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Received July 24, 2023, accepted October 13 2023, date of publication Decemvber 5, 2023

Application of statistical processes control for the performance 
improvement of a clinical engineering department

By Edgar González Campos1, Andrea Elizabeth Vázquez Rodriguez2, Fátima Jaqueline Rodríguez Trujillo2, Catherine Jazmín 
Ramírez Mendiola2

1 Instituto de Salud Pública del Estado de Guanajuato, Mexico

2 División de Ciencias e Ingenierías, Universidad de Guanajuato, Mexico

ABSTRACT

This article addresses the fundamental role of Statistical Process Control (SPC)   as a quality tool in the field of clinical engineer-
ing, to improve and optimize internal processes. This study describes the methodology used to apply the SPC in a reference 
hospital's clinical engineering department. Data was collected over an extensive period, involving multiple medical equipment 
and verification procedures. These data were analyzed using various statistical tools, such as control charts, Pareto charts, and 
descriptive statistics.
The results showed stability in the department's processes, which made it possible to identify areas for potential improvement. 
Statistical analyses revealed behavior patterns and trends that were not previously apparent. Based on these conclusions, specific 
modifications were proposed in the department's processes to optimize efficiency, reduce costs, and improve service quality.
The implementation of these modifications based on evidence suggests that they would positively impact the general perfor-
mance of the clinical engineering department if applied.   Key indicators could improve significantly, reflecting increased medical 
equipment reliability and availability, decreased unscheduled downtime, and increased satisfaction for department staff and 
equipment users.
In summary, this study highlights the importance of using SPC as a powerful improvement tool in clinical engineering. By adopt-
ing an approach based on data and scientific evidence, clinical engineering departments can achieve more efficient and effective 
management of their processes, contributing to higher-quality medical care and patient safety.

Keywords – Control chart, Equipment maintenance, SPC.

Copyright © 2023. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY): Creative Commons - Attribu-
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are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is 
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González Campos, Vázquez Rodriguez, Rodríguez Trujillo, Ramírez Mendiola: Application of statistical processes control for the 
performance improvement of a clinical engineering department 

J Global Clinical Engineering Vol.6 Issue 1: 2023  30

INTRODUCTION

The performance and efficiency of clinical engineer-
ing departments can be influenced by various factors 
involving human, material, and financial resources. In the 
same way, the intellectual capital of "know how" to carry 
out critical activities i.e., to have a sound and effective 
standardized methodology to realize procedures within 
the department's operation, serves as an asset of great 
value in organizations.  

Within these activities, equipment maintenance and 
continuous verification to ensure its correct operation 
emerge as critical activities. Although a series of classic 
activities are carried out in most clinical engineering 
departments, each department establishes its processes 
based on its conditions and scope. The standardization 
of methodologies that ensure the quality of the processes 
takes on significance in the impact these activities can 
have on the general operation of the department.1,2

The measurement of data on these processes and their 
statistical use represents a tool of great value in the search 
for improvement in their performance.3 Statistical process 
control (SPC) represents a series of tools among which 
control charts stand out. This has traditionally been used 
within different industries to improve processes based 
on evidence generated by their own data.4

Within the healthcare field, it has been considered a 
tool for research and improvement issues,5 as a tool for 
improving the culture of data measurement,6 and even 
for improvements in clinical issues.7–9

There are many statistical tools with endless applica-
tions within the field of biomedical/clinical engineering 
to be used in the search for improvement of the efficacy, 
effectiveness, and efficiency of its activities.10

In the same way, there are studies in which tools used 
within the statistical control of processes, such as the 
Pareto diagram, are deployed to improve the activities of 
a clinical engineering department, as Cecchini, Masselli, 
et al. described.11 Or the evidence-based maintenance 
method proposed by Wang12 in which using data gener-
ated by a medical equipment maintenance program could 
modify the entire program itself. However, the use of 

control charts as a complement to traditional statistical 
techniques and those associated with quality improve-
ment may represent a valuable option in the search for 
effective evidence-based improvements.

