Wednesday, September 11, 2013 1 Virtual Session #2 Track 2: Better Quality Through Better...

Wednesday, September 11, 2013

1

Virtual Session #2 Track 2:Better Quality Through Better Measurement

Part 1

Robert Lloyd, PhD, Dave Williams, PhDRebecca Steinfield, MA, Kim Werkmeister, Steve Tremain, MD

Robert Lloyd, PhD, IHI

Dave Williams, PhD, IHI

Rebecca Steinfield, MA, IHI

Kim Werkmeister, RN, Cynosure

Steve Tremain, MD, Cynosure

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Session #2 Faculty

Robert Lloyd

Robert Lloyd, PhD is Executive Director of Performance Improvement for the Institute for Healthcare Improvement (IHI). Dr. Lloyd provides leadership in the areas of performance improvement strategies, statistical process control methods, development of strategic dashboards and building capacity and capability for quality improvement. He also serves as lead faculty for various IHI initiatives and demonstration projects in the US, the UK, Sweden, Denmark, New Zealand and Africa. Before joining the IHI, Dr. Lloyd served as the Corporate Director of Quality Resource Services for Advocate Health Care (Oak Brook, IL). He also served as Senior Director of Quality Measurement for Lutheran General Health System (Park Ridge, IL), directed the American Hospital Association's Quality Measurement and Management Project (QMMP) and served in various leadership roles at the Hospital Association of Pennsylvania. The Pennsylvania State University awarded all three of Dr. Lloyd’s degrees. His doctorate is in agricultural economics and rural sociology. Dr. Lloyd has written many articles and chapters in books. He is also the co-author of the internationally acclaimed book, Measuring Quality Improvement in Healthcare: A Guide to Statistical Process Control Applications (American Society for Quality Press, 2001, 5th printing) and the author of Quality Health Care: A Guide to Developing and Using Indicators, 2004 by Jones and Bartlett (Sudbury, MA).

Dave Williams

David M. Williams, PhD, Improvement Advisor, truesimple Consulting, is also on the teaching faculty of The George Washington University School of Medicine and Health Sciences. He is also a Six Sigma Black Belt and serves as faculty and an Improvement Advisor (IA) for the Institute for Healthcare Improvement, supporting teams through leading system changes using the Model for Improvement. Dr. Williams started his career as an urban street paramedic. For the last decade, he has acted as an internal and external IA to governmental agencies, hospitals, and for-profit and not-for-profit organizations. He works with clients to improve their organizations by enabling appreciation of systems, understanding of data and variation, testing changes, and recognizing the influence of psychology. He has published nearly 100 articles, led intensive workshops, presented at major conferences to thousands of attendees, and writes a popular blog.

Rebecca Steinfield

Rebecca Steinfield earned her MA in Applied Psychology at Boston University and has been with IHI since 1996 in numerous capacities. She currently serves as an Improvement Advisor teaching courses and coaching teams on the Science of Improvement. Rebecca serves on the faculty for IHI's work on Improving Transitions from Hospital to Home, Kaiser Permanente's Performance Improvement Institute, and the Harvard Medical School Center for Primary Care Academic Innovations Collaborative.

Kim Werkmeister

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Kim Werkmeister, RN, BA, CPHQ is a National Improvement Advisor working with the American Hospital Association / Health Research Educational Trust Hospital Engagement Network (HEN). As an Improvement Advisor, she is responsible for working directly with State Hospital Associations to drive improvement in hospital acquired conditions and patient harm in hospitals across the country. In addition, she is the lead Improvement Advisor for the Hospital Engagement Network for the Perinatal Harm initiative, the VTE Reduction initiative and the Psychiatric Affinity Group initiative.

Prior to this, Ms. Werkmeister worked with hospitals across California to improve patient outcomes, implement best practices, set up Quality/Risk programs, and prepare for and respond to licensing and accreditation activities. She served as an improvement advisor for both the California Public Hospital Improvement Collaborative and the California Partnership for Health/Patient Safety First Collaborative. She also served as the lead improvement advisor for the ICU Mortality Reduction Collaborative, a project focusing on reduction of mortality and morbidity in ICU care across the state of California.

Ms. Werkmeister is a Registered Nurse and graduated with a Bachelor of Arts degree from California State University Fullerton.

