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Successful Implementation of IndustrialSuccessful Implementation of Industrial and Systems Engineering in Health Care

Victoria Jordan, PhDUniversity of Texas M. D. Anderson Cancer Center

IIE Webinar March 21 2012IIE Webinar – March 21, 2012

Victoria Jordan, Ph.D.Director, Quality Measurement & Engineering,

M. D. Anderson Cancer CenterM. D. Anderson Cancer Center

Over 25 years of experience providing management and statistical consulting in manufacturing, service, and heath care organizationsorganizations.

Ph.D. from Auburn University in Industrial and Systems Engineering with an emphasis in applied statistics. M.B.A. from Ohio State University, M.S. in Industrial and Systems Engineering from Auburn University and B S from theEngineering from Auburn University, and B.S. from the University of Kentucky in Statistics, with minors in Computer Science and Mathematics.

Six Sigma Master Black Belt (certified by ASQ and BMGi) Research interests include statistical quality control Six Sigma Research interests include statistical quality control, Six Sigma,

process optimization, mathematical simulation of patient flow, and applied statistics.

UT Health Services Fellow in Systems Engineering, Adjunct Professor in Business at UT McCombs and in Industrial Engineering at the University of HoustonUT McCombs and in Industrial Engineering at the University of Houston

Co-author of a McGraw-Hill textbook, Design of Experiments in Quality Engineering, author of several peer reviewed articles

Previously served on faculty in Industrial and Systems Engineering at Auburn University and in the Statistics department at Auburn Universityand in the Statistics department at Auburn University.

Sr member of the American Society for Quality, and the Institute of Industrial Engineers, member of the American Statistical Association

Objectives• Provide an overview of Industrial and Systems

Engineering and its role in healthcare• Discuss its alignment with Basic Continuous

Improvement and Operational Excellence efforts (and discuss how it enhances these efforts)

• Provide some examples of successful applications in healthcare pp

• Share implementation plan at the University of Texas Health SystemTexas Health System

2001 Institute of Medicine Report:2001 Institute of Medicine Report: Crossing the Quality Chasm

“Fundamental reform of health care is needed to ensure that all Americans receive care that is safe, effective,

patient-centered, timely, efficient, and equitable.”

Then: Kenneth I. Shine, M.D.President, Institute of Medicine1992-2002

Now: Kenneth I. Shine, M.D.Executive Vice Chancellor for Health AffairsThe University of Texas System2002- Present

UT Systems Engineering Mission y g g

A l i d t i l d tApply industrial and systems engineering tools and methods to further

advance clinical effectiveness and safety and improve operations across y p pthe health institutions of The University

of Texas Systemof Texas System.

Donald M. Berwick, MD“The Moral Test”

“I know that among the important dimensions of quality –

,Founder, Institute for Health Care Improvement

Former Administrator, Centers for Medicare and Medicaid (16 Months)

I know that, among the important dimensions of quality safety, effectiveness, patient-centered care, timeliness, efficiency, and equity – I am not sure any of us would h h “ ffi i ” th d ti f thave chosen “efficiency” – the reduction of waste – as our favorite. It’s not my favorite. Nonetheless, it is the quality dimension of our time.”

“I would go so far as to say that, for the next three to five years at least the credibility and leverage of the qualityyears at least, the credibility and leverage of the quality movement will rise or fall on its success in reducing the cost of health care – and, harder, returning that money to

th hil i i ti t i ”other uses – while improving patient experience.”

Systems Engineering in Healthcare • Make health care safe, timely, effective, efficient,

equitable, and patient centered• Design / layout new facilities• Reduce patient wait times

I ffi i d li th• Increase efficiency so we can deliver the same quality of care without increasing staff

• Eliminate non-value-added steps to improve• Eliminate non-value-added steps to improve efficiency, reduce cost

• Mathematical simulation to try new processesy p• Make care safer for patients• Identify when we need to add resources (staff or

equipment or rooms)

