TRUE BLUE Quest For Quality

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

Performance Improvement QAPI Quality Assurance - Performance Improvement 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.

Performance Improvement QA+PI=QAPI Quality Assurance Performance Improvement Compliance with standards Inspection Reactive Remove outliers Narrow Involves only a few Continuously improving processes Prevention Proactive Processes/Systems Systemic Involves entire IDT 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

QA vs QI QA- climate of defensiveness and a lack of cooperation Focus shifts to the “negative” end of performance QI – potential of high achievers to influence the process Focus shifts to the “positive” end of performance 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 Comprehensive and ongoing plan Includes all departments and functions Addresses safety, quality of care, QOL, resident choice, transitions Based on best available evidence QAPI plan

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

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

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

5 Elements of QAPI Systematic Analysis and Systemic action Root cause analysis Systems thinking 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.

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

Quality Improvement: Case1 “we did it!” some of you not convinced? Everybody gets pizza!!!!!!!!!!!!

Quality Improvement: Case 1 Let’s make it look good on a graph!!! Still not convinced?

Quality Improvement: Case 2 What about this graph? Ok? Not ok? DON comes to you with this data saying that here is a problem, she has determined that the physicians and nurse practitioners are to blame because they are not writing orders correctly. Your response: give it a couple more months Spend some time analyzing this slide, get ideas from attendees as to what they think might be going on

Quality Improvement: Case 2 What about this graph? Ok? Not ok? So now you agree that there is a problem that is not going away and decide to have a meeting with your medical staff to discuss the issue Spend some time analyzing this slide, get ideas from attendees as to what they think might be going on

Quality Improvement: Case 2 What about this graph? Ok? Not ok? Some improvement is noted, you and the DON are happy Spend some time analyzing this slide, get ideas from attendees as to what hey thin might be going on

Quality Improvement: Case 2 What about this graph? Ok? Not ok? You now feel great, as do the DON and administrator ( who usually gets in on the bandwagon when something good happens, but who you never see otherwise!). they see that you have these physicians under control- you feel like asking for a raise! Spend some time analyzing this slide, get ideas from attendees as to what they think might be going on

Quality Improvement: Case 2 What about this graph? Ok? Not ok? You didn’t get your raise but things aren’t so bad Spend some time analyzing this slide, get ideas from attendees as to what hey thin might be going on

Quality Improvement: Case 2 What about this graph? Ok? Not ok? Now the DON is upset again, you have another meeting with the medical staff, they tell you that it is the pharmacy that’s screwing things up- so you go to the pharmacy and have a high level meeting with the consultant pharmacist and his boss to fix things. Spend some time analyzing this slide, get ideas from attendees as to what they think might be going on

Quality Improvement: Case 2 What about this graph? Ok? Not ok? Great – we’re all on the same page, you are going to ask for a raise again Spend some time analyzing this slide, get ideas from attendees as to what they think might be going on

Quality Improvement: Case 2 What about this graph? Ok? Not ok? Well, it could be worse, we’re back where we started. Spend some time analyzing this slide, get ideas from attendees as to what they think might be going on

Quality Improvement: Case 2 What if we were to tell you that this was not medication error data but …………………. Is this “trend” right? How can you tell? What if we were to tell you that this wasn’t medication error data but ……………….– go to next slide!!!! Coin Flip Data

Basic Statistical Lesson 2 Key Concept -Variation Case 2: Coin Flip : 50 people- 25 times- # Heads This is coin flip data !!!!!! One can statistically determine the “limits” of variation 14-36 What would you say now about all the “theories” that we came up with

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

Process Variation Priority Quality Improvement This is what we will talk about today, If you remember nothing else from this session, remember this slide- the rest will come naturally!

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

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

Process Oriented Thinking Process- Definition Sequences of tasks aimed at accomplishing a particular outcome Transformation of inputs into outputs We’re going to start by talking about processes themselves

Basic Statistical Lesson 1 Given two different numbers, one will be larger This is the second most important slide to remember after process/variation/priority slide Or- Two numbers that are not the same : are different

Quality Improvement Two numbers!!!!

