a system dynamics based model for medical equipment...
TRANSCRIPT
A System Dynamics Based Model for Medical
Equipment Maintenance Procedure Planning in
Developing Countries
Sawsan Mekki, Manal Abdel Wahed, Khaled K. Wahba, Bassem K. Ouda
Systems and Biomedical Engineering Dept.
Faculty of Engineerig – Cairo University
Giza, Egypt
[email protected] – [email protected]
Abstract— Clinical Engineering (CE) department activities
include acquisition, maintenance of medical instrumentation,
health technology assessment, medical informatics, and risk
management. In this work the authors focus on maintenance
activities in developing countries where there is a lack of
acquisition planning, assessment, budgeting planning, and critical
equipment breakdown. In the past decade there has been an
explosion in the use of system dynamics modeling in healthcare.
In this paper a system dynamics based model for medical
equipment maintenance is designed; it incorporates 8 key
variables that influence the progress of medical equipment
maintenance. This model shows the effect of changing a key
variable on the others. This model is intended to maximize the
quality and to minimize the cost and time of medical equipment
maintenance. This is developed in a causal loop diagram, which is
a cause and effect diagram. iThink software has been used in the
development of this model, together with vensim in the
development of the causal loop diagram. The results show that
the critical variables for maintenance are the defect rate,
breakdown rate, and maintenance cost. In conclusion the medical
equipment maintenance cost determines the decision for
acquiring new equipment. The type and number of equipment to
be acquired is determined according to the available budget.
Index Terms— system dynamics, medical equipment
maintenance, simulation model.
I. INTRODUCTION
Biomedical technology is a valuable asset that is
strategically important to the operational effectiveness of
healthcare facilities [1]. CE department activities are divided to
first class activities related to acquisition and maintenance of
medical instrumentation, and second class activities like health
technology assessment, medical informatics, and risk
management [2]. In developing countries the principal
problems are how to correctly manage the devices
maintenance, to purchase the most suitable instrument,
planning device substitutions, ensure the correct functioning of
the instruments, and guarantee the availability of critical
devices every time they are needed.
Maintenance can be defined as the combination of all
technical and associated administrative actions intended to
retain an item or system in, or restore it to, a state in which it
can perform its required function [3]. A good maintenance
system is required for almost all equipment in order to
guarantee its performance, prevent failures and to extend its life
expectancy [4]. The breakdown of medical equipment in
service is of particular concern because of its possible use in
critical conditions. The signs of equipment failure may not
always be apparent to the clinical staff. Therefore scheduled
inspections help ensure the safety and efficacy of the medical
equipment [5]. Forward planning of maintenance requires
knowledge of maintenance requirements and the resources that
are required in order to perform maintenance; these resources
include labor, parts, materials and tool costs [6]. WU Hong [7]
discussed the Relativity in Purchase and Maintenance of
Medical Equipment. Regression models were built to predict
the future status of medical equipment to support decision
making [8].
Systems dynamics has become an important methodology
for understanding and formalizing conceptual process models.
There is clear potential for system dynamics to be employed in
support of health care policy [9]. It can be used to provide the
basis for a model of a feedback structure in decision-making,
which encapsulates the complexity of decision-making
behavior generated by the iteration of many nonlinear loops
over time [10]. The main feature of this methodology is that, it
permits to simulate the system elements and their links to
understand how the system will behave under different
conditions. In the field of system dynamics modeling, a system
is defined as a collection of elements that continually interact
over time to form a unified whole [11]. Medical equipment
maintenance is a time dependent procedure that can be a good
example of such system. This paper presents an application of
this methodology in the field of clinical engineering.
System dynamics has several applications and uses such as:
Total Quality Management (TQM) modeling [12],
experimental analysis of the dynamic structure and behavior of
managerial support systems [13], the dynamics of avian
influenza epidemics [14], hemodialysis performance control
[15], [16], [17], hospital waste management [18]. D C Lane
and E Husemann [19] aimed to assess the usefulness of system
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dynamics in a healthcare context and to elicit proposals
concerning ways of improving patient experience.
This paper describes a system dynamics based model for
medical equipment maintenance. While the model is simplified
compared to real CE department, it realistically captures time
delays, costs, and other parameters characterizing a CE
department. A simple questionnaire was prepared to collect real
value for the model variables. It is to be noted that it focuses on
the data of the radiology department. We distributed the
questionnaire by E-mail, personal communication (hand to
hand), and by the investigator filling. The distribution covered
Egypt and the Sudan as samples of developing countries. The
questionnaire was filled in Sudan by 3 hospitals, and in Egypt
by 12 hospitals. The collected data was used just to govern an
initial condition for the model to help setting the variable
ranges and to present real scenarios.