Steve Tremain

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Steve Tremain, MD, has championed quality and safety in various leadership positions for 30 years. He served as both Chief Medical Officer and Chief Medical Information Officer for IHI mentor hospital Contra Costa Regional Medical Center and Health Centers in California.

Under his executive sponsorship, Contra Costa achieved mentor status for its work in Medication Reconciliation, VAP, and Rapid Response Teams. Since then, Dr . Tremain has co-led several improvement collaboratives. He was the sole recipient of the National Association of Public Hospitals Patient Safety Award for 2007.

In 2012 Dr. Tremain joined the HEN initiative as one of just two Physician Improvement Advisors. His focus areas are Adverse Drug Events, Culture of Safety, and Messaging. He also supports VTE and OB Adverse Event prevention. He has worked on site in several states and presented plenaries and workshops for the HRET-HEN national and regional meetings.

Dr. Tremain has served as guest faculty for the IHI on several topics including physician engagement and whole system change. He currently serves as physician faculty for the Physician Communication Pilot of IHI-VHA West Coast Patient Experience Collaborative.

He is a graduate of the UCLA School of Medicine, he is board certified in Family Medicine and is a Certified Physician Executive.

Chat Time!

Please type the name of your organization and the number of people sitting in on the call

with you into the Chat Box.

Example: “Institute for Healthcare Improvement- 2

Remember to chat to ALL PARTICIPANTS!

Where are you today?

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Reflections onVirtual Session #1 August 21, 2013

• How have you used your understanding of the human side of change (motivation, resistance to change, adoption of new ideas) in the past few weeks to help with your improvement efforts?

• What help do you need, or advice might you offer your colleagues?

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General Objectivesfor Virtual Session #2

• To revisit Question 2 in the Model for Improvement (How will you know that a change is an improvement?)

• To be clear on why you are measuring? (Improvement, Judgment or Research)

• To understand the differences between types of measures.

• To identify the key elements of a data collection plan.

• To build increased knowledge and comfort with understanding variation statistically with run and control charts.

• Have teams report out on their progress, successes and barriers

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Session #2 Prework(Track 1 & 2)

IHI Open School ModulesQI 103 Lesson 1: Measurement Fundamentals QI 103 Lesson 2: Displaying Data IHI On Demand video by Dr. R. LloydBuilding Skills in Data Collection and Understanding Variationhttp://www.ihi.org/offerings/virtualprograms/ondemand/datacollection_variation/Pages/default.aspx

http://www.ihi.org/offerings/virtualprograms/ondemand/datacollection_variation/Pages/default.aspx



Aim

or Values

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Appreciation of a system

Understanding Variation

Theory of Knowledge

Human Behavior

QI

“The system of profound knowledge provides a lens. It provides a new map of theory by which to understand and optimize our organizations.” (Deming, Out of the Crisis)

It provides an opportunity for dialogue and

learning!

The Lens of Profound Knowledge

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A Model for Learning and Change

When you combine the 3 questions with

the…

PDSA cycle, you get…

…the Model for Improvement.

The Improvement Guide, API, 2009

Our focus today

1. By understanding the variation that lives within your data

2. By making good management decisions on this variation (i.e., don’t overreact to a special cause and don’t think that random movement of your data up and down is a signal of improvement).

How will we know that a change is an improvement?

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QI 103 : Lesson #1 Topics (Measurement Fundamentals)1. Project-level measures versus PDSA-level measures 2. Why are you measuring? 3. Key elements of a data collection plan 4. Building a families of measures (FOM).

QI 103 Lesson #2 Topics (Displaying Data)1. The value of plotting data over time 2. The basic elements of a run chart3. Elements of a run chart and how to interpret a run chart 4. Detecting random and nonrandom patterns in the data 5. The difference between common cause and special cause variation6. The difference between a run chart and a Shewhart chart

QI 103: Lessons 1 and 2Content

Questions? Concerns?

Observations?

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Measurement for improvement should be used to speed things up, not slow them down. The following tips are meant to help teams use measurement to accelerate improvement.

1. Plot data over time. 2. Seek usefulness, not perfection. 3. Use sampling appropriately. 4. Integrate measurement into the daily routine. 5. Use qualitative and quantitative data.

