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Applying Systems Engineering Principles in Improving Health Care Delivery


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In a highly publicized joint report, the National Academy of Engineering and the Institute of Medicine recently recommended the systematic application of systems engineering approaches for reforming our health care delivery system. For this to happen, medical professionals and managers need to understand and appreciate the power that systems engineering concepts and tools can bring to redesigning and improving health care environments and practices. To present and discuss fundamental concepts and tools of systems engineering and important parallels between systems engineering, health services, and implementation research as it pertains to the care of complex patients. An exploratory, qualitative review of systems engineering concepts and overview of ongoing applications of these concepts in the areas of hemodialysis, radiation therapy, and patient flow modeling. In this paper, we describe systems engineering 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. We discuss challenges and opportunities for bringing people with systems engineering skills into health care.
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Applying Systems Engineering Principles in Improving Health
Care Delivery
Renata Kopach-Konrad, MSc
, Mark Lawley, PhD
, Mike Criswell, MSN, RN, CCNS
Imran Hasan, MSc
, Santanu Chakraborty, MSc
, Joseph Pekny, PhD
and Bradley N. Doebbeling, MD, MSc
School of Biomedical Engineering, Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, IN, USA;
School of
Industrial Engineering, Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, IN, USA;
School of Nursing,
Regenstrief Center for Healthcare Engineering, Purdue University, West Lafayette, IN, USA;
School of Chemical Engineering, Regenstrief
Center for Healthcare Engineering, Purdue University, West Lafayette, IN, USA;
Health Services Research & Development Center of
Excellence on Implementing Evidence-based Practice, Roudebush Veterans Affairs Medical Center, Indianapolis, IN, USA;
Indiana University
Center for Health Services and Outcomes Research, Regenstrief Institute, Inc., Indianapolis, IN, USA;
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):4317
DOI: 10.1007/s11606-007-0292-3
© Society of General Internal Medicine 2007
The U.S. hea lth care delivery system is poorly prepared to
meet the growing health care needs of its population.
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.
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,
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-
Current care delivery systems are not designed to
support the c are of these complex patients, which requires
multiple provide rs and services.
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.
engineering focuses on coordina tion, synchronization, and
integration of co mplex sy stem s of pers onnel , informat ion,
materials, and financial resources.
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.
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.
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.
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
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
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-
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.
For example,
Endress et al.
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.
These methods are
useful in all steps. As an example, Steenstra et al.
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.
As an example,
Parachoor et al.
uses statistical process control to
benchmark hospital performance indicators against peer
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.
They are
usually applied in steps 3 and 4. Hauskrecht and Fraser
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.
These models are most e ffective in steps 3 5. As an
example, Brennan et al.
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.
These mod els support steps 35. As an ex ample, Koubek
et al.
provides a framework for proc ess usability
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.
methods support steps 15. As an example, Gupta
et al.
uses flow modeling for capacity planning in cardiac
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.
simulation is very useful for steps 35. Hung et al.
simulation to study patient flow in a pediatric emergency
For additional discussion of systems engineering tools in
improving health care delivery, we refer the reader to the NAE/
IOM report
and to the recently published Handbook of
Operations Research/Management Science Applications in
Health Care, edited by Brandeau et al.
For more general
applications and differing perspectives on systems engineer-
ing, we refer the reader to the INCOSE website at http://www.
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
Therapeutic Optimization
Therapeutic optimization models the social and clinical aspects
of an individual patient, treatment options , and relevant
environmental factors to customize a patientstreatment.
These models might use the patients 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.
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-
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.
Schaefer et al.
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.
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,
HIV treatment,
seizure warn-
and vaccine protocols.
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
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 partners 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
) 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
patients 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
patients 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
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.
Although systems engineering holds great promise for improv-
ing health care delivery, there are significant challenges
impeding its acceptance. The NAE/IOM report
discusses the
following challenges, which help lay the groundwork for
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
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.
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.
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.
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
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
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:
1. Starfield B. Deficiencies in US medical care. JAMA. 2000;284(17):
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):263545.
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 physicianpatient relationships: patients experiences, 19961999. J
Fam Pract. 2001;50:1239.
