Simulation Modeling of the Operating Room Based on SIMIO
Qian Zheng 1, Saifeng Chen 2, Jie Shen 1, Zeqing Liu 1,
Kai Fang 1, Wei Xiang 1,a
1
School of Mechanical and Mechanics, Ningbo University, China
2
Affiliated Hospital of School of Medicine of Ningbo University, China
a
email: xiangwei@nbu.edu.cn
Keywords: Operating Room, Simulation Modeling, SIMIO
Abstract. Operating rooms（OR） is one of the most demanding department in hospital. OR’s
process will directly influence the profits of hospital as well as the patients' satisfactory degree. On
the condition of a complex cooperation of ORs with variations, simulation has its advantage on
solving problems. From the perspective point of rational utilization of resources, the simulation
modeling of OR in big hospital by a latest simulation platform – SIMIO is proposed. The modeling
objects and the logic underline are studied, and a simulation model case is presented.
Introduction
Nowadays health care system becomes more sophisticated and hospital medical level has greatly
improved. However, some key processes still remain relatively original. Parts of them almost have
nothing different from those decades ago. The phenomenon of "waiting" and "delay" is quite
common in most hospitals. As investigations show, when the flow of patients, information and
material can't be smoothly operated, not only the patients' satisfaction will be decreased because of
“delay”, but also the medical care personnel's satisfaction will be decreased as the work intensity is
very high and working hours is very long. Thus, it's really vital to analyze the key process to
eliminate the bottlenecks and optimize the process of healthcare system. Operating rooms is one of
the most demanding departments in hospitals. It has complex cooperation with other departments,
including various surgical divisions, assist offices, observation rooms, anesthetic divisions etc.
Additionally, variability in caseloads, patient acuity, and specialty needs as well as artificial
variations impacts procedure of the operating room a lot and causes avoidable problems in
bottleneck areas. In the past, most researches for such complex operation process adopted the
qualitative analysis and quantitative statistic analysis. Such statistic analysis is based on historical
data and is a kind of retrospective studies which can only find the bottleneck but not provide
suggestions on rational utilization resources. This project explores the simulation modeling of the
operating room at a big hospital in Ningbo through the latest simulation platform—SIMIO.
Literature Review
There’re merely a few domestic hospitals explored operation flow because of the relatively limited
hospital management level compared to those in abroad. In addition, the limited level of
informationization in current hospital management further makes it difficult to gather large amount
of flow data for quantities analysis. Hence those few domestic researches were mainly focused on
macroscopically qualitative analysis or quantative analysis based on statistics analysis. From the
literature review, there’s no research published yet about the quantitative analysis of OR’s working
flow based on simulation technique in China.
However, the quantitative analysis based on simulation is conducted recently in several big
hospitals under the cooperation with local universities in other developed countries, like German,
America, and Canada etc. Relevant researches can be found in latest Winter Simulation Conference
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of these 3 years. Various "what-if" situations were assessed by the index of the efficiency of OR
[1-4]. Those researches were aimed to improve operating room’s utilization and to shorten the time
between surgeries. The main indexes used for evaluating the utilization of OR is the time. Our
research aims to focus on balancing the load of OR and to optimize allocation of resources through
simulation by the latest simulation platform – SIMIO.
Patients Flow of the OR
A comprehensive understanding of the whole processes of OR need to be established for building
the simulation model. Based on survey of the operating rooms in the Affiliated Hospital of School
of Medicine of Ningbo University (AHSMNU), some useful information and data were collected
for OR modeling. The OR department at AHSMNU works for both elective and emergency
surgeries. The elective slate runs from 8:00 to 15:30 from Monday to Friday each week at ORs
located in 5th floor. The emergency slate is usually arranged in the afternoon section, and there is
additional operating room at 4th floor used for very urgent emergency surgery during morning
section. On the weekends there is no elective slate and only one OR is staffed solely for emergency
procedures. Here this research will only consider the elective patients. In current AHSMNU’s OR
management, the flow process for OR is generalized as the flow chart shown in Fig. 1.
Surgery Schedule
Patient Delivery
No
OR available?
Yes, send to OR
OR Setup
POU for
Patient Waiting
Surgery (OR)
OR cleaning
PACU for recovery
Patient delivery back to wards
Fig. 1 The flow of current AHSMNU’s operating room
As shown in Fig. 1, the flow procedure starts from the surgical schedule. Such schedule shows
all operating rooms’ schedule of the next day’s surgery and personnel arrangements, like operating
room, assigned nurses and doctors and anesthesia etc. The patient is then picked by medical worker
from Inpatient Ward. If the assigned operating room is idle, the patient is sent to operating room
for surgery, otherwise, the patient is waiting at the Pre-Operation Unit (POU). The third stage is
operating room setup. The patient is interviewed by an OR nurse and is assessed by the anesthetist
before the surgery, meanwhile, nurse is preparing for equipment for the coming surgery. After
everything is ready for surgery, the doctor is starting the surgery. Different types of operation need
different time. After the surgery, according to the different situations of patients, the patient is taken
to Post Anesthesia Care Unit (PACU) to recovery or directly to the in-patient ward. Finally, the
operating room has to be cleaned by medical worker. The OR has the link to many other
departments, such as Pre-operative units and Inpatient departments. There are many factors
influencing patients’ surgery. If, for example, there are not enough beds in PACU, the patient will
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not be delivered from operating room, so the starting for next surgery has to be postponed.
The OR resources considered include operating rooms, beds, and others as listed in Table 1.
