2018 African Forum on Quality and Safety in Healthcare. Better Quality Through Better Measurement. Session Objectives

Transcription

1 2018 African Forum on Quality and Safety in Healthcare Better Quality Through Better Measurement Faculty Robert Lloyd, PhD, Vice President 20 February 2018 Session Objectives To evaluate your knowledge about the Quality Measurement Journey (QMJ). To describe the milestones in the QMJ. To link measurement to improvement. 2 Neither presenter has anything to disclose.

2 The framework for Learning and Change When you combine the 3 questions with the PDSA cycle, you get Our focus today the Model for Improvement. Langley, G. et al, The Improvement Guide, Jossey bass Publishing, What is your current level of knowledge about quality measurement? This self-assessment is designed to help quality facilitators and improvement team members gain a better understanding of where they personally stand with respect to the milestones in the Quality Measurement Journey (QMJ). What would your reaction be if you had to explain why it is preferable to plot data over time rather than using aggregated statistics and tests of significance? Can you construct a run chart or help a team decide which measure is more appropriate for their project? You may not be asked to do all of the things listed below today or even next week. But if you are facilitating a QI team or expect to be able to demonstrate improvement, sooner or later these questions will be posed. How will you deal with them? The place to start is to be honest with yourself and see how much you know about concepts and methods related to the QMJ. Once you have had this period of selfreflection, you will be ready to develop a learning plan for yourself and those on your improvement team. Source: R. Lloyd, Quality Health Care: A Guide to Developing and Using Indicators. 2 nd edition, Jones & Bartlett Publishers, 2017.

3 Exercise Measurement Self-Assessment 1.I'd definitely have to call in an outside expert to explain and apply this topic/method. 2.I'm not sure I could apply this appropriately to a project. 3.I am familiar with this topic but would have to study it further before applying it to a project. 4.I have knowledge about this topic, could apply it to a project but would not want to be asked to teach it to others. 5.I consider myself an expert in this area, could apply it easily to a project and could teach this topic/method to others. Source: R. Lloyd, Quality Health Care: A Guide to Developing and Using Indicators. 2 nd edition, Jones & Bartlett Publishers, Measurement Self-Assessment Source: R. Lloyd, Quality Health Care: A Guide to Developing and Using Indicators. 2 nd edition, Jones & Bartlett Publishers, Measurement Topic or Skill Help people in my organization determine why they are measuring (improvement, judgment or research) Move teams from concepts to specific quantifiable measures Building clear and unambiguous operational definitions for our measures Develop data collection plans (including stratification and sampling strategies) Explain why plotting data over time (dynamic display) is preferable to using aggregated data and summary statistics (static display) Explain the differences between random and non-random variation Construct run charts (including locating the median) Explain the reasoning behind the run chart rules Interpret run charts by applying the run chart rules Explain the statistical theory behind Shewhart control charts (e.g., sigma limits, zones, special cause rules) Describe the basic 7 Shewhart charts and when to use each one Help teams select the most appropriate Shewhart chart for their measures Describe the rules for special cause variation on a Shewhart chart Help teams link measurement to their improvement efforts Response Scale I'd definitely have to call in an outside expert to explain and apply this topic/method. 2. I'm not sure I could apply this appropriately to a project. 3. I am familiar with this topic but would have to study it further before applying it to a project. 4. I have knowledge about this topic, could apply it to a project but would not want to be asked to teach it to others. 5. I consider myself an expert in this area, could apply it easily to a project and could teach this topic/method to others.

4 Do you know the milestones in the Quality Measurement Journey (QMJ)? AIM* (How good? By when?) Concept Measure Operational Definitions Data Collection Plan Data Collection Analysis ACTION Source: R. Lloyd, Quality Health Care: A Guide to Developing and Using Indicators. 2 nd Edition, Jones & Bartlett Publishers, /R. Lloyd Milestones in the Quality Measurement Journey AIM reduce inpatient harm by 37% by the end of the calendar year Concept reduce inpatient falls Indicator Inpatient 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 QI Dept. for analysis Analysis control chart ACTION

5 A Complete QMJ: The CAUTI Case Study 9 Your QMJ begins with an AIM! What are We Trying to Accomplish? Aim Statement 10

6 Aim Statement Worksheet Project Topic: Aim statement (What s the problem? Why is it important? What are you going to do about it?) Do you have or can you develop a good Aim Statement? How good? By when? /R. Lloyd Milestones in the Quality Measurement Journey AIM (Why are you measuring?) Concept Measure Operational Definitions Data Collection Plan Data Collection Analysis ACTION 12 Source: R. Lloyd. Quality Health Care: A Guide to Developing and Using Indicators. 2 nd edition, Jones and Bartlett Publishers, 2017.