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
11. Park C. Fundamentals of Engineering Economics. New Jersey: Prentice-
Hall; 2003.
12. Neftci S. Principles of Financial Engineering. New York: Academic Press;
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):55778.
14. Ross S. Introductory Statistics, 2nd edn. New York: Academic Press;
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;39:1758.
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):22144. 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;7680.
22. LehtoM,BuckJ,BuckM.Introduction to Human Factors and
Ergonomics for Engineers. Hillsdale, NJ: Lawrence Erlbaum Associates;
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(13):22041.
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):111.
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, ONeill 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):510.
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):86772.
31. Kaye M, Baird C, McCloskey B, Oscar G, DAvirro M. Two years
sequential hemodynamic data on polytetrafluoroethylene (PTFE) grafts
used for hemodialysis. Proc Eur Dial Transplant Assoc. 1979;16:266
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):2436.
33. Alagoz O, Maillart L, Schaefer A, Roberts M. The optimal timing of
living-donor liver transplantation. Manage Sci. 2004;50(10):142030.
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;597616.
35. Shepard D, Ferris C, Olivera G, Mackie T. Optimizing the delivery of
radiation therapy to cancer patients. SIAM Rev. 1999;41(4):72144.
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):6579.
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;101:31938.
38. Su X, Zenios S. Patient choice in kidney allocation: a sequential
stochastic assignment model. Oper Res. 2005;53(3):44355.
39. Jeffrey A, Xia X, Craig I. When to initiate HIV therapy: a control
theoretic approach. IEEE Trans Biomed Eng. 2003;50(11):121320.
40. Pardalos P, Chaovalitwongse W, Iasemidis L, et al. Seizure warning
algorithm based on optimization and nonlinear dynamics. Math Program
Ser B. 2004;101:36585.
41. Wu J, Wein L, Perelson A. Optimization of influenza vaccine selection.
Oper Res. 2005;53(3):45676.
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
... Documentation templates also were an important tool for caring for suspected COVID-19 patients and rapidly evolved as knowledge developed about presenting symptoms, management, and isolation procedures; this process of using "dynamic templates" in times of clinical uncertainty also is important for other public health exigencies. Use of systems engineering to improve primary care also remains underutilized for understanding and designing reliable care processes (Kopach-Konrad et al., 2007;Pronovost et al., 2017;Watts et al., 2013). As illustrated, process maps and failure analysis can help study performance and identify improvement opportunities in processes such as these; related design and modeling methods can help generate further insights and develop more reliable and efficient processes (Benneyan, 1997;Kopach-Konrad et al., 2007). ...
... Use of systems engineering to improve primary care also remains underutilized for understanding and designing reliable care processes (Kopach-Konrad et al., 2007;Pronovost et al., 2017;Watts et al., 2013). As illustrated, process maps and failure analysis can help study performance and identify improvement opportunities in processes such as these; related design and modeling methods can help generate further insights and develop more reliable and efficient processes (Benneyan, 1997;Kopach-Konrad et al., 2007). ...
COVID-19 necessitated significant care redesign, including new ambulatory workflows to handle surge volumes, protect patients and staff, and ensure timely reliable care. Opportunities also exist to harvest lessons from workflow innovations to benefit routine care. We describe a dedicated COVID-19 ambulatory unit for closing testing and follow-up loops characterized by standardized workflows and electronic communication, documentation, and order placement. More than 85% of follow-ups were completed within 24 hours, with no observed staff, nor patient infections associated with unit operations. Identified issues include role confusion, staffing and gatekeeping bottlenecks, and patient reluctance to visit in person or discuss concerns with phone screeners.
... Systems engineering tools and methods are highly important to advance healthcare service delivery [4]. In general, "systems engineering is concerned with the design, control, and orchestration of system activities to meet performance objectives" [4]. ...
... Systems engineering tools and methods are highly important to advance healthcare service delivery [4]. In general, "systems engineering is concerned with the design, control, and orchestration of system activities to meet performance objectives" [4]. Several systems engineering tools, such as Lean, Six Sigma, Kaizen, and the Work-Out methodology, were proven effective in many healthcare settings. ...
