Successful Implementation of IndustrialSuccessful ... · Successful Implementation of...
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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
Created by: Office of Performance Improvement, UT MD Anderson Cancer Center
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)
Created by: Office of Performance Improvement, UT MD Anderson Cancer Center
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
Created by: Office of Performance Improvement, UT MD Anderson Cancer Center
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
Created by: Office of Performance Improvement, UT MD Anderson Cancer Center
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
Created by: Office of Performance Improvement, UT MD Anderson Cancer Center
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