Content uploaded by Bradley N Doebbeling
Author content
All content in this area was uploaded by Bradley N Doebbeling
Content may be subject to copyright.
Content uploaded by Bradley N Doebbeling
Author content
All content in this area was uploaded by Bradley N Doebbeling
Content may be subject to copyright.
Available via license: CC BY-NC 2.0
Content may be subject to copyright.
Available via license: CC BY-NC 2.0
Content may be subject to copyright.
Applying Systems Engineering Principles in Improving Health
Care Delivery
Renata Kopach-Konrad, MSc
2
, Mark Lawley, PhD
1
, Mike Criswell, MSN, RN, CCNS
3
,
Imran Hasan, MSc
2
, Santanu Chakraborty, MSc
2
, Joseph Pekny, PhD
4
,
and Bradley N. Doebbeling, MD, MSc
5,6,7
1
School of Biomedical Engineering, Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, IN, USA;
2
School of
Industrial Engineering, Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, IN, USA;
3
School of Nursing,
Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, IN, USA;
4
School of Chemical Engineering, Regenstrief
Center for Healthcare Engineering, Purdue University, West Lafayette, IN, USA;
5
Health Services Research & Development Center of
Excellence on Implementing Evidence-based Practice, Roudebush Veterans Affairs Medical Center, Indianapolis, IN, USA;
6
Indiana University
Center for Health Services and Outcomes Research, Regenstrief Institute, Inc., Indianapolis, IN, USA;
7
Department of Internal Medicine,
Indiana University School of Medicine, Indianapolis, IN, USA.
BACKGROUND: In a highly publicized joint report, the
National Academy of Engineering and the Institute of
Medicine recently recommended the systematic appli-
cation of systems engineering approaches for reforming
our health care delivery system. For this to happen,
medical professionals and managers need to under-
stand and appreciate the power that systems engineer-
ing concepts and tools can bring to redesigning and
improving health care environments and practices.
OBJECTIVE: To present and discuss fundamental
concepts and tools of systems engineering and impor-
tant parallels between systems e ngineering, health
services, and implementation research as it pertains to
the care of complex patients.
DESIGN: An exploratory, qualitative review of systems
engineering concepts and overview of ongoing applica-
tions of these concepts in the areas of hemodialysis,
radiation therapy, and patient flow modeling.
RESULTS: In this paper, we describe systems engineer-
ing as the process of identifying the system of interest,
choosing appropriate performance measures, selecting
the best modeling tool, studying model properties and
behavior under a variety of scenarios, and making
design and operational decisions for implementation.
CONCLUSIONS: We discuss challenges and opportuni-
ties for bringing people with systems engineering skills
into health care.
KEY WORDS: health care engineering; patient modeling; systems
approach; systems engineering; health services research.
J Gen Intern Med 22(Suppl 3):431–7
DOI: 10.1007/s11606-007-0292-3
© Society of General Internal Medicine 2007
INTRODUCTION
The U.S. hea lth care delivery system is poorly prepared to
meet the growing health care needs of its population.
1
Current limitations result in unexplained practice varia-
tions, gaps between evidence and practice, inequitable
patterns of utilization, unsusta ina ble cost increase s, and
poor safety.
2–4
Health care cos ts c onsume a growing pro-
portion of the economy, leaving the public, insurers, indus-
tries, and government straining under a financial burden.
Unnecessary services are provided far too often because
there is little coordination across sites or among providers,
5
yet care management, crossdisciplinary care, and preventive
care are ofte n unc overe d or poorly reimburs ed. Notably, 4 5%
of the U.S. population have chronic conditions requiring
care management. Of this population, 60 million, or roughly
half of those with chronic conditions, have multiple condi-
tions.
6
Current care delivery systems are not designed to
support the c are of these complex patients, which requires
multiple provide rs and services.