7 Moving from a Concept to Measure Hmmmm how do I move from a concept to an actual measure? Every concept can have MANY measures. Which one is most appropriate? 14 Vision End Result Ideal State

8 These are NOT measures! Reduce wait times Improve patient satisfaction Expand market share Be more efficient Increase health and well-being Reduce waste Improve our financial situation Reduce inpatient discharge delays Enhance Patient education Deliver safe services They are part of Every concept can have many measures! Source: R. Lloyd. Quality Health Care: A Guide to Developing and Using Indicators. 2 nd edition, Jones and Bartlett Publishers, Concept Potential Measures Access Number of days to the next 2 nd appointment Percent of add-ons who can be seen today Number of walk-in appointments The number of minutes a caller is on hold before talking to a staff person Number of phone calls requesting an appointment this week Wait Time Wait time from check-in to discharge Wait time from check-in to seeing doctor Time spent with doctor Time it takes to have follow-up work done in the office (labs, x-ray, ultra-sound, etc.) Management of Diabetes Patients Percent of diabetes patients with appropriate eye and foot exams done during an office visit Percent of all diabetes patient in glucose control Percent of patients engaged in self-management goals

9 Three Types of Measures Outcome Measures Point to qualities that stakeholders value (voice of the customer) Is this system meeting the needs of those who care about its operation? Is our improvement work making a meaningful impact? Process Measures Voice of the process. Are the parts/steps in the system performing as planned? Are processes reliable? Efficient? Patient-Centered? Are we on track to influence the Outcome measure(s)? Balancing Measures Are we producing unintended consequences in our efforts to improve? What other factors may be affecting results? Looking at a system from different directions/dimensions. What happened to the system as we improved the outcome and process measures? 17 Potential Set of Measures for Improvement in a Family Practice Clinic Topic Improve waiting time and patient satisfaction in the family practice clinic Outcome Measures Total Length of Stay (in minutes) for a scheduled appointment at the clinic % of patients marking Strongly Agree to the question: Would you recommend our clinic to family and friends? Process Measures Time from check-in till seeing the doctor Patient /staff comments on flow % of patient receiving discharge materials Wait time for ancillary services (lab, x-ray, ultra-sound) during a visit Balancing Measures Volume of patients % of patients leaving without being seen by the doctor Staff satisfaction Financials 2016 /R. Lloyd

10 Balancing Measures: Looking at the System from Different Dimensions Outcome (quality, time) Transaction (volume, no. of patients) Productivity (cycle time, efficiency, utilisation, flow, capacity, demand) Financial (charges, staff hours, materials) Appropriateness (validity, usefulness) Patient satisfaction (surveys, customer complaints) Staff satisfaction Balancing measures help keep you from sub-optimizing the system! Balancing Measures help you capture Unintended Consequences 20

11 Exercise Organizing your Measures 1. A starting point for any QI project is to move from concepts to measures that appropriately capture the concepts of interest. 2. Use the Organizing Your Measures Worksheet on the next page to start this part of your journey. 3. List the concepts of interest in the far left column. Then identify potential measures for these concepts in the second column. Remember that a single concept might have more than one potential measure. 4. Finally, indicate whether each potential measure is an Outcome, Process or Balancing measure /R. Lloyd Organizing Your Measures Worksheet Topic for Improvement: Concept Potential Measure(s) Outcome Process Balancing Source: R. Lloyd. Quality Health Care: A Guide to Developing and Using Indicators. 2 nd Edition, Jones & Bartlett Learning, /R. Lloyd

12 Example Organizing Your Measures Worksheet Topic for Improvement: Inpatient Falls Concept Potential Measure(s) Outcome Process Balancing Patient Harm Patient Harm Compliance Staff Education Assessment Time Inpatient falls rate Number of falls Percent of inpatients assessed for falls Percent of staff fully trained in falls assessment protocol The additional time it takes to conduct a proper falls assessment Source: R. Lloyd. Quality Health Care: A Guide to Developing and Using Indicators. 2 nd Edition, Jones & Bartlett Learning, /R. Lloyd Milestones in the Quality Measurement Journey AIM (Why are you measuring?) Concept Measure Operational Definitions Data Collection Plan Data Collection Analysis ACTION 24 Source: R. Lloyd. Quality Health Care: A Guide to Developing and Using Indicators. 2 nd edition, Jones and Bartlett Publishers, 2017.

