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Why 85% of Decisions Made in Your Organization are Wrong and How to Fix It!
 

Why 85% of Decisions Made in Your Organization are Wrong and How to Fix It!

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    Why 85% of Decisions Made in Your Organization are Wrong and How to Fix It! Why 85% of Decisions Made in Your Organization are Wrong and How to Fix It! Presentation Transcript

    • Why are 85% of yourdecisions wrong and how to fix it? John Bachman Bachman.john@mayo.edu
    • Strategies► Run up the hill► Something different-The foreman stopped set fire to an area and jumped into the burnt out area (escape fire) Beckoned his men to joint him► Men died…. All of them had their packs on and were carrying their axes or shovels
    • Points of the Story1. Danger happens and change is needed2. Escape Fires are present along the way3. Cling to our past and hold on to the familiar 1. Knowledge is learning something new. Wisdom is letting go of something old.
    • Why are 85% of yourdecisions wrong and how to fix it? John Bachman Bachman.john@mayo.edu
    • Outrageous► Escape Fire► Let go of things that are holding you back and are a part of your every day life!
    • That’s a lot of nerve► Edwards Deming► He felt over 90% of decisions designed to make things better were wrong► Father of Quality improvement
    • Quality Improvement► We have quality improvement!► Six Sigma Lean and all sorts of tools!► Organizations have improved!► Point to an area and see what we have done► We reorganized things and it is better► We have data to support it
    • Deming would smile at us► Language-overuse reduces meaning► Finding a better way to scrape burnt toast► Flavor of the month…. Of course you can improve things for a little while and then if you watch things it regresses► Reorganizations 25% successful at 4 years► Victims of a constantly changing environment and controlled by outside forces► We lead and get what we want and that is the problem
    • Deming would then say “Let us learn a new philosophy”
    • What is a System?
    • System Characteristics
    • What is the Aim of a System?► Person?► Office?► Health Care System?► Fastest way to get to the heart of an issue  Example Joan  Example Group Please the top► Better than that► How do we measure it?????
    • How do we manage a system?
    • Management by Results► Peter Drucker► “People on a balance sheet are recorded as liabilities”► 1950s► Extremely common method► Deming’s view  Neighbors  One of 14 points not to use it
    • Top Rung► Leadership sets up goals and objectives for the system► Give it to the rung below them► Give it to the rung below them► Give to the rung below them
    • MANAGEMENT BY RESULTS
    • Makes sense► Give Responsibility to other so they have freedom to act► Performance reviews► Control of resources► Logical and organized► “Command and Control”
    • Problems with Management of Results► Please the people above you-Give the results they need to see (Management gets what it wants)► Silos-nurses, secretaries, medical, financial► We have accountability of individuals? How much do we control?
    • Diabetes
    • Problems with Management of Results► Communication► Knowledge of processes  Top management 4%  Middle management 9%  Supervisors 74%  Frontline 100%► Implementation is success► Compliance vs Commitment► Numbers-Fear-Fudging-short term thinking
    • ► Elite group design future for the many (design team)  6-12 people supervisors professionals and workers  Analysis  Redesign  One best design► Shortcomings  Resistance  Misses subtleties  Long time  70% failure rate at 4 years
    • Management by Results► Summary: Better to do things right then to do the right thing.
    • What is the Alternative?
    • Different Approach-Profound Knowledge1. System2. Variation-Take a group next3. Knowledge4. Psychology
    • What is the difference between Data, Information, and Knowledge?