1
A recently published joint report from the National
Academy of Engineering (NAE) and Institute of Medicine
(IOM) a dvocated the widespread application of systems
engineering tools to improve health care delivery.
7
Systems
engineering focuses on coordina tion, synchronization, and
integration of co mplex sy stem s of pers onnel , informat ion,
materials, and financial resources.
8
This is achieved
through the application of mathematical modeling and
analysis techniques. Ove r the pas t 30 years , the c ontinuing
development and application of systems engineering meth-
ods has enabled the unprecedented growth in the manufac-
turing, logistics, distribution, and transportation sectors of
our economy.
7
Although drawing direct parallels between
other economic sec to rs and h ealth care delivery is pr oble m-
atic, many functions common to both have been significant-
ly improved in other sectors through engineering analysis.
These include inventory control and logistics, s cheduling,
operations management, project planning, facilities design,
process flow analysis, resource synchronization, e ngineering
economic analysis, and many others.
7
We believe that these
engineering approaches, properly mod ified and applied, can
provide similar high-level impacts in health care delivery.
Thus, o ur objective here is to provide an overview of key
systems engineering concepts and methods and explore
some illustrations of how these are being used to improve
health care delivery.
431
SYSTEMS ENGINEERING CONCEPTS
Systems engineering focuses on the design, control, and
orchestration of system activities to meet performance objec-
tives. Before discussing systems engineering tools and applica-
tions, we provide some basic definitions and key concepts.
Systems Definition and Operational Behavior
A system is a set of possibly diverse entities (patients, nurses,
physicians, etc.), each performing some set of functions. The
interaction of these entities as they perform their various
functions gives rise to a global system behavior.
System State, Resources, and Customers
The state of a system is an instantaneous snapshot of its
status. For example, the state of a medical/surgical unit in a
hospital might indicate that room 210 is occupied by patient
0901, wheelchair 11 is idle, and nurse 21 is on break. Thus,
room 210 is allocated to a patient, wheelchair 11 is available
for allocation, and nurse 21 is not currently available. Many
levels of resolution are possible in specifying the information
content of the system state.
System Operation, Event Occurrence, and State
Evolution
As a system operates, it moves from one state to the next
through the occurrence of enabled events. An event is enabled
when the preconditions for its occurrence are met. An event
occurs when its associated actions are performed. For exam-
ple, if an ICU has 30 beds, with 29 occupied and one available,
and there is a patient in the emergency department requesting
an ICU bed, then an ICU bed allocation event is enabled and
can be performed. This event is performed when the bed is
assigned to the patient. If another patient arrives needing an
ICU bed, the ICU bed allocation event is not enabled because a
bed is not available.
The sequence of states that the system traverses over some
time horizon is referred to as the state trace of the system.
There are many system state traces that might possibly evolve
and because of the presence of uncontrollable events such as
walk-in or ambulance arrivals, the future state trace is not
always predictable or controllable.
System Performance Measures
From a systems engineering perspective, a performance mea-
sure is a statistic computed from information in a given system
trace. Examples of performance measures could be number of
patients waiting for at least 30 minutes, the number of diabetic
patients who received an A1C blood test, or number of ICU
beds available during peak hours. System state traces that
yield desirable performance measures are preferable to those
that do not.
Event-based Systems
Health care delivery systems are examples of event-based
systems. The states of these systems evolve with the occurrence
of enabled events. The occurrence of events often initiates work
actions or physical processes (such as “deliver radiation
treatment”) that consume time. Typically, the time interval
between consecutive events appears random.
Systems Modeling
Systems modeling is the activity of identifying the most
relevant system characteristics and representing them in a
mathematical model. The model is then analyzed to learn
about and improve the behavior of the original system.
This process is significantly different from the hypothesis-
based clinical trial mode of research prevalent in medical
resea rch .
With this background, we now describe the process a
systems engineer undertakes in a health care improvement
project. There are six fundamental steps, which are conducted
iteratively. Each of the six will be briefly described.