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

Basic Statistical Lesson 1 Real Question 2 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?

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

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, it is amazing how much waste can be disguised as useful work.

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

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.

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% Key slide – drive this point home- processes not people!!!

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

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

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.

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

Process Variation Priority Quality Improvement This is what we will talk about today, If you remember nothing else from this session, remember this slide- the rest will come naturally!

Basic Statistical Lesson 2 Key Concept -Variation

Basic Statistical Lesson 2 Key Concept -Variation Walter Shewhart- 1920’s There is always variation in anything that is being measured In statistical thinking terms: there are inputs causing variation that are always present and conspire in random ways to affect a process’s output. Elaborate on this slide, spend some time – this is THE key concept for attendees to grasp before going forward Use travel time to work example – varies every day Ask if everyone understands, take questions! The go on to look at the cases that were given at the beginning

Basic Statistical Lesson 2 Key Concept -Variation Case 2: Coin Flip : 50 people- 25 times- # Heads This is coin flip data !!!!!! One can statistically determine the “limits” of variation 14-36 What would you say now about all the “theories” that we came up with

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

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

Basic Statistical Lesson 2 Key Concept -Variation Controlled (stable) variation Predictable within well-defined limits, but impossible to predict where any specific result will lie within those limits Controlled variation is due to the way that the processes and systems have been designed and built. Common Cause Limits may or may not be acceptable to one’s preconceived notions!!!

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

Basic Statistical Lesson 2 Key Concept -Variation Walter Shewhart- 1920’s 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 Consider the hospital that was looking at timely pre-op antibiotic use. One month they hit the target range 92% of the time, next month 84%, then 88%, then 80% at which time they decided they needed to do an inservice. The following month it was at 88% and everyone was happy. Subsequent months varied between 76 and 96%. Every time the value was 80% or less, they did an inservice, and invariably the next month or at most 2 months later they would be at 84% or above and all were happy. However, you might surmise from the fact that the data are multiples of 4 that the sample size each month was 25; and we are talking about the difference of 19/25 up to 24/25 Additionally, this was all random variation. If instead of wasting everybody’s time with an inservice they had done nothing, it is likely that the following month would have been better regardless.

Basic Statistical Lesson 2 Key Concept -Variation Walter Shewhart- 1920’s 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

Basic Statistical Lesson 2 Key Concept -Variation The 2-point Curve Common practice Last month to this month Last year to this year Last quarter to this quarter The usual way data is presented in nursing homes for Pressure ulcers, uti’s, falls

Basic Statistical Lesson 2 Key Concept -Variation The 2-point Curve The usual way data is presented in nursing homes for Pressure ulcers, uti’s, falls Are we better or worse based on 2 numbers!! Again happens all the time

Basic Statistical Lesson 2 Key Concept -Variation The 2-point Curve Variation w/ last years data The usual way data is presented in nursing homes for Pressure ulcers, uti’s, falls What about all the months in between??

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

3 Point Curves Upward Trend Downturn Rebound Setback Turnaround Downward Trend

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

Human tendency is to treat ALL variation as special cause Nurse picking wrong drug from drawer because they are all marked similarly -environmental error not personal error 62

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.

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

Quality Improvement: Case 1

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

Data Analysis- Control Charts Control chart - limits 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 Common Cause, will focus discussion on common cause strategies, utilization entire data set for analysis Answer: Experimentation, initiation of a P&T committee involving entire IDT, front line education of staff

Data Analysis- Run Charts- Rules Rule #1 – Trend A sequence of SEVEN or more points continuously increasing or decreasing (six successive increases or decreases) (21-199 points) Less than 21 points- SIX points needed Greater than 200 -EIGHT points needed Omit entirely any points that repeat the preceding value. Such points neither add to the length of the run nor break it. Remember the 1 point, 2 point , and 3 point curves that we talked about earlier!!

