Fundamentals of Mistake‐Proofing Your Operation Lab Quality Confab Conference November 7, 2012
Presented by M. Susan Stegall, Owner and CEO M. S. Stegall & Associates, LLC A Management Consulting Firm
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My Personal Interest in This Topic— To Err Is Human
As an administrative director of laboratory services in the 1980s, my hospital had a patient die during surgery from the transfusion of an ABO incompatible unit of blood. Lessons learned: Your laboratory is only as strong as your operating processes’ weakest link, The trick is in identifying that weakest link, preferably before an adverse patient outcome, and Value of a human Then doing something about it that will life is estimated prevent it from ever happening, i.e., preventing the failure mode in this at $5 M. (Google example. inquiry)
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1 Presentation Flow
Introduction and Background—Patient Safety Learning Objectives Key definitions within the “Path of Flow” Identifying Opportunities for Mistake Proofing Who drives a mistaking‐proofing initiative Integrating mistake‐proofing into your current operations Developing the business case for mistake‐proofing Quality tool approaches to mistake proofing Multiple choice quiz Selected Bibliography Case study exercise—teams use quality tools Certificate of completion
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Learning Objectives for You
Attendees will learn: 1. Key definitions of mistake‐proofing plus other terms 2. How to identify opportunities for mistake‐proofing Reactive—mistake proofing Proactive—error proofing 3. Who needs to drive mistake‐proofing initiatives 4. How to integrate mistake‐proofing initiatives into your current operations 5. How to identify and develop the business case for mistake‐ proofing 6. Quality tools that are useful in mistake‐proofing your processes 7. Team exercise: Learning the power of “First Pass Yield” to monitor error‐proofing successes plus use of other improvement tools.
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2 Background to Mistake Proofing— Focus Is on the Patients!
Institute of Medicine 2000 Report: “Don’t Kill Me! What I Want From To Err Is Human: Building a Safer the Health‐Care System” Author of Health System Potent Medicine: The Collaborative Cure for Healthcare by John Extrapolated model Toussaint, MD implied a range of deaths 1. Don’t Kill Me—hospital during inpatient admission related deaths rated by in the U.S. between some at No.6 cause of all 44,000 and 98,000 from deaths. preventable medical 2. Keep Me Healthy errors. 3. Don’t Keep Me in the Dark http://www.cnbc.com/id/48608480/
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10 Shocking Medical Mistakes by John Bonifield, CNN and Elizabeth Cohen, CNN Senior Medical Correspondent (June 2012)
1. Treating the wrong patient Laboratory equivalent = 2. Surgical souvenirs WBIT = Wrong Blood in Tube 3. Lost patient 4. Fake doctors 5. The ER waiting game 6. Air bubbles after chest tube removed 7. Operating on the wrong body part 8. Infection infestation 9. Lookalike tubes—chest & stomach 10. Waking up during surgery
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3 Deaths Avoidable through Health Care— U.S. Lags Behind Three European Countries
E. Nolte and C. M. McKee, "In Amenable Mortality—Deaths Avoidable Through Health Care—Progress in the US Lags That of Three European Countries," Health Affairs Web First, published online Aug. 29, 2012.
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Key Definitions
Defects are an “outcome” generated by a process and should be considered the problem you are attempting to solve using a variety of quality tools. Errors And Mistakes that occur during a process are causes of the defects and should be the focus of the analysis and subsequent error‐proofing process improvement. Zero Quality Control (ZQC): Is a quality control approach for achieving zero defects. ZQC assumes that defects are prevented by controlling the performance of a process so that it cannot produce a defect—even when a mistake is made by a machine or a person. First Pass Yield—First pass yield is a measure to evaluate the initial efficiency of a multistep production process.
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4 Key Definitions (cont.)
Root Cause Problem Solving—Attempts to solve problems by attempting to identify and then correct the root causes of events, as opposed to simply addressing their symptoms. Pareto Chart—A bar graph. The length of the bars represent frequency or cost (money or time) arranged in order from longest on the left to shortest on the right. May include actual counts and percentages along with a cumulative line. 5 Whys—The key is to encourage the trouble‐shooter to avoid assumptions and logic traps and instead trace the chain of causality in direct increments from the effect through any layers of abstraction to a root cause that still has some connection to the original problem. Note that the fifth why suggests a broken process or an alterable behavior, which is typical of reaching the root‐cause level. 11/12/2012 [email protected] | 330-337-6664 |http://www.msstegall-consulting.com 9
Key Definitions (cont.)