1. Define system purpose and scope, specify required func-
tions and resource types, and develop relevant performance
measures along with desired performance thresholds.
2. Specify, collect, and develop required data through data
collection methods.
3. Design, validate, and verify appropriate system models.
This involves selecting the right modeling tools, building
and validating the model.
4. Use the model to learn about system behavior to find the
best des ign alternati ve. The engineer of ten deve lops
appropriate experiments for the studying the model and
analyzing the results.
5. Use the results of step 4 to determine how to configure the
system for best performance. This involves specifying
equipment requirements, staffing levels and patterns,
scheduling procedures, workflows, and so forth. Sensitiv-
ity analysis is also important to determine how system
performance will be affected by perturbations to nominal
conditions.
6. Develop implementation and evaluation plans and coordi-
nate their performance.
With these six steps in mind, some of the most important
engineering methods are listed below with a brief explanation.
Many of these originate from the d iscipl ine of Industrial
Engineering, which is often synonymous with Systems Engi-
neering.
&
Project management models include project evaluation and
review technique (PERT) and critical path method (CPM)
techniques. These models capture task dependencies and
timing in the execution of a large project.
9
For example,
Endress et al.
10
uses the CPM to analyze patient and work
flow in an operating room environment.
&
Engineering economics and financial engineering models
are used to make cost-effective decisions on capital invest-
ments and portfolio optimization.
11,12
These methods are
useful in all steps. As an example, Steenstra et al.
13
uses
economic analysis techniques to analyze the effectiveness
of a multistage “return to work ” program for workers with
low back pain.
&
Statistical modeling is used to capture relationships,
patterns, correlations, and probabilistic structure in data.
In systems engineering, statistical methods are essential
432 Kopach-Konrad et al.: Systems Engineering Applied to Health Care JGIM
for input modeling and analysis in step 2, for any required
experimental design and analysis in step 4, and for many
quality control applications where a performance or quality
characteristic can be monitored and controlled over time.
Specific techniques include regression, design of experi-
ments, and statistical quality control.
14–16
As an example,
Parachoor et al.
17
uses statistical process control to
benchmark hospital performance indicators against peer
organizations.
&
Stochastic processes model the random nature of complex
systems and processes. These types of models can be used
to derive expected values and variances of performance
measures under a variety of conditions. They can also be
used to develop optimal decision policies, that is, decision-
making rules that optimize the expected system perfor-
mance while minimizing risk. Queuing models, Markov
chains, Brownian motion, and Markov decision processes
(MDP) are common types of stochastic models.
18
They are
usually applied in steps 3 and 4. Hauskrecht and Fraser
19
apply MDP to the treatment of heart disease.
&
Operations research models are well suited for optimal
resource allocation, determining how to cost effectively
distribute resources. Operations research methods also
provide the analytical foundation for important systems
applications such as patient flow, inventory control, and
scheduling. Common techniques include linear program-
ming, network flow analysis, and dynamic programming.
20
These models are most e ffective in steps 3 –5. As an
example, Brennan et al.
21
models pricing options, discount
rates, and organizational structure in regional health
information organizations.
&
Human factors models can be used to opti mize human
performance in complex systems. These models can
capture b oth cog nitive and ergonomi c conc erns a nd
include the area of human c omputer interactions.
22
These mod els support steps 3–5. As an ex ample, Koubek
et al.
23
provides a framework for proc ess usability
assessment.
&
Process flow models capture how work tasks need to be
sequenced, coordinated, and synchronized. Interleaving of
the work processes of system components allows the
modeling of resource competition and delays.
24
These
methods support steps 1–5. As an example, Gupta
et al.
25
uses flow modeling for capacity planning in cardiac
catheterization.
&
Discrete event simulation is perhaps the most commonly
applied systems engineering tool. In essence, these models
mimic system behavior in accelerated time. Simulation
models can easily capture a plethora of operational detail
and are easily used in experimentation.