This article aims to exemplify what was previously 
explained through the use of SPC tools for formulating 
improvement strategies in standardized processes of a 
clinical engineering department.

METHODS

The methodology followed can be divided into the five 
main phases shown in Figure 1. Only procedures related 
to routine verification of medical equipment in different 
hospital areas were considered.

To find opportunity areas through SPC, it is necessary 
to comply with specific characteristics to be evaluated in 
the processes. The first two activities were focused on this: 
the definition of criteria, evaluation of these criteria and 
selection of procedures to be analysed. The next two phases 
correspond to the deployment of the statistical analysis, 
first through data capture, followed by the development 
of control charts. Finally, based on the results obtained, 
improvement proposals were made to the department's 
management to be evaluated and, where appropriate, 
implemented. 

Each phase is explained in more detail below.

FIGURE 1. Followed methodology.



31 J Global Clinical Engineering Vol.6 Issue 1: 2023

González Campos, Vázquez Rodriguez, Rodríguez Trujillo, Ramírez Mendiola: Application of statistical processes control for the 
performance improvement of a clinical engineering department 

Selection criteria definition

Definition of selection criteria for the opportunity areas 
search was carried out considering the following factors:

• Standardized processes: The selected processes must 
be well standardized so that the data collection when 
performing them is carried out in a homogeneous 
way regardless of the personnel that carries it out, 
in addition to having written tools for capturing data 
generated during the routine.

• More than nine months of registers: Generated 
data by the processes in the lapse of the last nine 
months of operation were only considered to have 
an extended operation period, so the data reflects 
the closest possible reality of the department.

• Percentage compliance greater than 90% on the 
scheduled verification routines. The continuity and 
quantity of data in the measurements represent im-
portant factors in carrying out the statistical analysis 
of the processes since the consistency of the process 
with the generated data can be detected.

Processes selection based on the criteria of 
compliance

Once the selection criteria were defined, it determined 
which was compliant. Table 1 shows eight standardized 
procedures for carrying out verification routines in the 
department that met the first two selection criteria.

The routines for the vacuum and medical air systems 
imply verifying the work pressures for both hospital equip-
ment. The operation theatres, intensive care units and 
emergency department routines demand the verification 
of technical aspects of the medical equipment installed in 
those areas, such as correct functioning, autotest, etc. This 
equipment ranges from vital signs monitors to stretchers.

Finally, the verification routine of the defibrillators 
involves physical and functional verification of all the de-
fibrillators installed within the hospital through autotest 
and discharge proof.

After identifying the standardized processes, it veri-
fied the percentual compliance with carried-out routines. 
Figure 2 shows the results for this verification, obtaining 

compliance with the criteria in four of the eight processes; 
these correspond to:

• Emergency departments
• Defibrillators
• Medical air
• Vacuum systems

These were the procedures on which the statistical 
study was studied further using SPC tools.

Record capture in the database and selection of 
tools

The next phase of the methodology implied the capture 
of the registered data in department formats in the form of 

TABLE 1. Verification Routines Processes of the Clinical Engi-
neering Department.

FIGURE 2. Percentual compliance of done routines. 



González Campos, Vázquez Rodriguez, Rodríguez Trujillo, Ramírez Mendiola: Application of statistical processes control for the 
performance improvement of a clinical engineering department 

J Global Clinical Engineering Vol.6 Issue 1: 2023  32

verification sheets in a digital database within statistical 
software. In this stage, the types of control charts to be 
used were selected based on the information generated 
by the verification routine. 

Individual values and Moving range, or I-MR charts, were 
selected for medical air and vacuum system verification. 
This is due to the measured variable in each verification 
routine corresponding to an individual value, not a sub-
group. In this way, the behaviour of the systems can be 
explored based on measurements made periodically to 
the pressure variable generated by the system itself. The 
moving range chart indicates the variability between each 
measurement caused by comparing it with the immediate 
previous measurement; this information makes it possible 
to verify the process stability statistically.

