HCM 540 – Healthcare Operations
Management
Analyzing, Designing and
Managing Healthcare
Operations and Supporting
Managerial Decision Making
First Class Overview
 Introductions
 Course overview and administration
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Syllabus and course webs
Schedule of topics
 Overview of operations analysis/modeling/decision support
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Preview of modeling applications sprinkled in
 Lab session
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Dealing with the course webs
A model challenge
Fun With Uncertainty
Mark Isken
 BSE, MSE, Ph.D. in Industrial and Operations
Engineering from University of Michigan
 Operations analyst for William Beaumont Hospital
Henry Ford Health System , Health Services
Engineering (~7 years)
 Teach Business Analysis and Modeling (MIS 646)
and undergraduate information systems courses
 Faculty coordinator in Applied Technology in
Business program
Some of my Healthcare Operations
Analysis Experience
 Internal business analysis / decision support consultant
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Build models and analyze systems, create analytical tools and databases,
present to executive leadership, support managerial decision making
 Simulation modeling
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Critical care tower, Ptube systems, outpatient clinics, emergency centers,
inpatient obstetrics, many more
 Staffing and scheduling
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numerous ancillary services, nursing
tactical staff scheduling optimization models
 Database and decision support system development
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chest pain, lab courier routing, data mart for operations analysis
 Various statistical and operations analysis studies
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inpatient occupancy
surgical utilization and capacity allocation
Syllabus and Course Webs
http://www.sba.oakland.edu/faculty/isken/HCM540/
 HCM 540 Course Web site will be the place to go
for course information and materials.
 Announcements
 Downloads – readings, class materials
 Homework assignments
 We will also use WebCT for a few things
 Email
 Discussion forums
 Login with your GrizzlyID and SAIL password
Our Starting Point – The Givens
 Modern healthcare “systems” are extremely
complex conglomeration of human, machine,
material and information flow
 Huge $$$ spent on delivery of healthcare
 Access, cost, quality are important dimensions
 Systemic, economic and political issues can make
management of healthcare entities a wee bit
challenging
Cases in Point
 IOM report Crossing the Quality Chasm: A New
Health System for the 21st Century
 Crisis in our local healthcare system
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Detroit Health Care Stabilization Workgroup
Debate on CON and transfer of beds
 Groups for healthcare improvement
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Institute for Healthcare Improvement
Agency for Healthcare Research and Quality
The Leapfrog Group
Donabedian’s Quality Triad
 Structure
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facility planning
staffing
operational policies
 Process
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Our course will have two primary areas
of focus:
(1) supporting managerial decision
making
(2) analyzing and managing business
process flows
process physics – arrival, delays, service
patient, work, information flow
 Outcome
Donabedian, A. Med Care Rev (Medical care review.) 1980 Fall; 37(7): 653-98
Nature of healthcare service operations
 Spectrum from pure service to quasi-manufacturing
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Ex: diagnosis vs. lab testing
 High level of direct customer contact and participation
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Ex: ER, OB, call-centers, nursing care, check-in/out
 The products and services delivered are often complex and
difficult to define and measure
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Ex: classifying hospital patient types
 Demand has large component of uncertainty
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Ex: TOD/DOW, random events, random outcomes
 Demand has differing levels of urgency
 Large # of highly educated and highly trained service providers
 Poor service can result in everything from customer (patient,
physician, co-worker) dissatisfaction to highly adverse
consequences (injury or death)
So, what are your tough operational
decision problems?
 capacity planning
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beds
staff
time
diagnostic and treatment
equipment
 scheduling
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procedures
appointments
staff
 information systems
integration with operations
and decision making
 measuring and improving
patient satisfaction
 quality of outcomes
 designing/managing
process flows
 strategic and tactical
planning & decision
making
 logistics
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material handling and
management
routing/distribution
Example 1: Staffing the
Centralized Appointment Center
 A Common Problem:
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Patients complaining of long wait times on hold,
Operators complaining of understaffing,
A supervisor points out that at times the operators seem
to be sitting around with nothing to do,
Manager and supervisors calling for help to figure out
how many operators are needed and how they should be
scheduled
 So, what do you do?
Relevant Healthcare IS Trends
 Still many gaps in basic data collection
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Example: For what date did the patient want an
appointment?
 Loosely “integrated” healthcare systems often have
far from integrated information systems
 Continuing toward electronic medical record
 Data warehousing and decision support slowly
evolving
 Efforts to leverage the internet and figure out what
“e-healthcare” might be
Supporting Managerial Decision
Making with Decision Technology
Information
Systems
Critical Link
Quantitative
Methods
 Planning
 Operational analysis,
design and control
Operations
Management
Operations research
Management science
What is Operations Research &
Management Science?
 Application of scientifically based mathematical
modeling, data and information technology for
informed decision making.
 Build models to help understand complex systems
comprised of people, technology and processes.
 Related to applied mathematics, information
systems, computer science, economics, industrial
engineering, systems engineering
 Applied broadly in many industries
 Some History if you’re interested
A Few Roles of OR/MS in
INFORMSed decision making
 Problem structuring
 Evaluation of alternatives through data analysis and
modeling
 Quantify risk, uncertainty
 Complements management experience, knowledge,
and expertise
 Adds value to information and information systems
 Add insight and guide decision making
A Few OR/MS Applications
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Vehicle routing
Inventory control
Scheduling
Capacity planning
Decision analysis
Fraud detection
Yield management
 Financial modeling
 Design and analysis of
information and
telecommunications systems
 Customer service systems
 Military tactics/strategy
 Healthcare policy
The OR/MS Toolbox
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Statistics
Computer simulation
Queuing models
Forecasting
Decision analysis
Optimization
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Computer programming
Spreadsheets
Databases
IT
Business domain knowledge
Industrial Engineering (IE) and
OR/MS in Healthcare
 Pioneering work at Johns Hopkins in the 1950’s
 Many OR/MS applications and academic studies focusing
on healthcare in subsequent decades
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productivity and staffing, admissions scheduling, staff scheduling,
facility planning, medical decision making, patient flow modeling
 Management (industrial) engineering (ME) departments
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internal management consultants
crisis for the field in the late 80’s
role continues to evolve
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operations analysis
TQM/BPR facilitation
IS design/implementation support
decision support
small IS development
ME’s must focus on
high impact problems
and must work hand
in hand with IS/IT
folks.