13 An Operational Definition... is a description, in quantifiable terms, of what to measure and the steps to follow to measure it consistently. It gives communicable meaning to a concept Is clear and unambiguous Specifies measurement methods and equipment Identifies criteria 25 What is a goal? The whole ball or half the ball?? 2016 /R. Lloyd

14 How do you define the following healthcare concepts? Medication error Co-morbid conditions Teenage pregnancy Cancer waiting times Health inequalities Asthma admissions Childhood obesity Patient education Health and wellbeing Adding life to years and years to life Children's palliative care Safe services Smoking cessation Urgent care Complete history & physical Delayed discharges End of life care Falls (with/without injuries) Childhood immunizations Complete maternity service Patient engagement Moving services closer to home Successful breastfeeding Ambulatory care Access to health in deprived areas Diagnostics in the community Productive community services Vascular inequalities Breakthrough priorities Surgery start time Example Medication Error Operational Definition Measure Name: Percent of medication errors Numerator: Denominator: Data Collection: Number of outpatient medication orders with one or more errors. An error is defined as: wrong med, wrong dose, wrong route or wrong patient. Number of outpatient medication orders received by the family practice clinic pharmacy. This measure applies to all patients seen at the clinic The data will be stratified by type of order (new versus refill) and patient age The data will be tracked daily and grouped by week The data will be pulled from the pharmacy computer and the CPOE systems Initially all medication orders will be reviewed. A stratified proportional random sample will be considered once the variation in the process is fully understood and the volume of orders is analyzed.

15 Exercise Operational Definition 29 Select an improvement project that is work related or a personal improvement project. Select one measure from this project and develop an operational definition that is: Clear and unambiguous Specifies measurement methods and equipment Identifies criteria if appropriate. Use the Operational Definition Worksheet to guide and record your work. Operational Definition Worksheet 30 Measure Name: (Remember this should be specific and quantifiable, e.g., the time it takes to,the number of, the percent of or the rate of ) Operational Definition Define the specific components of this measure. Specify the numerator and denominator if it is a percent or a rate. If it is an average, identify the calculation for deriving the average. Include any special equipment needed to capture the data. If it is a score (such as a patient satisfaction score) describe how the score is derived. When a measure reflects concepts such as accuracy, complete, timely, or an error, describe the criteria to be used to determine accuracy. Can you develop a good Operational Definition? See Appendix D for a detailed Operational Definition worksheet 2016 /R. Lloyd

16 Milestones in the Quality Measurement Journey AIM (Why are you measuring?) Concept Measure Operational Definitions Data Collection Plan Data Collection Analysis ACTION Source: R. Lloyd. Quality Health Care: A Guide to Developing and Using Indicators. 2 nd edition, Jones and Bartlett Publishers, Key Aspects of Data Collection Stratification Sampling Methods Frequency of Data Collection Duration of Data Collection Source: R. Lloyd. Quality Health Care: A Guide to Developing and Using Indicators. 2 nd edition, Jones and Bartlett Publishers, 2017, Chapter 4.

17 Key Data Collection Strategies: Stratification Stratification Separation & classification of data according to predetermined categories Designed to discover patterns in the data For example, are there differences by shift, time of day, day of week, severity of patients, age, gender or type of procedure? Consider stratification BEFORE you collect the data Age & Gender Day of week Time of day or Shift Stat vs routine orders Severity of patients Units within a facility Socio-economic status Key Data Collection Strategies: Sampling Methods Probability Sampling Methods Simple random sampling Stratified random sampling Stratified proportional random sampling Systematic sampling Cluster sampling Non-probability Sampling Methods Convenience sampling Quota sampling Judgment sampling Do you need to pull a sample or do you take every occurrence of the data (i.e., collect data for the total population) Source: R. Lloyd. Quality Health Care: A Guide to Developing and Using Indicators. 2 nd edition, Jones and Bartlett Publishers, /R. Lloyd

18 How often and for how long do you need to collect data? Frequency the period of time in which you collect data (i.e., how often will you dip into the process to see the variation that exists?) Moment by moment (continuous monitoring)? Every hour? Every day? Once a week? Once a month? Duration how long you need to continue collecting data? Do you collect data on an on-going basis and not end until the measure is always at the specified target or goal? Do you conduct periodic audits? Do you just collect data at a single point in time to check the pulse of the process? 2015 /R. Lloyd Milestones in the Quality Measurement Journey AIM (Why are you measuring?) Concept Measure Operational Definitions Data Collection Plan Data Collection Analysis ACTION 36 Source: R. Lloyd. Quality Health Care: A Guide to Developing and Using Indicators. 2 nd edition, Jones and Bartlett Publishers, 2017.