II. METHODOLOGY
The model is based on three elements: the activities to be
simulated, the characteristics of healthcare facility, and the kind
of human resources that will be part of staff. First we
summarize the process, identify the key variables, link them on
a cause and effect diagram, establish a stock and flow diagram,
and finally simulate the model. The following sections explain
the work done in details.
A. Variables Identification
In the identification of the critical variables, various means
were used and these included examination of different
variables, and literature survey. The progress of maintenance of
medical equipment is influenced by 8 key variables. Table I
presents those variables, their specification, definitions,
equations and units.
TABLE I. THE MODEL KEY VARIABLES
No Variable Specifi
cation Definition Equations Units
1 Equipment
Defects Stock
The
number of
equipment
failures
Equipment
defects +
(defect
creation –
Defect
elimination
- Defect
elimination
by CM) *
dt
Devic
e
2 Breakdown Conver
tor
Number
Equipment
out of
services
0.5*equipm
ent defects
Devic
e/year
3 Takedown Conver
tor
Number of
equipment
in PM
1/equipmen
t defects
Devic
e/year
4
Planned
Maintenance
(PM)
Conver
tor
proactive
repair of
operable
equipment
PM
Skills/(plan
ning
capability*
Training)
Devic
e
5
Corrective
Maintenance
(CM)
Conver
tor
Repair
failed of
equipment
1*Breakdo
wn Rate
Devic
e
6 Maintenance
Cost
Conver
tor
Expense in
year
(defects*Br
eakdown
Rate)
7 Backlog
Defect Stock
Devices in
flow up
Defects
Backlog+(
Defect
Rate–
Defect
Resolution)
* dt
Devic
e
8 Defect Rate Inflow Change in
defects rate
Defects
Backlog*up
time
Devic
e/year
B. Developing the casual loop diagram
After defining the model variable, we deduced the
variables’ affecters. Table II shows those affecters (negative or
positive). The variables and their affecters are linked on one
diagram, the Casual Loop Diagram (CLD), Fig. 1 shows this
diagram. ithink software [20] was used to develop this diagram.
Affecters are represented by entering arrows to the variable;
outing arrows are affecters to another variable. The positive
sign on the arrow means positive effect, while the negative sign
means negative effect. Reinforcing Loops (R) means positive
feedback and Balancing Loops (B) means negative feedback.
TABLE II. VARIABLES AFFECTERS
Variable Positive affecters Negative affecters
Equipment Defects Defect creation Defect elimination
Breakdown Equipment defects Defect elimination
through CM
Takedown Equipment defects Defect elimination
through PM
Planned
Maintenance (PM)
Planned Maintenance
skills Defect creation
Corrective
Maintenance (CM) Function status Defect creation
Maintenance Cost Breakdown rate Pressure to cut cost
Backlog Defect Defect Rate Solution rate
Defect Rate New defects Speed of solutions
C. Stock and flow diagram (SFD)
After drawing the CLD we convert it to SFD using Vensim
software [21], Fig. 2 shows the model SFD. The following
are some commonly used expression:
Stock: A stock is represented by a simple rectangle. Stock is
integration.
Flow: The job of flows is to fill and drain accumulation,
processing of request. Flows are differentiations.
Convertor: The convertors serve as utilitarian role in software.
Connector: The connectors are line between variables.
The model simulates a typical CE department. There are three
roles: operations manager, maintenance manager, and spare
parts stores manager. When enough red markers accumulate,
the equipment breaks down and capacity falls. The
maintenance manager must call the company engineer to repair
the equipment and must go to the spare parts store to see if the
needed parts are available. If the parts are in stock, the
equipment is repaired. If not, the engineer must wait until they
are available or pay to have delivery expedited. Alternatively,
the maintenance manager can schedule planned work, ordering
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the needed parts in advance. Planned maintenance can only be
done, however, if the operations manager agrees to take
operating equipment out of service. Each round the participants
make decisions such as how much equipment to take down for
planned maintenance, how to company and maintenance
resources, and how many spare parts to order. Cost is recorded,
along with, uptime, inventories, and so on.
