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HCM 540 – Healthcare Operations Management
Analyzing, Designing and Managing Healthcare
Operations and Supporting Managerial Decision Making
First Class Overview
Introductions Course overview and administration
Syllabus and course webs Schedule of topics
Overview of operations analysis/modeling/decision support Preview of modeling applications sprinkled in
Lab session 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 Build models and analyze systems, create analytical tools and databases,
present to executive leadership, support managerial decision making Simulation modeling
Critical care tower, Ptube systems, outpatient clinics, emergency centers, inpatient obstetrics, many more
Staffing and scheduling numerous ancillary services, nursing tactical staff scheduling optimization models
Database and decision support system development chest pain, lab courier routing, data mart for operations analysis
Various statistical and operations analysis studies inpatient occupancy surgical utilization and capacity allocation
Syllabus and Course Webshttp://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 Detroit Health Care Stabilization Workgroup Debate on CON and transfer of beds
Groups for healthcare improvement Institute for Healthcare Improvement Agency for Healthcare Research and Quality The Leapfrog Group
Donabedian’s Quality Triad
Structure facility planning staffing operational policies
Process process physics – arrival, delays, service patient, work, information flow
Outcome
Our course will have two primary areas of focus:
(1) supporting managerial decision making
(2) analyzing and managing business process flows
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 Ex: diagnosis vs. lab testing
High level of direct customer contact and participation Ex: ER, OB, call-centers, nursing care, check-in/out
The products and services delivered are often complex and difficult to define and measure
Ex: classifying hospital patient types Demand has large component of uncertainty
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 beds staff time diagnostic and treatment
equipment scheduling
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
material handling and management
routing/distribution
Example 1: Staffing the Centralized Appointment Center
A Common Problem: 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
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
InformationSystems
QuantitativeMethods
OperationsManagement
Planning Operational analysis,
design and control
Operations researchManagement science
Critical Link
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 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 Statistics Computer simulation Queuing models Forecasting Decision analysis Optimization
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 productivity and staffing, admissions scheduling, staff scheduling, facility
planning, medical decision making, patient flow modeling Management (industrial) engineering (ME) departments
internal management consultants crisis for the field in the late 80’s role continues to evolve
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 historically corporate but
currently split between RO and Troy
reports to CFO formerly shared building with
IS 10-15 engineers and data techs
IE’s, clinical managers operations analysis,
facilitation, simulation, JCAHO, decision support
Mgt. Services @ HFHS 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 IE’s, physician, MHA, clinical
managers, academics
OR/MS for Healthcare Managers, Decision Makers, Administrators
1. Intelligent consumer perspective• 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)
2. End-user modeler perspective• 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
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 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
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 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
The scheduling challenge Complex relationship between variables
the physics of healthcare processes and services Difficulty quantifying outcomes and making
tradeoffs between multiple, often conflicting objectives capacity cost vs. wait time
Obtaining and using data Organization and political constraints and pressures Uncertainty and variability
Let’s have a little fun with uncertainty
Call Center Example RevisitedUsing a Descriptive Queueing Model
So, what’s so hard about it?
Parameter Units SymbolArrival Rate of Calls calls/hour aAverage Call Length hours/call bNumber of Staff people/hour c
Mathematical equations or simulation model
(2) Queueing Model(s)
(1) Inputs
Performance Measure Units Symbol
Expected Wait Time in Queue hours E[W q ]
Probability of Waiting in Queue #N/A P[W q >0]Probability of Waiting in Queue less than t seconds #N/A P[W q
t]
(3) Outputs
Given these
Predict these
The Grossly Simplified Scheduling Problem
• 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
Daily Staffing Requirments
17
1315
19
1416
11
0
5
10
15
20
Mon Tue Wed Thu Fri Sat SunNu
mbe
r of
Em
ploy
ees
Nee
ded
Required Staffing
OR/MS History - WWII
Summer of 1938 in England 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, problem-driven approach throughout WWII in U.S., GB, and Canada
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
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
1. PC's are cheap and extremely powerful 2. Huge interest and investment in ERP and data warehousing in business as people
realize value of integration and of data 3. E-commerce making even more data electronically available 4. E-commerce exposing businesses to their customers in ways never envisioned 5. The evolution of products like MS Excel and MS Access into very capable
platforms for end-user decision support activities 6. Many top business schools have created spreadsheet based modeling courses 7. 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
8. Wall Street has been hiring quants (math-jocks) to help create and maintain the complex mathematical models driving investing today
9. Financial engineering, marketing engineering