using data at the front line and across the system pat o connor jane murkin wendy sayan ros gray
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Why Do You Need Data and Information?. To plan for improvementFor testing changeFor tracking complianceFor determining outcomesFor monitoring long term progressTo tell their story. . 3. How Do We Know if a Change is an Improvement?.

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using data at the front line and across the system pat o connor jane murkin wendy sayan ros gray

Using Data at the Front-line and Across the SystemPat O’Connor/Jane MurkinWendy Sayan/Ros Gray

why do you need data and information
Why Do You Need Data and Information?

To plan for improvement

For testing change

For tracking compliance

For determining outcomes

For monitoring long term progress

To tell their story

how do we know if a change is an improvement
How Do We Know if a Change is an Improvement?

“You can’t fatten a cow by weighing it”

- Palestinian Proverb

  • Improvement is NOT about measurement
  • However…


how do we know if a change is an improvement4
How Do We Know if a Change is an Improvement?

“If you can’t measure it, you can’t manage IMPROVE it”


model for improvement
Model for Improvement

Using Data to understand progress toward the team’s aim

Using Data to answer the questions posed on in the plan for each PDSA cycle

The Improvement Guide, API

need for measurement
Need for Measurement
  • Improvement is not about measurement.
  • But measurement plays an important role:
  • Key measures are required to assess progress on team’s aim
  • Specific measures can be used for learning during PDSA cycles
  • Balancing measures are needed to assess whether the system as a whole is being improved
  • Data from the system (including from patients and staff) can be used to focus improvement and refine changes
reaction to data stages of facing reality
Reaction to Data Stages of Facing Reality

“The data are wrong”

“The data are right, but it’s not a problem”

“The data are right; it is a problem; but it is not my problem.”

“I accept the burden of improvement”

why are you measuring
Why are you measuring?




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


the three faces of performance measurement improvement accountability and research
“The Three Faces of Performance Measurement: Improvement, Accountability and Research”

“We are increasingly realizing not only how critical measurement is to the quality improvement we seek but also how counterproductive it can be to mix measurement for accountability or research with measurement for improvement.”

Lief Solberg, Gordon Mosser and Sharon McDonaldJournal on Quality Improvement vol. 23, no. 3, (March 1997), 135-147.

improvement vs research contrast of complementary methods
Improvement vs. ResearchContrast of Complementary Methods



  • Improve practice of health care


  • Test observable
  • Stable bias
  • Just enough data
  • Adaptation of the changes
  • Many sequential tests
  • Assess by statistical significance

Clinical Research


  • Create New clinical knowledge


  • Test blinded
  • Eliminate bias
  • Just in case data
  • Fixed hypotheses
  • One fixed test
  • Assess by statistical significance
three types of measures
Outcome Measures:Voice of the customer or patient. How is the system performing? What is the result?

Process Measures:Voice of the workings of the system. Are the parts/steps in the system performing as planned?

Balancing Measures:Looking at a system from different directions/dimensions. What happened to the system as we improved the outcome and process measures (e.g. unanticipated consequences, other factors influencing outcome)?

Three Types of Measures
integrate data collection for measures in daily work
Integrate Data Collection for Measures in Daily Work

Include the collection of data with another current work activity (for example, pain scores with other vital signs; data from office visit flowsheets)

Develop an easy-to-use data collection form or make Information Systems input and output easy for clinicians 

Clearly define roles and responsibilities for on going data collection

Set aside time to review data with all those that collect it  

Expectations for Improvement

When will my data start to move?

  • Process measures will start to move first.
  • Outcome measures will most likely lag behind process measures.
  • Balancing measures – just monitoring – not looking for movement (pay attention if there is movement).
The Quality Measurement Journey

AIM(Why are you measuring?)



Operational Definitions

Data Collection Plan

Data Collection



The Quality Measurement Journey

AIM– freedom from harm

Concept – reduce patient falls

Measure – IP falls rate (falls per 1000 patient days)

Operational Definitions - # falls/inpatient days

Data Collection Plan – monthly; no sampling; all IP units

Data Collection – unit submits data to RM; RM assembles and send to QM for analysis

Analysis – control chart

Tests of Change

ConceptPotential Measures

Hand Hygiene Ounces of hand gel used each day

Ounces of gel used per staff

Percent of staff washing their hands (before & after visiting a patient)

Medication Errors Percent of errors

Number of errors

Medication error rate

VAPs Percent of patients with a VAP

Number of VAPs in a month

The number of days without a VAP

Every concept can have many measures

balancing measures looking at the system from different dimensions
Balancing Measures: Looking at the System from Different Dimensions

Outcome (quality, time)

Transaction (volume, no. of patients)

Productivity (cycle time, efficiency, utilization, flow, capacity, demand)

Financial (charges, staff hours, materials)

Appropriateness (validity, usefulness)

Patient satisfaction (surveys, complaints)

Staff satisfaction

Topic: Improve Waiting Time and Patient Satisfaction in A & E


Perspective (O, P, B)










% patient receiving discharge materials

Patient volume

Total Length of Stay (LOS=wait time)

Time to registration

Staff satisfaction

Patient Satisfaction Scores

Availability of antibiotics

“Left without being seen” (LWBS)


Unit 1

Unit 2

Unit 3

Cycle time results for units 1, 2 and 3

Unit 2

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



Dynamic View

Static View

Static View


  • Every process displays variation:
  • Controlled variation
    • stable, consistent pattern of variation
    • “chance”, constant causes
  • Special cause variation
    • “assignable”
    • pattern changes over time

Static View

elements of a run chart
Elements of a Run Chart

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



X (CL)


look at the relationships
Look at the Relationships

GWP5a Compliance with PVC bundle

GWP1 Compliance with EWS

GWP6 Compliance with safety briefings

GWO1 Crash Calls

GWP5 Compliance with hand washing