III. RESULTS AND DISCUSSION
We were able to perform a first check using the
questionnaire answers; Table III was deduced based on the
average value of each variable. Figure 3 shows the result after
running the model under the initial conditions. Obviously
breakdowns reduce uptime (the total time during which the
equipment is valid and ready to be used and perform its
intended function whether with or without a patent on it). In
addition, most planned maintenance activity also reduces
uptime since planned maintenance frequently requires operable
equipment to be taken out of service so the needed work can be
done. High breakdown rates and low uptime mean the
company engineer is less able to meet all calls.
TABLE III. MODEL VARIABLES VALUES
Variable Initial value Range
Breakdown Rate 25% 3- 30%
Defect repair
(equipment) 100 equipment/year (4-150) equipment/year
Maintenance cost 100.000 LE (100.000-600.000) LE
Function status
after Repair 75% 50-100%
Initialized with high breakdowns and low uptime, the
maintenance manager's attempts to increase planned
maintenance are often rebuffed by the operations manager, who
faces pressure to meet demand, just as in the real world. For
the clinical engineer who sticks, to prevailing cost
minimization, reactive maintenance policies are able to keep
costs low for a while. But as defects build up they find their
uptime slowly sinking and costs gradually rising. Engineer who
follow through with a planned maintenance strategy
immediately find costs rising and uptime falling as equipment
is taken off line for planned maintenance. Soon, however, costs
begin to fall and uptime rises.
IV. CONCLUSION
In this paper a system dynamics based model for medical
equipment maintenance is designed. While the model is
simplified compared to real CE department, it realistically
captures time delays, costs, and other parameters characterizing
a CE department in developing countries. By compressing time
the model allows people to experience the worse before better
dynamic in a few hours instead of a few months. The results
show that the critical variables for maintenance are the defect
rate, breakdown rate, and maintenance cost. In conclusion the
medical equipment maintenance cost determines the decision
for acquiring new equipment. The type and number of
equipment to be acquired is determined according to the
available budget. An expansion of the presented model to
include other CE department activities is our future plan.
REFERENCES
[1] Yadin David, Wolf W. von Maltzahn, Michael R.Neuman,
Joseph D. Bronzino , Clinical Engineering– Principles and
Applications in Engineering, CRC Press, 2003.
[2] Gabriella Baslestra, Laura Gaetano, Daniele Puppato, "A model
for simulation of clinical Engineering Department activities",
30th Annual International IEEE EMBS Conference, Vancouver,
British Columbia, Canada, August 20-24, 2008.
[3] Rommert Dekker, “Applications of maintenance optimization
models: a review and analysis”, Reliability Engineering &
System Safety, Volume 51, Issue 3, pp. 229-240, March 1996.
[4] D. A. Cook, "A protocol for the measurement of down time of
medical equipment", The British Journal of Radiology, vol. 70,
pp. 279-290, 1997.
[5] Z. Bliznakov, G. Pappous, K. Bliznakova, N. Pallikarakis,
"Integreted software system for improving medical equipment
management", Biomedical Instrumentation & Technology, vol.
37, pp. 25-33, Jan. 2003.
[6] Dyro, J. , “Clinical Engineering Handbook”, Elsevier academic
press, issue 2003.
[7] W U Hong, "Relativity in Purchase and Maintenance of Medical
Equipment ", Chinese medical equipment journal, 2009-1. DOI:
CNKI:SUN:YNWS.0.2009-01-044.
[8] Manal Abdel Wahed, Amr A. Sharawi, Hanaa A. Badawi,
"Modeling of medical equipment maintenance in health care
facilities to support decision making", The 5th Cairo
International Biomedical Engineering Conference (CIBEC
2010), Cairo, Dec. 2010.
[9] Dangerfield, “System dynamics applications to European health
care issues”, Journal of the Operational Research Society vol.
50, pp. 345-353, 1999.
[10] Leslie A. Martin, Manas Ratha, System Dynamics in Education
Project, Massachusetts Institute of Technology, November 10,
1997, Latest Revision August, 2005, available in:
http://clexchange.org/ftp/documents/Roadmaps/RM9/D-4509-
4.pdf.
[11] Sally C. Brailsford, “System dynamics: what’s in it for
healthcare simulation modelers”, Proceedings of the 2008
Winter Simulation Conference, pp 1478- 1483, 7-10 Dec. 2008.
DOI: 10.1109/WSC.2008.4736227.
[12] Total Quality Management - ISEE systems, available in:
http://www.iseesystems.com/resources/Articles/Total Quality
Management.pdf.
[13] Thomas D. Clark Jr, Mary C., "An experimental analysis of the
dynamic structure and behavior of managerial support systems",
System Dynamics Review Vol. 24, Issue 2, pp 215–245,
Summer 2008, DOI: 10.1002/sdr.401.
