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TRUE BLUEQuest For Quality
PATIENT FOCUSED
CARE
CommuniCare
Assessments
Clinical Workflow
QAPI
MDS
Quality Measures
Clinical
Standards
Data Sanity
Matthew S. Wayne MD, CMDChief Medical Officer
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
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.
QAPIQuality Assurance -
Performance Improvement
QA+PI=QAPIQuality 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 http://www.hrsa.gov/healthit/toolbox/HealthITAdoptiontoolbox/QualityImprovement/whatarediffbtwqinqa.html
7
QA vs QI
Balestracci p285
5 Elements of QAPI
• Design & Scope• Governance & Leadership• Feedback, Data Systems and
Monitoring• Performance Improvement Projects
(PIPs)• Systematic Analysis & Systemic
Action
5 Elements of QAPI• Design and Scope
oComprehensive and ongoing plano Includes all departments and functionsoAddresses safety, quality of care, QOL,
resident choice, transitionsoBased on best available evidenceoQAPI plan
5 Elements of QAPI• Governance and Leadership
oBoards/owners and executive leadership• Buy in and support
o Training and organizational climate• Administration sees value
o Sufficient resourceso Sustainability
5 Elements of QAPI• Feedback, Data monitoring Systems, and
MonitoringoMultiple sources, including resident and
staffoBenchmarking and targetingoAdverse events
5 Elements of QAPI• Performance Improvement Projects
o Prioritized topics• Number of PIPs depend on the facility
programo Team Charteredo PDSA Cycle
5 Elements of QAPI• Systematic Analysis and Systemic action
oRoot cause analysiso Systems thinkingo Systematic changes as needed
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 2012. A final rule is likely to be issued by early 2013.
15
Quality Improvement: Case 1
Year 2000 Falls 93
Goal – reduce 10% next year
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Quality Improvement: Case1
Year 2000 2001Falls 93 80 14.0%Everybody gets pizza!!!!!!!!!!!!
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Quality Improvement: Case 1
Falls
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2000 2001
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Quality Improvement: Case 2
Case 2 "Medication Errors" - Graph 1
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Quality Improvement: Case 2
Case 2 "M edication Errors" - Graph 2
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Quality Improvement: Case 2
Case 2 "Medication Errors" - Graph 3
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Quality Improvement: Case 2
Case 2 "Medication Errors" - Graph 4
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Quality Improvement: Case 2
Case 2 "Medication Errors" - Graph 5
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
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Quality Improvement: Case 2
Case 2 "Medication Errors" - Graph 6
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Quality Improvement: Case 2
Case 2 "Medication Errors" - Graph 7
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Quality Improvement: Case 2
Case 2 "Medication Errors" - Graph 8
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1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25
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Quality Improvement: Case 2
What if we were to tell you that this was not medication error data but ………………….
Coin Flip Data
27
Basic Statistical Lesson 2
Key Concept -Variation
• Case 2: Coin Flip : 50 people- 25 times- # Heads
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1 3 5 7 9 11
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Key Concept -Variation
• We learn nothing of importance by comparing two or three results when they all come from a stable processMost data of importance to management are from stable processes
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Quality Improvement
•Process
• Variation
• Priority
30
Process Oriented Thinking
Systems Thinking• System - Definition
oA group of interdependent processesoA network of functions or activities
within an organization that work together for the aim of the organization
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 analyzedProcess
• Have beliefs, values, interests, needs
• Have roles which are made up of functions and tasks
People
32
Process Oriented Thinking
• Process- Definition
oSequences of tasks aimed at accomplishing a particular outcome
oTransformation of inputs into outputs
33
Basic Statistical Lesson 1
• Given two different numbers,
one will be larger
Or- Two numbers that are not the same :are different
34
Quality Improvement
Falls
70
75
80
85
90
95
2000 2001
35
Basic Statistical Lesson 1
• Is the process that produced the second number the same that produced the first number?
Real Question 1
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
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
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
Process• Third :
improving quality = improving process
• Problems :Breakdown in current work processes, or,Lack of consistent work process
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
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
Quality Improvement• Change in focus from the 15% to
the 85%:oThe processoNot people
43
Worker controllable problems
• People need to have the means:
oFor knowing what they were supposed to do
oFor knowing what they were actually doing
oTo close the loop between what they were doing and what they should be doing
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
Process Oriented Thinking
• Concentrating on the process inherent in any improvement situation leads to:oGreater cooperation due to a
common languageoElimination of blameoSimpler, more effective solutions
46
Quality Improvement
• Process
•Variation
• Priority
47
Basic Statistical Lesson 2
Key Concept -Variation
48
Basic Statistical Lesson 2
Key Concept -Variation
• Walter Shewhart- 1920’so 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
Basic Statistical Lesson 2
Key Concept -Variation
• Case 2: Coin Flip : 50 people- 25 times- # Heads
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Basic Statistical Lesson 2
Key Concept -Variation
• Questions to ask:
oFirst: Is the process stable? In other words, is the process in statistical control? oSecond: What are the causes of variation in the process?
