovery rather than punishing

outliers.

Desired User Distribution



Trust but Verify: The Importance of Broad Access AND Auditing Key Polarity: Grant Broad Access to Promote Utilization AND Rigorously Audit Appropriate Use of Data

https://www.idera.com/productssolutions/sqlserver/sqlcompliancemanager

Idera Compliance Manager

Audit tool example:

Idera Compliance Manager




Cards)














Analytical Services Working Group (ASWG) User Group

Purpose of ASWG:

Serve as a forum where analysts from the various

domains within the organization can collaborate to

define standards and share knowledge.



Analytical Services Working Group (ASWG)

Responsibility Summary

Define information and analytical standards.

• Standardize calculations and definitions.

• Recommend tools and processes.

• Establish data quality standards.

Provide technical and domain cross-training.

Information consumers (analysts) provide feedback to technical staff on tools, information,

performance, processes, etc…

Technical staff (analytics engineer team) provides status updates and notices to analysts on

infrastructure and content.



System-Wide Adoption

“Run your analytics department

like a small business.”

- Dale Sanders



Data-Driven Outcomes Improvement & Decision Making

Service

Oriented

Engaged,

High Contributing

Analytics Engineers Satisfied, Empowered

Analytics Customers

Scalable, Sustainable

Products

Employee

Focused

Technically

Sound

Analytic Asset Adoption

Running Your Analytics Department Like a Small Business



System-wide Adoption: Effective Marketing

Effectively Marketing your Analytics Department requires

Two key elements:

• Educate

– Let the organization know what’s available.

– Help them become more sophisticated users of analytics.

• Share Results

– Success breeds success.

– Publish Improvement Vignettes.



NO READMITS Bundle Successfully Lowers COPD Readmissions

Chronic Obstructive Pulmonary

Disease (COPD) is responsible for

approximately 135,000 deaths

annually, making it the third

leading cause of death in the U.S.

Nationally, there are

approximately 700,000

hospitalizations with the principal

diagnosis of COPD each year,

with one in five patients being

readmitted within 30 days. The

national average cost for a COPD

readmission is between $9,000

and $12,000.

95% of COPD patients assessed for readmission risk.

17% reduction in readmission rate. Approximately 34 fewer

patients with COPD readmitted each year, saving an

estimated $360,000 annually based on national benchmarks.

9% improvement in PCP notification.

97% of patients with COPD get an order set.

Building from work done by the Heart Failure team, MultiCare's

Medicine Collaborative developed a NOREADMITS bundle

consisting of nine interventions for patients with COPD who are not

mechanically ventilated. The intent of the bundle was to decrease

the likelihood of readmission. After less than nine months, they

achieved the following results:



[Headline – the One Sentence Grabber]

The Setting:

What is the situation and

context?

The Challenge:

What was the complication?

What problem are we solving?The Result:

What happened?

What was improved?

The Turning Point:

What interventions where used to make a difference?

How were analytics used in solving the problem?

What expertise was needed for the improvement?

Organization Logo



System-Wide Adoption: Improvement Governance

Stakeholders:Starting at the top, engage all stakeholders around a common vision.

Shared Understanding:Have a common understanding of organizational needs, capabilities, and readiness.

Alignment:Use a consistent improvement methodology, align incentives, and balance polarities.

Focus:Practice disciplined decision-making to prioritize, fund, organize, and sustain initiatives.

Key Objective of Improvement Governance:

Move from a loose federation of hospitals and clinics

that share supply purchasing . . .

. . . to an integrated care delivery system that delivers

consistent high quality, coordinate care everywhere

across the continuum at the lowest appropriate cost.

Governance Quest

HAS 16 Game




Cards)














Avoiding Conflict & Contention: Creating a Pause

“Freedom is the ability to pause between stimulus and response and in the

pause to choose.”