26,27
Discrete-event
simulation is very useful for steps 3–5. Hung et al.
28
use
simulation to study patient flow in a pediatric emergency
department.
For additional discussion of systems engineering tools in
improving health care delivery, we refer the reader to the NAE/
IOM report
7
and to the recently published Handbook of
Operations Research/Management Science Applications in
Health Care, edited by Brandeau et al.
29
For more general
applications and differing perspectives on systems engineer-
ing, we refer the reader to the INCOSE website at http://www.
incose.org/.
EXAMPLE SYSTEMS ENGINEERING APPLICATIONS
TO HEALTH CARE DELIVERY
We now provide two examples of how the tools discussed above
are being applied in health care. The first focuses on medical
decision making whereas the second focuses on hospital
management.
Therapeutic Optimization
Therapeutic optimization models the social and clinical aspects
of an individual patient, treatment options , and relevant
environmental factors to customize a patient’streatment.
These models might use the patient’s age, physical mobility,
comorbidities, social support, and so forth in selecting the
optimal treatment. We now briefly discuss several examples.
Hemodialysis is a method for removing waste products from
the blood stream of renal failure patients. The procedure
requires an arteriovenous (AV) fistula/graft, which joins an
artery and vein. Maintaining AV access is a significant problem
for many hemodialysis patients because of progressive steno-
sis. Mean patency (state of being unblocked) of a native fistula
is about 3 years and approximately 18 months for a synthetic
graft. Studies show that early stenosis detection and treatment
intervention (e.g., angioplasty or surgery) can successfully
reduce the chances of thrombosis.
30,31
Stenosis increases the
pressure in the access by reducing its diameter, eventually
rendering it unusable. Figure 1 illustrates the growth of the
venous access pressure ratio (VAPR) with increasing stenosis
before and after intervention. AV patency can be modeled as an
optimal stopping time problem (a common problem in stochas-
tic processes) in which, the pressure in the access represents
the state of the system. Decisions include when to intervene
with treatment, when to prepare a new AV site, and when to
abandon the current site and move to the next, all with the
objective of optimizing patency and expected patient life.
Therapeutic optimization is being used in liver transplanta-
tion.
32
When a liver becomes available, the transplant surgeon
can either accept or reject it based on liver condition, patient
compatibility, and waiting list characteristics. This decision
can be modeled using Markov decision processes (MDP). These
models can be solved to find an optimal decision rule that
maximizes the expected transplant success. The MDP ap-
proach is also useful for timing liver transplants from living
donors to maximize patient life expectancy.
33
Schaefer et al.
34
review of MDP approaches for treatment modeling.
Therapeutic optimization is being used to optimize radiation
therapy in cancer treatment. In this treatment, a specified
amount of radiation needs to be applied in a localized
cancerous region. To achieve this, the radiation must pass
through healthy t issue, which it can damage. Radiation
treatment technologies have evolved significantly and are now
highly computerized so that very fine beam modulation is
possible. Optimization models have been developed for con-
trolling the modulation mechanism so that the cancerous
region receives the prescribed dose while collateral damage to
surrounding health tissue is minimized.
35–37
The methods
being applied here include mix-integ er a nd network flow
programming from operations research.
Therapeutic optimization has also been applied to model
kidney allocation problems,
38
HIV treatment,
39
seizure warn-
ings,
40
and vaccine protocols.
41
433Kopach-Konrad et al.: Systems Engineering Applied to Health CareJGIM
Hospital Operations Modeling
Large hospitals are highly complex systems that are poorly
understood, extremely costly, and rife with inefficiency. Be-
cause of this complexity, there are no detailed models that
capture the overall operation of these systems from a systems
engineering perspective. The authors of this paper are partner-
ing with health providers to develop event-based (Petri net
42,43
)
models of acute care hospitals. The objective is to analyze
decision policies for bed al location, pati ent transf er and
discharge, and staff scheduling to help reduce costs and
improve quality of care.
