Basic Tools for Data Analysis Michael D. Chance MSM, MBA, MSQM, - - PDF document

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6/26/2013 Basic Tools for Data Analysis Michael D. Chance MSM, MBA, MSQM, CPHQ, CQIA, CSSGB Objectives 1. Define data. 2. Explain some barriers to successfully using data. 3. Explain the purpose and use of select quality data tools

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6/26/2013 1

Basic Tools for Data Analysis

Michael D. Chance

MSM, MBA, MSQM, CPHQ, CQIA, CSSGB

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Objectives

1.

Define data.

2.

Explain some barriers to successfully using data.

3.

Explain the purpose and use of select quality data tools

4.

Explain common misconceptions and limitations that arise from reporting “averages” or from relying on a single tool.

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What is Data? Data is: Factual information used as a basis for reasoning, discussion, or calculation.

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What Things are Considered Data?

  • Medications Given (Dosages, Routes, etc.)
  • Vital Signs / Symptoms
  • Patient Characteristics (Age, Sex, Race, etc)
  • Costs
  • Therapies
  • Staff Turnover, Working Hours, Ratios
  • Times (Admission, Discharge, etc.)
  • Anything we can document to measure

performance.

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“We measure performance in healthcare for two basic purposes. We measure first as a basis for making judgments and decisions… Second, we measure as the basis for future improvements”

Dennis O’Leary Former President, JCAHO

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Barriers To Putting Data Into Action

  • Don’t even know where to get data / info
  • Paralysis by analysis
  • No one is interested in it
  • Incorrect interpretation of data
  • Too complex to understand
  • Defensiveness
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Stages of Coping with Data

  • Stage I: “The data are wrong….”
  • Stage II: “The data are right, but it’s not

a problem…”

  • Stage III: “The data are right, it’s a

problem, but it’s not my problem…”

  • Stage IV: “The data are right, it’s a

problem, it’s my problem…”

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How Do We Make Sense of Data?

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Quality Improvement Tools

“If the only tool you have is a hammer, you will see every problem as a nail.”

Abraham Maslow, 1966

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Purpose of QI Tools

  • Describe and improve processes
  • Evaluate process or output

variation

  • Assist with decision-making
  • Analyze data in a variety of ways
  • Display information

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QI Tools Help Answer 5 Questions

  • 1. Where am I at?
  • 2. Where do I want to be?
  • 3. How do I get there?
  • 4. Am I still on the right path?
  • 5. How well did I do?

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Partial List of QI Tools

Affinity diagram Arrow diagram Balanced scorecard Benchmarking Box and whisker plot Brainstorming Cause-and-effect/Ishikawa/fishbone diagram Cause analysis tools Check sheet Control chart Critical incident Data collection and analysis tools Decision matrix Design of experiments (DOE) Evaluation and decision-making tools Failure mode effects analysis (FMEA) Fishbone/Ishikawa/cause-and-effect diagram Five S (5S) Five whys and five hows Flowchart Force field analysis Gage repeatability Gantt chart Histogram House of quality Idea creation tools Impact effort matrix Kano model Matrix diagram Mistake-proofing Multivoting Nine windows Nominal group technique Pareto chart Plan-do-check-act (PDCA) cycle or plan-do-study-act (PDSA) cycle Problem concentration diagram Process analysis tools Process decision program chart (PDPC) Project planning and implementation tools Quality function deployment (QFD) Relations diagram Scatter diagram Seven basic quality tools Seven new management and planning tools SIPOC+CM diagram SMART matrix Spaghetti diagram Stratification Success and effect diagram Survey Tree diagram Value stream mapping Voice of the customer table (VOCT)

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Purpose of QI Tools

However, you don’t have to use EVERY tool for every problem.

