A comparative analysis of experts' judgement methods for patient safety implementation - FMEA and fuzzy AHP

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384
I
nt. J. Productivity and Quality Management, Vol. 37, No. 3, 2022
Copyright © 2022 Inderscience Enterprises Ltd.
A comparative analysis of experts’ judgment methods
for patient safety implementation – FMEA and fuzzy
AHP
Seyedehfatemeh Golrizgashti*,
Mehrdad Keshmiri and Noshad Nazabadi
Department of Industrial Engineering,
South Tehran Branch,
Islamic Azad University,
Tehran, Iran
Email: sf_golrizgashti@azad.ac.ir
Email: mira20000m@gmail.com
Email: noshad.nn92n@gmail.com
*Corresponding author
Abstract: Regards to the complexity of healthcare systems, it is very important
to apply accurate and effective methods to manage probable risks to enhance
patient safety. The aim of this study is to identify probable risks by using
failure modes, effects analysis method and fuzzy analytic hierarchy process to
compare obtained results and define shortages of experts’ judgment methods.
This study has been conducted in electrocautery surgery in an operating room
at a hospital in Iran. The failures modes, their effects and causes are identified
by in-depth interviews, hospital documentation and fuzzy Delphi method.
Obtained results show that decision making based on experts’ judgment
methods may lead to different results, therefore applying accurate quantitative
methods to enhance reliability and patients’ safety is essential. While this study
identifies the potential risks of a complex surgery, comparing the obtained
results based on two methods is the most important contribution of this paper.
Keywords: quality management; patient safety; healthcare system; risk;
FMEA; FAHP; electrocautery surgery.
Reference to this paper should be made as follows: Golrizgashti, S.,
Keshmiri, M. and Nazabadi, N. (2022) ‘A comparative analysis of experts’
judgment methods for patient safety implementation – FMEA and fuzzy AHP’,
Int. J. Productivity and Quality Management, Vol. 37, No. 3, pp.384–404.
Biographical notes: Seyedehfatemeh Golrizgashti is currently an Assistant
Professor in the Department of Industrial Engineering, South Tehran Branch,
Islamic Azad University, Tehran, Iran. She has worked as a manager and
consultant in the home appliances industry, and based on her academic and
practical experience she has published several papers in different journals.
Mehrdad Keshmiri is currently an Industrial Engineer. He received his Master
degree from South Tehran Branch, Islamic Azad University, Tehran, Iran. He is
very motivated to research and publish paper.
Noshad Nazabadi is currently a master student of Industrial Engineering in the
Department of Industrial Engineering, South Tehran Branch, Islamic Azad
University, Tehran, Iran. He is very motivated to research and publish paper.

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1 Introduction
Risk management is one of the most important subjects which has been considered in
quality improvement and safety patient (Morello et al., 2013; Maher et al., 2019).
Improving quality of healthcare services and enhancing patients’ safety and also
decreasing medical errors are the most important issues in healthcare systems (Maamoun,
2009; Kolagar and Hosseini, 2019; Simsekler, 2019). Healthcare managers and
supervisors have to be encouraged to employ effective programs to reduce the health
sector risks (Chatman, 2010). Risk identification and evaluation are related to ‘safety
culture’ for patient safety but there is a gap to improve managing risk in healthcare
system by focusing on ‘risk identification practice’ (Simsekler, 2019). In recent years,
many researches have been done to identify, prioritise and analyse probable risks in
different subjects such as production and service systems such as healthcare
(Mosallanezhad and Ahmadi, 2018). Medical errors are the greatest problems in
healthcare systems (Dastjerdi et al., 2017).
Some researchers investigated that medical errors such as wrong procedure, patient
transfer and, etc. in an operating room sometimes occur in health care systems (Khoshbin
et al., 2009; van Beuzekom et al., 2012; Marshall and Emerson, 2012; Mosallanezhad and
Ahmadi, 2018). Assessing and analysing probable risks have to be done to identify errors
to apply suitable solutions to avoid occurring patients’ injuries systematically. Qualitative
‘risk scoring’ methods may decrease the accuracy of risk prioritisation in healthcare
system so some researchers recommended integrated approaches and some advices to
overcome existing challenges (Kaya et al., 2020).
For so many years, total quality management tools have been used in service
industries such as healthcare systems (Talib and Rahman, 2010). Some researchers
mentioned that TQM practices can improve productivity and customers’ satisfaction and
also decrease cost in different fields (Chen et al., 2004; Sweis et al., 2013). Recently,
using quality management tools such as FMEA has been increased to identify and
decrease failure risks (Rah et al., 2016). FMEA has been introduced as a proactively
approach for reducing errors and improving quality in healthcare performance and
patients’ safety including ‘healthcare process’, ‘hospital management’, ‘hospital
informatisation’ and ‘medical equipment and production’(Liu et al., 2019). So FMEA has
been used as an effective process-based tool to evaluate potential risks in different fields
widely (Card et al., 2012; Abdi et al., 2016; Mosallanezhad and Ahmadi, 2018) but it’s
needed to analyse the accuracy of its results in healthcare systems. Despite of being an
effective method, FMEA has some weaknesses. The disadvantage of this technique is that
the risk priority numbers are measured qualitatively, so they may have no high decision-
making capability. Some researchers believe that by combining FMEA and multi criteria
decision making (MCDM) techniques or fuzzy logic approach, risk detecting process will
be more effective (Kou et al., 2012). MCDM methods such as analytic hierarchy process
method (AHP) can be used for evaluating subjective and categorical parameters with
FMEA method (Ilbahar et al., 2018).
AHP as a decision making tool has been used in many fields by managers and
researchers such as healthcare and medical research (Vaidya and Kumar, 2006;
Valmohammadi, 2010; Hummel et al., 2014; Yuen, 2014; Motlagh et al., 2020).

