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Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd
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Page 1: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Healthcare Redesign:

Diagnostics

Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC

Bounty Brokers Pty Ltd

Page 2: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Disclaimers

All data published in this presentation is fictional except for published text/figures/tables.

Bounty Brokers P/L accepts no liability for any information provided or its use by participants.

"Portions of the input and output contained in this publication/book are printed with permission of Minitab Inc. All material remains the

exclusive property and copyright of Minitab Inc. All rights reserved."

Page 3: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Your Bio

• Introduction• Novice? • Expert?• Hands Up

– Table– Pie Chart– Bar graph– Run chart– Control chart– Control chart + others

Page 4: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Redesign Model: Measurement

Dip. Govt. PSPGOV50101

Page 5: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Today’s Program

Statistical Thinking

Statistical Methods

Improvement and Problem Solving

Page 6: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Objectives

Following the session, you should be better able to:• Value the use of dynamic data analysis and display• Understand that variation exists in all processes• Monitor a process over time to better understand it• Determine whether or not processes are ‘in control’ • See the effect of a change in a process• Provide a more accurate basis for prediction for the

purposes of planning, scheduling, budgeting, resource allocation, improvement, rewarding, etc…..

Page 7: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Focus of Capability Building

• Apply Deming’s principles• Monitor and improve core

processes• Monitor adverse events

– and the systems that produce them

• Learn to use statistical tools and techniques

• Turn data into information• React appropriately to variation

Page 8: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Statistical Thinkers Can…..

Statistical thinkers have skills to:

• Assign limited resources

• Determine if a change (decision) was effective

• Know when and if to ask “What happened”

• Understand the system before targets are set• Have confidence in making:

– More accurate predictions– Decisions to do something– Decisions to do nothing

Page 9: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

5 Problems

1. Limited capacity to appropriately collect, analyse, interpret, report and act on data.

2. Static data display3. Focus on the person instead of the

process4. No Common Language5. Data torturing

Page 10: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Problem 1: Limited capacity to appropriately collect, analyse, interpret,

report and act on data

Type 1 error: Take action or adjust performance when not warranted

Risk: Tampering» Increases variation

within a process» Wastes resources» Impacts

psychologically

Type 2 error: Take no action when warrantedRisk: Molehill grows into a mountain

Risk: Wasting timeDuplicating collecting, analysing, reporting, reviewing, communicating and discussing “new” information that is already ‘known

Risk: Wasting energyby looking for explanations of a perceived trend when nothing has ‘changed’

Page 11: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Problem 2: Static Data Display

Oct-06Nov-06Dec-06

Jan-06Feb-06Mar-06Apr-06May-06Jun-06Jul-06Aug-06Sep-06

Category

Audiology2006 Occasions of Service

LOS 1

LOS 2

LOS 3

1 3.4 4.51.2 3.1 31.6 3 3.61.9 3.3 1.92 3.2 3.7

2.2 2.8 42.3 2.6 3.62.5 3.2 3.52.3 3.3 2.52.7 3.1 42.9 3.4 2.52.8 3 3.32.7 2.8 3.93 3.1 2.3

2.8 2.9 3.72.9 1.9 2.62.9 2.5 2.73.1 2 4.23.6 2.4 33.8 2.2 1.63.6 2.6 3.33.4 2.4 3.13.6 2 3.94 4.3 3.3

3.9 3.8 3.24.1 4 2.24.1 3.8 4.24.6 4.2 2.74.5 4.1 2.74.8 3.8 1.1

Page 12: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Problem 3: Inappropriate People

FocusFocus on the person rather than the

process, by:

• Ranking

• Setting inappropriate goals

• Blaming or giving credit for things over which staff have little or no control

Page 13: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Problem 4: Data Torturing

Data Torturing: When data analysis goes beyond reasonable interpretation of the facts.

Page 14: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Problem 5: No Common Language

Page 15: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Solution

Statistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and MethodsStatistical Thinking and Methods

Page 16: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Why Statistical Thinking?

Many clinicians and other healthcare leaders underestimate the great contributions that

better statistical thinking could make toward reducing costs and improving

outcomes.

