data-driven quality improvement - ecri institute driven quality...1/20/2017 1 ©2015 ecri institute...

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©2015 ECRI INSTITUTE

Data-Driven Quality

Improvement

Patricia Stahura, RN, MSN

Senior Patient Safety Analyst/Consultant

ECRI Institute

January 26, 2017

©2017 ECRI INSTITUTE2

• Power Point Slides viewed here• Today’s session is recorded• Today’s slides and recording will be

posted to the ECRI website.

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How to Ask Questions

Please submit your text questions and comments using the Questions Panel

Remember . . .

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How to Download Slides

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For Physicians:

CME Accreditation Statement:

This live activity has been planned and implemented in accordance

with the Essential Areas and Policies of the Accreditation Council for

Continuing Medical Education (ACCME). ECRI Institute is accredited by

the ACCME to provide continuing medical education for physicians.

AMA Credit Designation Statement:

ECRI Institute designates this live activity for a maximum of 0.75 AMA

PRA Category 1 credits tm. Physicians should claim only the credit

commensurate with the extent of their participation in the activity.

All faculty members involved in this January 26, 2017, live webinar

Data-Driven Quality Improvement have disclosed that there are no

conflicts or financial affiliations.

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For Nurses:

This activity has been approved for up to 1.0 California State Nursing contact

hours by the provider, Debora Simmons, who is approved by the California Board

of Registered Nursing, Provider Number CEP 13677. Credit will only be issued to

individuals that are individually registered and attend the entire program.

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To be eligible for credits:

Credit will only be issued to individuals that are individually registered and

attend the entire program. Each individual participant must log on prior to the

start of the program and remain on the line for the entirety of the program. This

is how individual timed attendance is verified. In addition you must complete an

attestation survey included in the post webinar evaluation at the conclusion of

the webinar. Once all that information is verified, qualified attendees will receive

a certificate via e-mail within 60 days of today’s program.

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About ECRI Institute

Independent, not-for-profit applied research institute

focused on patient safety, healthcare quality, risk

management

ECRI Institute resources about quality and safety

Obtain username and password by contacting us at

[email protected] with your name and contact information

■ Sign up to receive notifications of monthly webinars

50-year history, 450-person staff

■ Evidence-Based Practice Center under the Agency for

Healthcare Research and Quality (AHRQ)

■ Federally designated Patient Safety Organization

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mailto:[email protected]

1/20/2017

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Learning Objectives

1. Review definitions of quality data

2. Differentiate types, sources, and functions of quality data

3. Identify the elements of collection, analysis, and

reporting

4. Recognize the value of data-based decision making

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Quality Program

Team approach with assigned roles

Goal-directed: the Quadruple Aim

Work is structured around areas of interest to reach goals

Plan brings measures, outcomes, and focused studies to

gain buy-in and ownership of a complex process

Continuous evaluation and improvement

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Data Definitions

Word Description

Data Set of collected facts

Numerical Measured or variable data and counted or attribute data

Qualitative Text or words

Quantitative Numbers

Benchmark Measures its performance against that of best in class

Target Goal to be achieved

Threshold Point or level at which something begins or changes

Dashboard Combines all your data sources, like a road map

Indicator Established measures to determine level of performance

Trigger Efficient manner of screening to identify harm and identify cases

for more detailed review

Source: American Society for Quality. Quality glossary. [cited 2016 Dec 14]. http://asq.org/glossary/d.html

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http://asq.org/glossary/d.html

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Data Example What the Data Tell Us

Outcome ► Core measures

► Average HgB A1C for a

population of diabetes

patients

► What you made, the end product, the actual results

► Whether change occurred that leads to the intended outcome

► Whether the services and care delivered are meeting the

goals of the organization

Process ► Throughput

► Variability of the process

► Trends

► Whether actual practices follow the recommended sequence

► How smoothly the process works, efficiency

► Whether the parts/steps in the system are performing as

planned

► Whether an action was completed

Structural ► Findings from AHRQ Culture

of Patient Survey

► Staffing levels

► Volume of procedures

► Underlying processes

► Reflects conditions in which clinicians care for patients

Exception ► Incident reports

► Broken equipment reports

► Breakdowns in a process or a problem

► What are the specific exceptions that indicate times when our

processes are not working as planned

Activity ► How many activities on time

► Cost per activity

► How effectively are we completing improvement activities to

address areas identified as process weaknesses

Composite ► Patient safety indicators

► Adverse events/

1,000 patient-days

► Combine the results of multiple performance measures to

provide a more comprehensive picture of quality care

Types of Data

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“What” Data and “Why” Data

► “WHAT” data tell us what happened

■ In January we had 210 falls

■ You can’t fix “what”

