Table Of ContentSpringerBriefs in Health Care Management
and Economics
For further volumes:
http://www.springer.com/series/10293
Alexander Kolker
Healthcare Management
Engineering: What Does
This Fancy Term Really
Mean?
The Use of Operations Management
Methodology for Quantitative
Decision-Making in Healthcare Settings
Alexander Kolker
Children’s Hospital and Health System
Milwaukee, WI 53201, USA
[email protected]
ISBN 978-1-4614-2067-5 e-ISBN 978-1-4614-2068-2
DOI 10.1007/978-1-4614-2068-2
Springer New York Dordrecht Heidelberg London
Library of Congress Control Number: 2011941803
© Alexander Kolker 2012
All rights reserved. This work may not be translated or copied in whole or in part without the written
permission of the publisher (Springer Science+Business Media, LLC, 233 Spring Street, New York,
NY 10013, USA), except for brief excerpts in connection with reviews or scholarly analysis. Use in
connection with any form of information storage and retrieval, electronic adaptation, computer software,
or by similar or dissimilar methodology now known or hereafter developed is forbidden.
The use in this publication of trade names, trademarks, service marks, and similar terms, even if they
are not identifi ed as such, is not to be taken as an expression of opinion as to whether or not they are
subject to proprietary rights.
Printed on acid-free paper
Springer is part of Springer Science+Business Media (www.springer.com)
You cannot do today’s job with yesterday’s
methods and be in business tomorrow.
Anonymous
Hunch and intuitive impressions are
essential for getting the work started,
but it is only through the quality of the
numbers at the end that the truth can be told.
S. Glantz, Primer of Biostatistics, 2005.
McGraw-Hill/L. Thomas, Biostatistics in
Medicine. Science, 198: 675, 1977
Preface
This Brief Series book illustrates in depth a concept of healthcare management
engineering and its domain for hospital and clinic operations. Predictive and ana-
lytic decision-making power of management engineering methodology is system-
atically compared to traditional management reasoning by applying both side by
side to analyze 26 concrete operational management problems adapted from hospi-
tal and clinic practice. The problem types include: clinic, bed, and operating rooms
capacity; patient fl ow; staffi ng and scheduling; resource allocation and optimiza-
tion; forecasting of patient volumes and seasonal variability; business intelligence
and data mining; and game theory application for allocating cost savings between
cooperating providers.
Detailed examples of applications are provided for quantitative methods such as
discrete event simulation, queuing analytic theory, linear and probabilistic optimi-
zation, forecasting of a time series, principal component decomposition of a dataset
and cluster analysis, and the Shapley value for fair gain sharing between cooperat-
ing participants. A summary of some fundamental management engineering prin-
ciples is provided.
The goal of the book is to help to bridge the gap in mutual understanding and
communication between management engineering professionals and hospital and
clinic administrators.
The book is intended primarily for hospital/clinic leadership who are in charge of
making managerial decisions. This book can also serve as a compendium of intro-
ductory problems/projects for graduate students in Healthcare Management and
Administration, as well as for MBA programs with an emphasis in Health care.
What Is This Book About?
Modern medicine has achieved great progress in treating individual patients. This
progress is based mainly on life science (molecular genetics, biophysics, and bio-
chemistry) and the development of medical devices, drugs, and imaging technology.
However, according to the highly publicized report “B uilding a Better Delivery
System: A New Engineering/Healthcare Partnership ” published jointly by the
National Academy of Engineering and Institute of Medicine, relatively little mate-
rial resources and technical talent have been devoted to the proper functioning of the
vii
viii Preface
overall healthcare delivery as an integrated system in which access to effi cient care
is delivered to many thousands of patients in an economically sustainable way (Reid
et al. 2005).
A system is generally defi ned as a set of interconnected elements-subsystems
(objects and/or people) that form a complex whole that behaves in ways that these
elements acting independently would not. The system boundaries can be defi ned at
different levels (scales). For example, a healthcare system can be defi ned at the
nationwide level; in this case, the main interdependent and connected elements of
the system are separate hospitals and large clinics and/or their networks, insurance
companies, government bodies, such as the center for Medicaid/Medicare services
(CMS), etc.
At a lower level, a system can be defi ned as a stand-alone hospital; in this case
the main interdependent and connected elements of the system are hospital depart-
ments, such as emergency, surgical, intensive care, etc.
Management engineering methodology can be applied at all system levels
(scales). However, specifi c method can be different depending on the system scale
and complexity. For example, system dynamics that operates mostly with mac-
rolevel patient volumes, large-scale patient categories, and large fi nancial fl ows and
allocations can be an appropriate method to analyzing the nationwide healthcare
system and analysis of policy issues. On the other hand, such a powerful methodol-
ogy as discrete event simulation that operates mostly with individual patients as
entities can be more appropriate to analyzing operations at lower scale systems such
as a separate hospital. At the same time, the separate hospital, despite a lower scale
level, is a complex system in itself, comprised of many interdependent departments
and units.
The focus of this book is the application of management engineering principles
and methodologies on the scale of a separate stand-alone hospital or a large clinic.
The report (Reid et al. 2005) referenced above provides strong convincing argu-
ments that a real impact on quality, effi ciency, and sustainability of the healthcare
system can be achieved by the systematic and widespread use of methods and prin-
ciples of system engineering or healthcare management engineering. Lawrence
(2010) strongly supports this view, “The opportunities for improvement are substan-
tial; the techniques of industrial engineering can drive large improvements in effi -
ciency and quality and safety when applied within the right context…,” and further,
“…As we improve performance, innovations will continue around the sick care sys-
tem, … in turn enabling the tools of industrial design to have their greatest impact.”
