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TRUE BLUE Quest For Quality. Data Sanity Matthew S. Wayne MD, CMD Chief Medical Officer.

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Presentation on theme: "TRUE BLUE Quest For Quality. Data Sanity Matthew S. Wayne MD, CMD Chief Medical Officer."— Presentation transcript:

1 TRUE BLUE Quest For Quality

2

3 Data Sanity Matthew S. Wayne MD, CMD Chief Medical Officer

4 Objectives Perform an in-depth evaluation of current data analysis processes and how they can be improved to improve the quality of care in your nursing home Review the 3 steps in proper data analysis Utilize control charts to analyze data in your nursing home Distinguish between common cause and special cause variation and discuss specific strategies to address both types of variation

5 5 ACA Provision Section 6102(c) of the Affordable Care Act (ACA) directs the Secretary to provide technical assistance and promulgate regulations for each nursing home to implement a QAPI system, and permits the Secretary to sequence these actions so the technical assistance is available prior to the regulations. QAPI Quality Assurance - Performance Improvement

6 QA+PI=QAPI Quality Assurance Compliance with standards Inspection Reactive Remove outliers Narrow Involves only a few Performance Improvement Continuously improving processes Prevention Proactive Processes/Systems Systemic Involves entire IDT 6 U.S. Department of Health and Human Services, Health Resources and Services Administration. Quality Improvement adapted from

7 7 QA vs QI Balestracci p285

8 5 Elements of QAPI Design & Scope Governance & Leadership Feedback, Data Systems and Monitoring Performance Improvement Projects (PIPs) Systematic Analysis & Systemic Action

9 5 Elements of QAPI Design and Scope o Comprehensive and ongoing plan o Includes all departments and functions o Addresses safety, quality of care, QOL, resident choice, transitions o Based on best available evidence o QAPI plan

10 5 Elements of QAPI Governance and Leadership o Boards/owners and executive leadership Buy in and support o Training and organizational climate Administration sees value o Sufficient resources o Sustainability

11 5 Elements of QAPI Feedback, Data monitoring Systems, and Monitoring o Multiple sources, including resident and staff o Benchmarking and targeting o Adverse events

12 5 Elements of QAPI Performance Improvement Projects o Prioritized topics Number of PIPs depend on the facility program o Team Chartered o PDSA Cycle

13 5 Elements of QAPI Systematic Analysis and Systemic action o Root cause analysis o Systems thinking o Systematic changes as needed

14 National Rollout: Timeline By statute, nursing homes will be expected to have QAPI programs in place that meet a defined standard, one year after CMS issues a QAPI rule. CMS expects to issue a draft regulation for comment in A final rule is likely to be issued by early 2013.

15 15 Quality Improvement: Case 1 Goal – reduce 10% next year

16 16 Quality Improvement: Case1 Everybody gets pizza!!!!!!!!!!!!

17 17 Quality Improvement: Case 1

18 18 Quality Improvement: Case 2

19 19 Quality Improvement: Case 2

20 20 Quality Improvement: Case 2

21 21 Quality Improvement: Case 2

22 22 Quality Improvement: Case 2

23 23 Quality Improvement: Case 2

24 24 Quality Improvement: Case 2

25 25 Quality Improvement: Case 2

26 26 Quality Improvement: Case 2 What if we were to tell you that this was not medication error data but …………………. Coin Flip Data

27 27 Basic Statistical Lesson 2 Key Concept -Variation Case 2: Coin Flip : 50 people- 25 times- # Heads

28 28 Key Concept -Variation We learn nothing of importance by comparing two or three results when they all come from a stable process Most data of importance to management are from stable processes

29 29 Quality Improvement Process Variation Priority

30 30 Process Oriented Thinking Systems Thinking System - Definition o A group of interdependent processes o A network of functions or activities within an organization that work together for the aim of the organization

31 The Big Picture Group of related interdependent processes working together to achieve a common goal Made up of a culture, structure and boundary System Sequence of tasks aimed at accomplishing a goal Produce data which can be analyzed Process Have beliefs, values, interests, needs Have roles which are made up of functions and tasks People

32 32 Process Oriented Thinking Process- Definition o Sequences of tasks aimed at accomplishing a particular outcome o Transformation of inputs into outputs

33 33 Basic Statistical Lesson 1 Given two different numbers, one will be larger Or- Two numbers that are not the same : are different