So convinced am I of the power of this principle of tracking over time that I would

suggest this: if you follow only one piece of advice from this lecture when you get

home, pick a measurement you care about and begin to plot it regularly over time.

You won't be sorry.“

D. Berwick 1995

Page 17: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Common Statistical Traps

• Average Value approach • Ranking • Poorly Presented Percentages• Trending• Smoothing • Tables• Circling

Page 18: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Average Value Approach

As the average is usually near to a midpoint set of data, one should expect to be:– above average about half of the time– below average about half of the time

Feel bad half the

time

Feel good half the time

Page 19: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Comparison to Averages

• Results in a characterization of either “above average” or “below average”

• Characterizes the world in a binary view:– “operating okay– “in trouble”

• Ignores the “dead band” of data on either side of an average

• Treats every fluctuation as important

Page 20: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

2 Bucket Average

Page 21: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

What is the difference between these 2 Patient

Groups?

Patient A: 50

Patient B: 250

Total: 300

Average: 150

Patient C: 140

Patient D: 160

Total = 300

Average = 150

Consider if this was waiting to see Dr. in ED?

What is the impact of these results on our patients?

Page 22: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Goals

Page 23: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

The Use and Abuse of Numerical Goals

“I never use

them”

“ I always provide one and let other people figure

out how to achieve it”Results in:

•Numerical targets expressed as a single point

•Unfairly holding people (departments) accountable for results they are incapable of achieving

•Achieving goals at the expense of other parts of the system

•Falsifying numbers

Page 24: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Goal /Target/ Specification Oriented

ApproachTargets (voice of the customer)

should be based on:• customer expectations• benchmarking• competitive requirements • knowledge of those who will do the work• voice of the process – current system

capability

Page 25: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

The Deceptiveness of Poorly Presented Percentage Data

A 2 patients, 1 died = 50% mortality B 20 patients, 1 died = 5% mortality C 200 patients, 1 died = 0.5% mortality

A 2 patients, 2 died = 100% mortalityB 20 patients, 2 died = 10% mortalityC 200 patients, 2 died = 1% mortality

Sample

Double the numerator with same sample size

You need to know the denominator (area of

opportunity)

Page 26: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Appropriateness of Trendlines

• Weight of a baby increases as it gets older

• Reduction in number of kilometres driven - predict that, within 12 months, will be driving minus 350 kilometres per month.

Page 27: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Trending: 6 Possible Sequences for 3

NumbersUpward Trend? Downturn? Setback?

Rebound? Turnaround? Downward trend?

Page 28: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Statistical Representation

of a Trend

15105

35

25

15

Index

C7

Run Chart: Statistical Representation of a Trend

• A sequence of SEVEN or more points continuously increasing or decreasing (SIX if < 20 observations)

• Omit entirely any points that repeat the preceding value: neither add to the length of the run nor do they break it.

Page 29: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Ranking

• Given two numbers if they are not the same, then one will be bigger.

• Ranking provides managers with a tool for choosing who goes, and in what order, as the ship begins to list

Page 30: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.
Page 31: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Statistical Thinking: Knowledge of Variation

Page 32: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Statistical Thinking

A philosophy of learning and

action based on the following fundamental principles:

»all work occurs in a system of interconnected processes

»variation exists in all processes»understanding and reducing variation are

the keys to success

Page 33: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

4 Approaches to Analysis, Interpretation

& Prediction• Average Value Approach

• Specification Approach (goal / target)

• Run chart approach

•Shewhart Control Chart Approach

Page 34: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Solution: Our Scientific Method

is Statistical Process Control (SPC)

0 10 20 30 40

0

5

10

15

Consecutive Observation Number

Indi

vid

ual V

alue

Graph Title & Date

Mean=5.561

UCL=14.14

LCL=-3.016

0102030405060708090

1stQtr

2ndQtr

3rdQtr

4thQtr

East

West

North

The control chart is the tool of choice to appropriately display

variation

Page 35: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Control Charts

• Provides a formal method to detect trends• Provides credibility and rigour at minimum

costs• Accepted industry standard with a long

history• Provides performance objective criteria• Balances false alarms and failures to detect