Conference Paper
This paper presents a conceptual framework for using systems thinking to solve cross-departmental problems in a healthcare setting. The General Electric Co. (GE) Work-Out methodology is adopted as an instance of systems engineering methodologies to coordinate the problem-solving journey by bringing all departments together to discuss the issue and generate solutions collectively. Two cases were examined: the period before applying the proposed framework (C1); and the period during the implementation of the framework (C2). The evaluation metrics were calculated for both cases and then compared. The results suggest that the proposed framework is powerful in reducing the resistance to the generated solutions because frontline employees were closely involved in the solution creation process. The estimated Acceptance of proposed solutions was 50% for C1 and 80% for C2. The estimated Resistance was 50% for C1 and 20% for C2. The number of meetings to generate solid solutions that can be implemented to solve the problem was three two-hour meetings in C2. C1 had many meetings scattered along a period of about five months. This paper focused on the solution generation process and the reduction of potential resistance among process stakeholders. However, to completely evaluate the approach, the implementation phase should be taken into consideration as well. This study demonstrated the role of systems thinking to effectively generate solutions for cross-departmental problems while minimizing the resistance among process stakeholders. By implementing the Work-Out methodology, common solutions for most of the process stakeholders were reached and approved for implementation.
... While failures to "close the loop" on concerning symptoms are an important challenge and vulnerability for busy primary care clinicians, these are exactly the types of process issues that lend themselves to systems engineering analysis and design approaches, increasingly advocated as a complement to other process improvement methods. [14][15][16][17][18] The field of systems engineering includes methods for analyzing, designing, and optimizing processes so they perform with high reliability across varied settings and populations. Engineers routinely talk of concepts and terms that are less familiar in healthcare process improvement but offer great potential (e.g., flow simplification, reduction of queues and push systems, non-value-added work, processes that assure loop closing, resilient systems that adapt and operate reliably under routine and novel stresses, and fault-recovery processes that minimize harm when failures occur nonetheless), and the application of systems engineering principles might help quality improvement efforts. ...
Reliable systems that track the continuation, progression, or resolution of a patient’s symptoms over time are essential for reliable diagnosis and ensuring that patients harboring more worrisome diagnoses are safely followed up. Given their first-contact role and increasing stresses on busy primary care clinicians and practices, new processes that make these tasks easier rather than creating more work for busy clinicians are especially needed. Some symptoms are sufficiently worrisome that they demand an urgent diagnosis and treatment while others result in a differential that can be more safely explored over time, or less differentiated and worrisome that they are best managed with the “test of time” to see if they resolve, worsen, or evolve into symptoms that are more worrisome. Regardless, it is essential that clinicians are able to reliably track symptoms over time, yet this capacity is rarely available or explicit. Working with systems engineers, we are developing prototypes for such systems and are working on their implementation and evaluation. In this commentary, we describe approaches to this essential, but underappreciated, problem in primary care.
... Furthermore, applying a systems science approach that leverages the expertise of healthcare system engineers to optimise coordination of scheduling protocols may also be necessary. 28 Greater involvement of other care team members, such as nurses, pharmacists and behavioural health, could also increase access. For example, at M Health Fairview, clinical pharmacists have the ability to prescribe medications under their collaborative practice agreement, so patients needing medication management could be seen by a pharmacist instead of their primary care provider. ...
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Objective A learning health system (LHS) uses data to generate evidence and answer questions required to continually improve system performance and patient care. Given the complexities of practice transformation, an area where LHS is particularly important is the study of primary care transformation (PCT) as PCT generates several practice-level questions that require study where the findings can be readily implemented. In May 2019, a large integrated health delivery system in Minnesota began implementation of a population management PCT in two of its 40 primary care clinics. In this model of care, patients are grouped into one of five service bundles based on their complexity of care; patient appointment lengths and services provided are then tailored to each service bundle. The objective of this study was to examine the use of a LHS in PCT by utilising the Consolidated Framework for Implementation Research (CFIR) to categorise implementation lessons from the initial two PCT clinics to inform further implementation of the PCT within the health system. Design This was a formative evaluation in which semistructured qualitative interviews were carried out. Observational field notes were also taken. Inductive coding of the data was performed and resultant codes were mapped to the CFIR. Setting Two suburban primary care clinics in the Twin Cities, Minnesota. Participants Twenty-two care team members from the first two clinics to adopt the PCT. Results Seventeen codes emerged to describe care team members' perceived implementation influences. Codes occurred in each of the five CFIR domains (intervention characteristics, outer setting, inner setting, characteristics of individuals and process), with most codes occurring in the 'inner setting' domain. Conclusions Using an LHS approach to determine early-stage implementation influences is key to guiding further PCT implementation, understanding modifications that need to be made and additional research that needs to occur.