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Histogram Pareto Chart Scatter Diagram Run Chart Control Chart

Basic Decision Making Toolbox

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Basic Decision Making Tools

  • Bar Charts
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Histograms

  • A bar graph that shows the distribution
  • f CONTINUOUS data
  • A snapshot of data taken from a process
  • Summarize large data sets graphically
  • Compare process results to specification
  • Communicate information to the team
  • Assist in decision making

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Histogram Creation

Range

  • No. of Occurrences

1 – 10 11 – 20 21 – 30 31 – 40 41 – 50 51 – 60 61 – 70 71 – 80 81 – 90 91+

Check Sheet:

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Histogram Creation

Histogram:

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Histogram Analysis

Absorption Time Frequency 40 36 32 28 24 20 20 15 10 5 Histogram of Absorption Time

30 5 X   

Variable N Mean St Dev Minimum Median Maximum Absorption Time 100 30.009 5.002 13.759 30.694 42.076

Mean (or Average): Standard Deviation:

Descriptive Statistics:

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  • Histograms are a

snapshot in time and show “distribution”.

  • They do NOT show

trends over time.

Histogram Analysis

5 10 15 20 25 5 10 15 20 25 5 10 15 20 25 1 2 3 4 5 6 7

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Basic Decision Making Tools

  • Pareto Chart
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What is a Pareto Chart?

  • Bar chart arranged in

descending order of height from left to right

  • Bars on left relatively more

important than those on right

  • Separates the "vital few"

from the "useful many" (Pareto Principle)

  • 80/20 Rule
  • 80% of the gain from 20% of

the categories

Budget Allocation

80 38 27 14 11 5 20 40 60 80 100

Code 20 Code 10 Code 50 Code 30 Code 40 Code 60

Thousands

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Why use a Pareto Chart?

  • Breaks big problems into smaller pieces
  • Displays causes or problems in priority order
  • Identifies most significant factors
  • Shows where to focus efforts
  • Allows better use of limited resources

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Case Study

Overall Rating

75% 80% 85% 90% 95% 100% 1-24 2-7 2-21 3-6 3-20 4-3 4-15 4-30 5-15 5-30 YTD Goal Satisfaction

Where do we start?

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Example - Pareto Chart

Delay: 27 Communication: 15 Procedural / Competency Issues: 15 Staff Issues: 13 Environment – Cold: 12 Documentation/Admin/Billing: 11 Appointments: 7 Entertainment: 4 Location: 4 Parking: 4 Gown Size: 3 Other: 7

N = 122

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Example - Pareto Chart

27 15 15 13 12 11 7 4 4 4 3 7

22% 34% 47% 57% 67% 76% 82% 85% 88% 92% 94% 100%

5 10 15 20 25 30 Delay Comm Proc/Comp Staff Environ Admin Appt Entertain Location Parking Gown Other 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Complaints Cumulative N = 122

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Example - Pareto Chart

27 15 15 13 12 11 7 4 4 4 3 7

22% 34% 47% 57% 67% 76% 82% 85% 88% 92% 94% 100%

5 10 15 20 25 30 Delay Comm Proc/Comp Staff Environ Admin Appt Entertain Location Parking Gown Other 0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100% Complaints Cumulative N = 122

Break point

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Basic Decision Making Tools

  • Scatter Diagram

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Scatter Diagrams

A graph of paired data points plotted on a table that helps identify the possible relationship between the changes observed in two different sets of variables.

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Why use Scatter Diagrams?

  • Supplies the data to confirm a hypothesis that two

variables are related.

  • Provides both a visual and statistical means to test

the strength of a potential relationship.

  • Provides a good follow-up to a Cause and Effect

Diagram to find out if there is more than just a consensus connection between causes and the effect.

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Interpreting Scatter Diagrams

1 2 3 4 5 6 7 8 9 10

Pain Free Time Amount of Drug Given Does Amount of Drug Given Affect Pain-Free Time?

Correlation

1 2 3 4 5 6 7 8 9 10

Patient Satisfaction Rating Patient Waiting Time Does Patient Waiting Time Affect Patient Satisfaction?

1 2 3 4 5 6 7 8 9 10

Minutes Out of Restraint Mg/Kg Drug Given Does Amount of Drug Given Affect Minutes Patient Out

  • f Restraints?

0.00 5.00 10.00 15.00 20.00 50 100 150 Pressure (PSI) Temperature (F) Pressure of Gas at Temperature

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Is there a correlation?