- Viktor Frankl



The 10 Worst Practices in Healthcare Analytic Interactions

1. Use Data as a Weapon.

2. Misrepresent Data.

3. Prevent Appropriate Data Access.

4. Disengage from the Process.

5. Highlight Data Imperfections / Discredit.

6. Analysis Paralysis.

7. Political Favoritism.

8. Budget Cuts Across the Board.

9. Delay a Decision with Stall Tactics.

10. Stick to the Status Quo.



Signs Someone Is Outside the Productive Zone

Blame others, distract attention, denial



Start With Compassion

When you don’t know what to try first,

lower the heat.

• Validate feelings, acknowledge loss.

• Simplify and clarify.

• Address the technical aspects.

• Break the problem into parts.

• Restore, add, or reallocate resources.

• Temporarily reclaim responsibility for tough issues.

• Give your attention.

• Take stock of what is available.

• A lot more time, enrich knowledge and skills.

Nobody misbehaves from a place of strength.



Avoiding Contention: Understand Intent & Impact

“I know that you believe you

understood what you think I said, but I

am not sure you realize that what you

heard is not what I meant.”

- Robert McCloskey



Other’s impact on me

My intention Intention of other

My impact

Harvard Negotiation Project

Mine Other’s

Intention

Impact



Mine Other’s

Intention

Impact




Our assumptions about intentionsare often wrong.

Good intentionsdo not make bad

impact unimportantor irrelevant.


Mine Other’s

Intention

Impact



Other’s

This is what I meant.

This is how it felt/seemed to me.

Is that what you meant?

How did it feel/land with you?


Mine

Intention

Impact




Cards)













Within the triple aim of data governance, our organization struggles

most with? 113 respondents

1. Data Quality. – 25%

2. Data Literacy. – 31%

3. Data Utilization. – 41%

4. We’re great at all three of these. – 3%

Poll Question #9

123


Which of these 5 practices have you seen at your organization?

(select all that apply) 109 respondents

1. Use Data as a Weapon. – 32%

2. Misrepresent Data. – 41%

3. Prevent Appropriate Data Access. – 39%

4. Disengage from the Process. – 54%

5. Highlight Data Imperfections / Discredit. – 55%

Poll Question #10

124



Spectrum - Key Takeaways and Lessons Learned

Set your organization up for success

• Invest in Infrastructure (Acquire Data, Grant Access, Build Actionable Metrics and Discover Insights).

• Train Key Roles and Skills (Train Analytics Engineers, Recruit Change Agents and Engage Key Stakeholders).

• Create a Data-Driven Culture (Embed Analytics in Workflow, Invest in 5 Core Capabilities for improvement 1) Leadership,

Governance & Culture, 2) Analytics, 3) Best Practice, 4) Adoption and 5) Financial Alignment).

Unleash data across the Spectrum of Improvement

• Perform On-going Opportunity Analysis – Asses Value, Effort, Capabilities, Capacity and Willingness.

• Enable Light Effort Improvements – Prerequisites 1) Grant Broad Access, 2) Analytic Toolset Alignment, 3) Training and Support,

4) Communication and 5) Analytic Leadership; Be Opportunistic – improve your Return on Luck.

• Invest in Deep Effort, High-Value Improvement - Pick ONE Improvement Methodology, and Organize consistent on-going

interdisciplinary teams.

• Understand No Margin, No Mission – Involve the Finance time early, Fund clinical outcomes with financial results in other

domains, and use Overhead Value Analysis to eliminate less value reports and point solutions.

Sustaining and Spreading Improvement

• Collaborate with information & resources – Establish Data Governance – increase Data Quality, Data Literacy and Data Access.

• Promote system-wide adoption – Create an Analytic Services Working Group to promote standards & knowledge sharing, Market

you Analytics like a small business and establish improvement governance.

• Reduce wasteful contention – Avoid worst practices such as using data as a weapon, help everyone engage in the work by staying

in the ”Productive Zone, and improving communication by understanding intent and impact.



A

Questions &Answers



Thank You

Unleashing Data: The Key To Driving Massive Improvements

Healthcare

Transcript of Unleashing Data: The Key To Driving Massive Improvements