Nonconforming units, also known as NP charts, were 
selected regarding the verification routines of medical equip-
ment in the emergency department and for the installed 
defibrillators. This chart evaluates the nonconforming 
portion of several measurements made. This chart was 
selected because there are a certain number of variables 
to be verified in each routine, which may be compliance or 
non-compliance. This number is constant in each routine. 
Each test variable was categorized depending on its result: 
"compliant" in case it was performed without problems 
or "non-compliant" in case it presented any detail.

Control charts development

The next phase consists of developing the control charts 
and the statistical analysis of the obtained results. Only 
I-MR charts were generated for the verification routines 
of the medical air and vacuum system; this is due to the 
results obtained that denote procedures in statistical 
control and it was not necessary to explore further.

Regarding the defibrillator verification routine, derived 
from the results obtained, the decision was made to go 
deeper through a Pareto diagram to make an improve-
ment proposal that could impact the department's work.

Finally, in the case of the emergency area and derived 
from the results obtained, it was not necessary to carry 
out a significant analysis to make proposals.

Improvement proposals

After the analysis of the obtained results, proposals for 
improvements in the processes of the clinical engineering 
department were formulated. All based on evidence from 
the same information that this department generated.

RESULTS

Figure 3 shows the NP chart obtained for the emergency 
department's equipment verification routine. It can be 
seen that the upper control limit is located at a value of 
1.008, which indicates a maximum of one non-conformity 
found per verification routine carried out in the period 
analysed. The nonconforming portion is located at the value 
of 0.093, which corresponds to a value of less than one 
non-conformity per verification routine performed. Lastly, 
the lower control limit is located at zero and corresponds 
to zero nonconformities found by the verification routine 
as the minimum value in the evaluated period. Within the 
presented values in the measurement period, only two 
values can be found in the upper control limit and one 
outside said limits. The value outside the control limits 
indicates two nonconformities in a verification routine. 
This value is considered atypical due to the stable trend 
of the evaluated process.

The emergency department dynamics implies an ac-
tive role on the part of the equipment user in terms of 
continuous verifications; this is due to the high patient 
turnover in the service. This is reflected in the data in the 
control chart, and it is difficult to find technical failures 
related to the equipment.

Regarding the verification routines for hospital de-
fibrillators, Figure 4 shows the obtained results. It has 
a value of 3.45 for the upper control limit, zero for the 
lower control limit, and 0.79 for the fraction nonconform-
ing. The chart interpretation indicates that, on average, 
there can be a maximum of between 3 and 4 technical 
failures per verification routine for the 15 equipment 
distributed throughout the hospital, approximately one 
failure per routine performed, and a minimum of zero 
failures found. Two atypical values outside the control 
limits are identified, carrying out a study regarding the 
type of failures that led to these values; a special situation 



33 J Global Clinical Engineering Vol.6 Issue 1: 2023

González Campos, Vázquez Rodriguez, Rodríguez Trujillo, Ramírez Mendiola: Application of statistical processes control for the 
performance improvement of a clinical engineering department 

was detected where the supply of printing paper for the 
equipment presented a delay in delivery by the supplier. 
Derived from these results, it was decided to carry out a 
deeper analysis that could offer a broader perspective of 
the behaviour of this process.

Figure 5 shows a Pareto diagram that identifies the 
equipment with the highest number of nonconformities 
in the measured period. It can be seen that 80% of the 
failures come from five specific defibrillators of the fif-
teen installed. These are installed in the areas of Nursery, 
Operating Theatre 1, Radiological Imaging, Emergency 
Department and Operating Theatre 2.

Finally, for the case of verification routines of the gas 
system, Figure 6 and Figure 7 show the I and MR control 
charts, respectively, for the case of the vacuum system. 
Chart I shows an upper control limit of ˗13.480 inHg, a 
lower control limit of ˗21.75 inHg, and an average value 
of ˗17.63 inHg. It is observed that there are no values 
outside the control limits; for its part, the system was 
programmed to operate at a value of ˗18 inHg, so this 
behavior presents reasonable statistical control.

On the other hand, the chart of moving ranges in Fig-
ure. 7 also denotes an excellent statistical control of the 
process with only one atypical data outside the control 
limits. Regarding this atypical value, a significant variation 

FIGURE 3. NP control chart for the emergency department.