34 34 Quality Improvement

35 35 Basic Statistical Lesson 1 Is the process that produced the second number the same that produced the first number? Real Question 1

36 36 Basic Statistical Lesson 1 If this number is different from a desired goal, is this variation from the goal due to common cause or special cause process? What is common cause? Special cause? Real Question 2

37 37 Process First: Your current processes are perfectly designed to get the results they are already getting and designed to get, with it's corollary: o insanity is doing things the way you have always done them while expecting different results

38 38 Process Second, the current process are also perfectly designed to take up more than 100% of people's time working in them, with it's corollary, o it is amazing how much waste can be disguised as useful work.

39 39 Process Third : improving quality = improving process Problems : Breakdown in current work processes, or, Lack of consistent work process

40 40 Process All work is a process All processes exhibit variation and have measurable values associated with them The performance of any component process is to be evaluated in terms of its contribution to the aim of the system.

41 41 85/15 Process Rule Individuals have direct control over only 15% of their work problems. The other 85% are controlled by the process in their work environment. Deming 4% - 96%

42 42 Quality Improvement Change in focus from the 15% to the 85%: o The process o Not people

43 43 Worker controllable problems People need to have the means: o For knowing what they were supposed to do o For knowing what they were actually doing o To close the loop between what they were doing and what they should be doing

44 44 It ’ s processes not people While we must still hold individuals responsible for high standards of performance, we now recognize that most errors result from faulty systems, not faulty people.

45 45 Process Oriented Thinking Concentrating on the process inherent in any improvement situation leads to: o Greater cooperation due to a common language o Elimination of blame o Simpler, more effective solutions

46 46 Quality Improvement Process Variation Priority

47 47 Basic Statistical Lesson 2 Key Concept -Variation

48 48 Basic Statistical Lesson 2 Key Concept -Variation Walter Shewhart ’ s o There is always variation in anything that is being measured o In statistical thinking terms: there are inputs causing variation that are always present and conspire in random ways to affect a process ’ s output.

49 49 Basic Statistical Lesson 2 Key Concept -Variation Case 2: Coin Flip : 50 people- 25 times- # Heads

50 50 Basic Statistical Lesson 2 Key Concept -Variation Questions to ask: o First: Is the process stable? In other words, is the process in statistical control? o Second: What are the causes of variation in the process?

51 51 Basic Statistical Lesson 2 Key Concept -Variation Two types of variation: o Controlled (stable) variation o Uncontrolled (unstable) variation

52 52 Basic Statistical Lesson 2 Key Concept -Variation Controlled (stable) variation o Predictable within well-defined limits, but impossible to predict where any specific result will lie within those limits o Controlled variation is due to the way that the processes and systems have been designed and built. o Common Cause

53 53 Basic Statistical Lesson 2 Key Concept -Variation Uncontrolled (unstable) variation o Process affected by special causes o Behavior changes unpredictably o No one can predict process capability

54 54 Basic Statistical Lesson 2 Key Concept -Variation Walter Shewhart ’ s o Two kinds of mistakes Mistake 1. Treating a fault, complaint, mistake, accident as if it came from a special cause when in fact there was nothing special at all, ie it came from the system: from random variation due to common causes – Tampering Sounding a false alarm

55 55 Basic Statistical Lesson 2 Key Concept -Variation Walter Shewhart ’ s o Two kinds of mistakes Mistake 2. Treating a fault, complaint, mistake, accident as if it came from a common cause, when in fact it was due to a special cause Missing a signal in the data

56 56 Basic Statistical Lesson 2 Key Concept -Variation The 2-point Curve o Common practice Last month to this month Last year to this year Last quarter to this quarter

57 57 Basic Statistical Lesson 2 Key Concept -Variation The 2-point Curve

58 58 Basic Statistical Lesson 2 Key Concept -Variation The 2-point Curve Variation w/ last years data

59 59 Basic Statistical Lesson 2 Key Concept -Variation The 3-point Curve - Trends o Also Common practice

60 3 Point Curves Upward Trend Rebound Downward Trend Turnaround Setback Downturn

61 61 Basic Statistical Lesson 2 Key Concept -Variation The 3-point Curve – “ Trends ” o False explanations given to each “ trend ” resulting in false solutions that increase variation and increase costs.

