mellott & associates llc statistics for the healthcare quality professional susan mellott phd,...

Mellott & Associates LLC www.mellottandassociates.com

Statistics for the Healthcare Quality Professional

Susan Mellott PhD, RN, CPHQ, FNAHQ


Objectives

Use basic statistical techniques to describe data and to evaluate data

Use or coordinate the use of statistical process control components

Use or Coordinate the use of process analysis tools to display data


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Baseball

Apple PieStatistics


What Type of Data?

The type of data that you have controls what tools, statistics and display format are utilized

Everything is driven from this


Type of Data

Category – Count Data Categories or groups Histogram Percentage Chi Squared

Measured Data Time, Volume, Money,

Scores Run Chart Control Chart Mean, Median, Mode,

Range T-test


Categorical Data Examples

Men vs Women Age Ranges (0-30, 31-50, 51-70, 71+) DRGs Types of falls Types of injuries Diagnosis Complications


Measured Data Examples

Temperature LOS Average daily census Scores on a test Pre and Post Test scores Anything over time


In Healthcare Quality

Use more measured data than categorical data, but there is a place for each

Use more Run and Control charts than Histograms


And then there are other Problem Solving Tools

Cause & effect diagram

Pareto diagram

Scatter diagram

Regression analysis


But First…… Some Basics


Reliability and Validity

Reliability – The ability to get the same answer time and time

again if nothing changes

Validity– To be sure you are counting or measuring what

you intended to count or measure


Reliability and Validity

Must have reliability before you can have validity

HINT: R comes before V in the alphabet


Reliability

Test / Retest reliability

Inter-Rater reliability ***


Validity

Face Validity– Lowest level

Criterion Validity ***– Based on criteria

Construct Validity– Hardest to obtain


Sampling

How you pick your sample influences what you can do with your results – Can you generalize the findings outside of the

sample you used– Are you confined to use your findings only in

relation to the sample itself

Probability vs Non-Probability– Usually use of combination of each type


Probability Sampling

You probably will be able to generalize your findings

Simple Random– All items have an equal chance of being chosen

Stratified Random– Creating 2 or more homogeneous groups and then

randomly selecting items– Men vs Women

Systemic Random– Every n’th case


Non-Probability Sampling

You probably will not be able to generalize your findings

Convenience– Using data readily available

Quota– Set number of data sets

Purposive– Demonstrate a desired characteristic– Expert sampling– Men vs Women


Data Collection

Must assure that measuring what you intend to measure– Pre-test your data collection tool!– Verify collection questions with requestor of the data

Must assure that all data collectors collect the data from the same places and in the same manner


Before Collect Data, Must:

Know how you want to manipulate data;

Know how you want to display data;

Know how you want to report data;

All of which is based on type of data!


Categorical Data

AKA:– Attribute, Qualitative, Nominal, Ordinal, Discrete

Examples:– # of members, patients, births, procedures,

occurrences, gender, ethnicity


Categorical Data

Usually Reported as:– % in each category, whole numbers

Usually Displayed as:– Histogram, pie chart

Usual statistical test of difference between groups:– Chi Squared


Measured Data

AKA:– Continuous, Variable, Quantitative

Examples:– Age, height, weight, temperature, time, charges,

LOS


Measured Data

Usually Reported as:– Mean, median, mode, minimum, maximum,

percentiles, whole and fractional numbers Usually displayed as:

– Run charts, control charts Usual statistical test of difference between

groups:– T-test, or in special cases a Z-test


Mean, Median, Mode

Mean:– Average– Can be influenced by outliers/extreme values

Median:– Middle of data– Best utilized when there are outliers

Mode:– Most frequently occurring numbers


Examples

2, 4, 6, 8, 10– Mean:– Median:

2, 4, 6, 8, 100– Mean:– Median:


Example

2, 4, 6, 7, 8, 10– Mean:– Median:

0, 0, 0, 0, 0, 7, 12, 26– Mean:– Median:


Examples

2, 4, 6, 8, 10– Mode:

2, 3, 3, 4, 6, 8, 10– Mode:

2, 3, 3, 4, 4, 4, 6, 8, 10– Mode:


STANDARD DEVIATION


Statistical Differences between Groups

Clinical differences occur before statistical differences

Categorical data – Chi Square

Measured data – T-test


Chi-Squared test

Similar to a t-test

Get an X2 value

Then look that value up on a p-value table like a t-test score


T-test

Reported as a ‘p’ score Ranges from 0 to 1 0.05 and less shows statistical differences