Management Engineering @
WBH and HFHS
 Mgt. Engineering @
WBH
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historically corporate but
currently split between
RO and Troy
reports to CFO
formerly shared building
with IS
10-15 engineers and data
techs
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IE’s, clinical managers
operations analysis,
facilitation, simulation,
JCAHO, decision support
 Mgt. Services @ HFHS
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Corporate department
reports to Exec. VP of Strategic
Planning (was Sahney)
major player in TQM, patient
focused care, “panels”, Open
Access
operations analysis, simulation,
decision support
springboard for management
positions
10-15 engineers and data techs
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IE’s, physician, MHA, clinical
managers, academics
OR/MS for Healthcare Managers,
Decision Makers, Administrators
1.
Intelligent consumer perspective
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working with technical analysts and interpreting technical
analyses (techie geeks like me)
working with consultants (beware the packaged solution)
you are customers for high value information (demand it)
End-user modeler perspective
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learn by doing, hands-on
Excel spreadsheets rule the world
build simple models, do data analysis
entrepreneurs do not have luxury of stable of analysts
be a more informed consumer of modeling, know what to ask for,
understand the realities of modeling and analysis
SO, WHY DON’T WE HEAD TO THE LAB...
Lab Session
 Course Web
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Structure
Downloading files from course web
 Modeling intro and challenges
 Fun with Uncertainty
 Anything else you want me to show you in Excel,
web, whatever
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Excel tutorial
Emailing files
Models
 Simplified representation or abstraction of reality.
 Capture essence of system without unnecessary
details
 Models tailored for specific types of problems
 Models help us understand the world
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Prediction (What if?)
Optimization (What’s best?)
Convergence of Data and
Models for Decision Support
 Data is retrospective, models offer possibility of
prediction
 Data is critical, but simply not enough for solving
many difficult business problems
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A routing example – Lab Couriers
A material handling example – Pneumatic Tube
A clinic capacity planning example – OB Clinic
What makes decision problems hard?
 Massive number of alternatives
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The scheduling challenge
 Complex relationship between variables
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the physics of healthcare processes and services
 Difficulty quantifying outcomes and making
tradeoffs between multiple, often conflicting
objectives
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capacity cost vs. wait time
 Obtaining and using data
 Organization and political constraints and pressures
 Uncertainty and variability
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Let’s have a little fun with uncertainty
Call Center Example Revisited
Using a Descriptive Queueing Model
So, what’s so hard about it?
Given these
(1) Inputs
Parameter
Arrival Rate of Calls
Average Call Length
Number of Staff
Units
Symbol
calls/hour
a
hours/call
b
people/hour
c
Predict these
(2) Queueing Model(s)
Mathematical equations or
simulation model
(3) Outputs
Performance Measure
Units
Symbol
Expected Wait Time in Queue
Probability of Waiting in Queue
Probability of Waiting in Queue
less than t seconds
hours
#N/A
E[W q ]
P[W q >0]
#N/A
P[W q £ t]
The Grossly Simplified Scheduling Problem
Number of Employees Needed
Daily Staffing Requirments
20
19
17
16
15
15
14
13
11
10
Required Staffing
5
0
Mon
T ue
Wed
T hu
Fri
Sat
Sun
• Staff works 5
consecutive days
• Can start any day of the
week
• Ex: T, W, Th, F, Sa
• Objective
• Minimize total amount of staff needed
• By Finding
• Number of employees starting their 5-day workstretch
each day of the week
• Subject to constraints
• Daily staffing requirements are met
OR/MS History - WWII
 Summer of 1938 in England
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scientists and operations folks of the RAF working together on how
to use radio waves to track incoming planes for defensive purposes
 Similar collaborative, multidisciplinary, practical, problemdriven approach throughout WWII in U.S., GB, and Canada
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capacity/limitations of radar
night bombing and use of navigational assistance
finding and attacking submarines
artillery deployment, ship convoy configuration
many more... (some still classified or just unclassified)
Postwar OR/MS
 Belief that OR/MS could be applied in civil and business
domains
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1953 – Operational Research Society (GB)
1952 – Operations Research Society of America
1953 – The Institute of Management Sciences
Over 40 such societies by mid 1990’s
 Growing pains in the 1970’s as focus on the mathematics of
the OR seemed to take precedence over application
 State of IT in 70’s and 80’s made practical use of many
models extremely difficult
 OR/MS diffused throughout many organizations in
numerous industries
 ORSA/TIMS merges in mid 1990-s to become INFORMS
Golden Age for OR/MS in Business
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PC's are cheap and extremely powerful
Huge interest and investment in ERP and data warehousing in
business as people realize value of integration and of data
E-commerce making even more data electronically available
E-commerce exposing businesses to their customers in ways never
envisioned
The evolution of products like MS Excel and MS Access into very
capable platforms for end-user decision support activities
Many top business schools have created spreadsheet based modeling
courses
The field of operations research/management science is popping up in
general business publications and information systems publications as
its value is becoming more widely recognized
Wall Street has been hiring quants (math-jocks) to help create and
maintain the complex mathematical models driving investing today
Financial engineering, marketing engineering