– Analogous to circuitry in smoke detectors

Page 36: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Control Chart Approach

3 concepts:– Variation (special / common cause / structural /

off-target)

– Pattern matching– Decision Making (optional):

» Do something / Do nothing» Assign limited resources» Determine who is/are the “best”

Page 37: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Variation and Pattern Matching

• Review of process variation when viewed with the mean can help to spot patterns of variation which are:– highly improbable– non-random– unnatural– detectable and therefore

assignable

Page 38: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

4 Types of Variation

• Off Target

• Common Cause

• Special Cause

• Structural

Page 39: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Variation

Variation is the constant

companion of any data

If there was no variation,

we would only need 1 number

Page 40: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Effective Measurement System

For accountability or Improvement:

• Objective, reliable, valid data• Control for confounding, e.g.. age, casemix

• Use of graphical presentations• Use of comparative data (over time and between hospitals)• Applying the scientific method when interpreting results

(hypothesis/experiment/test hypothesis)

• Indication of the magnitude of the expected statistical variation

Page 41: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Recommend/Mandate Control Charts

Who WhatAustralian Council on Healthcare Standards

(ACHS).

Clinical Indicator Users’ Manual 2006

Recommends that control charts be used to display

longitudinally, both the absolute numbers and rates

ACHS

Risk Management and Quality Improvement

Handbook. Version 1 2007.

Recommends control charts to display data and give information

for decision making.

NSWHealth.

Healthcare Associated Infection: Clinical

Indicator Manual 2008

NSW to report monthly rates with control charts and EWMA

NSWHealth.

CareSafe Performance Agreement 08/09

Incident Mgt. Indicator 13.3: Clinical RIBs reported to NSW

Health “Use control charts as presented in RIRC reports”

National Health and Medical Research Council

Pilot Program 2005-2007

Level 3-3 Evidence. Experimental study using a comparative

study without concurrent controls such as an interrupted time

series,

Therapeutic Advisory Group.

Indicators for Quality Use of Medicines in

Australian Hospitals. 2007

Promotes the use of control charts for 30 indicators

Page 42: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Recommend/Mandate Control Charts

Who WhatIndependent Pricing & Regulatory

Tribunal.

Framework for Performance Improvement

in Health September 2008

Recommendation 17 ”That NSW Department of Health

investigate models in other health services, such as 's

model of statistical process control charting, and

monitor their impact to see if they are appropriate to

adopt in the future.”NSWHealth and Centre for Healthcare Redesign

Statistical process control ce-learning course

SQUIRE.

Standards for Quality Improvement

Reporting Excellence.

Describes analytic methods used to demonstrate

effects of time as a variable (for example, control

charts) when reporting improvement projects, as

opposed to Introduction, Methods, Results, Discussion

(IMRAD).

Joint Commission (US) The healthcare accreditation agency requires that all

organisations submit control charts of their clinical

indicators

Page 43: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Bristol Royal Infirmary (BRI) Paediatric Cardiac Surgery

In a landmark article The UK Cardiac Surgical Register of mortality rates for children under one year old was analysed using control charts. The 1988-90 data showed that Bristol mortality rate was outside the control limits indicating special cause variation.

 

Five years later, data for the period 1991-1995 demonstrated that BRI was again above the upper control limit. Although external action to address concerns about paediatric cardiac surgery at BRI took place in 1998, monitoring using the control charts could have provided a basis for action some 11 years earlier.

Mohammed A Mohammed et al. Bristol, Shipman and Clinical Governance: Shewhart’s Forgotten Lessons. The Lancet, vol 357 February 10, 2001

Page 44: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Global Trigger Tool

“Plotting this data on control charts

will give you useful information

about trends and special causes

of variation in harm in your organisation”.

