









































45 J Global Clinical Engineering Vol.2 Issue 3: 2020

Received February 23, 2020, accepted May 19, 2020, date of publication May 29, 2020

Bedside Communication and Management of Vital 
Parameters and Alarms In Care-Intensive Environments: 
Simulation Model Development for the Clinical 
Effectiveness Analysis of an Innovative Technology 

By I. De Rosa1, A. Pepino2, G. Giaconia3, M. Guarino4  

1  Biomedical Engineer – Università degli Studi di Napoli Federico II
2 Professor of bioengineering – Università degli Studi di Napoli Federico II
3 Director of Economato and Clinical Engineering – Azienda Ospedaliera dei Colli di Napoli
4 Director of Emergency Department – Azienda Ospedaliera dei Colli di Napoli.

ABSTRACT
Background and Objective: The deliberation n.7301 of 31/12/2001 provides for the inclusion of a call system with acoustic 
and luminous signaling within the minimum equipment of the recovery ward. However, traditional call systems are inefficient 
since they are based on the following incorrect assumptions: patients and staff are unmoving, information sources are static, 
and assistance is unidirectional. Taking care of a patient involves different personnel who should be dynamic and should be 
able to exchange information. Furthermore, the high number of clinical calls and alarms might be an issue, as they are essential 
to fulfill patients’ needs, but could cause stress and additional workload for medical staff. Indeed, they sometimes ignore some 
calls or waste time on non-urgent requests. Also, the identification of an alarm and prompt intervention seems to be more dif-
ficult during travel. An ideal alarm system should have 100% sensitivity and specificity. However, the alarms are designed to be 
extremely sensitive, at the expense of specificity. The alarm fatigue, that is the work overload due to an excessive alarms number 
exposition, is a critical problem in terms of safety in the current clinical practice because it involves desensitization and alarm 
loss, and occasionally a patient's death.
Material and Methods: Appropriate approaches to notifications should be evaluated, including the effectiveness of mobile 
wireless technologies that are key to linking patients, staff, data, services, and medical devices which simplifies communications 
and workflows. Several issues related to the communication among staff members, between patient and caregiver, and regarding 
the alarms and vital parameters distribution in care-intensive environments have been analyzed. The focus was on the clinical 
effectiveness analysis of innovative technology to support the activities in the Emergency Department of the Azienda Ospedaliera 
dei Colli. Afterward, we created a simulation model with Simul8, so that a digital twin reproduces direct and indirect activities 
in two cases: with and without (What If and As Is model) the aid of the technology. 
Results and Conclusions: The model provides a set of Key Performance Indicators (number of performing activities, average 
alarm resolution time, wait time) on which the compensatory aggregation method is applied to obtain a single final score in 
both cases. This score is 52.5 in the As Is Model and 80 in the What If model. So, clinical effectiveness has been demonstrated.

Keywords – Alarm fatigue, safety, communication, clinical effectiveness, simulation model, workflow, vitalsigns. 

Copyright © 2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY): Creative Commons - Attribution 4.0 International 
- CC BY 4.0. The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original 
publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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De Rosa, Pepino, Giaconia, Guarino: Bedside Communication and Management of Vital Parameters and Alarms In Care-
Intensive Environments: Simulation Model Development for the Clinical Effectiveness Analysis of an Innovative Technology 

J Global Clinical Engineering Vol.2 Issue 3: 2020  46

INTRODUCTION
Different people are involved in the patient care 

process. All of them have to share and discuss informa-
tion about patient management. These people are not 
stationary but move around the hospital while engaging 
in multiple activities at the same time. This can include 
the manual recording of clinical data and filing medical 
records which increases the possibility of error and can 
impact the assistance response times. Furthermore, hear-
ing and correctly identifying an alarm signal and promptly 
intervening can be more difficult due to the movement 
of caregivers. As a result, both the interest and the use of 
information and communication technologies to support 
health services has increased. 