This type of work requires (1) the definition of the hospital
state, that is, the instantaneous state of hospital resources,
patients, and staff; (2) the events that move the hospital from
one state to the next; (3) projections on how patients will
continue to evolve through their care plans; (4) how patients
following care plans load hospital resources; and (5) surgical
schedules and projections capturing how patients arrive at the
hospital. Along with this, technology must be in place to
automatically monitor and update system state by tracking
patients and key resources.
At our partner’s facility, digital displays are coupled with
radiofrequency identification and other sensor and data entry
technologies to provide real-time patient tracking and bed
allocation information at a glance. Displays are mounted in
strategic positions within the hospital and in a centralized
control room where a bed controller focuses on managing
patient flow. Up-to-date information on where patients are,
how long they have been there, whether they are waiting, and,
if so, how long, is displayed and logged. Departmental state
sequences or traces are automatically saved to one or more
databases as patients are admitted, discharged, and trans-
ported throughout the system.
Much of the trace data are in the form of HL7 messaging (a
health care information systems message protocol
44
) between
the hospital information systems and the tracking software.
For each patient admitted, the information systems generate a
sequence of HL7 messages for postmedication orders, lab
requests, transfer and transportation orders, and so forth. In
fact, the set of HL7 messages generated for a given patient
represents much of what happens to the patient during the
patient’s stay. Because patient care plans for the individual
patient are rarely formally recorded, as such, they tend to
evolve with the patient stay, and exist in a piece-meal fashion
in the minds of physicians, nurses, and discharge planners.
One objective of this research is to make the care plans explicit
so they can be used for modeling hospital processes, workload
planning, and quality improvement. Figure 2 provides the
reconstructed care paths of two cardiac patients both requir-
ing percutaneous cardiovascular procedures. Their care paths
were rec ons truc ted from H L7 messages, although in the
interest of space, only the most significant events are illustrat-
ed. By comparing treatment paths o f th ese t wo pa tients
diagnosed under the same diagnosis-related group (DRG), it
is straightforward to identify the differences and similarities in
their treatments.
If patient care plans can be successfully reconstructed from
HL7 messaging, then the following benefits are likely. It will be
possible to very rapidly develop representative care plans for
classes of patients, which capture and reflect what is actually
happening to patients in the facility. The resource needs of
representative care plans can then be established, that is, for
each patient of a given type, it will be possible to use that
patient’s representative care plan to estimate the workload
that the patient imposes on the system. One very important
element of the hospital state can be modeled: The set of
patients current ly admitted and their residual care plans
Figure 1. Sample data for VAPR
434 Kopach-Konrad et al.: Systems Engineering Applied to Health Care JGIM
(what they have left to do). This will enable the development of
operational models that can provide insight into the short-
term behavior of the hospital, whether certain resources will be
overloaded in the short-term, or whether the hospital is likely
to go on a divert status. These models will then support
experimentation with new decision policies.
A sample Petri net model of a generic emergency department
is shown in Figure 3. The model explicitly represents the event
enabling and state transition structure of the system. Such
models support significant systems analysis activities. For
example, the model can be used to determine where the ED
is most resource-constrained and the impact of altering
resource levels. It can also be used to explicitly define divert
states and then, for any nondivert state, compute the number
and sequence of events that must occur to reach the nearest
divert state. Finally, the model can be used to identify circular
Figure 3. Sample Petri net model of a generic emergency department
Figure 2. Sample results for patient even traces for DRG 558
435Kopach-Konrad et al.: Systems Engineering Applied to Health CareJGIM
resource dependencies that can cause difficulties in system
operation.
Currently, we are developing similar models of all hospital
departments. We will then merge these models into one hospital
model that will capture resource dependencies that propagate
across the system. It will then be possible to explicitly see how
discharge policies affect daily operations in the ED and OR, and
how ED congestion and OR scheduling policies affect medical
surgical wards, ICU, and lab facilities over the short-term.