19 You have performance data. Now, what do you do with it? 37 Do you understanding variation conceptually? If I had to reduce my message for management to just a few words, I d say it all had to do with reducing variation. W. Edwards Deming

20 The Problem! Aggregated data presented in tabular formats or with summary statistics, will not help you measure the impact of process improvement efforts. Aggregated and summary statistics data can only lead to judgment, not to improvement. 39 Mean, Median, Mode Minimum, Maximum, Range, Standard Deviation Comparison of monthly or quarterly averages The average of a set of numbers can be created by many different distributions Measure X (CL) 65 Time 40

21 Managing a company by means of the monthly (or quarterly or yearly) reports is like trying to drive a car by watching the yellow line in the rear-view mirror. Myron Tribus As quoted in Wheeler, Donald. Understanding Variation: The Key to Managing Chaos. SPC Press, Inc., 1993: 4. If you are serious about your quality improvement efforts, you should be collecting and analyzed data as close to the production of work as possible. What would it take to collect data on individual patients waiting to see the doctor? To track the number of patients being assessed for pressure ulcers each day? The percent of did not attend appoints for each week? Most measures can be collected more frequently than monthly! If you don t understand the variation that lives in your data, you will be tempted to... Deny the data (It doesn t fit my view of reality!) See trends where there are no trends Try to explain natural variation as special events Blame and give credit to people for things over which they have no control Distort the process that produced the data Kill the messenger! 42

22 Dr. Walter A Shewhart W. Shewhart. Economic Control of Quality of Manufactured Product, 1931 A phenomenon will be said to be controlled when, through the use of past experience, we can predict, at least within limits, how the phenomenon may be expected to vary in the future What is the variation in one system over time? Walter A. Shewhart - early 1920 s, Bell Laboratories Dynamic View UCL Static View time 44 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

23 Types of Variation Common Cause Variation Is inherent in the design of the process Is due to regular, natural or ordinary causes Affects all the outcomes of a process Results in a stable process that is predictable Also known as random or unassignable variation 45 Special Cause Variation Is due to irregular or unnatural causes that are not inherent in the design of the process Affect some, but not necessarily all aspects of the process Results in an unstable process that is not predictable Also known as non-random or assignable variation Random (Common Cause) Variation /1/2008 3/8/2008 3/15/2008 3/22/2008 3/29/2008 4/5/2008 4/12/2008 4/19/2008 4/26/2008 5/3/2008 5/10/2008 5/17/2008 5/24/2008 5/31/2008 6/7/2008 Points equally likely above or below center line There will be a high data point and a low, but this is expected No trends or shifts or other patterns Courtesy of Richard Scoville, PhD, IHI Improvement Advisor

24 Non-Random or Special Cause Variation Two Sources of Special Causes Unintentional When the system is out of control and unstable due to unexpected forces Intentional When we re trying to change the system Courtesy of Richard Scoville, PhD, IHI Improvement Advisor Minutes ED to OR per Patient Holding the Gain: Isolated Femur Fractures Sequential Patients Point Variation exists! Random Variation (common cause) does not mean Good Variation. It only means that the process is stable and predictable. For example, if a patient s systolic blood pressure averaged around 165 and was usually between 160 and 170 mmhg, this might be stable and predictable but unacceptable. Similarly Non-Random (special cause) variation should not be viewed as Bad Variation. You could have a non-random variation that represents a very good result (e.g., a low turnaround time), which you would want to emulate. Non- Random merely means that the process is unstable and unpredictable.