    • Data is figures
    • LOOK AROUND YOU DATA DATA EVERYWHERE AND NOT A BIT TO THINKWe can explain data Numbers have power
    • Déjà vu? How many meetings?We make decisions by reacting to observedvariations in data or information??? Davis Balestracci www.davisdatasanity.com
    • Board Member #1: “Find out what happened!” “Why are we having more bacteremias?” Davis Balestracci www.davisdatasanity.com
    • Information1. Describes the present or past2. Data organized in patterns 1. Prejudice 2. Percentages3. Comparisons to other groups (benchmarking and rankings)4. Experiments or Pilots
    • Experiments (Implementation is Success)
    • 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08 Apr-08 May-08 Jun-08 Jul-08 Aug-08 Sep-08 Annualised Attrition rate Davis Balestracci Oct-08 But do we?www.davisdatasanity.com Nov-08 progress” Dec-08 Jan-09 Feb-09 Mar-09 Apr-09 May-09 Jun-09 “We have a trend of steady Jul-09 Aug-09
    • What is the trend? Nursing Attrition Rate (Monthly)3.0%2.5%2.0%1.5%1.0%0.5%0.0% May-08 May-09 Apr-08 Aug-07 Nov-07 Mar-08 Aug-08 Nov-08 Mar-09 Apr-09 Oct-07 Oct-08 Sep-07 Dec-07 Feb-09 Jul-07 Jan-08 Feb-08 Jun-08 Sep-08 Dec-08 Jul-08 Jan-09 Jun-09 Adults Hospital Private Hospitals Childrens Hospitals Mothers Hospitals Combined Hospitals Davis Balestracci www.davisdatasanity.com
    • Profound truth: Given two different numbers…SomethingImportant Yesterday Today …one will be larger! Davis Balestracci www.davisdatasanity.com
    • “Account for your performance!” “Upward Trend” “Downturn” “Rebound” This month… vs. last month… vs. the month before “Setback” “Turnaround” How much time is wasted explaining random variation? “Downward Trend” Davis Balestracci www.davisdatasanity.comTime
    • A=AA B=TB and C=LC L=BB Q=BEF=ML H=IY M=DC
    • “Let’s set some stretch goals”► The bottom 10% of the group will improve to the 50th percent► We will be in the top 80% of all quality markers
    • Example► Teachers  Your child has a test and is below average  Your child has now had two tests and is below average
    • How do we know what we are doing?
    • Knowledge► Rooster► Knowledge implies theory  Means it can be revised  Predictive► Future related
    • What would be a real trend???
    • Trend► Variation All processes vary Is it just background noise (common cause)► Variation Is this related to something new (special cause ) People Need to Solve their Problems!!!!
    • Tampering► Tampering: Treating common cause as special cause  Human tendency is to account for ALL variation as special  Looking for an explanation and finding one - positive or negative  Wastes time and resources► Deming: “The losses caused by tampering are incalculable.” Davis Balestracci www.davisdatasanity.com
    • Knowledge is Prediction
    • A statistical definition of “trend” "Sweat" Index Upward Trend Downward Trend Time TimeSpecial Cause – A sequence of SIX successive increasesor decreases Usually indicates a process “in transition” Davis Balestracci www.davisdatasanity.com
    • Diabetes
    • “I can’t wait for five or six increases!”►You don’t have to in the context of variation►Increase the frequency of measurement (quarterlyweekly)►Use other methods Davis Balestracci www.davisdatasanity.com
    • If We Talk Improvement:Knowledge=Plot the Dots►Eliminate tampering►Meaningful Interventions►Improved Conversations►Eliminate 75-85% of data and information  “Your current processes are perfectly designed to get the results you are already getting.” Davis Balestracci www.davisdatasanity.com
    • Plot the dots
    • Keep Sampling the Stream!