Griffin FA, Resar RK. IHI Global Trigger Tool for Measuring Adverse Events (2nd ed). IHI Innovation Series white paper, Cambridge, Massachusetts: IHI

Page 45: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Common Cause Variation

• Is an inherent part of every process:» chronic - often hidden

• Is random • Due to regular, natural, or ordinary

causes• Produces processes that are stable or “in

control”• If only common cause variation exists,

we can make predictions about the process

• Management “plans” for this

There are no lessons to be learned from comparing high dots to low

dots

Page 46: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

8 Tests

for Specia

l Causes

"Portions of the input and output contained in this publicationare printed with permission of Minitab Inc. All material remains theexclusive property and copyright of Minitab Inc. All rights reserved."

Page 47: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Special Cause Variation

• Due to irregular or unnatural causes– acute, often out of the

blue - significant• Not inherent to the process• Affects some, but

necessarily all outcomes in the process

• Produces processes that are unstable or “out of control”

• The process is unpredictable

403020100

40

30

20

10

0

-10

-20

Observation Number

Ind

ivid

ua

l V

alu

e

I Chart for C1

1

222

1

333

Mean=7.027

UCL=20.99

LCL=-6.936

Institute for Clinical Excellence. Blood Transfusion Improvement Collaborative. Final Report. 2003

Page 48: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

How Much Data?

Distance from the Baseline versus Time needed to Identify major Special Causes (MINITAB)

0

1

2

3

4

0 1 2 3 4 5 6 7 8 9 10

Months

Sta

nd

ard

De

viat

ion

s f

rom

B

ase

line

1 Point Outside 3S Control Limits

2 of 3 Outside 2 Standard Deviations

4 of 5 Outside 1 Standard Deviations

9 same side

6/7 points in a row up / dow n

Page 49: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Unlocking the Secrets of Simple Statistical

Methods

yx

nn

ss

yyxxyyxxyyxxnr

2211

1

Page 50: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Tools that Generate Knowledge for Improvement

Process/system Improvement Tools

Collaborative Work Tools

Planning & Analysis Tools

Statistical Thinking Tools

Flowchart Brainstorming Affinity Diagram

Run chart

Control Chart

Cause & Effect Nominal Group Technique

Force Field Analysis

Scatterplot

Pareto chart Multi-voting Prioritisation Matrices

Histogram

Degree of Difficulty

Page 51: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Three Uses of Control Charts

• Evaluate the past

• Evaluate the present

• Predict the range of values likely to see in the near future (where appropriate)

Page 52: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Average/Mean

Mean minus 3s

Mean plus 3s

The Standard Deviation

Page 53: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

6 Sigma

6s

UCL

LCL

Mean

0 10 20 30 40

0

5

10

15

Consecutive Observation Number

Ind

ivid

ua

l Va

lue

Graph Title & Date

Mean=5.561

UCL=14.14

LCL=-3.016

Page 54: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Basic Control Chart

A run chart with:• average (green

horizontal line) • control limits -three

standard deviations from the mean (red horizontal lines):– upper control limit (UCL)

and lower control limit (LCL),

– or +3 or -3 sigma limits

(+ or - 3.0SL)

13121110987654321

12

10

8

6

4

2

0

Observations in time sequence (x axis)

Num

ber

bein

g M

easu

red (

y a

xis)

UCL=12

LCL= minus 1

_X=5.38

Title of GraphDate

Page 55: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

-3SD -2SD -1SD +1SD +2SD +3SD

90-98%

60-75%

99-100%

3 sigma limits are notprobability limits - notbased on theory (thatrandom samples froman underlyingpopulation would givethis result by chance xtimes out of 100)

Empirical = observed.Only assumption is thatthese data are outputs of aprocess

Xbar

Empirical Rule

Page 56: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Control Limits are NOT Confidence Intervals

The control limits describe the natural variability of a process over time and are usually set to three standard deviations (SDs) or sigma.

Confidence limits of a distribution describe the degree of certainty that a given point is different from the average score (populations) – as when “outlier” performance is demonstrated using comparison data.

Page 57: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

NEEDACTION

NEEDS NOACTION

TAKE ACTION I TYPE 1OVER ADJUST

Take action or adjustperformance whennot warranted

TAKE NOACTION II TYPE 11

UNDER ADJUSTNo action taken whenaction is warranted

Because tampering is such a bad thing, common control charts have limits set to produce:

•low risk of tampering (type 1 error)•moderate risk of under-controlling (type 11 error)

Torki

2 or 3 Standard Deviations?