386 S. Golrizgashti et al.
Recently, fuzzy decision-making methods such as AHP have been applied in healthcare
and medical industry widely too (Mardani et al., 2019). One of the advantages of AHP
method is possibility for using both of qualitative and quantitative criteria and also using
fuzzy logic to obtain the accurate results (Motlagh et al., 2020). AHP has been used to
identify risks in healthcare system by some researchers too. So in this study FMEA and
AHP have been applied to identify potential risks in a healthcare case study.
Electrocautery as an effective popular process is used in many surgeries but it may
cause patient injury seriously (Bisinotto et al., 2016). Saaiq et al. (2012) discussed three
kinds of Electrocautery burns in their study by reviewing previous studies qualitatively.
They mentioned dangers of Electrocautery equipment to inform surgeons and healthcare
workers. They mentioned that in many hospitals, the monopole Electrocautery is still
used in surgery process and it may cause burn injury especially on the back of the patient
body. Therefore, the surgeons have to be aware of these risks to enhance patient safety.
Some studies have been carried out identifying and prioritising potential risks in
healthcare systems but there is still a lack of risk identification in surgeries and operating
rooms (Mesa, 2015). According to literature review, there is no study focusing on
Electrocautery risks in operating rooms, therefore this study aims to identify failure
modes in a complex surgery process.
And also regards to importance of patients’ safety, this research uses two different
methods includes traditional FMEA and fuzzy AHP to identify and evaluate failure
modes in electrocautery surgery. The obtained results of two methods are compared
based on a real case study to show the shortages and limitations of applying experts’
judgment methods in healthcare systems.
2 Literature review
2.1 FMEA in healthcare systems
FMEA as a useful approach has been used in some studies to identify potential risks and
their causes to apply effective strategies and practical solutions to enhance patients’
safety (Jain, 2017). Christian et al. (2006) identified major system factors that affect
patient safety based on a qualitative analysis and also used the hierarchic coding scheme
as a quantitative analysis in ten complex general surgeries in operating rooms of an
academic hospital. They could recognise potential problems that have negative impact on
team performance and patient safety. Cagliano et al. (2011) proposed a framework to
study probable risks that affect patient safety directly and indirectly. Their systematic
methodology included four progressive steps such as context analysis, process mapping,
risk identification and assessment, and failure modes and waste analysis.
FMEA has been used in healthcare systems widely such as hemodialysis process
(Ookalkar et al., 2009), pharmaceutical industry (Inoue and Yamada, 2010), dialysis unit
(Bonfant et al., 2010), processes of ICU unit (Yousefinezhadi et al., 2016), equipment
sterilisation (Huang et al., 2016), treatment of septic patients (Alamry et al., 2017),
medication management process (Jain, 2017). Using systematic approaches such as
FMEA is growing in healthcare systems but it seems that there is a lack of applying
accurate structured brainstorming and experts’ judgment approaches (Simsekler, 2019).
Chilakamarri et al. (2021) used ‘multi-disciplinary team’ and FMEA approach to
decrease risks and improve patients’ safety in ‘neurocritical care transitions’. They