51
Basic Statistical Lesson 2
Key Concept -Variation
• Two types of variation:
oControlled (stable) variation
oUncontrolled (unstable) variation
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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
Basic Statistical Lesson 2
Key Concept -Variation
• Uncontrolled (unstable) variation
oProcess affected by special causes
oBehavior changes unpredictably
oNo one can predict process capability
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Basic Statistical Lesson 2
Key Concept -Variation
• Walter Shewhart- 1920’so 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
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Basic Statistical Lesson 2
Key Concept -Variation
• Walter Shewhart- 1920’soTwo 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
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Basic Statistical Lesson 2
Key Concept -Variation
• The 2-point Curveo Common practice
• Last month to this month• Last year to this year• Last quarter to this quarter
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Basic Statistical Lesson 2
Key Concept -Variation
• The 2-point Curve
0
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Jan-10
Feb-10
Mar-10
Apr-10
May-10
Jun-10
Jul-10 Aug-10
Sep-10
Oct-10
Nov-10
Dec-10
Jan-11
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Basic Statistical Lesson 2
Key Concept -Variation
• The 2-point Curve Variation w/ last years data
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Jan-10
Feb-10
Mar-10
Apr-10
May-10
Jun-10
Jul-10
Aug-10
Sep-10
Oct-10
Nov-10
Dec-10
Jan-11
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Basic Statistical Lesson 2
Key Concept -Variation
• The 3-point Curve - TrendsoAlso Common practice
3 Point Curves
Upward Trend
Rebound
DownwardTrend
Turnaround
Setback
Downturn
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Basic Statistical Lesson 2
Key Concept -Variation
• The 3-point Curve – “Trends”
oFalse explanations given to each “trend” resulting in false solutions that increase variation and increase costs.
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Variation
•Human tendency is to treat ALL variation as special cause
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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.
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Data Analysis- Run Charts
• What information can you get from the run chart?
Stability
Common cause vs. special cause
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Quality Improvement: Case 1Falls with Median
0
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Data Analysis- Control Charts
• Control chartoTime plot of the data that includes lines added for the average and natural process variation.
67
Data Analysis- Control Charts
• Control chart - limitso 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.
Long Stay Residents Receiving an
Antipsychotic- One Facility
69
Data Analysis- Run Charts- Rules
• Rule #1 – Trendo A sequence of SEVEN or more points
continuously increasing or decreasing (six successive increases or decreases) (21-199 points)
o Less than 21 points- SIX points neededo 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.
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Quality Improvement: Case 4
Trends?
0
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NO
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Quality Improvement: Case 5
Trends?
0
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NO
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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
Quality Improvement: Case 5
“Clump of Eight?”
0
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1 3 5 7 9
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YES
Follow Up
Short Stay Pain- One Facility
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Data Analysis- Control Charts
• Control chart ruleso1. A special cause is indicated when a single point falls outside a control limit
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Special Cause• Trend• Clump of 8• Single data point falls outside control limits
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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
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
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Quality Improvement: Case 1Falls with Median
0
2
4
6
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14
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Quality Improvement
• Process
• Variation
•Priority
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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 problemso Juran 1920’s
• The “vital few” vs the “trivial many”
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.
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Key Concept -Improvement
• Process Improvement
oPhase 1 – stabilization oPhase 2 – active improvementoPhase 3 - monitoring
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Key Concept - Improvement
• Process Improvement
oPhase 1 – stabilization oPhase 2 – active improvementoPhase 3 - monitoring
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Key Concept - Improvement
• Process ImprovementoPhase 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
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Key Concept - Improvement
• Process Improvement
oPhase 1 – stabilization oPhase 2 -active improvementoPhase 3 - monitoring
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Key Concept - Improvement
• Process ImprovementoPhase 2 – active improvement
•Eliminate common causes• Pareto analysis• Fish-bones• Flow charting• Recalculate control limits
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Key Concept - Improvement
• Process Improvement
oPhase 1 – stabilization oPhase 2 – active improvementoPhase 3 - monitoring
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Key Concept - Improvement
• Process ImprovementoPhase 3 – monitoring
•Constant vigilance• Implement additional improvements as the need arises (Continuous Improvement)
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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
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Strategies for Reducing Variation
• Most problems arise from common causes.
• However, it is better to work on special causes first.oCloud the pictureoLess false leads
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
Short Stay Pain- One Facility
Follow Up
96
Improving a Stable Process
Common Cause Strategy
97
Improving an Stable Process Common Cause Strategy
oStratify
oExperiment
oDisaggregate the process
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Improving an Stable Process Common Cause Strategy
oStratify• 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.
Long Stay Residents With a UTI-
43 Facilities
Stratification- Individual Facilities
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 problemso Juran 1920’s
• The “vital few” vs the “trivial many”
102
Improving an Stable Process Common Cause Strategy
oExperiment• Make planned changes and learn from
the effects• Trying out ideas• Two keys to effective experimentation
oHaving good ideas to test – need in depth knowledge of how a process does and should work
oHaving good ways to assess and learn- Plan
• PDSA – Plan, Do, Study, Act
103
Improving an Stable Process Common Cause Strategy
oDisaggregate 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
Process analysis
• The most serious problems in service processes result from variation caused by:
The lack of agreed-upon processes
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
Process analysis• FlowchartsoAn opportunity for those involved
in a process to describe it’s current operation in a concise, visual way
oEstablish 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
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
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
YesNo
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
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
Fishbone Diagram
111
Weight Loss
Fishbone Diagram
112
Weight Loss
Type of Patient
Dietary Staffing
CNA assistance with meals
Food Not Appetizing
Fishbone Diagram
113
Weight Loss
Type of Patient
Hospice
Obese patienton dietOrtho
Rehab
Dietary Staffing
Holiday call-offs
Wages not competitive
NewDietician
CNA assistance with meals
Short staffed
Wages not competitive
Holiday call-offs
Inadequatetraining
Lack ofinterest
High toileting needs
Don’t understandimportance
Food Not Appetizing
Monotonous Menu
Wrong Temperature
Poor presentation
Fishbone Diagram
114
Generate SolutionsHow / 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
Long Stay Residents With a UTI-
43 Facilities
Follow Up
“Data Sanity, A Quantum Leap to Unprecedented
Results”
Davis Balestracci Jr., MS
MGMA Press, 2009