Quality Improvement: Case 4 Trends? NO Any trends? Remember – need seven points in a row up or down

Quality Improvement: Case 5 Trends? NO Any trends? Remember – need seven points in a row up or down

Data Analysis- Run Charts- Rules Rule #2 – “Clump of Eight” – the presence of a shift in the process A run of EIGHT consecutive points either all above or all below the median. It is broken and begins a new run when a data point literally crosses the median. Any data point on the median neither breaks nor adds to the current run Then, over the time period covered by the data, the process exhibited at least two different averages. The special cause may not have occurred at the beginning of the run Balestracci 149 Why “eight-in-a-row?” statistically, there is only a 0.8 % chance of eight consecutive points in a row above the median due to common causes, which is less than the statistical rule of thumb of 5%

Quality Improvement: Case 5 “Clump of Eight?” YES Any clump of eight? yes

Follow Up

Short Stay Pain- One Facility An example of special cause. Discussion will focus on doing a root cause analysis with special focus on the last several months. Answer: MDS nurses were moved on to the units, facility also increased their short stay residents. They also did not have an effective way of identifying new admissions having pain.

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

Special Cause Trend Clump of 8 Single data point falls outside control limits Balestracci 152

Data Analysis- Run Charts SPECIAL CAUSE VARIATION Indicates different processes at work, even if unintended or perhaps even desirable and appropriate Distinct shift(s) – due to outside interventions that have now become part of the everyday process inputs Process has changed P142 balestracci

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 P142 balestracci

Quality Improvement: Case 1

Priority Process Variation Quality Improvement Next topic – priority again, If you remember nothing else from this session, remember this slide- the rest will come naturally!

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 Juran 1920’s The “vital few” vs the “trivial many” Pareto – an Italian renaissance economist who studied the distribution of wealth

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. The bold bullet will be reiterated on next slide! 83

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

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

Key Concept - Improvement Process Improvement 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 86

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

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

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

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

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

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

Improving an Unstable Process Special Cause Strategy 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 An example of special cause. Discussion will focus on doing a root cause analysis with special focus on the last several months. Answer: MDS nurses were moved on to the units, facility also increased their short stay residents. They also did not have an effective way of identifying new admissions having pain.

Follow Up

Improving a Stable Process Common Cause Strategy

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

Improving an Stable Process Common Cause Strategy 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.

Long Stay Residents With a UTI- 43 Facilities Common Cause, focus on common cause strategies Answer: Stratify by buildings, certain buildings were using improper definitions, utilizing improper criteria for obtaining urine culture

Stratification- Individual Facilities

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 Juran 1920’s The “vital few” vs the “trivial many” Pareto – an Italian renaissance economist who studied the distribution of wealth

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

Improving an Stable Process Common Cause Strategy 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

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

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

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

Describing the Process Include “front-line” personnel They can tell you what is stopping them from doing their job. 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. IF 85% OF AN ORGANIZATION’S PROBLEMS ARE DUE TO BAD PROCESSES, AND WE WANT TO FIX THEM, THEN WE HAVE TO BE ABLE TO DESCRIBE THE PROCESSES. IT IS CRITICALLY IMPORTANT AT THIS STEP TO INCLUDE “FRONT-LINE” PERSONNEL. -THEY CAN TELL YOU WHAT IS STOPPING THEM FROM DOING THEIR JOB. -IT ALSO GIVES YOU AN OPPORTUNITY TO SEE IF THEY: 1. KNOW WHAT SHOULD BE DONE 2. KNOW HOW TO DO IT 3. UNDERSTAND WHY IT IS IMPORTANT 4. IF THEY THINK THEIR WAY IS BETTER THAN THE REQUIRED WAY, OR THINK THE REQUIRED WAY WON’T WORK A CASE EXAMPLE IS ORDER HERE. A LARGE MIDWEST TEACHING HOSPITAL ASSEMBLED A PROCESS IMPROVEMENT TEAM TO WORK ON IMPROVING INSULIN USE. THE PHARMACY ASSEMBLED 2 MANAGEMENT LEVEL PHARMACISTS, THE 2 DIABETES EDUCATOR NURSES, AND A PHYSICIAN FROM THE “PHARMACY AND THERAPEUTICS COMMITTEE”. WHEN ASKED TO DRAW A DIAGRAM OF THE CURRENT PROCESSES THE PHARMACISTS AND NURSE EDUCATORS WERE QUICKLY ABLE TO DIAGRAM THE PROCESS. HOWEVER, WHEN THE PHYSICIAN REQUESTED THAT FRONT LINE NURSING PERSONNEL BE INCLUDED, IT BECAME QUICKLY APPARENT THAT PROCESSES SUCH AS CHECKING A PATIENT’S ID BRACELET, CONFIRMING THE DOSE WITH A SECOND NURSE, AND RE-DRAWING A BLOOD SUGAR BEFORFE COVERING A SUGAR WHEN MORE THAN 2 HOURS HAD ELAPSED SINCE THE PREVIOUS BLOOD DRAW WERE NOT ROUTINELY HAPPENING AS HAD BEEN DIAGRAMMED BY THE ADMINISTRATIVE PERSONNEL. Case example is provided here to give a break from the slides and to emphasize the importance of involving “front-line” personnel in describing the current process(es). 107 107