Deductive Reasoning—Method of reasoning from general to particular, it is employed in deriving general laws or principles from the observed phenomenon. Convergent Thinking—Thinking that brings together information focused on solving a problem (especially solving problems that have a single correct solution) Divergent Thinking—Thinking that moves away in diverging directions so as to involve a variety of aspects and which sometimes lead to novel ideas and solutions; associated with creativity.
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5 Synonyms— Mistake
Error Lapse Fault Faux pas Blunder Muddle Slip‐up Confuse with Slip Mix up Gaffe Fail to appreciate Inaccuracy Misunderstand Oversight Misjudge Misstep Misinterpret Blooper Misconstrue Underestimate Confuse
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How to Identify Opportunities for “Mistake‐proofing” FOUR INSPECTION METHODS
SOURCE SOURCE INSPECTIONS: INFORMATIVE OBSERVATIONS: JUDGEMENT INSPECTIONS: ELIMINATE PREVENT DEFECTS INSPECTIONS: DEFECTS REDUCE DEFECTS ERROR PROOF DISCOVER DEFECTS QUALITY CONTROL STATISTICAL DESIGN GOOD VS. BAD CHECKS BEFORE QUALITY CONTROL OR AFTER EACH ZERO QUALITY STEP CONTROL
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6 Evolution to Zero QC Method Shigeo Shingo’s Book Zero Quality Control: Source Inspection and the Poka‐yoke System Baseline Stage: Judgment inspections Stage 1: Statistical Quality Control—Informative inspections—Reduces defects Stage 2: Encounter with Poka Yoke Methods (Mistake‐ proofing)—find it and fix it: Eliminates the opportunity to create defects Stage 3: Encounter with successive and self‐checks Stage 4: Sampling Inspections—Rational Stage 5: Encounter with Source Inspection Stage 6: The achievement of a month with Zero Defects
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Inspection versus Prevention
Inspection—Reactive Prevention—Proactive
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7 Countermeasures—Remedies: General Principles
1. Eliminate—error is impossible by design Source: Human Error by George A 2. Control against mistake by physical means Peters and Barbara J. Peters, pages 61 and 62. effective
3. Mitigate the consequences of the error 4. Enhance the detectability of the mistake Most 5. Institute procedural pathways for guidance to prevent errors
6. Maintain supervisory control and monitoring for mistakes 7. Provide posted instructions—specific, brief & clear to avoid errors 8. Use training to ensure correct procedures are known
Effectiveness 9. Provide technical manuals within the operating environment 10. Provide warning signs
11. Provide personal protective and other safety equipment when and where needed effective 12. Assume there is risk in your operation and provide insurance, a recall policy, and a public relations plan.
Least 11/12/2012 [email protected] | 330-337-6664 |http://www.msstegall-consulting.com 15
Mistake‐Proofing— Who’s Responsible and How to Integrate
Who’s Responsible: How to Integrate It:
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8 Goals of Mistake‐proofing
Patient Safety Employee Safety Visitor Safety Waste Reduction
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Examples
Laboratory Automation—Pre‐ Laboratory Automation— analytical (Moto Man website) Analytical (From Dark Daily)
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9 Examples
Inventory Management and Rotation Fire Alarms
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Mistake Proofing Devises Are Prevalent in Everyday Life & in Laboratory Medicine Types: Additional examples: Everyday life—road signs, car Limit switches keys, flash drives Proximity sensors Sensory devices— Laser displacement sensor refrigerator beeps if left Vision systems open Counters and timers Warning devices—blind spot warning indicator in newer Photoelectric sensors model cars & SUVs Ultrasonic sensors Shut down devices—safety Process measurement handle on push mowers and instruments riding mowers Specialty sensors
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10 Business Case for Mistake‐proofing