QI 103 Tips for BuildingEffective Measurement Systems

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QI 103 : Lesson #1 Topics (Measurement Fundamentals)1. Project-level measures versus PDSA-level measures 2. Why are you measuring? 3. Key elements of a data collection plan 4. Building a families of measures (FOM).

QI 103 Lesson #2 Topics (Displaying Data)1. The value of plotting data over time 2. The basic elements of a run chart3. Elements of a run chart and how to interpret a run chart 4. Detecting random and nonrandom patterns in the data 5. The difference between common cause and special cause variation6. The difference between a run chart and a Shewhart chart

QI 103: Lessons 1 and 2Content for Session #2

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Why are you measuring?

The answer to this question will guide your entire quality measurement journey!

Improvement?

Judgment?Research?

The Three Faces of Performance Measurement

Aspect Improvement Accountability Research

Aim Improvement of care(efficiency & effectiveness)

Comparison, choice, reassurance, motivation

for change

New knowledge(efficacy)

Methods:• Test Observability

Test observableNo test, evaluate current

performance Test blinded or controlled

• Bias Accept consistent bias Measure and adjust to reduce bias

Design to eliminate bias

• Sample Size “Just enough” data, small sequential samples

Obtain 100% of available, relevant data

“Just in case” data

• Flexibility of Hypothesis

Flexible hypotheses, changes as learning takes

placeNo hypothesis

Fixed hypothesis(null hypothesis)

• Testing Strategy Sequential tests No tests One large test

• Determining if a change is an improvement

Run charts or Shewhart control charts

(statistical process control)

No change focus(maybe compute a percent change or rank order the

results)

Hypothesis, statistical tests (t-test, F-test, chi square),

p-values

• Confidentiality of the data

Data used only by those involved with improvement

Data available for public consumption and review

Research subjects’ identities protected

byLief Solberg, Gordon Mosser and Sharon McDonald

Journal on Quality Improvement vol. 23, no. 3, (March 1997), 135-147.

21Integrating the Three Faces ofPerformance Measurement

Improvement Judgment

Research

The three faces of performance measurement should not be seen as mutually exclusive silos. This is not an either/or situation.

All three areas must be understood as a system. Individuals need to build skills in all three areas.

Organizations need translators who and be able to speak the language of each approach.

The problem is that individuals identify with one of the approaches and dismiss the value of the other two.

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Question Response Options1. In my organization we claim to be measuring for improvement but most of our time is actually spent measuring for judgment.

Strongly Agree Agree Not SureDisagree Strongly Disagree

2. In my organization there are very few people conducting classic research studies.


3. In my organization we typically use bar graphs, pie charts and aggregated summary statistics (e.g., the ALOS for the month) to display our data.


4. In my organization we collect data and measure things all the time but I am not sure what happens to the data once we submit it for tabulation.


If you responded Disagree or Strongly Disagree to any question, what criteria are used by this group to determine if data or improving or getting worse?

Poll #1Why are you measuring?

Let’s hear from a team working on VTEs

ILF Team Presentation #1

How has your Quality Measurement Journeybeen going? • Have you hit roadblocks?

• If so, what are they?

• Do you feel you are measuring for improvement, judgment or research?

• Are you collecting too much data? Not enough?

• Are you helping your teams use their data to become more efficient and effective?

Pursuing Perfection for VTE

Jane Northcutt, RNChief Quality Officer

Hospital Overview

• Located in Birmingham, Alabama• One of 15 General Acute Care Hospital Providers

in the Birmingham MSA (4-County)• Licensed Beds - 534

– 17 Rehabilitation Beds– 64 Psychiatric Beds

• Employees - 1,686• Physicians & Allied Health on Staff: 698 • Population for Primary Service Area - 437,957• Population for Secondary Service Area - 608,771

VTE - HEN Specific Run Charts

Aim?: By December 2013, 100% of patients will receive VTE prophylaxis by defined protocols, patient assessments or have documentation of contraindications.

Important? VTE is the #1 preventable cause of death in hospitalized patients.

Aim Statement

Changes Being Tested, Implemented or

Spread

Recommendations and Next Steps

Lessons Learned

Run Charts

•Assessment of patients for VTE prophylaxis is completed for Inpatients and ICU patients.

•Protocols are implemented timely.•VTE Discharge Instructions are completed.

•Patients requiring overlap therapy are identified with measures implemented.

•Surgery patients are assessed for appropriate prophylaxis.