► Other data tell us “WHY” something happened

■ 110 falls were related to medications and 100 were the

result of patients’ lack of awareness of their limitations

■ After you analyze and interpret, you can fix “why”

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Polling Question 1

Most raw data are _________________ data.

A. What

B. Why

C. Outcome

D. Composite

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Characteristics of Quality Data

Characteristic Description

Important To the hospital/clinic/patients and families

Valid Means what it should

Feasible Can be done or can be demonstrated

Reliable Can be replicated if pulled again

Predictable Providers document the same way consistently

Evidence

based

Blends the best available scientific knowledge

with clinical expertise

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Data Sources: Where Do We Find Data?

► Chart review

► Data from electronic health record (EHR):

electronic clinical quality measures (eCQM)

► Observations

► Claims

► Billing data

► Administrative data

► Surveys and interviews

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Data Functions: What Is the Purpose of Data?

Data prove quality Data drive quality

■ Tangible measure

■ Supports and authenticates

mission, vision, strategy

■ Connect EHR data with

quality goals

■ Quantifies Quadruple Aim

(i.e., readiness, better care,

better health, lower costs)

■ Monitors, protects, and

controls

■ Points to areas of future or

further quality improvement

efforts

■ Measures drive to

improvement

■ Alerts and triggers

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Data Objectives: Why Do We Measure?

► Regulations and accreditation

► Payment and reimbursement

► Standardization for comparisons

► Quality assurance to keep it the same

► Performance improvement to make it better

► Information for stakeholders

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Deciding What to Measure

► Quadruple Aim

► Regulation and accreditation

► “Report once”

► Key processes of care and services

► High risk—high volume—problem prone

►Patient experience

■ Quality/performance improvement—“Make me better”

■ Patient satisfaction—“Keep me comfortable”

■ Patient safety—“Don’t hurt me”

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Are the Data Meaningful?

► Generate more answers than questions

► Answer the question

► Provide insight

► Current, not too old or stale

► Valuable

► Actionable

► Make sense clinically

► Transparent

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Data Collection

►Data collection procedure

■ Chart abstraction

■ Direct observation

■ Who will collect data

■ Make sure it captures the workflow

■ Recording the data

■ Will the data need coding

■ Data entry or formatting

■ Frequency

■ Missing or incorrect data

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Using the EHR for Data Collection

Advantages Disadvantages

■ Staff trained in EHR use and data

entry

■ Information complete and consistent

■ Documentation is accurate and

timely

■ Data can be extracted from

reportable fields

■ Data analysis becomes less labor

intensive

■ Electronic interfacing of lab and

radiology data allows for rapid access

■ Easily accessed, shared, and

exchanged

■ Third-party data collection—

abstraction and submission

■ Data may be missing

■ Lack of experience

■ Data are incorrect

■ Data cannot be extracted from free text

■ Provider documentation deficiencies,

especially “negation” (provider should have

done the task but didn’t)

■ Extraction gaps or errors

■ Data abstraction

■ Bugs or glitches in the system

■ Incomplete file submission may result from

formatting files or data elements

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Is “Data” the Same as “Information”?