62 62Variation Human tendency is to treat ALL variation as special cause

63 63 Data Analysis- Run Charts Graphical representation of data over time Ignoring the time element implicit in every data set can lead to incorrect statistical conclusions.

64 64 Data Analysis- Run Charts What information can you get from the run chart? Stability Common cause vs. special cause

65 65 Quality Improvement: Case 1

66 66 Data Analysis- Control Charts Control chart o Time plot of the data that includes lines added for the average and natural process variation.

67 67 Data Analysis- Control Charts Control chart - limits o These limits represent a common cause range around the average where individual data points may be expected to fall if the underlying process does not change.

68 Long Stay Residents Receiving an Antipsychotic- One Facility

69 69 Data Analysis- Run Charts- Rules Rule #1 – Trend o A sequence of SEVEN or more points continuously increasing or decreasing (six successive increases or decreases) ( points) o Less than 21 points- SIX points needed o Greater than 200 -EIGHT points needed o Omit entirely any points that repeat the preceding value. Such points neither add to the length of the run nor break it.

70 70 Quality Improvement: Case 4 Trends? NO

71 71 Quality Improvement: Case 5 Trends? NO

72 72 Data Analysis- Run Charts- Rules Rule #2 – “ Clump of Eight ” – the presence of a shift in the process o A run of EIGHT consecutive points either all above or all below the median. o It is broken and begins a new run when a data point literally crosses the median. o Any data point on the median neither breaks nor adds to the current run o Then, over the time period covered by the data, the process exhibited at least two different averages. o The special cause may not have occurred at the beginning of the run

73 73 Quality Improvement: Case 5 “ Clump of Eight? ” YES

74 Follow Up

75 Short Stay Pain- One Facility

76 76 Data Analysis- Control Charts Control chart rules o 1. A special cause is indicated when a single point falls outside a control limit

77 77 Special Cause Trend Clump of 8 Single data point falls outside control limits

78 78 Data Analysis- Run Charts SPECIAL CAUSE VARIATION Indicates different processes at work, even if unintended or perhaps even desirable and appropriate o Distinct shift(s) – due to outside interventions that have now become part of the everyday process inputs o Process has changed

79 79 Data Analysis- Run Charts COMMON CAUSE VARIATION Each source (input) of common cause contributes a random, small amount of variation No one can predict in advance which particular source (input) will affect the process at any given time. However, the range of resulting outputs can be predicted Data points can not be treated and reacted to individually

80 80 Quality Improvement: Case 1

81 81 Quality Improvement Process Variation Priority

82 82 The Pareto Principle 80% of the observed variation in a process is caused by only 20% of the process inputs. 20% of the variation causes 80% of the problems o Juran 1920 ’ s The “ vital few ” vs the “ trivial many ”

83 83 The Pareto Principle Motivates staff to recognize the importance of identifying and exposing the real, underlying, hidden opportunities Special causes are isolated as a result, allowing a more specific action to focus on solving the problem. The goal must be to expose, locate and focus, and then further focus on a major opportunity that can have a significant impact.

84 84 Key Concept -Improvement Process Improvement o Phase 1 – stabilization o Phase 2 – active improvement o Phase 3 - monitoring

85 85 Key Concept - Improvement Process Improvement o Phase 1 – stabilization o Phase 2 – active improvement o Phase 3 - monitoring

86 86 Key Concept - Improvement Process Improvement o Phase 1 – stabilization Eliminate special causes Gets the process where it should have been in the first place Problem solving, putting out fires No real improvement at this level Control, Run charts

87 87 Key Concept - Improvement Process Improvement o Phase 1 – stabilization o Phase 2 -active improvement o Phase 3 - monitoring

88 88 Key Concept - Improvement Process Improvement o Phase 2 – active improvement Eliminate common causes Pareto analysis Fish-bones Flow charting Recalculate control limits

89 89 Key Concept - Improvement Process Improvement o Phase 1 – stabilization o Phase 2 – active improvement o Phase 3 - monitoring

90 90 Key Concept - Improvement Process Improvement o Phase 3 – monitoring Constant vigilance Implement additional improvements as the need arises (Continuous Improvement)

91 91 Strategies for Reducing Variation The differences between common cause and special cause variation require us to use different managerial approaches to deal with each if we are going to be effective