0 10.50.25

0.05


Histogram (Bar Chart)

What is it: – A display of comparisons between different

categories or groups

When used: – To look at differences between categories or

groups (non-statistical differences)

What it will tell you:– What the amount of differences look like


Histogram vs Run Chart vs Control Chart

Histogram – – only with categorical data

– Best to display one to three points in time

– If more than three points in time, better to use run chart

– Much more effective than a pie chart


Run Chart (Line Graph)

What is it:– A line graph display of performance changes over

time When used:

– To look at data over time What it will tell you:

– What is baseline performance?– Amount and type of variation in a process?– Is process changing over time?– Is change an improvement?


Histogram vs Run Chart vs Control Chart

Run Chart – – Categorical data over time

– Sequential Data points

– Display comparison between years, hospital units, stratification of data

– Prior to having enough data for Control Chart


Control Chart (Line Graph)

What is it:– A line graph display that compares actual

performance to the mean and includes upper and lower control limits

When used:– To display normal variations and out-of-control

variations

What it will tell you:– Common cause or special cause variation


Control Chart

Conceptually – Run chart with standard deviation curve

placed on its side


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Control Charts


R Chart Title

RUCL=0.395

CL=0.187

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6/88am

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10am 12pm 2pm 6/108am

10am 12pm 2pm 6/118am

10am 12pm 2pm 6/128am

10am 12pm

Date/Time/Period/Number

RangeUCL+2 sigma+1 sigmaAverage-1 sigma-2 sigmaLCL

n=19


Regression Analysis

Simple Regression– How one variable affects another variable– Example: number of calories eaten vs weight gain

Multiple Regression– How multiple variables affect another variable– Factors related to compliance with medication

regime


Regression Analysis

Scatter Diagram Pictorial representation of a simple

regression– Positive Relationship

Data goes upward in an oval shape

– Negative Relationship Data goes downward in an oval shape

– No relationship Data neither goes upward nor downward


Positive Correlation

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0 1 2 3 4 5


Negative Correlation

0

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No Relationship or Correlation

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Multiple Regression in Health Care Quality

Many examples where multiple regression could be an important tool: Factors related to compliance with medication

regime Factors related to infections Factors related to high cholesterol Factors related to flexibility & strength


Multiple Regression

Pareto diagram Cause & Effect diagram


Pareto Diagram

What is it:– Pictorial representation similar to multiple

regression When used:

– When you want to determine where to start to get the biggest ‘bang for your buck’

What it will tell you:– Where you should start in your improvement

efforts


Cause & Effect Diagram

What is it:– Pictorial representation similar to multiple

regression When used:

– When you want to determine where to start to get the biggest ‘bang for your buck’

What it will tell you:– Where you should start in your improvement

efforts


Cause & Effect

Use when “bad” things happen to determine how it happened

Use when want “Good” results to determine what you need to do to get those results


Cause & Effect Diagram

Effect

Cause


Isolation Shallow Staffing Limited Access to Specialists Master Calendar Speed of Change Mentor Program Medical Records Soldier Assistance Board Admin CBO Info Technology Library Empowered Staff

Clinical CBO Education Program

CME OPD NCOPD SGT’s Time

Life QualityLife QualityImprovementImprovement

Showplace ofShowplace ofMedical ReadinessMedical Readiness

World ClassWorld ClassServiceService

ProfessionalProfessionalExcellenceExcellence

OasisOasisofof

CareCare

Focus Focus

Community In-processing Lack of Measurement

ResourcesTime Community Wellness PAT

Wellness Focus Info Technology

Soldier Assist Board Awards Program

AMMED Council FSG

Youth Summer Turnover

Measurement NTC Surgeon

Leadership MASCAL/EPP

Info Technology NCO Corps

Management Orientation Access Info Flow Info Technology Turnover Team Work Staffing Restraints Committed Staff EC Issues Customer Focus Lack of Measurement MC/CS Division Ambulatory Care Center

Commitment to Vision

DRETS PT Test

FSG

© M. Ellicott


Summary

You first need to know what kind of data you are working with

Then you have to know the types of tools and statistics you can use with that kind of data

Then you design the data collection and plan the analysis of that data

mellott & associates llc statistics for the healthcare quality professional susan mellott phd,...

Documents

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mellott associates llc

data use

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data collectors

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