25 3 Issues 1. Does the process reflect random (i.e., common cause) variation? 2. If so, it is stable and therefore predictable. 3. Now, if it is predictable is the process capable under current operating conditions of meeting the established target or goal? The chart will tell you if the process is stable and predictable. You have to decide if the process is capable. If it is not capable: (1) reduce the variation or (2) redesign the entire process? Source: R. Lloyd. Quality Health Care: A Guide to Developing and Using Indicators. 2 nd edition, Jones and Bartlett Publishers, Finally, find examples that work for your discipline! Random Variation Non-Random Variation 1200 Holding the Gain: Isolated Femur Fractures Minutes ED to OR per Patient Sequential Patients Normal Sinus Rhythm (a.k.a. Random Variation) Ventricular Fibrillation (a.k.a. Non-Random Variation) Appreciation is extended to Dr. Douglas Brosnan, JD, MD, Vice Chair, Department of Emergency Medicine, Sutter Roseville Inpatient EHR Physician Champion for providing the example of normal sinus rhythm versus ventricular fibrillation.

26 Antal patienter med vårdtid < 6dygn i % vid primär elektiv knäplastik (operationsdag= dag1) Månad Attributes of a Leader Who Understands Variation Leaders understand the different ways that variation is viewed. They explain changes in terms of common causes and special causes. They use graphical methods to learn from data and expect others to consider variation in their decisions and actions. They understand the concept of stable and unstable processes and the potential losses due to tampering. Capability of a process or system is understood before changes are attempted. Dialogue Understanding Variation Select several measures which your organization tracks regularly. Do you and the leaders of your organization evaluate these measures according the criteria for common and special causes of variation? If not, what criteria do you use to determine if your measures are improving or getting worse? Antal patienter i %

27 Mo nth ED /1 00 Re tur ns M U C L = 0.88 A M ea n = LC L = M J J A Unplanned Returns to Ed w/in 72 Hours S O N D u ch ch a a r r t t 10 J F M A M J J A S Do you understanding variation statistically? Rate per 100 ED Patients STATIC VIEW Descriptive Statistics Mean, Median & Mode Minimum/Maximum/Range Standard Deviation Bar graphs/pie charts DYNAMIC VIEW Run Chart Control Chart (plot data over time) Statistical Process Control (SPC) 53 Annotated Time Series (the minimum standard for QI projects) Line Graph Run Chart Run and Shewhart (Control) Charts are the best tools to determine: The variation that lives in the process Control Chart If our improvement strategies have had the desired effect.

28 1. Make process performance visible Minutes ED to OR per Patient Current Process Performance: Isolated Femur Fractures Sequential Patients Three Uses of Statistical Process Control Tools Minutes ED to OR per Patient Process Improvement: Isolated Femur Fractures Sequential Patients 2. Determine if a change is an improvement 3. Determine if we are holding the gains Minutes ED to OR per Patient Holding the Gain: Isolated Femur Fractures Sequential Patients Elements of a Run Chart Ideally you should have between data points before 6.00 constructing 5.75 a run chart The centerline (CL) on a Run Chart is the Median 5.50 Measure Pounds of Red Bag Waste ~ X (CL) Median= Time Point Number Four statistical run rules are used to determine if non-random variation is present

29 Non-Random Rules for Run Charts A Shift: 6 or more A Trend 5 or more Too many or too few runs An astronomical data point Source: The Data Guide by L. Provost and S. Murray, Jossey-Bass Publishers, Why are Shewhart Charts preferred over Run Charts? Because Control Charts 1. Are more sensitive than run charts A run chart cannot detect special causes that are due to point-to-point variation (median versus the mean) Tests for detecting special causes can be used with control charts 2. Have the added feature of control limits, which allow us to determine if the process is stable (common cause variation) or not stable (special cause variation). 3. Can be used to define process capability. 4. Allow us to more accurately predict process behavior and future performance.

30 Elements of a Shewhart Control Chart Measure Number of Complaints An indication of a special cause (Upper Control Limit) UCL= A B C CL= C B A X (Mean) LCL= (Lower Control Limit) 5.0 Time Jan01 Mar01 May01 July01 Sept01 Nov01 Jan02 Mar02 May02 July02 Sept02 Nov02 Month Rules for Detecting Special Causes 60

31 Types of Quantitative Data Variables Data Attributes Data Defectives (occurrences plus non-occurrences) Nonconforming Units Defects (occurrences only) Nonconformities The Control Chart Decision Tree Source: R. Lloyd. Quality Health Care: A Guide to Developing and Using Indicators. 2 nd edition, Jones and Bartlett, Variables Data Decide on the type of data Attributes Data Yes More than one observation per subgroup? No No Occurrences & Nonoccurrences? Yes Yes Is there an equal area of opportunity? No X bar & S Average and Standard Deviation XmR Individual Measurement c-chart The number of Defects u-chart The Defect Rate p-chart The percent of Defective Units

32 63 Milestones in the Quality Measurement Journey AIM (Why are you measuring?) Concept Measures Operational Definitions Data Collection Plan Data Collection Analysis ACTION Source: R. Lloyd. Quality Health Care: A Guide to Developing and Using Indicators. 2 nd edition, Jones and Bartlett Publishers, 2017.