Information and communication technologies offer 
powerful tools to restructure health service processes. 
Nowadays, there is a growing range of communication 
channels, media, and devices, which makes it possible to 
provide these services. A growing literature on the value 
of communication in the healthcare sector has already 
been developed. 

Although there has been advanced research in highly 
specific areas (i.e., telemedicine), the clinical adoption of 
simpler services, such as voice mail or email, are still not 
common in many health services. This situation would 
change if we realized that the biggest information reposi-
tory in healthcare is the heads of the people who work 
in it, and the biggest information network is the complex 
network of conversations that connects the actions of 
these individuals.1 

Even small clinical teams can generate large and com-
plex communication spaces. The clinical communication 
space is also characterized by numerous interruptions, 
poor communication systems, and inadequate practices.

The participants are often separated by time and space. 
We have synchronous communication in case the attend-
ees exchange messages simultaneously, asynchronous if 
not (Table 1).

Therefore, care devices and hospital information sys-
tems should be integrated to encourage the exchange of 
information among caregivers and to provide structured 
data for improving the timely and effective coordination 
of care.

The goal is to provide an easy way to acquire and 
insert clinical data into the hospital registration system, 
through the use of mobile, lightweight, portable devices. 
Linking patients, staff, data, services and medical devices 
simplifies communications and workflows. 

Appropriate approaches to notifications should be 
evaluated, including the effectiveness of mobile wire-
less technologies to reduce alarm fatigue. The analysis 
is focused on the current structure and organization of 
the Emergency Department of Hospital CTO of Napoli 
(Azienda Ospedaliera dei Colli of Napoli). 

This work aims to propose and test a new organizational, 
technological, and managerial network which is capable 
of optimizing the hospital's response to the individual's 
need for health and guarantee caregivers the ability to 
carry out their clinical activities within the system

The study involves the analysis of the clinical effective-
ness (one of the nine domains defined by the EUnetHTA 
Core Model) of a technology that supports the department 
activities. For this analysis, a digital twin of the healthcare 
process was developed using Simul8, to which a set of 
indicators was calculated. Finally, the compensatory ag-
gregation method was applied to the selected indicators, 
to obtain a single value that allowed the evaluation of the 
clinical effectiveness of this technology.

STATE OF ART
In this work, studies and solutions in literature that 

face these problems have been analyzed. One of the most 
representative is Hendrich’s study.2 After equipping each 
nurse with a Personal Digital Assistant (PDA) for record-
ing activities and a bracelet capable of measuring skin 
temperature and displacements to assess energy expen-
diture and distances traveled, this study showed that a 

TABLE 1. Values Measured in a Patient Session

Sound Images Data

synchronous Telephone Video 
conferencing

Electronic 
cards, 

documents

asynchronous Voice mail Letters, notes, 
image archive fax, email



47 J Global Clinical Engineering Vol.2 Issue 3: 2020

De Rosa, Pepino, Giaconia, Guarino: Bedside Communication and Management of Vital Parameters and Alarms In Care-
Intensive Environments: Simulation Model Development for the Clinical Effectiveness Analysis of an Innovative Technology 

nurse spends 37% of her time in the patient's room and 
43% in the nursing station (Figure 1). 

The main nurse activities are the documentation (i.e., 
the compilation of medical records, acceptance, and 
discharge documents) and the coordination of the treat-
ment process (i.e., the communication with other team 
members to establish the best approach for the patient). 
A total of 19% of their time (less than 1/5 of the time) 
was dedicated to direct patient care activities and only 7% 
was dedicated to the monitoring of vital signs (Figure 2).

The Manhattan Medical Research Adoption Study of June 
2012 found that the use of mobile devices in healthcare 
is pervasive (Figure 3). The majority of the interviewed 

clinicians (87%) confirmed the adoption of smartphones 
and tablets in the workplace to improve resources and 
information at the point of care.3

Company policies for the use of mobile devices can be 
BYOD (Bring Your Own Device) or COPE (Corporate Owned, 
Personally Enabled). In the first case, the company’s initial 
investment is less, but there are lots of hidden costs and 
risks, such as distractions, which can lead to clinical risk 
situations, cybersecurity issues, and data loss problems. 