Page 58: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

What was the Question?

The Choice of Control Chart is determined by the:

• Research Question:• Number of falls (x)• Patients that fell

• Area of Opportunity

Page 59: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Area of Opportunity

MEASURE AREA OF OPPORTUNITY

Number of bacteria Agar plate

Number of referrals to Dr.

Day

Number of dents A car

Miles Gallon

Number of complaints Bed days

Annual mortality number Patients who could die each year

Number of vaginal births All births

Page 60: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Choosing a Control Chart

Minitab® Statistical Software

Page 61: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Variable /Continuous Data

Normal / non-Normal models

Page 62: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Variable Data - 2 Graphs

• Continuous data has no denominator to estimate variation - uses own variability:– Xbar-S Chart

»average subgroup standard deviations

– Xbar-R Chart»average of subgroup ranges

– I MR Chart»artificial subgroups created from individual

measurements

Page 63: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Common Cause Variation

121110987654321

6.6

6.4

6.2

6.0

Observation

Indiv

idual V

alu

e

_X=6.2717

UCL=6.6633

LCL=5.8800

121110987654321

0.45

0.30

0.15

0.00

Observation

Movin

g R

ange

__MR=0.1473

UCL=0.4812

LCL=0

Average LOS

6.7

6.6

6.5

6.4

6.3

6.2

6.1

6.0

5.9

5.8C1

Indiv

idual V

alu

e

_X=6.2717

UCL=6.6633

LCL=5.8800

Average LOS

Individuals Moving Range (IMR chart)

Individuals / X Chart

Page 64: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Individuals Chart: special cause variation

0 5 10 15 20 25 30 35 40 45

0

100

200

300

Consecutive patients

Min

ute

s

Time spent waiting

1

5

1

Mean =142.2

3.0SL=295.5

-3.0SL=-11.23

Page 65: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Length of Stay

Page 66: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Case study: Is there a Difference?

Balestracci

Variable n= Average LOS

Standard

Deviation

LOS 1 30 3.027 0.978

LOS 2 30 3.073 0.6680

LOS 3 30 3.127 0.8175

Page 67: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Case study: Appropriate Analysis?

Observation

Ind

ivid

ua

l Va

lue

30272421181512963

5

4

3

2

1

_X=3.027

UCL=3.595

LCL=2.458

1

11

111

1

16

11

1

222

2

22

22

13

11

11

1

11

I Chart of los1

Observation

Indiv

idual V

alu

e

30272421181512963

4.5

4.0

3.5

3.0

2.5

2.0

_X=3.073

UCL=4.119

LCL=2.028

5

51

5

5

5

1

1

6

6

5

6

11

I Chart of los2

Observation

Indiv

idual V

alu

e

30272421181512963

6

5

4

3

2

1

0

_X=3.127

UCL=5.841

LCL=0.412

I Chart of los3

Balestracci

Page 68: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Binomial Model

Defectives

‘p’ & ‘np’ charts

Page 69: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Data Categorised by the Binomial Model

• Count of occurrences and non-occurrences when the area of opportunity is known and equal, e.g.:

Head / tail Acceptable /not acceptable e.g. audits Infection/no infection Full bed/empty bed Operation / cancellation Working/broken Dead/alive• Patient fall/no patient fall• Either/or………Defective/Not………….Fraction

Page 70: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Any Percentage Data=P Chart

Page 71: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Chair-Step Limits

Sample

Pro

port

ion

252321191715131197531

0.6

0.5

0.4

0.3

0.2

0.1

0.0

_P=0.1613

UCL=0.2796

LCL=0.0430

Tests performed with unequal sample sizes

P Chart of Compliance all Elements of Care Bundle

Page 72: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Historical Control Chart

NSW Therapeutic Advisory Group Inc. Indicators for Quality Use of Medicine in Australian Hospitals. 2007

Page 73: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Where are the Control Limits?