... Interest in healthcare operations management has grown tremendously during the past decade among both academics and practitioners. With that interest has come a growing body of work that focuses on operational efficiency issues as they relate to managing hospital resources, congestion/overcrowding, and patient flow [14,21,33,41,42,43,48,64] using methodological tools of operations research. Specific problems of interest within this body of work have included a variety of topics, including but, not limited to capacity planning [7,32,33,53,62,70], staffing [37,67,75,78], resource allocation [19,29,54,73], patient scheduling [19,22,26,28,33], preemptive discharge management [27,44], and patient rerouting [74]. ...
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End-stage renal disease (ESRD) is a direful diagnosis for which regular (i.e., periodically scheduled) dialysis is typically the only immediate and accessible treatment. ESRD patients who are uninsured are in a high-risk category as they do not have access to regular treatment and have to rely on safety-net hospitals, funded by county governments, for access to dialysis. Since no national funding provides scheduled dialysis to this population, their only option is to seek dialysis under "emergency" conditions. These conditions are such that without urgent medical attention in the Emergency Room (ER), the patient's life is under threat. Hence, ER serves as a screening stage for gaining access to regular dialysis by the uninsured, and the resulting practice is known as "compassionate dialysis," a type of emergent dialysis treatment frequently offered at county hospitals serving uninsured ESRD patients. For a typical compassionate dialysis practice, existing county policy is such that patients are subject to a screening protocol upon arrival in the ER. The protocol serves to assess the severity of the patients' condition in the ER, and, hence, a certain fraction of the patients may not be offered treatment, i.e., these patients have to revisit the hospital at a later time, potentially within a few hours due to the nature of the underlying disease. The fraction of patients not offered the treatment is referred to as the screening threshold. As documented in the literature, the practice is costly and leads to significant congestion and treatment delays. Motivated by a real-life compassionate dialysis practice, we employ process flow mapping to gain a better understanding of the patient flow and identify inefficiencies and bottlenecks caused by the screening protocol of the existing county policy. We use simulation modeling to examine and estimate various system and patient-oriented metrics as a function of stochastic arrival rates and service times. Our eventual goal is to explore and analyze two proposals as alternatives to the current practice: one modifies the existing screening threshold based on the available capacity, and the other schedules and consolidates the future revisits of patients. We analyze and compare the effectiveness of both proposals using simulation optimization approaches. Ultimately, our goal is to propose solutions for alleviating congestion and treatment delays, and to inform hospital administrators and policy-makers about such solutions.
... To explain a system which is bounded rationally, the system's processes and the environment in which it is embedded (and hence, to which it adapts) must first be understood (Simon, 1990). With this in mind, this article will recognise that macro-level social phenomena are implemented through the actions and minds of (rationally bounded) individuals (Castelfranchi, 2000) and take the view of healthcare systems as dynamic, complex, and adaptive systems (Kopach-Konrad et al., 2007;Lipsitz, 2012;Martin, 2017;Miles, 2009;Sturmberg et al., 2012) embedded in specific contexts (social, economic, cultural, geographical, ecological). Similar systems approaches have subsequently been used to provide recommendations to reduce errors, failures, and maladaptive conditions in tertiary health surgery operating rooms (Dankelman and Grimbergen, 2005): standardization of procedures, processes, and toolkits; optimizing the information collection and dissemination process by using checklists and reminders; optimization of equipment and technology and standardization with lifecycle planning; reduction of sociotechnical system complexity; and comprehensive but cost-effective training and education programmes. ...