•Build in discharge documentation tools in electronic system to populate on all patients.

• Identify patients from radiological studies for identification of needed overlap therapy and have PharmD review for appropriate coverage.

•Set required timeline actions before the timeline actually ends.

•Order set with physician to design with physician driven education to medical staff.

•Use PI Referral Form for staff involved to document why variance occurred and lesson learned.

Project Title: Reducing VTE by Improved compliance with VTE Measures Date: 8/10/2013

Hospital Name: Trinity Medical Center State: Alabama

© 2012 Institute for Healthcare Improvement

Team Members

Self Assessment Score = _5__

•Re-assess protocols and current actions with identified variances.

•Evaluate effect of new electronic clinical documentation on compliance.

CQOCore Measure AnalystPharmDPhysician Liaison Director of Surgical ServicesCNOICU Nursing DirectorMed-Surge Nursing Director

Medical VTE Prophylaxis

AIM (How good? By when?)

Concept Measure

Operational Definitions Data Collection Plan Data Collection

Analysis

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The Quality Measurement JourneySource: R. Lloyd. Quality Health Care: A Guide to Developing and Using Indicators. Jones and Bartlett Publishers, 2004.

ACTION

AIM (How good? By when?)

Concept

Measure

Operational Definitions

Data Collection Plan

Data Collection

Analysis

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The Quality Measurement JourneySource: R. Lloyd. Quality Health Care: A Guide to Developing and Using Indicators. Jones and Bartlett Publishers, 2004.

ACTION

“A phenomenon will be said to be

controlled when, through the use of

past experience, we can predict, at least

within limits, how the phenomenon may be expected to vary in

the future.”Shewhart - Economic Control of

Quality of Manufactured Product, 1931

Dr. Walter A Shewhart

“What is the variation in one system over time?” Walter A. Shewhart - early 1920’s, Bell Laboratories

Every process displays variation:• Controlled variation

stable, consistent pattern of variation“chance,” constant causes

• Special cause variation“assignable” pattern changes over time

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time

UCL

LCL

Static View

Static V

iewStatic View

Dynamic View

Types of Variation

Common Cause Variation• Is inherent in the design of the

process

• Is due to regular, natural or ordinary causes

• Affects all the outcomes of a process

• Results in a “stable” process that is predictable

• Also known as random or unassignable variation

Special Cause Variation• Is due to irregular or unnatural

causes that are not inherent in the design of the process

• Affect some, but not necessarily all aspects of the process

• Results in an “unstable” process that is not predictable

• Also known as non-random or assignable variation

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Point …

Common Cause does not mean “Good Variation.” It only means that the process is stable and predictable. For example, if a patient’s systolic blood pressure averaged around 165 and was usually between 160 and 170 mmHg, this might be stable and predictable but it is unacceptable against the goal.

Similarly Special Cause variation should not be viewed as “Bad Variation.” You could have a special cause that represents a very good result (e.g., a low turnaround time), which you would want to emulate. Special Cause merely means that the process is unstable and unpredictable.

You have to decide if the output of the process is acceptable!

12/9

5

2/96

4/96

6/96

8/96

10/9

6

12/9

6

2/97

4/97

6/97

8/97

10/9

7

12/9

7

2/98

4/98

6/98

8/98

10/9

8

12/9

8

2/99

4/99

6/99

m ont h

Perc

ent

C-se

ctions

0. 0

5. 0

10. 0

15. 0

20. 0

25. 0

30. 0

35. 0

UCL=27. 7018

CL=18. 0246

LCL=8. 3473

P er cent of Cesar ean S ections P er for m ed Dec 95 - Jun 99

Common Cause Variation

Normal Sinus Rhythm (a.k.a. Common Cause

Variation)

Week

Num

ber

of M

edica

tions

Err

ors

per

100

0 Pat

ient

Days

0. 0

2. 5

5. 0

7. 5

10. 0

12. 5

15. 0

17. 5

20. 0

22. 5

UCL=13. 39461

CL=4. 42048

LCL=0. 00000

M edication E rror Rate

Atrial Flutter Rhythm (a.k.a. Special Cause Variation)

Special Cause Variation

There are many examples of Common and Special Causes of Variation in healthcare. Find ones that work for your you.