Activity Documentation Yes /No Decision point No Yes Yes No MDS Admission Nursing Assessment MDS completed Does Falls RAP trigger? No Yes Identify modifiable (intrinsic or extrinsic) risk factors Is there any other reason to believe patient is at high falls Risk? Yes Establish care plan No HERE IS AN EXAMPLE- falls assessment This very basic but just gives an idea Other examples – AMDA CPGs Write care plan in chart and on aide assignment sheets Routine precautions 108 108

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. 109

Fishbone Diagram A FISHBONE DIAGRAM MAY BE USEFUL IN IDENTIFYING EITHER COMMON CAUSES OR SPECIAL CAUSE VARIATION. (THINK OF IT AS GENERATING A DIFFERENTIAL DIAGNOSIS- YOU DO THIS EVERY DAY FOR YOUR PATIENTS. AS MEDICAL DIRECTOR YOU CAN APPLY THE SAME SKILLS FOR DIAGNOSING YOUR FACILITY) LET’S CONSIDER WEIGHT LOSS SEVERAL FACTORS COULD POSSIBLY CONTRIBUTE TO WEIGHT LOSS. THE FOOD MAY NOT BE APPETIZING, WHICH IN TURN MAY BE DUE DO POOR PRESENTATION WRONG TEMPERATURE, OR A MONOTONOUS MENU CeNA’S MAY NOT BE PROVIDING ASSISTANCE WITH MEALS, WHICH MAY BE BECAUSE: THEY LACK ADEQUATE TRAINING THEY DON’T CARE THEY DON’T UNDERSTAND THE IMPORTANCE OF ASSISTING THE RESIDENTS OR BECAUSE THEY ARE SHORT STAFFED. SHORT STAFFING IN TURN MAY BE DUE TO POOR WAGES HOLIDAY CALL-OFFS OR A LOT OF RESIDENTS WITH HIGH TOILETING NEEEDS THE TYPE OF PATIENT MAY BE A FACTOR HOSPICE PATIENTS MAY BE EXPECTED TO LOSE WEIGHT OBESE PATIENTS MAY ACTUALLY BE PLACED ON DIETS THESE WOULD BE VERY DIFFERENT FROM YOU TYPICAL ORTHOPEDIC REHAB PATIENT. DIETARY DEPARTMENT STAFFING COULD ALSO BE AN ISSUE THERE MAY BE A NEW DIETICIAN WAGES FOR THE DIETARY STAFF MAY NOT BE COMPETETIVE HOLIDAY CALL OFFS MAY OCCUR This is an animated slide to provide a change of pace and emphasize that we are dissecting the process, trying to get down to root causes. 110 110