Thus, the scope of healthcare management engineering fi eld can broadly be
defi ned as a systematic way of developing managerial decisions for effi cient allocating
of material, human, and fi nancial resources needed for delivery of high-quality care
using the various mathematical and computer simulation methods. (The term “man-
agement engineering” is sometimes substituted by the terms “operations research,”
“system engineering,” “industrial engineering,” “operations management”, or “man-
agement science.” All these terms have a similar meaning).
Management engineering methodology has become indispensable in addressing
pressing hospital issues, such as:
Preface ix
– Capacity: How many beds are required for a department or unit? How many
procedure rooms, operating rooms or pieces of equipment are needed for differ-
ent services?
– Staffi ng: How many nurses, physicians, and other providers are needed for a
particular shift in a unit (department) in order to best achieve operational and
service performance objectives?
– Scheduling: What are the optimized staff schedules that help not only in deliver-
ing a safe and effi cient care for patients, but also take into account staff prefer-
ences and convenience?
– Patient fl ow: What patient wait time is acceptable (if any at all) at the service
stations in order to achieve the system throughput goals?
– Resource allocation: Is it more effi cient to use specialized resources or pooled
(interchangeable) resources (operating/procedure rooms, beds, equipment, and
staff)? Does it make economic sense to keep some patient service lines (or drop
them at all)?
– Forecasting: How to forecast the future patient volumes (demand) or transaction
volumes for short- and long-term budget planning and other planning purposes?
– Optimized geographic location of facilities and facilities layout.
– Design of the facility optimized workfl ow.
– Defi ning and measuring staff productivity.
– Optimizing a supply chain and inventory management.
– Extracting useful information from raw data sets for marketing and budget plan-
ning using Business Intelligence and Data Mining.
This list can easily be extended to include any other area of operational manage-
ment that requires quantitative analysis to justify decision-making.
The ultimate goal and the holy grail of management engineering methodology is
to provide an aid and guidance to effi ciently managing hospital operations, i.e.,
reducing the costs of using resources for delivery of care while keeping high safety
and outcome standards for patients.
It can be said that the entire hospital is a patient; management engineering meth-
odology is a medical fi eld with different specialties developed to address different
conditions and operational problems; and the management engineer serves as a doc-
tor who diagnoses the operational disease and develops a treatment plan for an ail-
ing hospital and its operations.
No concept or methodology can truly be convincing without multiple concrete
and practically relevant examples of its application. Kopach-Konrad et al. (2007)
state, “…we believe that widespread success will only come when a critical mass of
health care organizations recognize its [healthcare engineering] value through con-
crete examples. Only then will these organizations promote changes needed for its
adoption.” The author of this book shares this belief.
In this book traditional managerial decision-making and management engineering
methodology are applied side by side to analyze 26 concrete operational manage-
ment problems adapted from a hospital and clinic practice. The focus is on explain-
ing why management engineering results are often different from the typical,
x Preface
traditional “common sense” management approach. The problems included in this
book are somewhat simplifi ed and adapted to focus on the fundamental principles
of quantitative decision-making. However, even simplifi ed, most of the problems
are not trivial.
It is not possible to illustrate all of the above-mentioned management engineer-
ing applications in one Brief Series book. This book covers detailed examples of
fi ve types of problems.
The fi rst type is one of the most practically important and widespread problems
of dynamic supply and demand balance. This includes 11 capacity and patient fl ow
problems and 5 staffi ng and scheduling problems. It is widely acknowledged that
the most powerful and versatile methodology for analyzing these kinds of problems
is discrete event simulation (DES).
At the same time, queuing analytic theory (QAT) is often recommended as a
means of analyzing hospital capacity, patient fl ow, and staffi ng issues (Litvak 2007;
McManus et al. 2004; Haraden et al. 2003). However, such a recommendation under-
estimates some serious practical limitations of QA theory for hospital applications
(D’Alesandro 2008) but overestimates diffi culties of using DES. These two method-
ologies (QAT and DES) are applied to the same problems (along with a traditional
managerial approach) to demonstrate side by side the pros and cons of each.
Discrete event simulation software package used to develop example models in
this book is ProcessModel 5.3.0 (Process Model, Inc., http://www.processmodel.
com ). However practically any other high-level commercially available DES pack-
age can also be used, such as ProModel, Arena, Simul8, AnyLogic, Simio, FlexSim
and many others. All provide a user-friendly graphical interface that makes the
efforts of building realistic simulation models no more demanding than the efforts
to make simplifi cations, adjustments, and calibrations to develop rather complex but
limited analytic queuing models.
Swain (2007), Abu-Taieh and El Sheikh (2007), Hlupic (2000), and Nikoukaran
(1999) provided a review and a comparative study of dozens of commercially avail-
able discrete event simulation packages.
The second type of problem includes fi ve problems for the linear and probabilis-
tic resource optimization and allocation. This section includes linear optimization
of patient service volumes for different service lines, optimal staffi ng for 24/7 three-
shift operations, physician resident scheduling to meet Institute of Medicine (IOM)
new restricted resident work hours for day and night shifts, optimized specimen
mass screening testing aimed at reducing the overall number of tests per specimen,
and the projection of the expected number of patients discharged from the Emergency
Department given the time that a patient has already stayed in ED (the use of the
concept of the conditional probability of discharge).
The third type of problem includes two problems for forecasting of a time series
using past data points. It is argued that the past data points used for forecasting the
future data points should be strongly correlated to each other. It is illustrated that the
strongly correlated past data points can be identifi ed from the autocorrelation func-
tion of the time series. It is further illustrated that a powerful forecasting procedure
for the time series can be a recursive technique. Its application is demonstrated