Data

■ Facts, raw, unorganized, input, disparate, random,

low value

Information

■ Evolved, structured, refined, organized, processed,

analyzed, data set, context, meaning

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Reviewing the Data

► Raw data

► Information

► Seven tools of quality

■ Cause and effect diagram

■ Check sheet

■ Control chart

■ Flowchart

■ Histogram

■ Pareto chart

■ Scatter diagram

Source: American Society for Quality. Quality glossary. [cited 2016 Dec 14]. http://asq.org/glossary/d.html

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Analysis and Decision Making

► Data complexity

► Siloed and scattered

► Meaningful information

► Deep dive

► Identify areas and causes of variability

► Process of translating data

► Interpretation

► Database, pivot tables, time series

► Dashboard

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Elements of a Quality Dashboard

► Financial data

(outcome + satisfaction + safety / cost = value)

► Clinical quality

► Operational data

► Satisfaction data

► Condensed to 15–30 metrics

► Graphic display

► Triggers targets or threshold

► Benchmarks

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Polling Question 2

On quality dashboards, the colors red, yellow, and green

have standardized meanings:

Does not meet, Meets, Exceeds

A. True

B. False

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sample dashboardRM Risk Management

Measure, Key Performance Indicator Target 1stQ 2ndQ 3rdQ 4thQ Annual

Incident Reports

Clinic # of incident reports Freq

Clinic Incident reports filed within 24 hrs of event 90%

SREs/SIs

Clinic # of Serious reportable events/serious incidents Freq

RM Dept Determination of preventability completed by RM

within 30 days of SRE

90%

RM Dept # of SREs not billed because of preventability

analysis

Freq

Root-Cause Analyses (RCAs)

RM Dept # of RCAs completed per qtr Freq

QA Quality Management Health Care Aquired Infections

IC Dept Total HAI 1

IC Dept CAUTI 1.06

IC Dept SSI 0.86

IC Dept CLABSI 0.54

IC Dept C.Diff 0.9

IC Dept Employee Influenza Vaccination Rate 90%

Antibiotic Stewardship

Ph Dept #convert IV to oral 60%

Ph Dept Culture/test prior to treatment 70%

Key IndicatorsQM Dept Mortality/risk adjusted 0.9 1.5 1.3 1.2 1.2QM Dept Readmission 15.5 17 14 23 17QM Dept C-sect before 39 weeks 22% 32 34 30 30QM Dept Patient Satisfaction 80% 78% 76% 79% 78%QM Dept All cause readiness 88% 90 90 91 90

HEDIS

QM Dept Diabetes screen complete 90%

QM Dept Tobacco counseling 95%

QM Dept Cancer screen 65%

PS Patient Safety

PSO PSI composite 1%

PSO Adverse events/1000 pt days 8%

PSO Adeverse events/100 admissions 4%

PSO % admissions with AE 23%

Dashboard Key - Performance

Improved/exceeded expectations T

Acceptable/needs improvement T-10

Not meeting target, action needed >T-10

Proprietary and Confidential

Copyright ECRI Institute, 2016

Dashboard Key - Performance

Improved/exceeded

expectations T

Acceptable/needs

improvement T-10

Not meeting target, action

needed

>T-

10

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Military Health System (MHS) Dashboard• Internally transparent for staff with CAC

• Website to gain access https://carepoint.health.mil


How Do You Know There Is Improvement?

Look at the data, your measurement

Check against the goal for expected compliance level

Match the data against the target

Check against the threshold

Minimal acceptable level of performance

All improvement will require change, but not all change

will result in improvement

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https://carepoint.health.mil/

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Analysis . . . Ask WHY ?

Look for patterns and trends

Determine the cause

Identify opportunities for improvement

Convert the data into actionable data

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Sound Decision Making:

Continue or Change Direction?

Magnitude

Direction

Variability

Rate

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Continue or Change Direction?

Question What It Tells You Quality Tool

Magnitude How much? Too much or too little

How does it compare to others

Limits

Goals

Benchmarks

Direction Better or worse?

Increasing or

decreasing?

Improving or declining

Fewer or more

Longer or shorter

Crossing

averages

Trends

Variability Is it under control or

out of control?

Nice and steady improvement

Predictable versus random

Bouncing all over the place

Smooth or spikes

Control

charts

Rate How fast is it

changing?