Fishbone Diagram Weight Loss 111 A FISHBONE DIAGRAM MAY BE USEFUL IN IDENTIFYING EITHER COMMON CAUSES OR SPECIAL CAUSE VARIATION. (THINK OF IT AS GENERATING A DIFFERENTIAL DIAGNOSIS- YOU DO THIS EVERY DAY FOR YOUR PATIENTS. AS MEDICAL DIRECTOR YOU CAN APPLY THE SAME SKILLS FOR DIAGNOSING YOUR FACILITY) LET’S CONSIDER WEIGHT LOSS SEVERAL FACTORS COULD POSSIBLY CONTRIBUTE TO WEIGHT LOSS. THE FOOD MAY NOT BE APPETIZING, WHICH IN TURN MAY BE DUE DO POOR PRESENTATION WRONG TEMPERATURE, OR A MONOTONOUS MENU CeNA’S MAY NOT BE PROVIDING ASSISTANCE WITH MEALS, WHICH MAY BE BECAUSE: THEY LACK ADEQUATE TRAINING THEY DON’T CARE THEY DON’T UNDERSTAND THE IMPORTANCE OF ASSISTING THE RESIDENTS OR BECAUSE THEY ARE SHORT STAFFED. SHORT STAFFING IN TURN MAY BE DUE TO POOR WAGES HOLIDAY CALL-OFFS OR A LOT OF RESIDENTS WITH HIGH TOILETING NEEEDS THE TYPE OF PATIENT MAY BE A FACTOR HOSPICE PATIENTS MAY BE EXPECTED TO LOSE WEIGHT OBESE PATIENTS MAY ACTUALLY BE PLACED ON DIETS THESE WOULD BE VERY DIFFERENT FROM YOU TYPICAL ORTHOPEDIC REHAB PATIENT. DIETARY DEPARTMENT STAFFING COULD ALSO BE AN ISSUE THERE MAY BE A NEW DIETICIAN WAGES FOR THE DIETARY STAFF MAY NOT BE COMPETETIVE HOLIDAY CALL OFFS MAY OCCUR This is an animated slide to provide a change of pace and emphasize that we are dissecting the process, trying to get down to root causes. 111 111

Fishbone Diagram CNA assistance with meals Type of Patient Weight Loss A FISHBONE DIAGRAM MAY BE USEFUL IN IDENTIFYING EITHER COMMON CAUSES OR SPECIAL CAUSE VARIATION. (THINK OF IT AS GENERATING A DIFFERENTIAL DIAGNOSIS- YOU DO THIS EVERY DAY FOR YOUR PATIENTS. AS MEDICAL DIRECTOR YOU CAN APPLY THE SAME SKILLS FOR DIAGNOSING YOUR FACILITY) LET’S CONSIDER WEIGHT LOSS SEVERAL FACTORS COULD POSSIBLY CONTRIBUTE TO WEIGHT LOSS. THE FOOD MAY NOT BE APPETIZING, WHICH IN TURN MAY BE DUE DO POOR PRESENTATION WRONG TEMPERATURE, OR A MONOTONOUS MENU CeNA’S MAY NOT BE PROVIDING ASSISTANCE WITH MEALS, WHICH MAY BE BECAUSE: THEY LACK ADEQUATE TRAINING THEY DON’T CARE THEY DON’T UNDERSTAND THE IMPORTANCE OF ASSISTING THE RESIDENTS OR BECAUSE THEY ARE SHORT STAFFED. SHORT STAFFING IN TURN MAY BE DUE TO POOR WAGES HOLIDAY CALL-OFFS OR A LOT OF RESIDENTS WITH HIGH TOILETING NEEEDS THE TYPE OF PATIENT MAY BE A FACTOR HOSPICE PATIENTS MAY BE EXPECTED TO LOSE WEIGHT OBESE PATIENTS MAY ACTUALLY BE PLACED ON DIETS THESE WOULD BE VERY DIFFERENT FROM YOU TYPICAL ORTHOPEDIC REHAB PATIENT. DIETARY DEPARTMENT STAFFING COULD ALSO BE AN ISSUE THERE MAY BE A NEW DIETICIAN WAGES FOR THE DIETARY STAFF MAY NOT BE COMPETETIVE HOLIDAY CALL OFFS MAY OCCUR This is an animated slide to provide a change of pace and emphasize that we are dissecting the process, trying to get down to root causes. Dietary Staffing Food Not Appetizing 112 112

