stegall-fundamentals of mistagal-proofing your operation · identifying opportunities for mistake...

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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

11/12/2012 [email protected] | 330-337-6664 |http://www.msstegall-consulting.com

[email protected] | 330-337-6664 |http://www.msstegall-consulting.com

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

Then doing something about it that will prevent it from ever happening, i.e., preventing the failure mode in this example.

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Value of a human life is estimated at $5 M. (Google inquiry)

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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 terms2. How to identify opportunities for mistake‐proofing

Reactive—mistake proofing Proactive—error proofing

3. Who needs to drive mistake‐proofing initiatives4. How to integrate mistake‐proofing initiatives into your current

operations5. How to identify and develop the business case for mistake‐

proofing6. Quality tools that are useful in mistake‐proofing your processes7. Team exercise: Learning the power of “First Pass Yield” to

monitor error‐proofing successes plus use of other improvement tools.

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Background to Mistake Proofing—Focus Is on the Patients!

Institute of Medicine 2000 Report: To Err Is Human: Building a Safer Health System

Extrapolated model implied a range of deaths during inpatient admission in the U.S. between 44,000 and 98,000 from preventable medical errors.

“Don’t Kill Me! What I Want From the Health‐Care System” Author of Potent Medicine: The Collaborative Cure for Healthcare by John Toussaint, MD

1. Don’t Kill Me—hospital related deaths rated by some at No.6 cause of all deaths.

2. Keep Me Healthy

3. Don’t Keep Me in the Dark

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http://www.cnbc.com/id/48608480/


10 Shocking Medical Mistakes by John Bonifield, CNN and

Elizabeth Cohen, CNN Senior Medical Correspondent (June 2012)

1. Treating the wrong patient

2. Surgical souvenirs

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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Laboratory equivalent =WBIT = Wrong Blood in Tube

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Deaths Avoidable through Health Care—U.S. Lags Behind Three European Countries

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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.


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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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.

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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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Synonyms—Mistake

Error Fault Blunder Slip‐up Slip Gaffe Inaccuracy Oversight Misstep Blooper Underestimate

Lapse Faux pas Muddle Confuse with Mix up Fail to appreciate Misunderstand Misjudge Misinterpret Misconstrue Confuse

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How to Identify Opportunities for “Mistake‐proofing”

FOUR INSPECTION METHODS

JUDGEMENT INSPECTIONS:

DISCOVER DEFECTS

GOOD VS. BAD

INFORMATIVE INSPECTIONS:

REDUCE DEFECTS

STATISTICAL QUALITY CONTROL

SOURCE INSPECTIONS:

ELIMINATE DEFECTS

QUALITY CONTROL CHECKS BEFORE OR AFTER EACH

STEP

SOURCE OBSERVATIONS:

PREVENT DEFECTS

ERROR PROOF DESIGN

ZERO QUALITY CONTROL

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Evolution to Zero QC MethodShigeo 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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Countermeasures—Remedies:General Principles

1. Eliminate—error is impossible by design

2. Control against mistake by physical means

3. Mitigate the consequences of the error

4. Enhance the detectability of the mistake

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

9. Provide technical manuals within the operating environment

10. Provide warning signs

11. Provide personal protective and other safety equipment when and where needed

12. Assume there is risk in your operation and provide insurance, a recall policy, and a public relations plan.

11/12/2012 15Least effective Effectiveness

Most effective

Source: Human Error by George A Peters and Barbara J. Peters, pages 61 and 62.


Mistake‐Proofing—Who’s Responsible and How to Integrate

Who’s Responsible: How to Integrate It:

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Goals of Mistake‐proofing

Patient Safety

Employee Safety

Visitor Safety

Waste Reduction

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Examples

Laboratory Automation—Pre‐analytical (Moto Man website)

Laboratory Automation—Analytical (From Dark Daily)

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Examples

Inventory Management and Rotation Fire Alarms

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Mistake Proofing Devises Are Prevalent in Everyday Life & in

Laboratory Medicine

Types:

Everyday life—road signs, car keys, flash drives

Sensory devices—refrigerator beeps if left open

Warning devices—blind spot warning indicator in newer model cars & SUVs

Shut down devices—safety handle on push mowers and riding mowers

Additional examples:

Limit switches

Proximity sensors

Laser displacement sensor

Vision systems

Counters and timers

Photoelectric sensors

Ultrasonic sensors

Process measurement instruments

Specialty sensors

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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 silo11/12/2012 21


Business Case: Mistake ProofingResults 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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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

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Path of Flow Value‐adding Process Steps Input measure

Unit

Count/Day Defects/Day

Defects÷Unit

Count/Day

Throughput Yield:

1‐Defects %

Calculation‐‐

Division

Calculation‐‐

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

60.70%

Calculation‐‐

Multiplication

First Pass Yield‐‐Current State

First Pass Yield‐‐‐‐‐>

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Tool 1: First Pass Yield—with Defect Reduction: Feedback Loop

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Path of Flow Value‐adding Process Steps Input measure

Unit

Count/Day Defects/Day

Defects÷Unit

Count/Day

Throughput Yield:

1‐Defects %

Calculation‐‐

Division

Calculation‐‐

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 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

67.44%

Calculation‐‐

Multiplication

First Pass Yield‐‐Defects Reduced



Tool 1: First Pass Yield—with Error Elimination, a.k.a. Success Every Time

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Path of Flow Value‐adding Process Steps Input measure Unit Count/Day Defects/Day

Defects÷Unit

Count/Day

Throughput Yield:

1‐Defects %

Calculation‐‐

Division

Calculation‐‐

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 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

100.00%Calculation‐‐

Multiplication

First Pass Yield: Mistake‐proofing


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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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Fishbone Diagram

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Fishbone Cause and Effect Diagram

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Goal: Mistake‐

Proofing IHC Reporting

MachinesMethods

People

Materials Environment

Measures

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Brainstorm People as a Causative Agent

5 Whys—Root Cause Exploration

POOR TRAINING

Measure it!

Why?

Why?

Why?

Why?

OVERBURDENED

Prove it!

Why?

Why?

Why?

Why?

SHORT STAFFED

Measure it!

Why?

Why?

Why?

Why?

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Brainstorm Methods as a Causative Agent

5 Whys—Root Cause Exploration

Manually Transpose Results from Score Sheet

Measure it!

Why?

Why?

Why?

Why?

OVERBURDENED—BIG SPECIMEN DUMPS

Prove it!

Why?

Why?

Why?

Why?

EVERY ORDER IS AN EXCEPTION

Measure it!

Why?

Why?

Why?

Why?

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Mistake‐proofing Solutions

Error Prevention Solutions

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.

Evidence‐based critical thinking

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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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Multiple Choice Quiz

Which of the following Lean wastes is the focus of error‐proofing?

1. Overproduction

2. Inventory

3. Defects

4. Transportation

5. Motion

6. Foregone talent

7. Over processing

8. Waiting

Classify these mistake proofing actions as reactive or proactive:

A. Liquid sensors on chemistry & hematology analyzers

B. Metal detectors at airport screening

C. Barcode readers for reagent packages placed on instruments

D. A delta check of >24% on a chemistry profile—suspecting WBIT (wrong blood in tube).

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Multiple Choice Quiz (cont.)

Does ZQC infer that statistical quality control is not required—the entire process has been error‐proofed?

Yes

No

Which of the following Lean Six Sigma tools could you also use to error‐proof a process?

a) Failure mode affects analysis (Six Sigma)

b) A3 Analysis (Lean)

c) DMAIC (Six Sigma)

d) Value Stream Mapping (Lean)

e) None of the above

f) All of the above

The business case for error‐proofing may include which of the following:

a. Labor savings

b. Productivity improvements

c. Investment costs

d. Customer satisfaction improvement

e. Improvement in patient safety

f. All of the above

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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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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 Summarizeyour 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

requisitions3. Specimen draw—defect input measure is missing specimens4. Courier transport—defect is missing specimen shipment bags5. Delivery receipt—defect input measure is # of mismatched specimens/requisition

order6. 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 call8. Automated instrument Testing—defect input measure is number of specimens that

do not auto verify9. 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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Case Study Exercise—Data

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Path of Flow Process Steps Model Input measure Average Unit

Count/Day

Average

Defects/Day

Pre, Pre‐analytical Ordering using Requisition Requisition 2,500 250

Pre‐analytical

Obtain Patient Specimen at

Physician Office or Patient Service

Center

Specimens 7,500 80

Pre‐analyticalTransport Patient requisition &

specimens via CourierBio Bags 2,500 13

Pre‐analytical Specimen Delivery Receipt Requisitions & Specimens 7,500 200

Pre‐analyticalData entry‐‐Demographics & Test

OrdersRequisitions ‐‐15 Data elem 37,500 5,625

Pre‐analytical Specimen Processing & Sorting Specimens 7,500 150

AnalyticalAutomated Instrument Testing &

Auto VerificationSpecimens 7,500 200

AnalyticalResolve Technical limit and Delta

check Patient samplesSpecimens (Average 5%) 375 20

Post‐analyticalReport distribution‐‐paper copies

onlyReports 7,500 30


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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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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