33 Measurement Self-Assessment Source: R. Lloyd, Quality Health Care: A Guide to Developing and Using Indicators. 2 nd edition, Jones & Bartlett Publishers, Measurement Topic or Skill Help people in my organization determine why they are measuring (improvement, judgment or research) Move teams from concepts to specific quantifiable measures Building clear and unambiguous operational definitions for our measures Develop data collection plans (including stratification and sampling strategies) Explain why plotting data over time (dynamic display) is preferable to using aggregated data and summary statistics (static display) Explain the differences between random and non-random variation Construct run charts (including locating the median) Explain the reasoning behind the run chart rules Interpret run charts by applying the run chart rules Explain the statistical theory behind Shewhart control charts (e.g., sigma limits, zones, special cause rules) Describe the basic 7 Shewhart charts and when to use each one Help teams select the most appropriate Shewhart chart for their measures Describe the rules for special cause variation on a Shewhart chart Help teams link measurement to their improvement efforts Response Scale I'd definitely have to call in an outside expert to explain and apply this topic/method. 2. I'm not sure I could apply this appropriately to a project. 3. I am familiar with this topic but would have to study it further before applying it to a project. 4. I have knowledge about this topic, could apply it to a project but would not want to be asked to teach it to others. 5. I consider myself an expert in this area, could apply it easily to a project and could teach this topic/method to others. Tips for building an effective measurement system Seek useful measures not perfection Think about stratification Use sampling (when appropriate) Integrate measurement into daily routine Collect qualitative and quantitative data Plot data over time 2017 Institute for Healthcare 2015 Improvement/R. C. Lloyd & Associates Lloyd

34 But realize that the charts don t tell you The reasons(s) for a Special Cause. Whether or not a Common Cause process should be improved (is the performance of the process acceptable?) How the process should actually be improved or redesigned. 67 A Simple Improvement Plan 1. Which process do you want to improve or redesign? 2. Does the process contain common or special cause variation? 3. How do you plan on actually making improvements? What strategies do you plan to follow to make things better? 4. What effect (if any) did your plan have on the process performance? SPC methods and tools will help you answer Questions 2 & 4. YOU need to figure out the answers to Questions 1 & 3.

35 Finally, remember that data are a necessary part of the Sequence of Improvement Make part of routine operations Sustaining improvements and Spreading changes to other locations Test under a variety of conditions Implementing a change Theory and Prediction Developing a change Testing a change Additional Resources You can access the following free videos from the IHI website: Dr. Lloyd has over 20 Whiteboard Videos that explain the concepts, tool and methods of QI in 4-8 minutes. Also Dr. Lloyd s On Demand Videos can also be accessed from the IHI Website: Deming s System of Profound Knowledge and the Model for Improvement Data Collection and Understanding Variation Using Run and Control Charts 70

36 Measuring Quality Improvement in Healthcare: A Guide to Statistical Process Control Applications (ASQ Press, 2000), Dr. Lloyd s books 1 st Edition 2 nd Edition Quality HealthCare: A Guide to Developing and Using Indicators, (Jones & Bartlett Learning (2004, 1 st edition) with the 2 nd edition to be released in the September of In the UK Jones & Bartlett books are sold through /R. Lloyd Thank you for joining me today! Good luck with your Measurement Quality Journey! Contact Information: Dr. Robert Lloyd: 72

37 Bio for Robert Lloyd, Robert Lloyd, PhD, Vice President, Institute for Healthcare Improvement provides leadership in the areas of performance improvement strategies, statistical process control methods, development of strategic dashboards and capacity and capability building for quality improvement. He also serves as faculty for the IHI Improvement Advisor (IA) Professional Development programme, the Improvement Science in Action (ISIA) programme, the Improvement Coach programme. Dr. Lloyd provides leadership for IHI s work in the US, Canada, the UK, Sweden, Denmark, Norway, Africa, the Middle East, India, New Zealand and Australia. Dr. Lloyd is the author of three books and numerous articles and chapters on quality measurement and the science of improvement. He lives in Chicago with his wife Gwenn, daughter Devon and ever entertaining dog Cricket..