Created by: Office of Performance Improvement, UT MDAnderson Cancer Center

Components of Systems Engineering

• Frontline Improvement Methods Six Sigma

L

• Human Factors Error Proofing

S f t Lean Standardization

• Quality Engineering Methods

Safety Ergonomics

• LogisticsQua ty g ee g et ods Statistical Quality Control Reliability Engineering Economics

og st cs Facility Design Layout Supply Chain / Inventory

Managementg g

• Process Optimization Methods Operations Research

S h d li

• Data Mining and Analytics Clinical Informatics Clinical Decision Making Scheduling

Simulation Staffing Models

Clinical Decision Making Reporting

Created by: Office of Performance Improvement, UT MDAnderson Cancer Center

“Front Line” Improvement Methods: DFSS, Lean, and DMAIC

OVERALL PROGRAMSLean DMAICDFSS

and DMAIC

ELIMINATE WASTE,

IMPROVE CYCLE TIME

DESIGN PREDICTIVE

QUALITY INTO PRODUCTS

ELIMINATE DEFECTS, REDUCE

VARIABILITY

LEAN Variation Reduction

Lead-time CapableRobust

Design for Six Sigma• Predictability• Feasibility• Efficiency• Capability

• Flow Mapping • Waste Elimination• Cycle Time• WIP Reduction

• Requirements allocation• Capability assessment• Robust Design• Predictable Product Quality Capab ty

• Accuracy• Operations and Design

The “I” in DMAIC ma become DFSSThe “I” in DMAIC may become DFSS

Douglas C. Montgomery, Introduction to Statistical Quality Control, John Wiley & Sons, 2006

LeanF i d i t ( d )• Focus is on reducing waste (muda)

• Standardization, Value Stream Mapping, 5S, and other tools are used to streamline processes

• Often combined with “Rapid Improvement” cyclesy

• Elements included in the “Toyota Production System”Production System

• Most notably in healthcare at Virginia Mason in SeattleMason in Seattle

Virginia Masong

In two years (2002-2004) Va Mason showed the f ll i i tfollowing improvements:

• Inventory down 53% ($1.35 million)Productivity up 36% (158 FTE’s deployed to other• Productivity up 36% (158 FTE’s deployed to other, value-added, jobs)

• Floor space required down 41% (22 200 sf)Floor space required down 41% (22,200 sf)• Lead time down 65% (23,082 hours)• People – Distance traveled down 44%People Distance traveled down 44%• Product – Distance traveled down 72%• Set up time down 82%p

Charles Kenney, Transforming Health Care, CRC Press, 2011.

Link Between Systems Engineering and Lean or Six Sigma Efforts

S t Thi ki

gOptimization

Mathematical Programming Queuing

Heuristics

System Dynamics

Human Factors Analysis

Systems Thinking Forecasting

i Si i

Operations ResearchDecision Analysis

Network Analysis

Constraint Base SchedulingData Mining

Facility Design Layout

Scheduling

Human Factors Analysis Classification System

Evidenced Based MedicineEthnographympl

exity

Discrete Event Simulation

Statistical Analysis

Network Analysis

Inventory Modeling

Decision Trees

y g y

Co

Statistical Process Control

Six Sigma

Lean

Root Cause Analysis

Fault Tree Analysis Failure Mode Effect Analysis

Quality Function Deployment

QI Leadership Patient Safety

Plan Do Study ActFront Line

Usability

Project Management

Process Mapping

Time/Motion Study

Basic Quality Tools

Clinical Variation

Plan-Do-Study-Act

Decision Making

Line Methods

Created by: Office of Performance Improvement, UT MD Anderson Cancer Center, with idea from the Standards and Practice Department, Mayo Clinic, 2010

Quantitative

j g

Qualitative

More Advanced Systems Engineering Methods and Toolsand Tools

• Quality Engineering Methods• Human Factors

Error Proofing Statistical Quality Control Reliability Engineering Economics

Safety Ergonomics

• Logistics• Process Optimization Methods

Operations Research S h d li

• Logistics Facility Design Layout Supply Chain / Inventory

Management Scheduling Simulation Staffing Models

Management

• Data Mining and Analytics Clinical Informatics Clinical Decision Making Reporting

Created by: Office of Performance Improvement, UT MD Anderson Cancer Center

UT Example –Nurse Scheduling Problem in ORNurse Scheduling Problem in OR

• Develop two integer programming nurse scheduling models Nurse Assignment Model (NAM)Nurse Assignment Model (NAM) Nurse Lunch Model (NLM)

