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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 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.

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

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

  2. 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

  3. 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

  4. How Do We Know if a Change is an Improvement? “If you can’t measure it, you can’t manage IMPROVE it” 4

  5. 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

  6. 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

  7. 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”

  8. Why are you measuring? Research? Judgment? Improvement? The answer to this question will guide your entire quality measurement journey! 8

  9. “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.

  10. The Three Faces of Performance Measurement

  11. 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

  12. 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

  13. 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  

  14. 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).

  15. The Quality Measurement Journey AIM(Why are you measuring?) Concept Measure Operational Definitions Data Collection Plan Data Collection Analysis ACTION

  16. 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

  17. Potential Set of Measures for Improvement in the ED

  18. 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

  19. 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

  20. 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

  21. Unit 1 Unit 2 Unit 3 Cycle time results for units 1, 2 and 3 Unit 2

  22. “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

  23. Elements of a Run Chart The centerline (CL) on a Run Chart is the Median ~ Measure X (CL) Time

  24. Let the Data tell the story- Annotations

  25. Presenting your data with Time Series

  26. 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

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