Interpreting Scatter Diagrams

97.9 98 98.1 98.2 98.3 98.4 98.5 98.6 98.7 98.8 98.9 99 99.1 99.2 99.3 99.4 99.5 99.6 99.7 99.8 99.9 100 10 20 30 40 50 60 70 Outcome Input

Scatter Diagram

Outcome (y) Linear (Outcome (y))

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Beware of False Correlation

Interpreting Scatter Diagrams

97.9 98 98.1 98.2 98.3 98.4 98.5 98.6 98.7 98.8 98.9 99 99.1 99.2 99.3 99.4 99.5 99.6 99.7 99.8 99.9 100 10 20 30 40 50 60 70 Outcome Input

Scatter Diagram

Outcome (y) Linear (Outcome (y))

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Basic Decision Making Tools

  • Run Chart

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What is a Run Chart?

A line graph of data points plotted in chronological order that helps detect special causes of variation.

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What is a Run Chart?

  • A running record of process behavior over time.
  • Requires no statistical calculations.
  • Shows process behavior at a glance.
  • Can detect some special causes.
  • Time sequence is plotted on horizontal axis.
  • Measure of interest is always plotted on the

vertical axis.

  • Center Line is the mean score.
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Run Chart

Patient Restraint Rate

0.00% 0.10% 0.20% 0.30% 0.40% 0.50% 0.60% 0.70% 0.80% 0.90% 1.00% Jan-02 Feb-02 Mar-02 Apr-02 May-02 Jun-02 Jul-02 Aug-02 Sep-02 Oct-02 Nov-02 Dec-02 Jan-03 Feb-03 Mar-03 Apr-03 May-03 Jun-03 Jul-03 Aug-03 Sep-03 Oct-03 Nov-03 Percentage Restrained

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Basic Decision Making Tools

  • Control Chart

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What is a Control Chart?

A statistical tool used to distinguish between process variation resulting from common causes and variation resulting from special causes.

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When people do not understand variation

  • see trends where there are no trends
  • blame and give credit to others for things over

which they have little or no control

  • build barriers, decrease morale, and create an

atmosphere of fear

  • never be able to fully understand past

performance, make predictions about the future and make significant improvements in processes

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Control Chart

  • Elements of a Control Chart

Measurement Scale Horizontal Axis x-axis Time Units or Sequence

10 9 8 7 6 5 4 3 2 1

Centerline

1 5 10 15 20

Vertical Axis y-axis Upper Control Limit (UCL) Lower Control Limit (LCL)

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Why use Control Charts?

  • Monitor process variation over time
  • Differentiate between special cause and

common cause variation

  • Assess effectiveness of changes
  • Establish the basis for determining process

capability

  • Communicate process performance
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Control Chart vs Bar Chart

90 73 74 35 43 51 48

5 10 15 20 25 30 35 40 45 50 55 60 65 70 75 80 85 90 95 100

Sun Mon Tue Wed Thu Fri Sat

Consecutive Patients Time in Minutes Daily Avg Goal Weekly Avg

N=250

WOW!

Goal <60 Avg = 59

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Control Chart vs Bar Chart

25 50 75 100 125 150 175 200 225 250 275

Consecutive Patients Time in Minutes Door to MD Avg

N=250

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Control Chart vs Bar Chart

25 50 75 100 125 150 175 200 225 250 275

Consecutive Patients Time in Minutes

Door to MD Avg Goal UCL LCL

N=250

Goal = <60 Mean = 59 Std Dev = 55 UCL = 224 LCL = 0

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Control Chart vs Bar Chart

25 50 75 100 125 150 175 200 225 250 275

Consecutive Patients Time in Minutes

Door to MD Avg Goal UCL LCL

N=250

Goal = <60 Mean = 59 Std Dev = 12 UCL = 95 LCL = 23

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Improvement Strategies

After making a Run Chart or a Control chart, what’s next?

The type of variation determines your approach: SPECIAL CAUSE VARIATION?

  • If negative, eliminate it.
  • If positive, emulate it.
  • But don’t change the process!

COMMON CAUSE VARIATION?

  • If process is functioning at an unacceptable level, change the process!
  • Don’t “tamper” with individual data points!

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Resources

The Memory Jogger 2

  • Goal QPC

The Quality Toolbox

  • Nancy Tague
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Basic Tools for Data Analysis

Michael D. Chance MSM, MBA, MSQM, CQIA, CPHQ, CSSGB Quality Improvement Specialist Phone: 832-824-1308 Email: mdchance@texaschildrens.org