• Consider nurse scheduling attributes in an operating suiteConsider nurse scheduling attributes in an operating suite Case specialties Procedure complexities Nurse skill levels

• Consider different aspects of goals Minimizing nurse over times and idle timesg Maximizing surgery case demand satisfactions

• Provide efficient schedule for each nurse to assure coverage, g ,reduce idle time, and reduce overtime


Simulation

• Mathematical model of the process• Effective tool for answering “what if”

questionsq• Can be used effectively for estimating

demands for resourcesdemands for resources• Can demonstrate patient flow challenges,

bottlenecks and impacts of plannedbottlenecks, and impacts of planned improvements

Examples of Simulation Applications • Evaluate the impact on Emergency Center

length of stay by allocating a number of bedslength of stay by allocating a number of beds for less than 23-hour observation patients

• Evaluate different methods to allocate exam• Evaluate different methods to allocate exam rooms in outpatient centersE l t i t f i t i• Evaluate impact of new equipment in pharmacy

• Determine Operating Room Requirements• Determine ICU Bed Requirementsq• Optimize facility design

Therapeutic Optimization

• Hemodialysis – Modeling AV patency as an ideal stopping time problem to decide whenideal stopping time problem to decide when to intervene with treatment, etc. (Kopach-Konrad et. al., 2007)

• Liver transplant decision rules on accepting / rejecting available livers (Kopach-Konrad, et. al., 2007)

• Optimizing radiation therapyin cancer (MDACC and UH)(Lim, et. al., 2007)

Prostate OARProstate Tumor

OAR (Rectum)


Facility Design Layouty g y

• Determine most efficient location of buildings, rooms, equipment, interior design, etc.

• May use basic tools such as Spaghetti y p gDiagrams, Relationship Diagrams

• May use more complicated tools such as ORMay use more complicated tools such as OR, simulation, etc.

Layout Example: BeforeFindings: 1. Layout not visual

control friendly2. Many isolated y

islands3. Workstation layout

not standardized

21Created by: Office of Performance Improvement, UT MD Anderson Cancer Center

Layout Example: After

Note for Improving:1. Layout follows specimen flow

• 2. Fewer isolated islands• 3. Layout includes use of visual controls• 4. Supply replenishment driven by usagepp y p y g


Materials Management / Inventory

• Maximize system opportunities for shared inventoriesinventories

• Pharmacy costs • Correlated demand and supply

Human Factors Knowledge BaseDisciplines SkillsEngineering• Systems Analysis• Industrial Design

Systems Approach• Process Integration• Performance

Applied Operations Analysis and Development

• Cost

Anatomy• Anthropometry

Evaluation• Experimental Design• Equipment / System Test

Physiology• Biomechanics• Biochemistry• Tolerance

Industrial Design and Analysis• Process Improvement• Workplace Design

Psychology Safety

Human Factors

EngineeringPsychology• Experimental Design• Memory and Learning• Perception

Safety• Reduce Injuries• Reduce Human Error

Mathematics• Statistics

Training• Process

g g

Systems approach to analyze and fit tasks to human capabilities and skills

• Statistics • Process• Tools

Computer Science• Interactive Systems• Automation

• Optimize technology and human user interactions

• Minimize error and injury

Created by: Ron Hoffman, Office of Performance Improvement, UT MD Anderson Cancer Center

Your Point of View

Attach the oxygen mask and tubing to the green spigot.the green spigot.

Design suggestion:

A better design eliminates the potential for this problem to occur by creating a clear spigot and simply allowing the user to correlate air with yellow and oxygen withthe user to correlate air with yellow and oxygen with green.

Error Proofing Example• Infant abduction sensor locks the exit inInfant abduction sensor locks the exit in

case of an abduction An electronic device, or “tag,” is designed to be , g, g

clamped to the infant’s umbilical cord The tag ensures that the infant is not removed from

the nursery. If the infant is removed without authorization, alarms sound, specified doors lock, and the elevators automatically return to the securedand the elevators automatically return to the secured maternity floor; the elevator doors remain open.