In this article, we argue for a novel adaptation of the Human Factors Analysis and Classification System (HFACS) to proactive incidence prevention in the public health and in particular, during and in response to COVID-19. HFACS is a framework of causal categories of human errors typically applied for systematic retrospective incident analysis in high-risk domains. By leveraging this approach proactively, appropriate, and targeted measures can be quickly identified and established to mitigate potential errors at different levels within the public health system (from tertiary and secondary healthcare workers to primary public health officials, regulators, and policymakers).
... The magnitude of the initial event is the cumulative sum of the magnitude of all branches and the probability of occurrence of an event equals to all probabilities of occurrence in the associated branch. The Sensitivity analysis is needed to determine how system target will be influenced by an event ( [67]). The DES enables us to carry out the sensitivity analysis by testing different probable value of CPs and Ups (risks) for each scenario. ...
A wide variety of projects are performed in healthcare in order to improve managerial decisions for efficient allocation of material, staff, and financial resources. However, traditional approaches do not take into account the inevitable process variability, uncertainty, scale, and interconnections that are critical for making efficient managerial decisions in real healthcare systems. Thus, engineers are increasingly involved in using computer simulation which is able to capture all these factors into efficient managerial decision-making. But, when compared to other applications, the simulation project in healthcare is more likely to fail and has a poor success rate. This chapter draws on the discrete event simulation (DES) project in healthcare and discusses the agenda of DES projects in healthcare focusing on (1) a characterization of DES project and simulation process, (2) risks on DES project in healthcare, and (3) applicable risk analysis methods. This chapter contains a real case study—an emergency department—which seems to be the most popular area of DES projects in healthcare.
Operating room throughput variability with spinal procedures revealed task inefficiency and potential safety concerns. Using the DMAIC framework (Define, Measure, Analyze, Improve, Control), a transdisciplinary team conducted a quality improvement (QI) research project to identify and address safety concerns with prone patient positioning. The main problem with patient positioning was undefined standard practice. Clinicians reported prone patient positioning for spinal surgery patients is physically demanding. Thus, the team conducted a rapid upper limb assessment for injury risk during patient positioning and identified a greater risk of clinician injury in the manual transfer process. The QI research team recommended the mechanical process of rotating patients with the Jackson Table to improve workload for the Surgical Team and developed training and design enhancements to support this workflow. The DMAIC quality framework enabled clinician collaboration with researchers to develop interventions to support a standardized process during prone patient positioning with the Jackson Table.
This paper develops an approach that articulates methods characterized as soft and hard Operational Research. This approach is known in the literature as Multimethodology, Hybrid Modeling, or Mixing Methods. As a Multi-Paradigm Hybrid Study, the present research provides an interface between the qualitative and quantitative modeling of the problem addressed. In this way, it facilitates the engagement of managers with analysts in the search for solutions to problems. The applied objective is to improve the performance of production processes at a prosthetics and orthotics factory located in Rio de Janeiro, Brazil. It applies a multimethodology to support evaluation and intervention using knowledge mapping and a discrete-event simulation model. Lessons learned from integrated workshops enabled the devising of an alternative improvement scenario for orthotics manufacturing. Theoretical results are validated through effective intervention, providing an increase in production capacity and reduction of the manufacturing lead time. This was made possible through the understanding and engagement of stakeholders during the modeling process.
