Using data at the front line and across the system pat o connor jane murkin wendy sayan ros gray l.jpg
This presentation is the property of its rightful owner.
Sponsored Links
1 / 27

Using Data at the Front-line and Across the System Pat O’Connor/Jane Murkin Wendy Sayan/Ros Gray PowerPoint PPT Presentation


  • 76 Views
  • Uploaded on
  • Presentation posted in: General

Using Data at the Front-line and Across the System Pat O’Connor/Jane Murkin Wendy Sayan/Ros Gray. 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.

Download Presentation

Using Data at the Front-line and Across the System Pat O’Connor/Jane Murkin Wendy Sayan/Ros Gray

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Using data at the front line and across the system pat o connor jane murkin wendy sayan ros gray l.jpg

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


Why do you need data and information l.jpg

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 l.jpg

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…

3


How do we know if a change is an improvement4 l.jpg

How Do We Know if a Change is an Improvement?

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

4


Model for improvement l.jpg

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 l.jpg

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 l.jpg

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 l.jpg

Why are you measuring?

Research?

Judgment?

Improvement?

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

8


The three faces of performance measurement improvement accountability and research l.jpg

“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.


The three faces of performance measurement l.jpg

The Three Faces of Performance Measurement


Improvement vs research contrast of complementary methods l.jpg

Improvement vs. ResearchContrast of Complementary Methods

Improvement

Aim:

  • Improve practice of health care

    Methods:

  • Test observable

  • Stable bias

  • Just enough data

  • Adaptation of the changes

  • Many sequential tests

  • Assess by statistical significance

Clinical Research

Aim:

  • Create New clinical knowledge

    Methods:

  • Test blinded

  • Eliminate bias

  • Just in case data

  • Fixed hypotheses

  • One fixed test

  • Assess by statistical significance


Three types of measures l.jpg

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 l.jpg

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  


Slide14 l.jpg

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).


Slide15 l.jpg

The Quality Measurement Journey

AIM(Why are you measuring?)

Concept

Measure

Operational Definitions

Data Collection Plan

Data Collection

Analysis

ACTION


Slide16 l.jpg

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


Potential set of measures for improvement in the ed l.jpg

Potential Set of Measures for Improvement in the ED


Slide18 l.jpg

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 ErrorsPercent of errors

Number of errors

Medication error rate

VAPsPercent 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 l.jpg

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


Slide20 l.jpg

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

Measure

Perspective (O, P, B)

P

B

O

P

B

O

P

B

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)

Costs


Slide21 l.jpg

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 l.jpg

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

UCL

time

Dynamic View

Static View

Static View

LCL

  • 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 l.jpg

Elements of a Run Chart

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

~

Measure

X (CL)

Time


Let the data tell the story annotations l.jpg

Let the Data tell the story- Annotations


Presenting your data with time series l.jpg

Presenting your data with Time Series


Look at the relationships l.jpg

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


  • Login