In the hospital, it would be appropriate to provide 
caregivers with dedicated devices. The main features of 
these devices should be that they are high quality, light-
weight to support mobility; robust and resistant to the 
action of aggressive detergents or disinfection solvents 
to reduce infections; impermeable, have a longer battery 
life, and good network coverage.

Another important problem is alarm fatigue. Sendelbach’s 
research showed that from 72–99% of clinical alarms are 
false alarms.4 The high number of false alarms has led 
to the alarm fatigue problem. Alarm fatigue an overload 
of work that occurs when caregivers are exposed to an 
excessive number of alarms it may lead to desensitization 
and loss of the alarms.

The research should evaluate various approaches to 
alarm notification, including the effectiveness of wireless 
technology and to increase the specificity of the alarms 
without a significant loss of sensitivity. This research aims 
to figure out these problems by focusing on the analysis 
of the clinical effectiveness of innovative technology in 
support of the ward activities.

The technology is modular and includes patient receiv-
ers, bed modules, conversation modules (to allow patients 
to quickly communicate with caregivers and control the 

FIGURE 1.  Nurse activities: Location.

FIGURE 2.  Nurse activities: Subcategory.

FIGURE 3.  Smartphone and tablet use in hospital.



De Rosa, Pepino, Giaconia, Guarino: Bedside Communication and Management of Vital Parameters and Alarms In Care-
Intensive Environments: Simulation Model Development for the Clinical Effectiveness Analysis of an Innovative Technology 

J Global Clinical Engineering Vol.2 Issue 3: 2020  48

environment), modules with inputs to connect medical 
devices for monitoring remotely and for the alarm notifi-
cation, door modules, peripheral modules, corridor lights 
and displays, personnel consoles, signalers, gateways, and 
passive bus concentrators (Figure 4).5

METHODS
Clinical effectiveness is one of the nine domains defined 

in the EUnetHTA Core Model; a multidisciplinary evalua-
tion model born from the EUnetHTA project funded by the 
European Union since 2006. The nine domains are devel-
oped by a multidisciplinary and multi-professional team.

This work involves the analysis of the fourth domain: 
the evaluation of the clinical effectiveness of the technol-
ogy. Effectiveness represents the benefit obtained by using 
technology in a real work context, whereas the efficacy 
represents the benefit in ideal conditions.

Simulation can be seen as a valid method for assess-
ing effectiveness, especially in situations where there is 
a lack of data in the literature and there is no possibility 
of directly observing the use of technology. Simulating 
consists of reproducing as accurately as possible the 
functioning of a system to study its responses to the 
change of the external environment, even before putting 
the change into action, through the analysis of suitably 
chosen performance indicators, called Key Performance 
Indicators (KPI).

For this analysis, a digital twin of the healthcare pro-
cess is developed using Simul8. A digital twin is a digital 
replica of physical systems, devices, processes, people, 

places. Simul8 is a simulation software product by SIMUL8 
Corporation, used for the simulation of systems that in-
volve the processing of discrete entities in discrete time. 
Through a model developed with Simul8, it is possible 
to test real scenarios in a virtual environment. Simul8 
allows simulation of the process, defining activities, times, 
resources, work shifts, to obtain a model representing 
the entire workflow with reasonable reliability. A Simul8 
simulation revolves around the processing of work items. 
They enter the system through the work entry points, pass 
through the work centers, can temporarily reside in the 
queues (storage areas), and terminate their path in the 
process through the work exit points. The work centers may 
need specific resources to process the represented activity. 
Simul8 outputs can be graphs, statistics, numeric values.