Introduction Our institution previously initiated a perioperative surgical home initiative to improve quality and efficiency across the hospital arc of care of primary total knee arthroplasty (TKA) and total hip arthroplasty (THA) patients. Phase II of this project aimed to 1) expand the perioperative surgical home to include revision THAs and TKAs, hip preservation procedures, and reconstructions after oncologic resections, 2) expand the project to include the preoperative phase, and 3) further refine the perioperative surgical home goals accomplished in Phase I. Patients and Methods Phase II of the orthopedic surgery and anesthesiology surgical improvement strategies (OASIS) project ran from July 2018-July 2019. The evaluated arc of care spanned from the preoperative surgical consult visit through 90 days postoperative in the expanded population described above. Results Mean length of stay decreased from 2.2 days to 2.0 days (p<0.001), 90-day readmission decreased from 3.0% to 1.6% (p<0.001), and Press-Ganey scores increased from 77.1 to 79.2 (97th percentile). Mean and maximum pain scores and opioid consumption remained unchanged (lowest p=0.31). Annual surgical volume increased by 10%. Composite changes in surgical volume and cost-reductions equaled $5 million. Conclusion Application of previously successful health systems engineering tools and methods in Phase I of OASIS enabled additional evolution of an orthopedic perioperative surgical home to encompass more diverse and complex patient populations while increasing system-wide quality, safety, and financial outcomes. Improved process and outcomes metrics reflected increased efficiency across the episode of care without untoward effects. Level of Evidence III Therapeutic
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Appreciative Inquiry (AI) has been described in a myriad of ways: a radically affirmative approach to change that completely lets go of problem-based management, 1 the most important advance in action research in the past decade, 2 and organization development's philosopher stone. 3 Summing up AI is difficult—it is a philosophy of knowing, a methodologyfor managing change, an approach to leadership and human development. Here is a practice-oriented definition: Appreciative inquiry is the cooperative search for the best in people, their organizations, and the world around them. It involves systematic discovery of what gives a system " life " when it is most effective and capable in economic, ecological, and humun terms. AI involves the art undpructice of asking questions that strengthen a system's capacity to
Emphasizing customer oriented design and operation, Introduction to Human Factors and Ergonomics for Engineers explores the behavioral, physical, and mathematical foundations of the discipline and how to apply them to improve the human, societal, and economic well being of systems and organizations. The book discusses product design, such as tools, machines, or systems as well as the tasks or jobs people perform, and environments in which people live. The authors explore methods of obtaining these objectives, uniquely approaching the topic from an engineering perspective as well as a psychological standpoint. The 22 chapters of this book, coupled with the extensive appendices, provide valuable tools for students and practicing engineers in human centered design and operation of equipment, work place, and organizations in order to optimize performance, satisfaction, and effectiveness. Covering physical and cognitive ergonomics, the book is an excellent source for valuable information on safe, effective, enjoyable, and productive design of products and services that require interaction between humans and the environment.
Five new chapters, numerous additions to existing chapters, and an expanded collection of questions and exercises make this Second Edition an essential part of everyone's library. Between defining swaps on its first page and presenting a case study on its last, Neftci's introduction to financial engineering shows readers how to create financial assets in static and dynamic environments. Poised among intuition, actual events, and financial mathematics, this book can be used to solve problems in risk management, taxation, regulation, and above all, pricing. * The Second Edition presents 5 new chapters on structured product engineering, credit markets and instruments, and principle protection techniques, among other topics * Additions, clarifications, and illustrations throughout the volume show these instruments at work instead of explaining how they should act * The Solutions Manual enhances the text by presenting additional cases and solutions to exercises.
BACKGROUND Our objective was to examine how patients of primary care physicians are responding to a changing health care environment. The quality of their relationship with their primary care physicians and their experience with organizational features of care were monitored over a 3-year period. METHODS This was a longitudinal observational study (1996-1999). Participants completed a self-administered questionnaire at baseline and at follow-up. The questionnaires included measures of primary care quality from the Primary Care Assessment Survey (PCAS). We included insured adults employed by the Commonwealth of Massachusetts who remained with one primary care physician throughout the study period (n=2383). The outcomes were unadjusted mean scale score changes in each of the 8 PCAS over the 3 years and associated standardized difference scores (effect sizes). The 8 PCAS scales measured relationship quality (4 scales: communication, interpersonal treatment, physician's knowledge of the patient, patient trust) and organizational features of care (4 scales: financial access, organizational access, visit-based continuity, integration of care). RESULTS There were significant declines in 3 of the 4 relationship scales: communication (effect size [ES] = -0.095), interpersonal treatment (ES = -0.115), and trust (ES = -0.046). Improvement was observed in physician's knowledge of the patient (ES = -0.051). There was a significant decline in organizational access (ES = -0.165) and an increase in visit-based continuity (ES = 0.060). There were no significant changes in financial access and integration of care indexes. CONCLUSIONS The declines in access and 3 of the 4 indexes of physician-patient relationship quality are of concern, especially if they signify a trend.