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Appropriate Management Response to Common & Special Causes of Variation

Type of variation

Right Choice

Wrong Choice

Consequences of making the wrong

choice

Is the process stable?YES NO

Only Common

Change the process if unacceptable

Treat normal variation as a special cause (tampering)

Increased variation!

Special + Common

Changethe process

Wasted resources!

Investigate the origin of the special cause

Attributes of a Leader WhoUnderstands Variation

Leaders understand the different ways that variation is viewed.

They explain changes in terms of common causes and special causes.

They use graphical methods to learn from data and expect others to consider variation in their decisions and actions.

They understand the concept of stable and unstable processes and the potential losses due to tampering.

Capability of a process or system is understood before changes are attempted.

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Question Response Options1. The Board evaluates our data using criteria for common and special cause variation


2. Senior Management evaluates our data using criteria for common and special cause variation


3. Front-line Managers evaluate our data using criteria for common and special cause variation


4. Staff Members evaluate our data using criteria for common and special cause variation


If you responded Disagree or Strongly Disagree to any question, what criteria are used by this group to determine if data or improving or getting worse?

Poll #2Attributes of a Leader Who Understands

Variation

How can I depict variation?

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STATIC VIEWDescriptive Statistics

Mean, Median & ModeMinimum/Maximum/Range

Standard DeviationBar graphs/Pie charts

DYNAMIC VIEWRun Chart

Control Chart(plot data over time)

Statistical Process Control (SPC)

Ra

te p

er

10

0 E

D P

atie

nts

Unplanned Returns to Ed w/in 72 Hours

M41.78

17

A43.89

26

M39.86

13

J40.03

16

J38.01

24

A43.43

27

S39.21

19

O41.90

14

N41.78

33

D43.00

20

J39.66

17

F40.03

22

M48.21

29

A43.89

17

M39.86

36

J36.21

19

J41.78

22

A43.89

24

S31.45

22

MonthED/100

Returnsu chart

0.0

0.2

0.4

0.6

0.8

1.0

1.2

UCL = 0.88

Mean = 0.54

LCL = 0.19

How do we analyze variation for quality improvement?

Run and Control Charts are the best tools to determine

if our improvement strategies have had the

desired effect.

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Process Improvement: Isolated Femur Fractures

0

200

400

600

800

1000

1200

1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64Sequential Patients

Min

utes

ED

to O

R p

er

Pat

ient

Holding the Gain: Isolated Femur Fractures

0

200

400

600

800

1000

1200


Min

utes

ED

to O

R p

er

Pat

ient

3. Determine if we are holding the gains

Current Process Performance: Isolated Femur Fractures

0

200

400

600

800

1000

1200


Min

utes

ED

to

OR

per

P

atie

nt

Three Primary Uses of

SPC Charts

2. Determine if a change is an improvement

1. Make process performance visible

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Elements of a Run Chart

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29

Point Number

Po

un

ds

of R

ed

Ba

g W

ast

e

3.25

3.50

3.75

4.00

4.25

4.50

4.75

5.00

5.25

5.50

5.75

6.00

Median=4.610

Mea

sure

Time

The centerline (CL) on a Run Chart is the Median

Four simple run rules are used to determine if non-random variation is present

X (CL)~

42Non-Random Rules for Run Charts

A Shift: 6 or more

An astronomical data point

Too many or too few runs

A Trend5 or more

Source: The Data Guide by L. Provost and S. Murray, Austin, Texas, February, 2007: p3-10.

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Jan01 Mar01 May01 July01 Sept01 Nov01 Jan02 Mar02 May02 July02 Sept02 Nov02

Month

Nu

mb

er

of

Co

mp

lain

ts

5.0

10.0

15.0

20.0

25.0

30.0

35.0

40.0

45.0

50.0

A

B

C

C

B

A

UCL=44.855

CL=29.250

LCL=13.645

An indication of a special cause

Mea

sure

Time

X (Mean)

(Upper Control Limit)

(Lower Control Limit)

Elements of a Control (Shewhart) Chart

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A single point outside the control limits

Six consecutive points increasing (trend up) ordecreasing (trend down)

Two our of three consecutive points near a controllimit (outer one-third)

Eight or more consecutive points above or belowthe centerline

Fifteen consecutive points close to the centerline(inner one-third)

1.

5.

4.

3.

2.