    • Bread-and-butter tool: Run ChartTime ordered plot with the MEDIAN as reference Median=Middle Value Davis Balestracci www.davisdatasanity.com
    • Special Cause: A consecutive sequence of 8 or more points on one side of the median Indicates a probable shift in the process during this time period Davis Balestracci www.davisdatasanity.com
    • What would you do now? - Plot the dots!Have thingschanged? Davis Balestracci www.davisdatasanity.com
    • “What is happening with NICU Infections?” 16 1400 14 1200 12 1000 10 800 # Infections 8 600 #Patients 6 400 4 2 200 0 0 1 2 3 4 5 6 7 8 9 101112131415161718 Infection Rate 2.5 2Plot the rate Infecction Rate 1.5 1(Infections/Patients) 0.5 0 -0.5 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 Month Davis Balestracci www.davisdatasanity.com
    • West 93.6% Do you do “Percent Compliance?” 90.8% 90.2% Red…Yellow…Green… 90.1% 91.8% 90.7% 90.1% 91.7% 89.7% 89.8% 88.5% 91.0% 89.7% 91.1% 90.1% 90.1% 91.1% IF you plot the dots … 90.8% 91.4% Davis Balestracci 91.9% www.davisdatasanity.com
    • With a stable process, we can construct a CONTROL CHART 93.6% 93.2% 90.6% 87.9%Week-to-week EXPECTED variation:  3.5% Davis Balestracci www.davisdatasanity.com
    • 0.0% 5.0% 10.0% 15.0% 20.0% 25.0% Jul-07 Aug-07 Sep-07 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08 Apr-08 May-08 Jun-08 Jul-08 Aug-08 Sep-08 Annualised Attrition rate Davis Balestracci Oct-08www.davisdatasanity.com Nov-08 Dec-08 Jan-09 Feb-09 Mar-09 Remember this?… Apr-09 May-09 Jun-09 Jul-09 Aug-09
    • Is there as steady trend? Annualized Attrition Rate Special Cause Flag 25.00% 20.00% Individual Value 15.00% 10.00% 5.00% 0.00% Oct-07 Dec-07 Oct-08 Dec-08 Aug-07 Feb-08 Apr-08 Apr-09 Jun-08 Aug-08 Feb-09 Jun-09 Davis Balestracci Periodwww.davisdatasanity.com
    • Did we improve the A1c?What about the LDL?
    • Constructing Run and Control Charts► Quality Academy templates http://mayoweb.mayo.edu/quality-learning/qa- templates.html  Select C-chart 5e  Copy 50 cells to paste  View control chart  Relabel chart title and axes► Copy and paste chart into document► Get Software► http://asq.org/learn-about-quality/data-collection- analysis-tools/overview/control-chart.html
    • Amundson and Scott
    • Amundsen-Exploring► Preparation  Dolphin was eaten  Expeditions  Read original work-Thinking
    • Scott ► Tradition of Britain “We can do it” “We are English” ► Scientific background
    • Scott used Ponies and Motorized Sleds (Manly thing to push) Ponies died and motorized sleds stopped working in a week 4500 calories given 7000 used for hauling
    • AmundsenAmundsen used dogs…. Excessive use of animalsAbundance of materials carried redundancy
    • Base Camps
    • Concept of the 20 mile March► Amundsen used the 20 mile march► Good days or bad would try to go 15-20 Miles.► Scott would on fair weather go long Distances and then not be ready if bad Weather came► Point was constancy of purpose!
    • What did they do to be successful► Preparation-know what status of environment is-(Stable System)► Used proven methods (control and run charts)► Constancy of purpose (PDSA cycles)
    • Lets Take an Example► Our office  We were having issues with “open access”  All the slots would fill up and we would call an access alert  Patients not happy  Clinicians not happy because mess-hours ►Add more slots that would be open-lose continuity ►Add more slots in general ►Decrease panel Size
    • What should we plot?
    • Unfilled Slots and Sames► We could go to the computer and get this information► It has been around for yearsIt would lead you completely astray!
    • Step 1 Get together and discuss what things mean!► Same=a slot that is open and kept open for people who call in on that day► Actually at 3 PM the preceding day it starts► Actually a doctor can put a person in if no space► Actually we will put a person in who is a bleeding heart
    • Deming►“Thereis no meaning to anything!!!”
    • Raw Data► Kept track of it on a graph (Put the numbers in the template) We used Mayo You can use ASQI (google calculate control chart)
    • Step 2► We plotted people who needed to be seen that day and from the previous day► We plotted unfilled slots
    • We Looked at a Stable System► We shared it with everyone► This is the PowerPoint We Used
    • Access is a Big Issue for us!
    • Access Can be Broken Down► Availability On a given day can a patient be seen► Access (Continuity) Can the patient see own doctor► Affordability Can a patient afford it► Acceptability Can a patient come in at a time of choosing
    • We are Going to Look at AvailabilityIf I am Sick Can I be Seen at Baldwin today It is really nice to see a doctor when I am sick!!!