For this analysis, the analyzed KPIs are:
• number of performing direct activities compared to 

the total number of required direct activities;
• average alarm resolution time;
• waiting time.
It is possible to apply the compensatory aggregative 

method on them, to obtain a single decision support score.

P = Priority score
w_(i )= i-th weight
V_i = i-th indicator value

CONTEST OF APPLICATION
Mobile handheld devices show greater benefits in 

high care-intensive environments where time is critical 
and rapid response is crucial. The application context of 
the technology is the Emergency Department of Hospital 
CTO (Napoli). This consists of a First Aid located on the 
ground floor, equipped with 4 beds in the Observation 
Area, 2 beds in the red code room and 2 beds in the yel-
low code room (Figure 5). 

The ward is located on the fourth floor and has 18 beds 
divided into 7 rooms (4 with 3 beds and 3 with 2 beds). 
There is also a nurse station (Figure 6).

FIGURE 4.  Ascom Telligence technology.



49 J Global Clinical Engineering Vol.2 Issue 3: 2020

De Rosa, Pepino, Giaconia, Guarino: Bedside Communication and Management of Vital Parameters and Alarms In Care-
Intensive Environments: Simulation Model Development for the Clinical Effectiveness Analysis of an Innovative Technology 

Considering the number of beds, it was determined 
that 26 devices should be installed (Table 2).

Regarding the organization of the emergency-urgency 
team, in the worst case (the one with the highest number 
of caregivers) there were:

• 3 doctors, 8 nurses (2 of them are always in the tri-
age area) and 1 social and health professional in the 
First Aid (ground floor), for a total of 12 units; and

• 2 doctors, 5 nurses and 1 social and health profes-
sional in the ward (fourth floor), for a total of 8 units. 

The number of caregivers present at the same time 
was 20. It was determined that 20 smartphones should 
be given to caregivers (Table 3). 

ELABORATION OF SIMULATION MODEL
The development of the simulation model foresaw a 

first phase, in collaboration with the emergency medicine 
staff, in which all possible activities carried out in the 
ward were identified. During this analysis, several direct 
activities (completed at the bedside) and indirect activi-
ties were selected, in three different periods of the day 

FIGURE 5.  First Aid – ground floor.

FIGURE 6.  Emergency Department - fourth floor.

TABLE 2. Bed Locations

BEDS

First Aid 4 2 red code room
2 yellow code room

Observation Area 4

Ward 18

TABLE 3. Values Measured in a Patient Session

First aid Observation area Ward

Doctors
2 (7:00 – 15:00)
2 (15:00 – 23:00)
2 (23:00 – 7:00)

1 (7:00 – 15:00)
1 (15:00 – 23:00)
1 (23:00 – 7:00)

2 (7:00 – 15:00)
1 (15:00 – 23:00)
1 (23:00 – 7:00)

Nurses
7 (7:00 – 15:00)
7 (15:00 – 23:00)
6 (23:00 – 7:00)

1 (7:00 – 15:00)
1 (15:00 – 23:00)
1 (23:00 – 7:00)

5 (7:00 – 15:00)
3 (15:00 – 23:00)
3 (23:00 – 7:00)

Auxiliary 
staff

1 (7:00 – 15:00)
1 (15:00 – 23:00)
1 (23:00 – 7:00)

1 (7:00 – 15:00)
1 (15:00 – 23:00)
1 (23:00 – 7:00)



De Rosa, Pepino, Giaconia, Guarino: Bedside Communication and Management of Vital Parameters and Alarms In Care-
Intensive Environments: Simulation Model Development for the Clinical Effectiveness Analysis of an Innovative Technology 

J Global Clinical Engineering Vol.2 Issue 3: 2020  50

(morning, afternoon, and night) which reflect the different 
work shifts and staff availability. 

All staff members filled out a questionnaire. Their an-
swers and the direct observation of the process allow the 
definition of the frequency and duration of their activities. 