Rules for Detecting Special Causes on Shewhart Charts

Rule #1: 1 point outside the +/- 3 sigma limits A point exactly on a control limit is not considered outside the limit . When there is not a lower or upper control limit Rule 1 does not apply to the side missing the limit.

Rule #2: 8 successive consecutive points above (or below) the centerlineA point exactly on the centerline does not cancel or count towards a shift.

Rule #3: 6 or more consecutive points steadily increasing or decreasingTies between two consecutive points do not cancel or add to a trend. When control charts have varying limits due to varying numbers of measurements within subgroups, then rule #3 should not be applied.

Rule #4: 2 out of 3 successive points in Zone A or beyond

When there is not a lower or upper control limit Rule 4 does not apply to the side missing a limit.

Rule #5: 15 consecutive points in Zone C on either side of the centerlineThis is known as “hugging the centerline”

Notes on Special Cause Rules

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So Which Is More Useful…a Run Chart or a Shewhart Chart?

What do you think?

Enter your thoughts into the Webex Chat Area

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For many improvement projects, an annotated run chart is all that is needed to learn whether the changes made have led to an improvement in the measure of interest. If the changes have led to a new level of system performance, the run chart will provide the evidence required to claim an improvement.

In some cases, however, the run chart is not clear and a Shewhart chart can be useful in clarifying if a meaningful change has occurred. The limits on the Shewhart chart allow the detection of a special cause with just one data point. While the rules on the run chart are probability based, the limits and rules associated with Shewhart charts are economical and empirically based.


Source: IHI Open School QI 103 Lesson 2 page 9

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Shewhart charts have the added feature of control limits, which allow us to determine if the process is stable (common cause variation) or not stable (special cause variation). That way we can tell if there’s some unusual factor(s) making the data fluctuate in a non-random manner.

For this reason, Shewhart charts also allow us to more accurately predict the future performance of the process.

Recommendations• Start off using a line chart (just plot the dots!)• Then move to a run chart when you have about 10-12 data points• If the run chart does not tell a clear story, consider moving to a

Shewhart chart• Remember you should have 15-20 data points to make a Shewhart

chart.


Source: IHI Open School QI 103 Lesson 2 page 9

So, why are Control Charts preferredover Run Charts?

Because Control Charts…1. Are more sensitive than run charts

• A run chart cannot detect special causes that are due to point-to-point variation (median versus the mean)

• Tests for detecting special causes can be used with control charts

2. Have the added feature of control limits, and zones which allow us to determine if the process is stable (common cause variation) or not stable (special cause variation).

3. Can be used to define process capability.

4. Allow us to more accurately predict process behavior and future performance.

ILF Team Presentation #2

How has your Quality Measurement Journeybeen going? • Have you hit roadblocks?

• If so, what are they?

• Have you placed all your measures on run or control charts?

• If not why is this not being done?

• Do you use common and special cause criteria to understand the variation in your data?

Let’s hear from a team working on ADEs

Josh Holland, PharmD.Clinical Psychiatric Pharmacist

About UsCoxHealth

•Springfield (Cox North/Cox South/Meyer Orthopedic and Rehab)• Monett •Branson •Four campus system in Southwest MO•Over 800 beds

Our FacilityCox North

•Inpatient Psychiatric•Four units/approx 20 beds each•Pediatric, Geriatric, two adult units•Level II Trauma Center (ER) •Majority of patients admitted through the ER

Column1 Column2 Column3Column

4 Column5 Column6 Column7 Column8 Column9 Column10 Column11

Month/Year

Date Station Physician Total Wrong Wrong Wrong No longer Drug#

Right/ Accuracy Patient

Meds Drug Strength Frequency Takes Missed#

Wrong Rate

Totals

Aim: Decrease Medication reconciliation error rate from paper to electronic to less than 5% by December 31, 2013 .

Why is this project important?ADEs are a common cause of hospital harm. Studies attribute 42-60 percent to ADEs. At CoxHealth we want to ensure patient safety with accuracy.

Aim Statement

Changes being Tested, Implemented or Spread

Recommendations and Next Steps

Lessons Learned

Run Charts

S: Measure error rate when transferring medication reconciliation from paper to electronic on admission.S: For medication education and engagement, started using medication consults as well as medication groups to try and increase patient knowledge and compliance. Use patient activation scores to guide approach to patient counseling.T: New process to improve accuracy of medication reconciliation. Call patient’s pharmacy/ies for accurate medication list and dose. New measurement run charts.