Quantify the wasted resources, financially. Here are a few examples: Staff members time spent looking for specimens Staff members time spent acquiring missing patient and billing demographics Cost of repeat analytical runs—Labor, supplies, reagents, QC materials Lost clients due to poor quality Cost of adding a missed order Staff and supervisory time spent on performing quality checks at the end of accessioning requisitions Staff members time spent on customer complaints Patient safety impact—impact outside the lab silo 11/12/2012 [email protected] | 330-337-6664 |http://www.msstegall-consulting.com 21
Business Case: Mistake Proofing Results of Henry Ford Health System— Do No Harm Program
Documented cost savings of harm prevention—a patient safety goal: Eliminate pressure ulcers by using the right kind of mattress—saved $10.6 M over 3 years Preventing urinary infections—saved $5M over 4 years Malpractice insurance decreases of $26M by reducing patient mortality rates down to 1.4% (a 30% plus reduction)
Source: ASQ Webinar, September 2012
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11 Lean Six Sigma Tools
Tool 1: First Pass Yield Tool 2: Pareto Chart Tool 3: Cause & Effect Investigation Tool 4: PDCA—Error Proofing Plan Using Lean ZQC as goal Zero Quality Control Source inspection Poka‐yoke system • Mistake Proofing • Error Proofing
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Tool 1: First Pass Yield— Current State Inspection
First Pass Yield‐‐Current State Unit Defects÷Unit Throughput Yield: Path of Flow Value‐adding Process Steps Input measure Count/Day Defects/Day Count/Day 1‐Defects % Calculation‐‐ Calculation‐‐ Division Subtraction Pre‐analytical Accessioning Cases 65.00 5.00 0.07692 1.00000 0.92308 Pre‐analytical Test Selection Cases 65.00 1.00 0.01538 1.00000 0.98462 Pre‐analytical Ordering‐‐data entry Cases 65.00 3.00 0.04615 1.00000 0.95385 Analytical Histology Cases 65.00 4.00 0.06154 1.00000 0.93846 Analytical IHC Antibody Runs 25.00 2.00 0.08000 1.00000 0.92000 Analytical Sign Out (Slide Review) Cases 65.00 1.00 0.01538 1.00000 0.98462 Analytical Additional test selection Cases 5.00 ‐ ‐ 1.00000 1.00000 Analytical Reporting‐‐Computer entry IHC Results 200.00 20.00 0.10000 1.00000 0.90000 Analytical Transcription Cases 35.00 2.00 0.05714 1.00000 0.94286 Post‐analytical Verification Cases 65.00 1.00 0.01538 1.00000 0.98462 Post‐analytical Report distribution Cases 70.00 1.00 0.01429 1.00000 0.98571 First Pass Yield‐‐‐‐‐> 60.70% Calculation‐‐ Multiplication
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12 Tool 1: First Pass Yield— with Defect Reduction: Feedback Loop
First Pass Yield‐‐Defects Reduced Unit Defects÷Unit Throughput Yield: Path of Flow Value‐adding Process Steps Input measure Count/Day Defects/Day Count/Day 1‐Defects % Calculation‐‐ Calculation‐‐ Division Subtraction Pre‐analytical Accessioning Cases 65.00 5.00 0.07692 1.00000 0.92308 Pre‐analytical Test Selection Cases 65.00 1.00 0.01538 1.00000 0.98462 Pre‐analytical Ordering‐‐data entry Cases 65.00 3.00 0.04615 1.00000 0.95385 Analytical Histology Cases 65.00 4.00 0.06154 1.00000 0.93846 Analytical IHC Antibody Runs 25.00 2.00 0.08000 1.00000 0.92000 Analytical Sign Out (Slide Review) Cases 65.00 1.00 0.01538 1.00000 0.98462 Analytical Additional test selection Cases 5.00 ‐ ‐ 1.00000 1.00000 Analytical Reporting‐‐Computer entry IHC Results 200.00 ‐ ‐ 1.00000 1.00000 Analytical Transcription Cases 35.00 2.00 0.05714 1.00000 0.94286 Post‐analytical Verification Cases 65.00 1.00 0.01538 1.00000 0.98462 Post‐analytical Report distribution Cases 70.00 1.00 0.01429 1.00000 0.98571 First Pass Yield‐‐‐‐‐> 67.44% Calculation‐‐ Multiplication
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Tool 1: First Pass Yield— with Error Elimination, a.k.a. Success Every Time