Fishbone Diagram CNA assistance with meals Type of Patient Weight Loss Short staffed Type of Patient Inadequate training High toileting needs Holiday call-offs Hospice Lack of interest Obese patient on diet Wages not competitive Ortho Rehab Don’t understand importance Weight Loss New Dietician A FISHBONE DIAGRAM MAY BE USEFUL IN IDENTIFYING EITHER COMMON CAUSES OR SPECIAL CAUSE VARIATION. (THINK OF IT AS GENERATING A DIFFERENTIAL DIAGNOSIS- YOU DO THIS EVERY DAY FOR YOUR PATIENTS. AS MEDICAL DIRECTOR YOU CAN APPLY THE SAME SKILLS FOR DIAGNOSING YOUR FACILITY) LET’S CONSIDER WEIGHT LOSS SEVERAL FACTORS COULD POSSIBLY CONTRIBUTE TO WEIGHT LOSS. THE FOOD MAY NOT BE APPETIZING, WHICH IN TURN MAY BE DUE DO POOR PRESENTATION WRONG TEMPERATURE, OR A MONOTONOUS MENU CeNA’S MAY NOT BE PROVIDING ASSISTANCE WITH MEALS, WHICH MAY BE BECAUSE: THEY LACK ADEQUATE TRAINING THEY DON’T CARE THEY DON’T UNDERSTAND THE IMPORTANCE OF ASSISTING THE RESIDENTS OR BECAUSE THEY ARE SHORT STAFFED. SHORT STAFFING IN TURN MAY BE DUE TO POOR WAGES HOLIDAY CALL-OFFS OR A LOT OF RESIDENTS WITH HIGH TOILETING NEEEDS THE TYPE OF PATIENT MAY BE A FACTOR HOSPICE PATIENTS MAY BE EXPECTED TO LOSE WEIGHT OBESE PATIENTS MAY ACTUALLY BE PLACED ON DIETS THESE WOULD BE VERY DIFFERENT FROM YOU TYPICAL ORTHOPEDIC REHAB PATIENT. DIETARY DEPARTMENT STAFFING COULD ALSO BE AN ISSUE THERE MAY BE A NEW DIETICIAN WAGES FOR THE DIETARY STAFF MAY NOT BE COMPETETIVE HOLIDAY CALL OFFS MAY OCCUR This is an animated slide to provide a change of pace and emphasize that we are dissecting the process, trying to get down to root causes. Wages not competitive Poor presentation Holiday call-offs Wrong Temperature Dietary Staffing Monotonous Menu Food Not Appetizing 113 113

Generate Solutions How / How Form How? Greater variety of supplements How? Improve Caloric Supplementation How? Optimal timing of supplements Goal: Decrease number of residents losing weight How? Limit # of therapeutic diets available How? Eliminate restrictive diets ONCE YOU HAVE IDENTIFIED THE ROOT CAUSES, IT IS TIME TO GENERATE POSSIBLE SOLUTIONS. BRAINSTORMING MAY AGAIN BE HELPFUL, BUT AN ADDITIONAL TOOL TO HELP FOCUS ON SOLUTIONS IS THE “HOW/HOW FORM” THIS IS AN EXAMPLE OF A “HOW-HOW FORM”. IT IDENTIFIES SPECIFIC ACTIONS TO BE TAKEN TO ACCOMPLISH THE GOAL, AND HONES THOSE ACTIONS DOWN WITH PROGRESSIVELY MORE DETAIL; IDEALLY TO THE LEVEL THAT THE ACTION COULD BE PUT IN THE DETAILED JOB DESCRIPTION OF A FRONT-LINE EMPLOYEE. This slide introduces the “How-How Form”. How? Team to review need for restrictions on individual patients How? Provide garnishes How? Improve food appearance How? Table settings 114 114

Long Stay Residents With a UTI- 43 Facilities Common Cause, focus on common cause strategies Answer: Stratify by buildings, certain buildings were using improper definitions, utilizing improper criteria for obtaining urine culture

Follow Up

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