92 92 Strategies for Reducing Variation Most problems arise from common causes. However, it is better to work on special causes first. o Cloud the picture o Less false leads

93 93 Improving an Unstable Process Special Cause Strategy 1.Get timely data so special causes are signaled quickly Indicators that give a clear signal when something affects our results Act rapidly or the trail will grow cold Look for ways to monitor process factors that are highly correlated with process outputs

94 Short Stay Pain- One Facility

95 Follow Up

96 96 Improving a Stable Process Common Cause Strategy

97 97 Improving an Stable Process Common Cause Strategy o Stratify o Experiment o Disaggregate the process

98 98 Improving an Stable Process Common Cause Strategy o Stratify Sort data into groups or categories based on different factors Look for patterns in the way that the data points cluster or do not cluster that may point to the source of the trouble Focusing to identify the leverage points where a little effort bring major improvement. Must have information on conditions related to the data: type of job, day of week, shift, unit, etc.

99 Long Stay Residents With a UTI- 43 Facilities

100 Stratification- Individual Facilities

101 101 The Pareto Principle 80% of the observed variation in a process is caused by only 20% of the process inputs. 20% of the variation causes 80% of the problems o Juran 1920 ’ s The “ vital few ” vs the “ trivial many ”

102 102 Improving an Stable Process Common Cause Strategy o Experiment Make planned changes and learn from the effects Trying out ideas Two keys to effective experimentation o Having good ideas to test – need in depth knowledge of how a process does and should work o Having good ways to assess and learn- Plan PDSA – Plan, Do, Study, Act

103 103 Improving an Stable Process Common Cause Strategy o Disaggregate the process Divide the process into component pieces and manage the pieces Every process has multiple steps or phases that can be monitored and improved individually Making the elements of the process visible through measurements and data Special causes may be buried in components of a process

104 104 Process analysis The most serious problems in service processes result from variation caused by: The lack of agreed-upon processes

105 105 Process analysis A lack of agreed-upon processes o Unintended variation in individual work processes o Management ’ s perceptions of these processes o There can be big differences between what is written down- the way the system is intended, or thought to operate, and what actually happens

106 106 Process analysis Flowcharts o An opportunity for those involved in a process to describe it ’ s current operation in a concise, visual way o Establish agreement on what the current process actually is o If a process can not be written down, it probably does not exist or it functions more on whim or “ gut- feeling ”

107 107 Describing the Process Include “front-line” personnel o They can tell you what is stopping them from doing their job. o Also gives you an opportunity to see if they: Know what should be done. Know how to do it. Understand why it is important. Think their way is better than the required way.

108 108 MDS completed Does Falls RAP trigger? Is there any other reason to believe patient is at high falls Risk? Routine precautions Identify modifiable (intrinsic or extrinsic) risk factors Yes No Yes Establish care plan Write care plan in chart and on aide assignment sheets Activity Documentation Yes /No Decision point Admission Nursing Assessment

109 109 Process analysis Fishbone Diagrams Show the causes of a certain event. A Fishbone or Ishikawa Diagram can be useful to break down (in successive layers of detail) root causes that potentially contribute to a particular effect.

110 110 Fishbone Diagram

111 111 Weight Loss Fishbone Diagram

112 112 Weight Loss Type of Patient Dietary Staffing CNA assistance with meals Food Not Appetizing Fishbone Diagram

113 113 Weight Loss Type of Patient Hospice Obese patient on diet Ortho Rehab Dietary Staffing Holiday call-offs Wages not competitive New Dietician CNA assistance with meals Short staffed Wages not competitive Holiday call-offs Inadequate training Lack of interest High toileting needs Don’t understand importance Food Not Appetizing Monotonous Menu Wrong Temperature Poor presentation Fishbone Diagram

114 114 Generate Solutions How / How Form Goal: Decrease number of residents losing weight How? Improve Caloric Supplementation How? Eliminate restrictive diets How? Improve food appearance How? Greater variety of supplements How? Limit # of therapeutic diets available How? Optimal timing of supplements How? Team to review need for restrictions on individual patients How? Provide garnishes How? Table settings

115 Long Stay Residents With a UTI- 43 Facilities

116 Follow Up

117 “Data Sanity, A Quantum Leap to Unprecedented Results” Davis Balestracci Jr., MS MGMA Press, 2009


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