The direct activities identified were:
• therapy administration;
• vital parameters control;
• patient hygiene;
• withdrawal, catheterization, medications;
• tours;
• alarm management;
• bed calls; and
• health status updates.

The indirect activities identified were:
• emergency in the First Aid department;
• medical record filling;
• drug preparation and therapy;
• medication warehouse management;
• instrument management;
• briefing with colleagues;
• patient disposal activities;
• patient acceptance activity;
• conducting diagnostic tests;
• transfers to other facilities; and
• exam requests.

For direct activities, the work item corresponded to 
every care need at the bed. Every work item was processed 
in a work center and for each of them an operating time 
may be defined, depending on three different levels of 
patient complexity. Every activity was made by one or 
more people (nurses, doctors, and auxiliary staff) re-
cruited on predefined shifts. These activities involve the 
movements of caregivers into the department and the 
operation time reflects the distance between the nurse 
station and the room from which the assistance need 
originated. Regarding indirect activities, the work items no 
longer represent patient needs but the repetitions of the 

individual activities and their duration was independent 
of the patient complexity level. 

After having entered all the required data, the simu-
lation model could run on different time frames and at 
different speeds through a dedicated cursor. It was also 
possible to obtain indications regarding the trend of the 
variables that characterize the functioning of the model 
through a series of graphs selected by the user. During 
execution, the icons and animations facilitate understand-
ing of the workflow.

Before using the model, it was necessary to verify whether 
the model could represent a reasonable approximation 
of reality. An approach divided into two successive steps 
was adopted for its validation outlined below.

• Formal validation: evaluation of the code correctness.
• Structural validation: comparison between the be-

havior of the simulation model and the real system, 
to assess whether and how much the model can be 
considered a good approximation of reality. The struc-
tural validation consists of two successive moments:
• open-box validation: the staff evaluate the model:
• black-box validation: the results are compared with 

the data obtained from the real system.6

According to these validations, it was possible to find 
that the simulation model implemented constituted a 
good approximation reality.

THE AS IS MODEL
The As Is model consists of the evaluation of the 

workflow characteristics and performance in the cur-
rent configuration. The As Is model created is described 
in Figure 7. 

To define the model, 5 steps needed to be implemented. 
1. Identification of direct and indirect activities.
2. Identification of the frequency and duration of all ac-

tivities through direct observation and questionnaire.
3. Identification of the resources that complete the activi-

ties, taking into account the work shifts.
4. Validation of the model.
5. Analysis of KPIs.



51 J Global Clinical Engineering Vol.2 Issue 3: 2020

De Rosa, Pepino, Giaconia, Guarino: Bedside Communication and Management of Vital Parameters and Alarms In Care-
Intensive Environments: Simulation Model Development for the Clinical Effectiveness Analysis of an Innovative Technology 

Each work item generated by the Start Point related to 
direct activities represented a care needs at the patient's 
bedside. Considering that there were 18 beds in the ward, 
it was estimated that these needs occurred every 5 min-
utes. A label was associated with each work item and set 
on a distribution that represents the frequency of each 
patient’s bedside care needs (Figure 8).

Each care need may come from a different bed, so each 
generated work item was associated with an additional 
label, set on a different distribution which defined the 
distance from the nurse station and takes into account 
that rooms 1, 2, 3 and 4 have 3 beds and rooms 5, 6 and 
7 have 2 beds (Figure 9).

FIGURE 7.  As Is model.

FIGURE 8.  Distribution of patient bedside care needs.



De Rosa, Pepino, Giaconia, Guarino: Bedside Communication and Management of Vital Parameters and Alarms In Care-
Intensive Environments: Simulation Model Development for the Clinical Effectiveness Analysis of an Innovative Technology 

J Global Clinical Engineering Vol.2 Issue 3: 2020  52

The movements were modeled through an activity 
whose operation time was defined by the time taken to 
reach the room the call comes from.

