Measuring Quality Improvement in

Healthcare

Rosemary Ellis, MSNDirector of Quality

792-0855ellisro@musc.edu

(Turning Data into Information)

Being responsible for a performance improvement project or analysis of a performance indicator can be a daunting task if you are not familiar with Statistical Process Control (SPC)

What is SPC

Statistical Process Control (SPC) Definition:

A data driven method for decision making based on the understanding of process variation.

Understanding SPC

Whether you are looking at medication delivery processes, or promptness in delivering food trays, the first thing you want to do is understand your process as it currently exists. In order to do this you must collect

some data.

Understanding SPCImportant things to consider when

considering data collection: Select only 1 or 2 key indicators that will

tell you how the process is performing. Develop a data collection plan that includes

reasons for collecting data, how the data will be used, an operational definition of the measurement, who and how the data will be collected.

Understanding SPC

QUESTIONS TO CONSIDER:What do you want to measure?Why are you measuring the data?How will you collect the data?Who will monitor the data?What will you do with the reports of

the analysis of the data?

Understanding SPC

How Much Data is Required? JCAHO PI .3.1.1 requires 5% or 30

whichever is greater. Control and run charts require a

minimum of 15 data points to be accurate (25 or more is best).

Measuring a process over time captures the best illustration of how the process is functioning.

Understanding SPC

Turning data into information After collecting the data you have to

analyze the data. Control and run charts will only tell you

about the predictability and capability of your process

In order to determine predictability you have to know something about variation in a process.

Understanding SPC

What is Variation in a process and why is it important? Every process has variation in its outputs and

inputs. This means that no two products- be they

components, reports, services - will ever be the same.

If your job is to fill quart bottles, there will always be some inconsistencies; there will always be a trace more or less than a quart no matter how well you do your job.

Understanding SPC

What is Variation in a process and why is it important? One of the main culprits working to make processes

unreliable or erratic is variation. A process whose capability and performance are

consistent and well understood generally produce a consistent product.

For example, computerized physician order entry results in a process with fewer handoffs, with fewer opportunities for variation, therefore leading to a more predictable time of order fill and time to patient.

Understanding SPC

VariationThere are two types of variation in a

process: Common Cause and Special Cause Common Cause Variation Variance inherent in the process which

is a result of how the process is performed

Understanding SPC

The next slide depicts common cause variation (with the exception of the first two data points)

Common Cause Variation is typically due to a large number of small sources

of variation. it is the sum of small sources of variation that

determines the inherent variation of the process it determines the process limits and its capability

as it is currently operated

Education about your role in safe care: Inpatient

UCL=85.5

CL=78.0

LCL=70.4

50

55

60

65

70

75

80

85

90

95

100

Oct-01 Nov-01 Dec-01 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

Month/Year

Mea

n

Data Points

UCL

A

B

Average

B

A

LCL

Goal is 90%

Understanding SPC

The previous slide would be considered “in control” (with the exception of the first two data points) this means unless with a change to the process

occurs the goal of 90% is not obtainable. this process is capable of achieving only

between 70 and 85 as it currently functions. additional information gathering is required to

determine what change would result in an improvement.

Understanding SPC

Special Cause Variation is depicted in the following slide (circled data is area of special cause)

Variance that can be attributed to a particular sourceEquipment problemAbnormal fluctuation in volumeSeasonal VariationFailure to follow procedure which could lead

to and increase in errors

Seclusion and Restraint

CL=0.04

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

Nov-00 Dec-00 Jan-01 Feb-01 Mar-01 Apr-01 May-01 Jun-01 Jul-01 Aug-01 Sep-01

November 00 through September 01

rate

per

pat

ient

day U

UCL

A

B

Average

B

A

LCL

Understanding SPC

The following are additional tools to help you turn data into information.

Understanding SPC

Basic Graphs - KEEP IT SIMPLEPie Chart-proportionalPareto Diagram-longest to shortestHistogram-frequency of distributionRun Chart-process over time

Basic graphs are easily compiled in Microsoft Excel

1999 Events Reported by Unit

Pie Chart

Top 5 Risk Management Issues (1999)

Fall found on floor Other IV Infiltrated Med-Other Med-Potential

Co

un

t

Pareto Analysis

Length of Stay

0

2

4

6

8

10

12

14

1 2 3 4 5 6 7

Length of Stay

Fre

qu

en

cyHistogram

Medication Errors Mar 96 to Dec 98

0

20

40

60

80

100

120

Month

Nu

mb

er

of

err

ors

re

po

rte

dRun Chart

Inpatient Fall per Month

Jan-00

Feb-00

Mar-00

Apr-00

May-00

Jun-00

Jul-00

Aug-00

Sep-00

Oct-00

Nov-00

Dec-00

Jan-01

Feb-01

Mar-01

Apr-01

May-01

Jun-01

Jul-01

Aug-01

Sep-01

Oct-01

Nov-01

Fa

lls

Re

po

rte

d

Falls

Mean

UCL

LCL

Control Chart

Understanding SPC

Additional components of graphs Name of creator Date created Source of information-you want your

data to be credible

Know the type of variation in the process before you make changes

The consequences of of not knowing the type of variation are: seeing trends where there are no trends blaming individuals for things they have no

control over. giving credit to others for things they have no

control over. “tampering” or making changes to a process

(without knowing the type of variation) can actually make it worse.

Making Changes in a processIf you have a process that demonstrates

only Common Cause: variation then you are faced with several decisions: if you you satisfied with the performance

then continue to monitor. you may determine that the process

performs so poorly you design a new process.

you may need to gather additional information before making any changes.

Making Changes in a processIf you have a process that

demonstrates Special Cause: you must eliminate the special cause

first (if you are able) the harder job begins because

eliminating common causes requires in-depth knowledge of the subject matter.

you may need to gather additional information before making any changes

All data used in this presentation is for the purpose of example only

END