Slow or fast change

Slow and steady

Plenty of time versus emergency

Trends

Slopes

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Jan

ua

ry

Feb

rua

ry

Ma

rch

Ap

ril

Ma

y

Jun

e

July

Au

gu

st

Se

pte

mb

er

Oct

ob

er

No

ve

mb

er

De

cem

be

r

Ye

ar

in R

ev

iew

CLABSI Rate 0.020 0.063 0.000 0.000 0.000 0.125 0.031 0.000 0.170 0.113 0.167 0.068 0.063

# of CLABSI 1 4 0 0 0 7 2 0 8 6 5 3 36

# of central l ine days 50 63 44 32 48 56 64 38 47 53 30 44 569

Central Line Utilization Rate 0.12 0.15 0.12 0.09 0.12 0.17 0.16 0.10 0.13 0.12 0.08 0.11 0.12

# of central l ine days 50 63 44 32 48 56 64 38 47 53 30 44 569

# of patient days 402 422 378 352 396 324 388 392 376 428 400 388 4646

Charts reviewed 0 0 0 0 0 0 0 0 0 0 0 0 0

Checklist Incompletion Percent N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A N/A# of incomplete process checks

before procedure 0 0 0 0 0 0 0 0 0 0 0 0 Before Procedure0

# of incomplete steps

prior to l ine insertion 0 0 0 0 0 0 0 0 0 0 0 0

Prior to Line Insertion0


during the procedure 0 0 0 0 0 0 0 0 0 0 0 0

During the Procedure0


after the procedure 0 0 0 0 0 0 0 0 0 0 0 0

After the Procedure0

View Reports (Dashboard) >>

Central Line-Associated Bloodstream Infection (CLABSI) Worksheet for Year

Data Worksheet

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Central Line-Associated Bloodstream Infection (CLABSI) Dashboard for Year

Comments

0.12

0.15

0.12

0.09

0.12

0.17 0.16

0.10

0.13 0.12

0.08

0.11

0.00

0.05

0.10

0.15

0.20

Central Line Utilization Rate (central line days /patient days)

0.020

0.063

0.000 0.000 0.000

0.125

0.031

0.000

0.170

0.113

0.167

0.068

0.0000.0200.0400.0600.0800.1000.1200.1400.1600.180

CLABSI Rate (central line count /central line days)

Magnitude

Direction

Variability

Rate

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Polling Question 3

Refer to the previous slide:

Your supervisor asks you to explain the CLABSI* dashboard

performance report. You tell her that ___________.

A. Improvement is declining

B. The CLABSI rate is going in the wrong direction

C. Performance is out of control

D. It’s bad

E. A, B, and C

* Central line–associated bloodstream infection

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Decision Making

Skill set

Blend experience and evidence

Synthesize disparate data

Magnitude, direction, variability, rate

Identify risks

Identify areas of variation and opportunities

Bias

Gut instinct

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Gut Instincts

► Experience and/or emotional filter

► No hard analytical data or information

► Only source available

► Refined analytics but instincts rule

► Refine intuitive decision making

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Value of Data-Based Decision Making

► Quantifies

► Supports a common language

► Follows a framework

► Identifies risks

► Defines fact versus opinion

► Increases credibility and reliability

► Bias free

► Explains performance

► Classifies priorities

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Make the Decision

► Analysis paralysis

► Learn to make the best decision possible

► Even if the data set is incomplete

► Law of diminishing returns

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Reporting the Right Level of Data

Level of organization Level of information

Senior leaders Summary, strategic

Emerging concerns

Long-term strategic and financial goals

Committees Dig deep, granular or specific cases

Recommendations

Directors Shorter-term tactical goals for the month,

quarter, year

Managers/supervisors Meeting daily and weekly

Staff My work and contribution

Organization’s work

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Quality Stories

Rudyard Kipling wrote, “If history were taught in the form of

stories, it would never be forgotten.”