Detect error Prevent errorForced control ◎Shut downWarningSensory alertSensory alert

Safety / Ergonomics

• Patient Safety • Reducing injuries to staff (such as back• Reducing injuries to staff (such as back

injuries, stress from repetitive movements)• Relationship between buildings and• Relationship between buildings and

healthcare outcomes (e.g. sunlight, air flow systems accessibility)systems, accessibility)

• Mechanical simulation to improve performanceperformance

• HFACS classification of errors

Task behavior assessment methods

OrganizationalInfluences

Wrong Site or Procedure Event 1

UnsafeSupervision

OrganizationalClimate

ResourceManagement

OperationalProcess

PlannedInappropriate

Operation

InadequateSupervision

Failed toCorrectProblem

Preconditionsfor Unsafe Acts

SupervisoryViolation

EnvironmentalFactors

PhysicalEnvironment

Personal & InterpersonalFactors

Condition ofEmployees

AdverseMentalStates

TechnologicalEnvironment

AdversePhysiological

States

CommunicationCoordination

Fitnessfor Duty

Physical& Mental

LimitationsEnvironment StatesEnvironment States& Planning

for Duty

Errors

Unsafe Acts

Violations

Limitations

Skill-basedErrors

DecisionErrors

PerceptualErrors

RoutineViolations

ExceptionalViolation

Courtesy of Ron Hoffman and Cindy Segal, UT MD Anderson Cancer Center

Our Implementation Plan

Not just a series of projects ….

Background

• March - Jordan named Health Chancellor’s Fellow for Systems EngineeringFellow for Systems Engineering

• May – System-wide Steering Committee f dformed

• Aug – “White paper” and recommendation presented to Dr. Shine

• Aug – Board of Regents granted fundingg g g g• April – Grant submissions due

Progress to Date –Steering CommitteeSteering Committee

Four subcommittees were established to:Four subcommittees were established to:1. Identify coursework and resources for front-line

education 2. Define the guidelines for an RFP to solicit grant

applications pp3. Plan for communication and a conference to

share information4. Develop formal process for internships and

sabbaticals

Curriculum and Certificates t Th L lat Three Levels

Prepares learner to Prepares learner toPrepares learner to participate on

improvement teams

Prepares learner to initiate and lead

local improvement efforts

Prepares learner to lead and champion

institutional improvement efforts


Criteria for Grant Selection• Impact on care delivery patient experience and cost through• Impact on care delivery, patient experience, and cost through

improvement science• Transformative impact and relationship to health institution’s p p

strategic goals• Productive use of Systems Engineering tools and

methodologiesmethodologies• Timeline, milestones, and metrics for clinical, operational, and

financial outcomes• Collaboration among areas & disciplines within the health

institution• Collaboration with other UT institutions• Spreadability and scalability within and across UT health

institutionsinstitutions

Conference

• Oct 10 at MDACCK k i l d• Keynote speakers include: Dr. Kenneth Shine, Executive Vice Chancellor for Health Affairs, The

University of TexasUniversity of Texas Dr. Steve Spear, Senior Lecturer, Massachusetts Institute of

Technology; Senior Fellow, Institute for Healthcare Improvement Dr Dennis Cortese Foundation Professor and Director Health Care Dr. Dennis Cortese, Foundation Professor and Director, Health Care

Delivery and Policy Program, Arizona State University; Emeritus President and CEO, Mayo Clinic

B k t i St t i Pl i d• Breakout sessions on Strategic Planning and Ind and Systems Engr tools and applications

Internships and Sabbaticals• Industrial Engineering capstone projects• Industrial Engineering student interns (part• Industrial Engineering student interns (part-

time and summer)St d t j t ith B i S h l• Student projects with Business School

• Graduate students’ dissertation research• First faculty sabbatical at MD Anderson this fall• Internships for Medical and Nursing schoolsInternships for Medical and Nursing schools• Subcommittee formed to develop formal plan

by June 2012by June 2012

Lessons LearnedLessons Learned

• Importance of collaborationImportance of collaboration• Importance of communication• Need for integration of strategic

planningplanning• Role of leadership• Vast opportunity

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