FIGURE 4. NP chart for the defibrillator verification routines.

FIGURE 5. Pareto chart for defibrillator failures.

FIGURE 6. I chart for the vacuum system verification routines.



González Campos, Vázquez Rodriguez, Rodríguez Trujillo, Ramírez Mendiola: Application of statistical processes control for the 
performance improvement of a clinical engineering department 

J Global Clinical Engineering Vol.6 Issue 1: 2023  34

in the hospitalized patient number between one measure-
ment and another was detected as a possible attributable 
cause. This issue caused variability in the range of data. 
However, outside of said identified cause, the data pres-
ents consistency in the statistical control denoted in the 
chart of Figure 6.

DISCUSSION

The data obtained for the selected processes denote 
their stability over time. This means the data tends to 
behave similarly except for specific and atypical situa-
tions. However, it would be essential to conduct more 
extensive analysis in time. It is suggested to analyse the 
time of at least one year of data collection to rule out 
that the behaviour may be affected by temporary issues.

In the case of the verifications of the equipment in 
the emergency department and the gas systems, it was 
recommended to the clinical engineering department to 
extend the time between verifications so that they could 
focus their work on other activities that require higher 
priority. The stability represented in the control charts 
gives the certainty that no values will require monitoring 
as closely as it was carried out; therefore, it is possible to 
carry out fewer verifications with the certainty that the 
processes work correctly. If problems arise from imple-
menting this strategy, it would be convenient to return 
to close monitoring.

Regarding the results obtained from the verification 
process of the hospital defibrillators, a proposal was made 
to the clinical engineering department to reinforce the 
monitoring of the equipment that represents the largest 
number of failures to control the nonconformities that the 
equipment could present. When dealing with life support 
equipment, a failure at the time of the operation could 
have serious consequences.

Once the failures have been solved or the processes 
regarding the nonconformities presented have been 
controlled, it could be considered to return to the weekly 
verifications or extend the time between them.

CONCLUSIONS

The results show good general statistical control for 
the selected processes. Based on these data, it can be 
assumed that the proposed strategies respond to real 
situations that occur in hospital operations.

However, it is important to highlight that the process 
selection was done to comply with the necessary charac-
teristics mentioned for the data. The standardized collec-
tion of data and sufficient data over time encourage the 
behaviour description of the process to be as similar to 
reality as possible. In this context, it is possible to make 
effective suggestions for improvement strategies based on 
evidence, in the opposite case for the other processes whose 
data was insufficient to develop the tools satisfactorily.

Accomplishing all the data characteristics, statistical 
process control arises as an effective strategy for evidence-
based efficiency improvement in the clinical engineering 
department.

The proposals to the clinical engineering department aim 
to guide its operation toward the needs detected through 
the statistical analysis of its generated data. If they were 
developed with insufficient or incorrect information, it is 
possible that the improvement strategies had been guided 
towards incorrect guidelines and were not effective.

Similarly, modifications could be considered to be done 
to all the department's verification processes so that while 
the verification times are prolonged in some of them, in 
others, they become more constant, focusing on priority 

FIGURE 7. MR chart for the vacuum system verification routines.



35 J Global Clinical Engineering Vol.6 Issue 1: 2023

González Campos, Vázquez Rodriguez, Rodríguez Trujillo, Ramírez Mendiola: Application of statistical processes control for the 
performance improvement of a clinical engineering department 

points and detection based on evidence. However, it would 
be necessary first to achieve the correct standardization 
of each process and capture sufficient data over time to 
deploy the same strategy.

Finally, it could be considered to go even deeper into 
the analysis of the statistics generated through individual 
studies of the non-conformities found. Through a categori-
zation by type of non-conformity, improvement strategies 
regarding verification routines could be directed toward 
more specific issues.

ACKNOWLEDGMENTS

The research team acknowledges biomedical engineer 
César González for giving access to the clinical engineering 
department data to elaborate on this work.

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http://dx.doi.org/10.31354/globalce.v4i1.87