Sample

Sam

ple

Count

18161412108642

1.50

1.25

1.00

0.75

0.50

__NP=1UCL=1LCL=1

Triage Category 1Proportion meet DoH Goal

2008-2010

Page 74: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Poisson Distribution

C charts

U charts

Area of opportunity may not be known

Page 75: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Run Chart or Control Chart?

464136312621161161

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

Sample

Sam

ple

Count

Per

Unit

_U=0.3

Complaints per Thousand Bed days

Q: How high

is too high?

Page 76: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

U Chart

464136312621161161

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0.0

Sample

Sam

ple

Count

Per

Unit

_U=0.3

LCL=0

UCL=0.7

Complaints per Thousand Bed days

Tests performed with unequal sample sizes

Page 77: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Is the Process Capable of Reaching Target?

Page 78: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Process NOT CAPABLE of Meeting Goal

Sample

Sam

ple

Count

2321191715131197531

60

50

40

30

20

_C=41.58

UCL=60.93

LCL=22.24

Weekly Complications

Goal

Page 79: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Process IS CAPABLE of Meeting Goal

90

85

80

75

70

65

60

55

Months

Indiv

idual V

alu

e

_X=83%

UCL=90%

LCL=77%

Goal: 80%

11

22

2

5555

Proportion Patients Would Recommend Happy HospitalMonthly Sample 50 Patients

Page 80: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Comparisons: ANOM

St. V

incen

ts

RPAH

Insti

tute

RNSH

Roya

l New

castle

POW

Oran

ge Ba

se

Nepe

an

John

Hun

ter

HKH

Conc

ord

4.0

3.5

3.0

2.5

2.0

1.5

1.0

Facilities

Sam

ple

Count

Per

Unit

_U=2.465

Units Transfused by Facility

Clinical Excellence Commission BloodWatch Program

Page 81: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Process Capability

Aim:Process

spread is smaller than and contained within the specification spread

20161284

LSL: 4.0 USL: 8.4 mmol/litre

LSL 4Target *USL 8.3Sample Mean 5.74845Sample N 97StDev(Within) 0.476507StDev(Overall) 2.6834

Process Data

Cp 1.50CPL 1.22CPU 1.78Cpk 1.22

Pp 0.27PPL 0.22PPU 0.32Ppk 0.22

Overall Capability

CapabilityPotential (Within)

Process Capability of BSL

Page 82: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Harold Shipman

In 2000, Harold Shipman, a general practitioner in Manchester (U.K.) was convicted of murdering 15 of his patients and of forging the will of one.

 The clinical audit revealed clear evidence of a higher level

of death than would have been expected and not just in the more recent years. It was concluded that the excess of death did not appear to be explicable of grounds that Shipman’s practice served populations with markedly different demographic or health profiles.

      

Mohammed A Mohammed et al. Bristol, Shipman and Clinical Governance: Shewhart’s Forgotten Lessons. The Lancet, vol 357 February 10, 2001

Page 83: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Early Warning of Poor Performance

19971992198719821977199819931988198319781973

300

250

200

150

100

50

0

Year

Death

per

Thousa

nd P

atients

_U=98.3

UCL=205.5

LCL=0

Comparative GPs Shipman1

1

222

1

Harold Shipman Versus Comparative GP Death Rate/ 1000 PatientsFemales aged 75 Years or Above

1973 - 1998

Department Health: Harold Shipman's Clinical Practice 1974 - 1998

n=11

n=17.5

For women aged 75 years or over, it was predicted that

Shipman had 177 more deaths than expected.

A summary of the review of Shipman’s Clinical practice found Shipman issued 521 death certificates compared with the highest number of any of six comparison practitioners being 210.

The excess number of deaths were evidenced from the first few years of Shipman’s career as a GP; An excess of deaths occurred at home or in his practice premises.

Page 84: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

How Will we Know that a Change is an

Improvement?

Q2: How will we know that a change is an improvement?

Teams use quantitative measures to determine if a specific change actually leads to an improvement. e.g. Proportion of reconciled medications.

