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