IMPEDIMENTS TO SYSTEMS ENGINEERING
APPROACHES IN HEALTH CARE
Although systems engineering holds great promise for improv-
ing health care delivery, there are significant challenges
impeding its acceptance. The NAE/IOM report
7
discusses the
following challenges, which help lay the groundwork for
implementation.
From the examples discussed above, it is clear that systems
engineering techniques have extensive data requirements. The
management of this data requires expensive integrated infor-
mation systems that are often not available in the health care
sector.
Reimbursement practices and regulations provide little
incentive for investing in quality and systems improvement.
Thus, managerial support for systems improvement can be
difficult to obtain. Indeed, there seems to be little understand-
ing of the characteristics that health care organizations need to
foster to enable adoption of systems engineering techniques
and solutions. One possible approach is to use appreciative
inquiry to help uncover enablers and motivators that will help
systems engineering methods gain wider acceptance.
45
Health care has a culture of rigid division of labor. This
functional compartmentalization does not optimally support
the application of tools that transcend and span functional
areas, especially when they induc e significant changes in
traditional relationships. Furthermore, because engineering
professionals have not traditionally had a presence in health
care delivery, the uncertainty associated with the role and
status of another functional area can spawn skepticism and
fear. This underscores the importance of considering social
and communication issues in the evaluation and implementa-
tion of any sociotechnical system.
46
Very few health care providers are trained to think analyt-
ically about how health care delivery should function. Thus, it
is often difficult for these professionals to appreciate the
contributions that systems engineering approaches can bring.
Conversely, engineering professionals often have little, if any,
education in health care delivery.
Fortunately, universities, professional organizations, gov-
ernment agencies, and philanthropic foundations are begin-
ning to act. In the future, we believe these organizations will
provide leadership, training, and funding opportunities.
SUMMARY AND CONCLUSION
In this paper, we described systems engineering as the process
of identifying the system of interest, choosing appropriate
performance measures, selecting the best mo deling tool,
studying model properties and behavior under a variety of
scenarios, and making design and operational decisions for
implementation. We covered the basic concepts and tools of
systems engineering and provided examples of ongoing work in
their application to complex patients. Potential impediments
were also discussed.
In conclusion, systems engineering approaches have been
instrumental in coordina ting the growth, operation, and
synchronization of many information-rich and technologically
complex economic sectors, most notably manufacturing,
transportation, and supply chain logistics. Whereas we are
excited about the f uture of health care engineering and
anticipate its ultimate success in helping to reengineer health
care delivery, we believe widespread success will only come
when a critical mass of health care organizations recognize its
value through concrete examples. Only then will these organi-
zations promote the organizational changes needed for its
adoption.
ACKNOWLEDGMENTS: This research was partially supported by
funding from the Department of Veterans Affairs, Veterans Health
Administration, and also partially supported by HSRD Center grant
no. HFP 04-148. The authors also appreciate the comments of
attendees of the VA State of the Art Conference on Complexity, the
comments of the SOTA and SGIM reviewers, and comments from
Steven Witz, PhD. The opinions expressed here are those of the
authors and do not necessarily reflect those of the Veterans Health
Administration.
Conflict of Interest: None disclosed.
Corresponding Author: Mark Lawley, PhD;School of Biomedical
Engineering, Regenstrief Center for Healthcare Engineering, Purdue
University, West Lafayette, IN, USA (e-mail: malawley@purdue.edu).
REFERENCES
1. Starfield B. Deficiencies in US medical care. JAMA. 2000;284(17):
2184–5.
2. McGlynn E, Asch S, Adams J, et al. The quality of health care delivered
to adults in the United States. N Engl J Med. 2003;348(26):2635–45.
3. American College of Physicians. The Advanced Medical Home: a
Patient-centered, Physician-guided Model of Health Care. United States:
American College of Physicians Monograph; 2005.