First Pass Yield: Mistake‐proofing Defects÷Unit Throughput Yield: Path of Flow Value‐adding Process Steps Input measure Unit Count/Day Defects/Day Count/Day 1‐Defects % Calculation‐‐ Calculation‐‐ Division Subtraction Pre‐analytical Accessioning Cases 65.00 ‐ ‐ 1.00000 1.00000 Pre‐analytical Test Selection Cases 65.00 ‐ ‐ 1.00000 1.00000 Pre‐analytical Ordering‐‐data entry Cases 65.00 ‐ ‐ 1.00000 1.00000 Analytical Histology Cases 65.00 ‐ ‐ 1.00000 1.00000 Analytical IHC Antibody Runs 25.00 ‐ ‐ 1.00000 1.00000 Analytical Sign Out (Slide Review) Cases 65.00 ‐ ‐ 1.00000 1.00000 Analytical Additional test selection Cases 5.00 ‐ ‐ 1.00000 1.00000 Analytical Reporting‐‐Computer entry IHC Results 200.00 ‐ ‐ 1.00000 1.00000 Analytical Transcription Cases 35.00 ‐ ‐ 1.00000 1.00000 Post‐analytical Verification Cases 65.00 ‐ ‐ 1.00000 1.00000 Post‐analytical Report distribution Cases 70.00 ‐ ‐ 1.00000 1.00000 First Pass Yield‐‐‐‐‐> 100.00% Calculation‐‐ Multiplication
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13 Tool 1: First Pass Yield Dashboard Summary
Baseline metric = 60.70% With error reduction (SQC) = 67.44% With error elimination (ZQC) = 100%
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Pareto Chart
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14 Fishbone Diagram
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Fishbone Cause and Effect Diagram
Machines Methods People
Goal: Mistake‐ Proofing IHC Reporting
Materials Environment Measures
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15 Brainstorm People as a Causative Agent
5 Whys—Root Cause Exploration
SHORT STAFFED POOR TRAINING OVERBURDENED Measure it! Measure it! Prove it! Why? Why? Why? Why? Why? Why? Why? Why? Why? Why? Why? Why?
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Brainstorm Methods as a Causative Agent
5 Whys—Root Cause Exploration
EVERY ORDER IS AN Manually Transpose OVERBURDENED—BIG EXCEPTION Results from Score Sheet SPECIMEN DUMPS Measure it! Measure it! Prove it! Why? Why? Why? Why? Why? Why? Why? Why? Why? Why? Why? Why?
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16 Mistake‐proofing Solutions
Error Prevention Solutions Evidence‐based critical thinking Measurement proved that the transcription from paper score card was the root cause of this problem. Mistake‐proofing solution: 1. Eliminate paper score card 2. Pathologist enters results directly into the computer as they interpret the IHC results.
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Rapid Process Improvement— PDCA or Kaizen
Plan: Computerize the IHC scoring Train pathologists on the new resulting screens Train pathologists on the verification step. Do: Communicate and implement Check: For problems and revise new process till it stabilizes. Act: Institutionalize the changed procedure; Affirm that the new process has eliminated the transcription errors.
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17 Multiple Choice Quiz
Which of the following Lean Classify these mistake proofing wastes is the focus of error‐ actions as reactive or proactive: proofing? A. Liquid sensors on chemistry 1. Overproduction & hematology analyzers 2. Inventory B. Metal detectors at airport 3. Defects screening 4. Transportation C. Barcode readers for reagent packages placed on 5. Motion instruments 6. Foregone talent D. A delta check of >24% on a 7. Over processing chemistry profile— 8. Waiting suspecting WBIT (wrong blood in tube).
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Multiple Choice Quiz (cont.)
Does ZQC infer that statistical quality The business case for error‐ control is not required—the entire proofing may include which of process has been error‐proofed? the following: Yes a. Labor savings No b. Productivity improvements Which of the following Lean Six Sigma c. Investment costs tools could you also use to error‐proof d. Customer satisfaction a process? improvement a) Failure mode affects analysis (Six e. Improvement in patient Sigma) safety b) A3 Analysis (Lean) f. All of the above c) DMAIC (Six Sigma) d) Value Stream Mapping (Lean) e) None of the above f) All of the above 11/12/2012 [email protected] | 330-337-6664 |http://www.msstegall-consulting.com 36
18 Multiple Choice Quiz (cont.)
Name one good and one poor countermeasure used to prevent human errors in laboratories today.