Nolan et al

Page 85: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

3 Ways to Get Better Numbers

1. Improve the System2. Distort the System3. Distort the Figures

»Outliers» Inliars»Darn Liars

Page 86: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.
Page 87: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Strategies for Getting Better Results

• Dis-aggregate:– e.g. LOS

• Stratify: aggregate and chop and

splice– e.g. Patient falls,

Cancelled OR cases• Experiment

– e.g. Waiting list management

• Standardisation

Page 88: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Disaggregate: LOS

• Pre-op

• Intra-op

• Recovery

• Post-op Ward

• Rehabilitation

Page 89: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Stratification: Slicing

FACTORS EXAMPLES – slice the data by….

WHAT Type of adverse event, Triage code, Cost

WHEN Month, Day of week, Time of day

WHERE Area health service, Facility, Location, e.g. sacrum

WHO Other GPs

Page 90: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Stratify: Comparing AHS

HGFEDCBA

90

80

70

60

50

Perc

ent

80%

Complaints Resolved within 35 DaysComparison of AHS Performance

BMKDoH

Page 91: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Pareto Principle

BackFin

gerChest

NeckThroat

HandE ye

Others

124 48 30 23 22 15 13 65

36.5 14.1 8.8 6.8 6.5 4.4 3.8 19.1

36.5 50.6 59.4 66.2 72.6 77.1 80.9 100.0

0

50

100

150

200

250

300

350

0

20

40

60

80

100

Location

Count

Percent

Cum %

Pe

rce

nt

Co

un

t

Paret Chart: Location of Staff Injury

80% of the trouble comes from 20% of the problem

Page 92: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Standardisation

Ganley H. Cameron M. Critical Paths – A Continuous Quality Improvement Approach to Improving Patient Care . The Quality Magazine. Australian Quality Council 1996.

Page 93: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Ganley HE, Cameron MJ. Momentum, Australian Quality Council. 2001

Page 94: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Ganley HE, Cameron MJ. Momentum, Australian Quality Council. 2001

Page 95: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.
Page 96: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Pro

port

ion

Mar-2007Mar-2006

0.9

0.8

0.7

0.6

0.5

0.4

0.3

0.2

_P=57%

UCL=78%

LCL=35%

1

1

Composite Compliance: Proportion Leg Ulcer Bundle ImplementedMarch 2006 & March 2007

Sample Size 6 & 7Tests performed with unequal sample sizes

Pro

port

ion

Mar-2007Mar-2006

1.0

0.8

0.6

0.4

0.2

0.0

_P=84%

UCL=100%

LCL=68%

Composite Compliance: Proportion of Leg Ulcer Bundle ImplementedMarch 2006 & March 2007

Sample Size 6 & 7

26%

O’Brien M, Lawton J, Conn C, Ganley H. Best Practice Wound Care. International Wound Journal. Wiley-Blackwell. 7 (4) 2011.

Page 97: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Causation:Feeling Challenged?

Sample

Sam

ple

Count

2321191715131197531

12

10

8

6

4

2

0

_C=1.43

UCL=5.03

LCL=0

1

C Chart of Damage Index

80757065605550

12

10

8

6

4

2

0

Degrees Fahrenheit

Dam

age Index

Challenger Data: Relationship between Temperature / Likelihood of Damage

Page 98: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

The Aim is Improvement

Common cause variation reduced

Process improved

Special causes present

Process out of control - unpredictable

Special causes eliminated

Process under control - predictable

Adapted from R. Lendon

Page 99: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Take Away Messages

Plot the dots - make the variation visible • Include sample size information• Interpret smoothed data with caution• Wary of drawing conclusion from few data points• Employ subject matter expertise to understand

data• Take care in extrapolating data• Stop tampering• Be willing to think differently

Plot the dots - make the variation visible

Page 100: Healthcare Redesign: Diagnostics Helen Ganley RN, CM, Cert IV QMA, Adv.Dip.QM, MQIHC Bounty Brokers Pty Ltd.

Deming’s Common Principles for Action

A common focus on ....................................................................Quality

A common vision ................................................High Quality Service

achieved by fighting

A common enemy .............................................................Variability

using

A common method ............................................Process Improvement

and communicated through

A common

language................................Statistics


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