4. Kohn L, Corrigan J, Donaldson M. To Err is Human Building a Safer
Health System Committee on Quality of Health Care in America.
Washington DC: Institute of Medicine National Academy Press; 1999.
5. Murphy J, Chang H, Montgomery J, Rogers W, Safran D. The quality
of physician–patient relationships: patients experiences, 1996–1999. J
Fam Pract. 2001;50:123–9.
6. Wu S, Green A. A Projection of Chronic Illness Prevalence and Cost
Inflation. Santa Monica, Calif: RAND Health; 2000.
7. Proctor P, Compton WD, Grossman J, Fanjiang G. Building a Better
Delivery System: a New Engineering/Health Care Partnership, Commit-
tee on Engineering and the Health Care System. Washington DC:
National Academy of Engineering and Institute of Medicine, National
Academy Press; 2005.
8. Kossiakoff A, Sweet W. Systems Engineering Principles and Practice.
New York: Wiley; 2003.
9. Kerzner H. Project Management: A Systems Approach to Planning,
Scheduling, and Controlling, 8th edn. New Jersey: Wiley; 2003.
10. Endress A, Aydeniz B, Wallwiener D, Kurek R. The critical path
method to analyze and modify OR-workflow: integration of an image
documentation system. Minim Invasive Ther Allied Technol. 2006;15
(3):177–86
11. Park C. Fundamentals of Engineering Economics. New Jersey: Prentice-
Hall; 2003.
12. Neftci S. Principles of Financial Engineering. New York: Academic Press;
2004.
436 Kopach-Konrad et al.: Systems Engineering Applied to Health Care JGIM
13. Steenstra I, Anema J, van Tulder M, Bongers P, de Vet H, van
Mechelen W. Economic evaluation of a multi-stage return to work
program for workers on sick-leave due to low back pain. J Occup
Rehabil. 2006;16(4):557–78.
14. Ross S. Introductory Statistics, 2nd edn. New York: Academic Press;
2005.
15. Montgomery D. Design and Analysis of Experiments, 6th edn. New
Jersey: Wiley; 2004.
16. Montgomery D, Peck E, Vining G. Introduction to Linear Regression
Analysis, 3rd edn. New Jersey: Wiley-Interscience; 2001.
17. Parachoor S, Rosow E, Enderle A. Knowledge management system for
benchmarking performance indicators using statistical process control
(SPC) and virtual instrumentation (VI). Biomed Sci Instrum. 2003;39:175–8.
18. Durrett R. Essentials of Stochastic Processes. New York: Springer; 2001.
19. Hauskrecht M, Fraser H. Planning treatment of ischemic heart disease
with partially observable Markov decision processes. Artif Intell Med.
2000;18(3):221–44. Mar
20. Rardin R. Optimization in Operations Research. New Jersey: Prentice-
Hall; 1997.
21. Brennan P, Ferris M, Robinson S, Wright S, Marquard J. Modeling
participation in the NHII: operations research approach. AMIA Annu
Symp Proc. 2005;76–80.
22. LehtoM,BuckJ,BuckM.Introduction to Human Factors and
Ergonomics for Engineers. Hillsdale, NJ: Lawrence Erlbaum Associates;
2007.
23. Koubek R, Benysh D, Buck M, Harvey C, Reynolds M. The develop-
ment of a theoretic framework and design tool for process usability
assessment. Ergonomics 2003;46(1–3):220–41.
24. vanderAalst W, vanHee K. Workflow Management: Models, Methods,
and Systems. Cambridge, Mass: The MIT Press; 2004.
25. Gupta D, Natarajan M, Gafni A, Wang L, Shilton D, Holder D, Yusuf S.
Capacity planning for cardiac catheterization: a case study. Health
Policy. 2007;82(1):1–11.