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Case Study Exercise
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19 Case Study Exercise for the Fundamentals of Mistake‐Proofing Presentation at Lab Quality Confab 2012
Task 1: Calculate “First Pass Yield” Current State based on the information on the right. See form provided Task 2: Develop “Pareto Chart” using the various errors listed within your current state “First Pass Yield” model. Note: The defects should be sorted highest frequency to lowest frequency on your bar‐graph chart Task 3: Brainstorm An Error‐Proofing Solutions and Summarize your team’s Plan: Kaizen Event aimed at error‐proofing the cause of the major defect listed on your Pareto Chart. Task 4: Recast your “First Pass Yield” based on the expected change in frequency for the error you selected to eliminate. Task 5: Team Presentations: 5 Minutes for Each Team
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Case Study Exercise— Case Study Scenario:
1. The Lab: Hospital‐based outreach testing. The Path of flow is as follows: 2. Physician office ordering using paper requisitions—defect input measure is requisitions 3. Specimen draw—defect input measure is missing specimens 4. Courier transport—defect is missing specimen shipment bags 5. Delivery receipt—defect input measure is # of mismatched specimens/requisition order 6. Data entry—defect input measure is incorrect data elements (Averages 15 data inputs/requisition) 7. Specimen processing & sorting—defect input measure is # of problem specimens that require a customer call 8. Automated instrument Testing—defect input measure is number of specimens that do not auto verify 9. Resolve/Retest Technical Limit And Delta Check Samples—defect is number of specimens that cannot be resolved and reported (Requires redraw) 10. Report distribution—defects input measure is the number of reports that are not printed within 24 hours and delivered.
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20 Case Study Exercise— Data
Average Unit Average Path of Flow Process Steps Model Input measure Count/Day Defects/Day
Pre, Pre‐analytical Ordering using Requisition Requisition 2,500 250 Obtain Patient Specimen at Pre‐analytical Physician Office or Patient Service Specimens 7,500 80 Center Transport Patient requisition & Pre‐analytical Bio Bags 2,500 13 specimens via Courier Pre‐analytical Specimen Delivery Receipt Requisitions & Specimens 7,500 200 Data entry‐‐Demographics & Test Pre‐analytical Requisitions ‐‐15 Data elem 37,500 5,625 Orders Pre‐analytical Specimen Processing & Sorting Specimens 7,500 150 Automated Instrument Testing & Analytical Specimens 7,500 200 Auto Verification Resolve Technical limit and Delta Analytical Specimens (Average 5%) 375 20 check Patient samples Report distribution‐‐paper copies Post‐analytical Reports 7,500 30 only
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Selected Bibliography
Casey, John J. Strategic Error‐Proofing. New York: CRC Press, Taylor & Francis Group, a Productivity Press Book, 2009. Burns, Joseph. Root Cause Analysis Used to Find Source of Errors. The Dark Report, Volume XIX, Number 12, August 27, 2012 Grout, PhD. John. Prepared for the Agency for Healthcare Research and Quality. Mistake‐Proofing the Design of Health Care Processes. Rockville, MD: AHRQ Publication No. 07‐0020, May 2007 Okes, Duke. Root Cause Analysis, the Core of Problem Solving and Corrective Action. Milwaukee, Wisconsin: ASQ Quality Press, 2009.
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21 Selected Bibliography (cont.)
Peters, George A. and Peters, Barbara J. Human Error, Causes and Control. New York: CRC Press, Taylor & Francis Group, 2006 Created by the Productivity Press Development Team. Mistake‐ Proofing for Operators: The ZQC System. Boca Raton, London, New York: CRC Press, Taylor & Francis Group, a Productivity Press Book, Reprinted 2010. Straseski, PhD, Joely. Making Delta Checks an Essential Quality Improvement Tool. Presentation at G2 Intelligence, Lab Institute 2012. ARUP Laboratories, October 2012 Shingo, Shigeo as translated by Dillion, Andrew P. Zero Quality Control: Source Inspection and the Poka‐yoke System. Portland, Oregon: Productivity Press, 1986
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