The 7 possible paths defining the movements from 
the nurse station to the patient’s room have the following 
lengths (measured by AutoCAD): 15.64 m; 23.82 m; 30.62 
m; 35.53 m; 41.76 m; 44.47 m; 42.37 m. Considering that 
the operator average speed is 5 km/h, the travel times are 
calculated. Operation Time is defined for the movement 
activities and depends on the distances (Figure 10). 

Operation Times of the other activities, on the other 
hand, take into account the level of complexity of the 
patient (Figure 11).

The model determined that only 50% of cases did 
the bedside activities end directly at the bedside: the 
caregiver was often forced to make multiple movements 
to satisfy the patient’s needs. The model took into ac-
count the alarm acknowledgment times (due to the lack 
of a centralized monitoring system) and the possibility 
of losing the alarms (due to the alarm fatigue problem). 
Moreover, each activity was associated with one or more 
resources (Figure 12).

Three shifts were identified.
1. Shift from 07.00 to 15.00, in which 5 nurses and 3 

doctors are available.

FIGURE 9.  Distribution of nurse station-bed distances.

FIGURE 10.  Routes on the Emergency department map.

FIGURE 11.  Visual Logic code for defining the movement 
activities Operation Times.

FIGURE 12.  Visual Logic code for defining shifts.



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De Rosa, Pepino, Giaconia, Guarino: Bedside Communication and Management of Vital Parameters and Alarms In Care-
Intensive Environments: Simulation Model Development for the Clinical Effectiveness Analysis of an Innovative Technology 

2. Shift from 15.00 to 23.00, in which 5 nurses and 2 
doctors are available.

3. Shift from 23.00 to 07.00, in which 3 nurses and 2 
doctors are available.

One social and health professional was always available. 

Regarding indirect activities, the work item no longer 
represented a patient’s bedside needs but the single rep-
etitions of the individual activities. Resources, frequency, 
and duration were also appropriately associated with the 
indirect activities and were independent of the patient 
complexity level.

The simulation time was 24 hours, every day for one 
month. According to the validation, the simulation model 
implemented constituted a reasonable approximation of 
the real system. It might, therefore, be suitably modified 
to study the response to the introduction of technology. 
Improvements to the workflow were introduced in the 
What If model to check the introduction of the technol-
ogy’s impact on KPIs.

THE WHAT IF MODEL
The What If model created is shown in Figure 13.

FIGURE 13.  What If model.
This model was created by properly modifying the As 

Is model, taking into account the activities on which the 
technology operates.

The main differences with the As Is model are:
1. The calls end directly to the bed, without making further 

movements. It may happen because the caregivers al-
ready know the patient’s need even before going there.

2. The vital signs can be remotely checked.
3. The patient’s health status can be updated via devices. 



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Intensive Environments: Simulation Model Development for the Clinical Effectiveness Analysis of an Innovative Technology 

J Global Clinical Engineering Vol.2 Issue 3: 2020  54

4. There is no longer an alarm acknowledgment time since 
the technology reports the bed from which the alarm 
goes off. Furthermore, there are no more lost alarms.
Indirect activities remain unchanged.

RESULTS
The KPIs are:

1. The number of completed direct activities compared 
to the required activities. The ratio between the two 
numbers ranges from 0 to 1: it is 1 when the number 
of completed activities is equal to the required ones 
(ideal case), 0 if none of the required activities is 
completed (worst case).

2. Average time to resolve an alarm.

3. Waiting time for direct activities.

The weights for the application of the compensatory 
aggregation method, chosen in collaboration with the 
staff, are: 50 for V1, 20 for V2, 30 for V3. The sum of the 
weights is 100 and the ideal values of V1, V2 and V3 is 1.

At the end of the simulation, the data related to the 
identified KPIs are presented. 

