HCM 540 – Healthcare Operations Management

1. HCM 540 – Healthcare Operations Management Analyzing, Designing and Managing Healthcare Operations and Supporting Managerial Decision Making

2. 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

3. 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

4. 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

5. 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

6. 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

7. 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

8. Donabedian’s Quality Triad • Our course will have two primary areas of focus: • supporting managerial decision making • analyzing and managing business process flows • Structure • facility planning • staffing • operational policies • Process • process physics – arrival, delays, service • patient, work, information flow • Outcome Donabedian, A. Med Care Rev (Medical care review.) 1980 Fall; 37(7): 653-98

9. 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)

10. 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 So, what are your tough operational decision problems?

11. 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?

12. 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

13. Supporting Managerial Decision Making with Decision Technology Information Systems Quantitative Methods Critical Link • Planning • Operational analysis, design and control Operations research Management science Operations Management

14. 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

15. 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

16. 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 A Few OR/MS Applications The OR/MS Toolbox • Statistics • Computer simulation • Queuing models • Forecasting • Decision analysis • Optimization • Computer programming • Spreadsheets • Databases • IT • Business domain knowledge

17. 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.

18. 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 Management Engineering @ WBH and HFHS

19. OR/MS for Healthcare Managers, Decision Makers, Administrators • 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) • 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...

20. 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

21. 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?)

22. 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

23. 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

24. Call Center Example RevisitedUsing a Descriptive Queueing ModelSo, what’s so hard about it? Given these (1) Inputs Predict these (2) Queueing Model(s) Mathematical equations or simulation model (3) Outputs

25. 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

26. 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)

27. 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

28. Golden Age for OR/MS in Business • 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