Data will be remembered if presented in the right way

Dashboards, slides, spreadsheets, or graphs tell a story

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Tips on Writing a Quality Report

► Determine who is your audience

► Be concise and well organized

► Make it easy to scan

► Engage the audience

► Make report culturally appropriate

► Be thoughtful about statistics and data

Source: Agency for Healthcare Research and Quality. Tips on writing a quality report. 2011 Jul [cited 2016 Dec

14]. http://www.ahrq.gov/professionals/quality-patient-safety/talkingquality/resources/writing/index.html

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Acting on the Data

Provide feedback to providers

Report both successes and deficiencies

Plan, Do, Study, Act (PDSA)

5 S’s: Sort, Straighten, Shine, Standardize, Sustain

Set monitoring schedules to evaluate and maintain

improvement

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http://www.ahrq.gov/professionals/quality-patient-safety/talkingquality/resources/writing/index.html

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Summary: Remember the Purpose of Data

Data prove quality Data drive quality

■ Tangible measure

■ Supports and authenticates

mission, vision, strategy

■ Connects EHR data with

quality goals

■ Quantifies Quadruple Aim

(i.e., readiness, better care,

better health, lower costs)

■ Monitors, protects, and

controls

■ Points to areas of future or

further quality improvement

efforts

■ Measures drive to

improvement

■ Alerts and triggers

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References Agency for Healthcare Research and Quality. Tips on writing a quality report. 2011 Jul [cited

2016 Dec 14]. http://www.ahrq.gov/professionals/quality-patient-

safety/talkingquality/resources/writing/index.html

American Health Information Management Association (AHIMA). Data quality management

model (2015 update). 2015 Oct [cited 2016 Dec 15].

http://library.ahima.org/PB/DataQualityModel#.WDOF5OQVCUk

American Society for Quality:

o Quality glossary. [cited 2016 Dec 14]. http://asq.org/glossary/d.html

o Quality tools and templates. 2016 [cited]. http://asq.org/learn-about-quality/tools-

templates.html

Centers for Medicare and Medicaid Services:

o Domestic Lean Goddess. Quality improvement video series. CFMC, the Learning and

Action Network National Coordinating Center.

https://www.cms.gov/Medicare/Provider-Enrollment-and-

Certification/QAPI/Downloads/QAPI-Domestic-Lean-Goddess.pdf

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http://www.ahrq.gov/professionals/quality-patient-safety/talkingquality/resources/writing/index.html

http://library.ahima.org/PB/DataQualityModel


http://asq.org/learn-about-quality/tools-templates.html

https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/QAPI/Downloads/QAPI-Domestic-Lean-Goddess.pdf

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References (cont.)

o MMS Blueprint. CMS measures management system blueprint (the Blueprint) v 12.0.

2016 Jun 7 [cited]. https://www.cms.gov/Medicare/Quality-Initiatives-Patient-

Assessment-Instruments/MMS/MMS-Blueprint.html

o A process tool framework. https://www.cms.gov/Medicare/Provider-Enrollment-and-

Certification/QAPI/Downloads/ProcessToolFramework.pdf

ECRI Institute:

o Quality improvement/quality assurance toolkit. 2012 Aug 1 [cited].

https://www.ecri.org/components/PPRM/Pages/QAToolkit.aspx

o The use of EHRs for quality improvement [webinar]. 2013 May 29 [cited].

https://www.ecri.org/components/HRSA/Pages/AC_EHRsforQI.aspx

Groves P, Kayyali B, Knott D, Van Kuiken. Center for US Health System Reform Business

Technology Office. The big-data revolution in US health care: accelerating value and

innovation. McKinsey Co. 2013 Jan [cited].

http://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/the-

big-data-revolution-in-us-health-care

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References (cont.)HealthIT.gov:

o EHR incentives & certification. Meaningful use definitions & objectives. 2015 Feb 6

[cited]. https://www.healthit.gov/providers-professionals/meaningful-use-definition-

objectives

o Learn EHR basics. 2014 May 21 [cited]. https://www.healthit.gov/providers-

professionals/learn-ehr-basics

Institute for Healthcare Improvement:

o Griffin FA, Resar RK. IHI global trigger tool for measuring adverse events, 2nd ed. IHI

Innovation Series white paper. [cited].

http://www.ihi.org/resources/pages/IHIWhitePapers/IHIGlobalTriggerToolWhitePaper.

aspx

o Plan-Do-Study-Act (PDSA) worksheet. [cited].

http://www.ihi.org/resources/Pages/Tools/PlanDoStudyActWorksheet.aspx

o Kuhn TM, Barr MS, Gardner LA, Baker DW. EHR-based quality measurement &

reporting: critical for meaningful use and health care improvement. A policy paper of

the American College of Physicians. 2010 Feb [cited].