The As Is model showed the results below 
1. A total of 70% of bedside care needs are fulfilled.
2. The average resolution time for an alarm was 28.57 

minutes.
3. The waiting time to complete direct activities was 

9.23 minutes.
KPIs for the As Is model are outlined in Figure 14 and 

the Compensatory Aggregation Method of the As Is Model 
is presented in Table 4.

The What If model shows the following results:
1. A total of 90% of bedside care needs are fulfilled.
2. The average resolution time for an alarm is 15.86 

minutes.
3. The waiting time to complete direct activities is 5.16 

minutes.
KPIs for the What If model are outlined in Figure 15 

and the Compensatory Aggregation Method of the As Is 
Model is presented in Table 5.

TABLE 4. Bed Locations

Vi 0.7 0.065 0.54

wi 50 20 30

0,7*50 + 0,065*20+0.54*30 = 52.5

FIGURE 14.  Compensatory Aggregation Method – As Is Model

FIGURE 15.  KPIs – What If model.

TABLE 5. Compensatory Aggregation Method – What If 
Model

Vi 0.9 0.64 0.74

wi 50 20 30

0,9*50 + 0,64*20+0,74*30 = 80



55 J Global Clinical Engineering Vol.2 Issue 3: 2020

De Rosa, Pepino, Giaconia, Guarino: Bedside Communication and Management of Vital Parameters and Alarms In Care-
Intensive Environments: Simulation Model Development for the Clinical Effectiveness Analysis of an Innovative Technology 

Considering that in the ideal case the final score is 100, 
from the compensatory aggregation method it results 
that this is 52.5 in the current model, 80 in the model 
that simulates the introduction of technology. The clinical 
effectiveness of the innovative technology for the com-
munication and distribution of alarms and vital signs is 
therefore demonstrated.

DISCUSSION
The analysis of the clinical effectiveness of the technol-

ogy studied in this work was based on the compensatory 
aggregation method applied on the KPIs obtained by the 
simulation models created with Simul8 (see Table 4 and 
Table 5).

The above-mentioned approach allows for:
• estimation of the organizational changes, which are 

generally complex to analyses in other ways;
• assessment of the operating conditions of the 

department;
• determination if, and how much, the resources oper-

ate in compliant conditions;
• determination of which resources intervene to im-

prove the workflow; and
• determination of which activities should be modified.

This methodology was also very educative for the care-
givers who had the opportunity to systematically analyzes 
their work organization both during the analysis phase 
and the discussion of the simulation results.

Having more precise measurement results of the real 
system behavior would be desirable: unfortunately, it is 
very complicated to obtain in an operating environment 
such as a hospital ward, even more in a high-intensity 
care environment like an emergency department. To ob-
tain precise estimates, it would be necessary to measure 
the completed activities at the bedside in daily life with 
precise tools and for a longer time. 

The main limitation of this work, like most of the works 
based on simulation models, is the difficulty to compare 
the results of the simulations with the results of the real 
world, despite the effort of the formal and structural valida-
tions. The analysis began from a real-world measurement 
of the process: it was empirical and based on subjective 

assessments of the caregivers and on the observation of 
ward activities. The reliability of the model and results 
depended on the reliability of the indications given by the 
caregivers and also the observations made internally in 
the ward. However, the model and methodology used can 
be considered a sufficient basis for further customizations 
in many case studies.

CONCLUSIONS
The clinical effectiveness of the technology supporting 

the ward activities was demonstrated with the simula-
tion method, in situations in which validated scientific 
literature was not yet developed.

The As Is model has a good adherence to reality – both 
formal and structural validation were used. According 
to the caregivers, the As Is model represented a good 
approximation of reality, but the comparison should be 
made on indicators that can be accurately measured. It 
is not always possible, especially in emergency medicine 
departments. The What If model could be improved with 
the analysis of some data from realities where the tech-
nology is already in use.

The simulation model offers the possibility to find 
out the resources and activities that need to be modified 
for improving the workflow. The simulation model has 
increased the awareness of hospital employees regarding 
the complexity of the processes.

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