26. Law A, Kelton D. Simulation Modeling and Analysis, 3rd edn. London:
McGraw-Hill; 1999.
27. Kelton D, Sadowski R, Sturrock D. Simulation with Arena, 4th edn.
London: McGraw-Hill; 2007.
28. Hung G, Whitehouse S, O’Neill C, Gray A, Kissoon N. Computer
modeling of patient flow in a pediatric emergency department using
discrete event simulation. Pediatr Emerg Care 2007;23(1):5–10.
29. Brandeau M, Sainfort F, Pierskalla W, eds. Handbook of Operations
Research/Management Science Applications in Health Care. Dordrecht:
Kluwer Academic Publishers; 2004.
30. Sullivan KL, Besarab A, Bonn J, Shapiro MJ, Gardiner GA, Moritz MJ.
Hemodynamics of Failing Dialysis Grafts. J Radiol. 1993;186(3):867–72.
31. Kaye M, Baird C, McCloskey B, Oscar G, D’Avirro M. Two years
sequential hemodynamic data on polytetrafluoroethylene (PTFE) grafts
used for hemodialysis. Proc Eur Dial Transplant Assoc. 1979;16:266–
71
32. Alagoz O, Maillart L, Schaefer A , Robe rts, M. Determining the
acceptance of cadaveric livers using an implicit model of the waiting list.
Oper Res. 2007;55(1):24– 36.
33. Alagoz O, Maillart L, Schaefer A, Roberts M. The optimal timing of
living-donor liver transplantation. Manage Sci. 2004;50(10):1420–30.
34. Schaefer A, Bailey M, Shechter S, Roberts M. Medical decisions using
Markov decision processes. In: Sainfort F, Brandeau M, Pierskalla W, eds.
Handbook of Operations Research/Management Science Applications in
Health Care. Dordrecht: Kluwer Academic Publishers, 2004;597–616.
35. Shepard D, Ferris C, Olivera G, Mackie T. Optimizing the delivery of
radiation therapy to cancer patients. SIAM Rev. 1999;41(4):721–44.
36. Rardin R, Preciado-Walters F, Langer F, Thai V. Column generation for
IMRT cancer therapy optimization with implementable segments. Ann
Oper Res. 2006;148(1):65–79.
37. Rardin R, Preciado-Walters F, Langer F, Thai V. A coupled column
generation, mixed-integer approach to optimal planning of intensity
modulated radiation therapy for cancer. Math Program. 2004;101:319–38.
38. Su X, Zenios S. Patient choice in kidney allocation: a sequential
stochastic assignment model. Oper Res. 2005;53(3):443–55.
39. Jeffrey A, Xia X, Craig I. When to initiate HIV therapy: a control
theoretic approach. IEEE Trans Biomed Eng. 2003;50(11):1213–20.
40. Pardalos P, Chaovalitwongse W, Iasemidis L, et al. Seizure warning
algorithm based on optimization and nonlinear dynamics. Math Program
Ser B. 2004;101:365–85.
41. Wu J, Wein L, Perelson A. Optimization of influenza vaccine selection.
Oper Res. 2005;53(3):456–76.
42. Peterson J. Petri Net Theory and the Modeling of Systems. New Jersey:
Prentice-Hall; 1981.
43. Haas P. Stochastic Petri Nets Modeling, Stability, Simulation. New York:
Springer; 2002.
44. American National Standards Institute. HL7 Version 3 Normative
Edition, Health Level Seven. United States: American National Stan-
dards Institute; 2006.
45. Cooperrider D, Whitney D. Appreciative Inquiry. San Francisco, CA:
Berrett-Koehler Communications; 1999.
46. Goldstein M, Coleman R, Tu S, et al.Translating research into practice:
sociotechnical integration of automated decision support for hypertension
in three medical centers. J Am Med Inform Assoc. 2004;11(5):368 –76.
437Kopach-Konrad et al.: Systems Engineering Applied to Health CareJGIM