https://www.acponline.org/acp_policy/policies/ehr_quality_measurement_critical_me

aning_hc_2010.pdf

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https://www.cms.gov/Medicare/Quality-Initiatives-Patient-Assessment-Instruments/MMS/MMS-Blueprint.html

https://www.cms.gov/Medicare/Provider-Enrollment-and-Certification/QAPI/Downloads/ProcessToolFramework.pdf

https://www.ecri.org/components/PPRM/Pages/QAToolkit.aspx

https://www.ecri.org/components/HRSA/Pages/AC_EHRsforQI.aspx

http://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/the-big-data-revolution-in-us-health-care

https://www.healthit.gov/providers-professionals/meaningful-use-definition-objectives

https://www.healthit.gov/providers-professionals/learn-ehr-basics

http://www.ihi.org/resources/pages/IHIWhitePapers/IHIGlobalTriggerToolWhitePaper.aspx

http://www.ihi.org/resources/Pages/Tools/PlanDoStudyActWorksheet.aspx

https://www.acponline.org/acp_policy/policies/ehr_quality_measurement_critical_meaning_hc_2010.pdf

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References (cont.)Model Systems Knowledge Translation Center. Charts and graphs to communicate research

findings. [cited]. http://www.msktc.org/Knowledge-Translation/Charts-Graphs-2

Moen RD, Norman CL. Circling back: clearing up myths about the Deming cycle and seeing

how it keeps evolving. Qualityprogress.com. 2010 Nov [cited].

http://www.apiweb.org/circling-back.pdf

Myatt M. 6 Tips for making better decisions. Forbes. 2012 Mar 28 [cited].

http://www.forbes.com/sites/mikemyatt/2012/03/28/6-tips-for-making-better-

decisions/#71d74ffe9f54

National Committee for Quality Assurance (NCQA). HEDIS measures. [cited].

http://www.ncqa.org/HEDISQualityMeasurement/HEDISMeasures.aspx

National Quality Forum. Phrase book: a plain language guide to NQF jargon.

Paranjpe P. How to use data analytics to engage physicians. Health Technology

Management 2016 Feb 23 [cited]. https://www.healthmgttech.com/how-to-use-data-

analytics-to-engage-physicians

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References (cont.)

Rosen A. Are we getting better at measuring patient safety? AHRQ PSNET. Patient Safety

Network. 2010 Nov [cited]. https://psnet.ahrq.gov/perspectives/perspective/94/are-

we-getting-better-at-measuring-patient-safety#Table

Sittig DF, Singh H. Electronic health records and national patient-safety goals. N Engl J

Med 2012 Nov 8;367(19): 1854-60.

http://www.nejm.org/doi/pdf/10.1056/NEJMsb1205420 PubMed:

https://www.ncbi.nlm.nih.gov/pubmed/23134389

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http://www.msktc.org/Knowledge-Translation/Charts-Graphs-2

http://www.apiweb.org/circling-back.pdf

http://www.forbes.com/sites/mikemyatt/2012/03/28/6-tips-for-making-better-decisions/#71d74ffe9f54

http://www.ncqa.org/HEDISQualityMeasurement/HEDISMeasures.aspx

https://www.healthmgttech.com/how-to-use-data-analytics-to-engage-physicians

https://psnet.ahrq.gov/perspectives/perspective/94/are-we-getting-better-at-measuring-patient-safety#Table

http://www.nejm.org/doi/pdf/10.1056/NEJMsb1205420

https://www.ncbi.nlm.nih.gov/pubmed/23134389

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Upcoming Webinar Dates and Topics

Date* Topic

February 23, 2017 Introducing the Global Trigger Tool to Improve Quality and Patient Safety

March 23, 2017 Healthcare Resolution and

Disclosure

April 27, 2017 Caring for the Second Victim

* All webinars are held the fourth Thursday of the month

from 1–2 p.m. eastern.

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©2017 ECRI INSTITUTE

[email protected]

(610) 825-6000, x5800

Thank you!

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