Table 1 The resources in OR
Item
Quantities (capacities)
Operating rooms
10
Beds in PACU
6
Beds in POU
6
Operating room nurses
20
Delivery beds
10
Anesthetists
6
Other staffs (for transport, cleaning)
6
Simulation modeling under SIMIO
A simulation model is built according to the patient flow in the OR. Now, there are lots of
simulation software available, like ARENA, WITNESS and FLEXSIM, etc. SIMIO is used as the
simulation tool in this research. It has functions of visual, interactive, and interpretative modeling
and supports the large scale application for both discrete and continuous system modeling. It
adopted the “event-oriented”, the “process-oriented”, the “object-oriented”, as well as the latest
modeling method of “Agent-based Simulation”, and supported these four kinds of modeling method
of seamless cohesion. Compared with other available simulation software, SIMIO has a unique
architecture with “Agent-based”. It sets the real design concept based on “objects” which can be
easily extended based on real objects. In addition, SIMIO provides the most advanced real-time 3D
technology, which strengthened the interaction of simulation.
Modeling Objects in SIMIO. According to the resources used in current AHSMNU’s operating
room, several “objects” are abstracted in SIMIO as the basic modeling object shown in Table 2.
Table 2 Modeling objects in SIMIO
the objects in real process
The objects in simio
The source of patient
SOURCE
Operating room
SERVER
PACU room
Delivering bed
VEHICLE
the path from wards to ORs
TIME PATH
Separate isolation
SINK
Nurse, Medical Worker
NURSE
Anaesthetists
DOCTOR
Source of patient – SOURCE. SOURCE is the starting point to the simulation model. There
maybe two ways of source in simulation: schedule or random. The source from schedule is an input
data table which includes information of surgery type and the assigned OR and personnel. Such
schedule source is usually used to validate the simulation model. While the random source is used
for “what-if” simulation so as to optimize OR flow process. By statistic analysis of the collected
data, the source of patients arrives to model in negative exponential function, and with different
percentages for different surgery types.
Surgery process – SERVER. SERVER is used as the object to represent the surgery process
(including surgery, OR setup, and OR cleaning) in operation room. The buffer capacity of the
SERVER is set to allow only one entry for each time. The detail parameters like the processing time
depends on the surgeries’ type. Based on the collected data of the OR, several surgeries’ processing
time can be found in Table 3.
PACU – SERVER. Patient stays in PACU for recovery. The recovery time is defined as the
processing time of SERVER, which is determined by surgery type.
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Patient delivery – VEHICLE. Patient has to be delivered by delivery bed from impatient ward to
assigned operating room, and vice versa. The delivery bed is represented as VEHICLE, which is
defined as on call Taxi type. Such VEHICLE follows the path from ward to OR with the defined
speed.
Delivery path – TIME PATH. The delivery time from impatient ward to assigned OR depends on
the distances. Therefore, such path is defined as the TIME PATH. It allows the VEHICLE to travel
along such path with specific speed.
Separate isolation– SINK. Separate isolation, abstracted as SINK, is defined as the ending point
of the OR procedure. Patient passes this place to their impatient ward.
Nurse, Medical Worker – NURSE. A new object is defined as NURSE in simulation model of
OR. It is further classified as OR nurse and general medical worker for different working type.
For example, Medical worker can do works like patient delivery and OR cleaning, while OR nurse
must can only assist doctor working in OR. Nurses return to Preparation room when they finish
tasks. Preparation room can hold all nurses and medical workers. The surgery has to be waited until
the required number of nurses is available.
Anesthetists – DOCTOR. Doctor from different surgery division is defined as a new object
DOCTOR. Usually the doctor will not be the main shortage source for OR management. Therefore,
in this research only the anesthetists are considered as the useful objects which means that the
surgery has to be waited until assigned anesthetist is ready.
Modeling Logic among Objects. Through setting the Logic among each object during the OR,
the event can correctly trigger appropriate behavior. For example, the detail logic defined in
delivery bed in this research can be found in Table 3. e.g. Once the surgery operation is finished, a
requirement for on-call delivery bed is triggered to pick up next patient.
Table 3 Logic of delivery bed to other objects in simulation model of OR
Next
Triggered
Resource
Capacity
Location
destination
event
Surgery
Delivery bed
1
Ward
POU
finished
Delivery bed
1
POU
OR
OR available
Anesthetist
Delivery bed
1
OR
PACU
required
Delivery bed
1
PACU
ward
Recovery time
Data Collection of OR. Data are essential for a simulation model. Some information of main
surgeries operated in AHSMNU are recorded, like resource numbers, time of patient delivery, OR
setup, PACU recovery etc. These data are necessary to validate the simulation model. So far,
around two weeks elective patients’ data are collected from AHSMNU. The total number of
patients is 210. Here the UFS surgery is listed as an example. The distribution function by statistic
analysis is given in Table 4, which is used in simulation model.
Table 4 Main distributions of processing time for UFS surgery
Items
Function of time
Patient Transfer
UNIFORM（9,4）
OR setup
TRIANGLE（5,41,22.7）
Surgery
UNIFORM（10,53）
OR cleaning
DURATION (10)
Case study. The whole simulation model is build in SIMIO. Here we only consider the ORs in
5 floor for elective patient slate. A snap-shot window of the 3-dimensional simulation model in
SIMIO is shown in Fig. 2
th
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Fig. 2
The snap-shot of the simulation model of OR in SIMIO
Conclusion and Future Work
By understanding the flow process of current operating room management in AHSMNU, the useful
flow data were collected and analyzed by statistic analysis. The related modeling objects in SIMIO
as well as the logic underline are established and a simulation model with 10 ORs and 20 nurses
was build for simulation analysis. In the future work, we will focus on quatitative analysis based on
simulation. The evaluating index will be focused on the workload of ORs. What-if simulation will
be done to provide a suggestion on OR’s process re-construction so as to optimize the resource
allocation.
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