    • We want a balance betweensupply of patients and openingsPatients Patients inTaken Careof No alerts, cramming, or compromise in care
    • How Many Slots Do We Need toServe Each Day Who Call in and are Seen That Day?► 40 per day► 60 per day► 80 per day► 100 per day► 150 per day
    • The answer ► About 60!If you look at the next graph you can learn a few things
    • TOTAL DAY MORNING AFTERNOON Average is 25 Average is 42Spikes INDICATE MONDAYS MONDAYS ARE ALWAYS BUSIEST DAYS OF THE WEEK FOLLOWING!!!MORINGS 25, AFTERNOONS 42 AND TOTAL 67
    • VARIATION► Averages of 65 are nice but not practical► You can have one foot in ice water and another in boiling water and on average you are fine► We need 0-140 slots a day!► No wonder we have problems!!!
    • Deming said “The Key toProcess Improvement is Decrease Variation We can…. Look at next graph…
    • This is the Next Step► Decreasevariation► Implement a new system
    • Eliminate MondaysLook at this less variation if we just eliminate Mondays!!!!!!
    • Mondays has more activity by almost 50% TOTALAM on consecutive Mondays PMs
    • Interesting► Ifwe eliminate Mondays=Variation reduced by 40► Mondays average 100 slots► We use as many slots on Monday PM as we do for whole days Tues to Thurs► Two days (Biggest Numbers are Tuesdays after Busy Mondays)► So our first step would be
    • Eliminate Mondays!
    • We are Kidding► However We Need to Have Mondays Handled Differently then Tues-Friday► We have on average 40 people scheduled the day before On Monday we average 25 from the weekend► There are some other add ons ER and Hospital Visits are higher
    • So Step 1 in Improving Availability► Treat Monday as Something Different► We can expect more patients and more complex patients.► If we handle Monday better will Tuesday be better??► We have set up a PDSA group to work on Monday
    • So that is where we are1. We know that we have a stable system but with a great deal of variability2. By using Data a group will help with making Monday better for handling increased demand
    • It makes sense► Yes, This is not revolutionary but we have data to support it so when we make changes we can see impact► This is going to take awhile but we are moving in a direction of continuous improvement!
    • What did we come up with► Monday Afternoon was total blocked  Monday PM First 30 minutes used for Hospital Recheck, ER Recheck, Newborns  Doctor could override system and see patient (no one else)  We will have excess access and expect some afternoons will be quiet but what ever happens Monday will be taken care of!!!
    • Family Medicine BaldwinWeekly Fill Rates• Fill rates in Q4 2011 mainly between 90% and 95%• Overall daily and weekly fill rates declined early in 2012• Fill rates stabilized from Feb 2012 through July 2012 • Ranged between 85 and 90% – more acceptable/manageable
    • Baldwin -- Monday PM Fill RatesFilled in Last 2 Days• Pre-Pilot: 7%• During Pilot: 24%During Day Fill Rate• Pre-Pilot: 32%• During Pilot: 52%End of Day Unfilled Rate• Pre-Pilot: 6%• During Pilot: 21%
    • Baldwin – Mondays PM Slots Filled Same Day - Monday Only (Or 1st Day of Week)Same Day Slots Filled 140• The counts of PM slots filled 120 Pre-Pilot Pilot Phase UCL=136.0 during the day has ranged Slots Filled Same Day 100 _ from 70 to 115 over the past 80 X=90 six months, significantly 60 higher than in 2011 40 LCL=44.0 20 0 Sept-2011 . . . . . . . . . July-2012 DateOverall Slots Filled• Average counts of filled slots Monday Filled Slots has slightly decreased 400 UCL=392.0 Pre-Pilot Pilot Phase during the pilot phase (one 350 of the goals) Filled Slots _• Variability of filled slots is 300 X=299.7 similar during both phases 250 LCL=207.4 200 Sept-2011 . . . . . . . . . July-2012 Date