[14] Burak Eskici, and Burak Türkgülü, "Modeling the Dynamics of
Avian Influenza Epidemics and Possible Pandemics ",
Proceedings of the 25th International Conference of the System
Dynamics Society and 50th Anniversary Celebration, Boston,
July 29 – August 2, 2007. Available in:
http://www.systemdynamics.org/conferences/2007/proceed/pape
rs/ESKIC371.pdf.
[15] Ahmad Taher Azar, and Khaled M. Wahba, "Biofeedback
Control of Ultrafiltration for Prevention of Hemodialysis
Induced Hypotension", Proceedings of the 26th International
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Defects insrease
operations
Equipment Quality
spare Part Quality
Equipments
defectsDefect Elimination
trough PM
Planned
Maintenance quality
Planned
Maintenance Effort
Takedown Rate
Uptime
Breakdown Rate
Reactive
Maintenance Effort
Pressure to keep
equipment Running
equipment
Available for PM
Maintenance Cost
Pressure to cut
Cost
Design
Improvement
PlannedMaintenance
Skills
Traning
Report
Status function
--
+-
+
+
++
--
+
Reactive
Maintenance quality
Defect Elimination
trough Repair
+
-
+
+
+
B2 B1
-
-
+
B3
Damage
+ +
R1
+ +
Maintenance
Budget
+
-
R7
R3
-
+
-
+
-
+
+
+
+- -
R2
R5
R6
R4
R8
Defect Rate
DefectsDefect Resolution
work pressureeffort to repair
life cyclequality of
maintenance
workweek
+
+
+
+
+
+
+
+-
-
B4
B5
B6
+
-
-
Fig. 1. The model casual loop diagram
Conference of the System Dynamics Society, Athens, Greece,
July 20 – 24, 2008. Available in:
http://www.systemdynamics.org/conferences/2008/proceed/pape
rs/AZAR110.pdf.
[16] Ahmad Taher Azar, Khaled M. Wahba, Abdalla S. A.
Mohamed, "System Dynamics Highlights the Effect of
Maintenance on Hemodialysis Performance", Proceedings of the
25th International Conference of the System Dynamics Society
and 50th Anniversary Celebration, Boston, July 29 – August 2,
2007. Available in:
http://www.systemdynamics.org/conferences/2007/proceed/pape
rs/AZAR124.pdf.
[17] Ahmad Taher Azar, and D. Khaled M. Wahba,"Association
between Neural Network and System Dynamics to Predict
Dialysis Dose during Hemodialysis", Proceedings of the 26th
International Conference of the System Dynamics Society,
Athens, Greece, July 20 – 24, 2008. Available in:
http://www.systemdynamics.org/conferences/2008/proceed/pape
rs/AZAR111.pdf.
[18] ] Mochammad Chaerul, Masaru Tanaka, Ashok V. Shekdar, "A
system dynamics approach for hospital waste management",
Waste Management, vol. 28, pp. 442–449, 2008.
[19] D C Lane and E Husemann, "System dynamics mapping of
acute patient flows", Journal of the Operational Research
Society vol. 59, 213-224 (February 2008) |
doi:10.1057/palgrave.jors.2602498, available in:
http://www.palgrave-
journals.com/jors/journal/v59/n2/full/2602498a.html.
[20] ithink software,
http://www.iseesystems.com/softwares/Business/ithinkSoftware.
aspx.
[21] Vensim software, http://www.vensim.com/
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operations equipment qualitypart quality
damage
equipment def f ects
def ect creation
Def ect elemination
by PM
Takedown Rate
Def ect elemination
by CM
PMCM
Beakdowm RatePM ef f orts
uptime
CM ef f orts
Pressure to run
machanics f or PM
Deliv ery Reliability
Pressure to cut
Maintenance Costs
Design ef f orts
planning and capapility
Traning
PM Skills
Def ects Backlog
Def ect Rate
Def ect Resolution
work pressure
ef f ort to repair
staf f Lev el
work week Fig. 2. The model Stock and Flow Diagram
Page 1
1.00 4.00 7.00 10.00 13.00
Months
1:
1:
1:
2:
2:
2:
3:
3:
3:
4:
4:
4:
5:
5:
5:
1
4
7
0
10
20
1
4
7
-15
-5
5
-6
-3
0
1: M Costs 2: eauipmةt def f ects 3: Beakdowm Rate 4: Takedown Rate 5: uptime
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
Fig. 3. Result of running the model under initial conditions
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