Project Title: Preventing Psychiatric Adverse Drug EventsDate:8/31/13 Hospital Name: CoxHealth State: MO

© 2012 Institute for Healthcare Improvement

Self Assessment Score 1-5 = 4

•Education and improvement of documentation decreases ADEs•Improving medication reconciliation helps to avoid ADEs in the hospital•Call patient’s pharmacy for Medication List more accurate and takes less time.

Jan-12

Feb-12

Mar-12

Apr-12

May-12

Jun-12

Jul-12

Aug-12

Sep-12

Oct-12

Nov-12

Dec-12

Jan-13

Feb-13

Mar-13

Apr-13

May-13

Jun-13

Jul-13

70%

75%

80%

85%

90%

95%

100%Median

CoxHealth Admission Medication Reconciliation

Ap

Ju Ju A S Oc

N D Ja F M Ap

M Ju Ju

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

Cox Health Discharge Medication Reconciliation

Temporary staff unfa-miliar with electronic med rec process

No reason found for decrease will monitor for trend

Oct-12

Nov-12

Dec-12

Jan-13

Feb-13

Mar-13

Apr-13

May-13

Jun-13Jul-1

3

0

5

10

15

20

25

10

20 20 20 20 20 20 20 20 20

5 6 6

12

4

1

4 5 5

1 40% 20% 15% 25% 20% 5% 15% 20% 15% 5%

Medication Transcription Errors from Paper to Electronic

# Patients

# Patients with Errors

Number Of Errors

Potential Harm Rate

Potential harm 40% Oct’12 to 5% July’13

Evaluate how reduction occurred and duplicate reduction in potential harm rate from medication transcription errors from paper to electronic.

Jun-13 Jul-130%5%

10%15%20%25%30%35%

Adequate Accuracy on Medication Reconciliation on Admission

PercentMedian

Percent

Baseline

Jun-13 Jul-1357%

59%

61%

63%

65%

67%

Accuracy of Medication on MR Form per Patient Pharmacy

PercentMedian

Baseline

Wordle: Measurement Challenges and Concerns

Virtual Session #3 Pre-workSept. 25, 2013

• IHI Open School QI 103: Lesson 3: Learning from Measures

• Benneyan, J, Lloyd, R and Plsek, P. “Statistical Process Control as a Tool for Research and Health Care Improvement” Journal of Quality and Safety in Healthcare. British Medical Journal, Spring 2003.

IHI On Demand video, “Using Run and Control Charts to Understand Variation” http://www.ihi.org/offerings/VirtualPrograms/OnDemand/Run_ControlCharts/Pages/default.aspx

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http://www.ihi.org/offerings/VirtualPrograms/OnDemand/Run_ControlCharts/Pages/default.aspx




Session #3 ObjectivesSept. 25, 2013

The focus in Track Two will be on:

• Understanding probability and non-probability sampling strategies, the advantages and disadvantages of each and when it is appropriate to use them

• Understanding how stratification and rational subgroups which can lead to a deeper understanding of the variation that lives in your data

• Construction and interpretation of Shewhart control charts, including charts for rare events

• Applying these measurement principles to VTEs and ADEs

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In summary, measurement is central to a team’s ability to improve

• The purpose of measurement in QI work is for learning not judgment!• All measures have limitations, but the limitations do not negate their

value for learning. • You need a balanced set of measures reported daily, weekly or monthly

to determine if the process has improved, stayed the same or become worse.

• These measures should be linked to the team’s Aim.• Measures should be used to guide improvement and test changes.• Measures should be integrated into the team’s daily routine.• Data should be plotted over time on annotate graphs.• Focus on the Vital Few!

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Sustaining improvements and Spreading changes to other locations

Developing a change

Implementing a change

Testing a change Act Plan

Study Do

Theory and Prediction

Test under a variety of conditions

Make part of routine operations

Measurement is n

eeded throughout th

e sequence

The Sequence of Improvement requires Measurement

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Thanks for joining us today.Please join us again on

September 25 1:00 – 3:00 PM CT

Take care…Bob, Dave, Rebecca,

Kim and Steve

Wednesday, September 11, 2013 1 Virtual Session #2 Track 2: Better Quality Through Better...

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