    • Baldwin – Tuesday / WednesdayTuesday Filled Slots Tuesday Filled Slots• Average number of filled 450 Pre-Pilot Pilot Phase slots is slightly higher during 400 UCL=396.7 pilot phase with significantly 350 Filled Slots _ less variability X=329.3 300 LCL=261.9 250 200 Sept-2011 . . . . . . . . . July-2012 Date Wednesday Filled SlotsWednesday Filled Slots 400• Average number of filled Pre-Pilot Pilot Phase slots is slightly higher during 350 UCL=352.3 pilot phase with significantly 300 Filled Slots _ less variability X=276.3 250 200 LCL=200.3 150 Sept-2011 . . . . . . . . . July-2012 Date
    • Baldwin – Thursday / Friday Slots Thursday Filled SlotsThursday Filled Slots 400• Average number of filled 1 Pre-Pilot Pilot Phase UCL=376.0 slots is higher during pilot 350 phase with significantly less _ Filled Slots 300 X=291.1 variability 250 LCL=206.2 200 Sept-2011 . . . . . . . . . July-2012 Date Friday Filled SlotsFriday Filled Slots 400• The average number of filled Pre-Pilot Pilot Phase UCL=383.3 slots is higher during the 350 pilot phase with less _ Filled Slots 300 X=300.2 variability 250 LCL=217.1 200 Sept-2011 . . . . . . . . . July-2012 Date
    • Other Items► Stopped churning on Mondays  Phone calls  Triage  Complex patients now seen by own clinician ► Decreased testing ► Decreased coming back► Providers very happy Patients very happy► No access alerts  Providers being ill not an issue  Monday AM Team meetings► Further PDSA Thursday for complex pts► More likely to have add ons
    • Constancy of Purpose► Weekly to monthly graphs► PDSA Thursday PM for Complex patients from hospital, ER, or Newborn► Monday AM team meetings► Not an issue…  Moving on to appt times
    • How do we make continual improvement► Don Berwick  Outsiders Management can make judgments on improvement  Insiders are the ones who can make improvement  Constancy of Purpose
    • Resources► Web Site: http://www.qualityandtraining.com  This has short films for teams about this  IHI Open School ►www.ihi.org/offerings/ihiopenschool/Pages/default .aspx
    • Books► Fundamentals of Health Care Improvement  www.ihi.org/.../ihiopenschool/.../Fundamenta lsofHealthCareImprovement.aspx► Deep Change► www.amazon.com/Deep-Change- Discovering...Bass.../0787902446► Great by Choice  www.amazon.com/Great-Choice-Uncertainty- Luck.../0062120999
    • Best All Around► DATA SANITY► davisdatasanity.com/  Weekly newsletter  Book
    • Variation John Bachman MDSaunders of Primary Care Mayo FoundationBachman.john@mayo.edu
    • Different Approach-Profound Knowledge1. System2. Variation3. Knowledge4. Psychology
    • Variation► All Processes Vary► Reacting to individual fluctuations is tampering► Not understanding a special cause leads to tampering► Tampering is successful initially and then over times falls apart because of the 8 influences on processes
    • Response to Variation“If I had to reduce my message for management to just a few words, I’d say that it all had to do with reducing variation” W.E.DEMING
    • RED BEAD EXPERIMENT
    • Control Chart Template (free)► http://asq.org/learn-about-quality/data- collection-analysis-tools/overview/control- chart.html
    • Funnel ExperimentIf you aim at the target and drop the ball 50 times
    • You Get a Circle
    • What Causes Variation?
    • Imagine► You are leading a parade……. People cause variation
    • Now you are pushing a wheelbarrow Machines may alter variation
    • You Come to a BuildingWhere Someone Gives you instructions Methods Cause Variation
    • Someone DumpsCement into Your Wheelbarrow Materials Alter Variation
    • Materials go to a Certain Line Measurement Varies
    • Go Out in the Sun where it is Hot Environment causes variation
    • Causes of Variation OUTSIDE INSIDE1. People 1. Materials2. Machines 2. Measurement3. Methods 3. Environment MONEY AND MAINTENANCE
    • How to use????► My car will not start► Fish Bone Diagram
    • Instructions► Draw a line► Draw 6-8 bones of causes of variation► Use sticky notes to put on the sides of causes► Keep drilling deeper 5 Whys?► Brainstorming Not to think
    • Funnel Rule 1 Variation► Management or people look at the pattern and think they can just improve it
    • Rule 2 Messed up by people► Drop a ball on the target► Measure the distance from the ball and go 180 degrees in the opposite direction► You will now be dropping a ball on the target again.► Now….. Measure the distance between the impact of two balls and go 180 degrees opposite from the second impact► Keep using last ball as your marker
    • Rule 2 Tweaking!Simply put, your reference point for adjustment is the last event. We base our decision on last result and tweak it By chance it may improve “Confirming Evidence Trap”
    • Variation increases 40%
    • Examples► Adjusting a process when a part is out of specifications  Complaint from a patient► Operator adjustments without the aid of control charts► Changing company policy based on the latest attitude survey► Recalibrating instruments to a standard  Gun firing in the morning► Adjusting the quota to reflect current output► Stock market reaction to last months deficit
    • Rule 3► Drop the ball and measure distance from the target. Measure the distance from the target and go 180 degrees opposite► Firea gun…. Adjust in the opposite and fire again Simply put, your reference point for adjustment is a standard.
    • More Variation in Different Quadrants
    • Rule 3 Compensation for Errors► Illicit drugs. Enforcement improves so drugs become scarcer. The price goes up which stimulates the import of more drugs. The cycle repeats.► Gambler increases his bet to cover losses► Farmers doing supply and demand► Unfilled Slots in appt schedule
    • Rule 4 There we go to the Milky Way► Drop the Ball► Aim at the Last landing place
    • Examples► History passed down from generation to generation.► Worker training replacements in succession► Adjustment of time of meeting based on last actual starting time.► Use of last board cut as a pattern for the next board.► Sitting in a circle with a number of people. One person whispers a secret to the next person who in turns whispers it to the next person and so on.► Train the trainer
    • Examples► Normal Variation► Tweaking► Compensating► Going to the Milky Way
    • Reorganizations► 1-4 success after 3-4 years► Start out great and then run out of steam Why?
    • Process► If I implement a change…Does it make a difference…..
    • Quality templates have information
    • Enough Theory-Example
    • Gathering Data-IHI Open School► Students from College or Medical Schoo► Faculty from school and clinic► Students learn 18 courses and receive certification► Develop projects► Free
    • Get Then Together and Do a Project
    • Where to get materials for projects?
    • Materials to use
    • Lets look at it► Workbook on out patient
    • Started a Study► Shadowing and Interviewing Patients by IHI students► Initially free► Purpose 1 Measure Times and Get Impressions of Patients► Purpose 2 See about Hand Hygiene
    • Look at what we learned!Average time waiting 15 minutes Median is 12 minutes
    • Average time waiting 10 minutes Median in 9
    • Waiting in Room 12 minutes Median is 9 minutes
    • Time spent with clinician 19 minutes Median 16.5 minutes
    • 61 minutes is average Median is 62 minutes
    • Percentages
    • Look what we have► 15 30 45 minute appts► 50% of the time patients are waiting► Stable system► Intervention
    • Another Project Participation by Design► This is what is happening with diabetes
    • What we have done► Variation….  Bead test  Funnel test► Examples  IHI open school  Appointments  Diabetes-Participation by design
    • I have no control!!!
    • ► His response was very quick► “Go back to your work”► “ Teach”► “ Be patient”► “Share your knowledge► He most likely would have loved the next painting……
    • What is the Title of This Painting?? On display at the Getty Center
    • Crazy parade? Health CareSystems? Chaos?? No…. On display at the Getty Center
    • Christ Entry Into Brussels 1989! Where is Christ???? On display at the Getty Center
    • Christ Entry Into Brussels 1989!► First painting to put Christ in background► Point is in the crazy parade one person influence only those around him Who knows maybe you will Help change the parade! Deming thought you could On display at the Getty Center
    • Homework► Find a partner► Select an area of improvement you wish to see implemented and how you might plot the dots!!!!
    • ► http://intqhc.oxfordjournals.org/content/10/ 1/69.full.pdf► http://www.statit.com/services/CQIOvervie w.pdf