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showed acceptable level of decreasing in potential failures and enhancing in patients’
safety in emergency department.
Traditional FMEA method has some limitations (Maheswaran and Loganathan, 2013;
Kumru and Kumru, 2013). These limitations includes considering same importance for
three risk factors, calculating duplicated RPN’s and also ignoring indirect relations
between risk factors (Chang and Cheng, 2010; Kumru and Kumru, 2013; Stojkovic et al.,
2017). Some researchers have been mentioned some weaknesses such as relatively long
time and potential errors in obtained results in human-based approaches such as FMEA
method (Kobo-Greenhut et al., 2020). So, some researchers have been applied different
methods such as MCDM methods, mathematical programming, artificial intelligence and
their combinations to overcome the limitations of the traditional RPN method and
enhance results reliability (Liu et al., 2013).
Pal and Sharma (2017) mentioned that traditional RPN can’t manage failure modes
effectively because some disappointment modes may be assigned to the most noteworthy
despite of their low importance. They proposed an integrated Lean Six Sigma approach
by using AHP–FMEA for healthcare service quality management. Some researchers used
FMEA under fuzziness approach introduced by Zadeh (1965) in healthcare systems to
increase FMEA efficiency (Kahraman et al., 2003; Mosallanezhad and Ahmadi, 2018).
Some researchers used linguistic variables and fuzzy rating to assess risk factors and
calculate fuzzy RPN in healthcare systems (Wang et al., 2009; Vencheh and Aghajani,
2013; Jamshidi et al., 2015; Dağsuyu et al., 2016; Chanamool and Naenna, 2016).
Abbasgholizadeh Rahimi et al. (2013) used an integrated fuzzy cost-based service-
specific FMEA, grey relational analysis and profitability theory to identify and minimise
failures in healthcare diagnosis service. They proposed effective solutions to reduce
failure occurrence probability and their costs. Structured what-if technique was
introduced to identify and assess risks more efficient than FMEA method in healthcare
systems (Card et al., 2012). Liu et al. (2014) proposed a model based on FMEA, fuzzy set
theory and MULTIMOORA method to determine and prioritise high-risk failure modes
for healthcare facility. In complex processes, small number of experts who participate in
FMEA approach may be insufficient so to cover this shortage, Liu et al. (2018) proposed
a new approach by using cluster analysis with large group of experts. They applied
entropy-based method to calculate the weights of risk factors in blood transfusion system.
Boral et al., (2020) integrated fuzzy AHP method with the modified fuzzy multi-attribute
ideal real comparative analysis to improve FMEA shortages. They also compared
obtained risks by using MCDM methods such as FVIKOR, FCOPRAS, FMOORA,
FMABAC and FTOPSIS. They showed different results by using different methods and
believed that their proposed model is more efficient in complex systems. Song et al.,
(2020) used ‘Swiss cheese’ model and ‘SHEL’ model to identify human errors in using
medical devices. They developed FMEA- Rough set theory-Grey relational analysis to
evaluate defined errors.
The Joint Commission on Accreditation of Healthcare Organizations (JCAHO)
proposed healthcare failure modes and effects analysis (HFMEA) to identify, analyse and
evaluate healthcare failure modes as modified FMEA version to apply effective solutions
(JCAHO, 2001). According to HFMEA™ method the severity of failure modes are
determined based on a 4-point scale (minor, moderate, major, or catastrophic) and the
probability of the occurrence of the failure modes are determined based on a 4-point scale

388 S. Golrizgashti et al.
(remote, uncommon, occasional, or frequent) and the hazard scores are calculated by
multiplying S and O, (Habraken et al., 2009; Yue et al., 2012; Bhalla et al., 2014). Some
researchers used decision tree analysis (DTA) to define criticality and detectability of
failure modes (DeRosier and Stalhandske, 2002; Habraken et al., 2009; Yue et al., 2012).
Weinstein et al. (2005) used HFMEA to identify potential failure modes of
sterilisation and usage of surgical instruments. They calculated hazard scores based on
severity and occurrence by using designed flow chart diagrams and preventive solutions.
Habraken et al. (2009) applied HFMEA™ in Dutch health care. They identified barriers
and failure causes and suggested some solutions to decrease probability of failure modes
and their negative effects. They analysed positive (systematic approach) and negative
(time–consuming method) feedbacks to improve applying HFMEA and enhancing results
accuracy. They believed that good conduction in experts’ team affects HFMEA
efficiency. HFMEA was applied to identify and prioritise failure modes of chemotherapy
process with using flowchart diagram (Cheng et al., 2012). Cheng et al. (2012) evaluated
11 failure modes with high hazard scores and could decrease Chemotherapy prescription
errors effectively. Yue et al. (2012) applied HFMEA to identify and prioritise probable
failure modes of secondary infusions. They proposed practical solutions to decrease
severity of obtained risks. Bhalla et al. (2014) proposed applying HFMEA to define
failure modes of peripheral nerve catheters in pediatric patients. They identified 96
failure modes and proposed 19 effective solutions to enhance patients’ safety. HFMEA
was used to identify failure modes of blood transfusion process, in pediatric emergency
(Dehnavieh et al., 2015). They used theory of inventive problem solving (TRIZ) to
propose proactive solutions to prevent occurrence of failure modes.
2.2 AHP in healthcare systems
Hsieh et al. (2018) mentioned that applying multiple criteria decision making (MCDM)
methods can be a useful method to analysis risk criteria in healthcare systems. There are
some studies which used AHP as MCDM method to calculate weights of risks and their
priority (Ilbahar et al., 2018). Brent et al., (2007) used AHP to identify risks of infection
in developing countries to improve healthcare management. The AHP approach was used
to identify and prioritise risk levels of five cancer types (Abdullah et al., 2009). Pecchia
et al. (2011) applied AHP to determine priorities of fall risks in community-dwelling
older people. They identified 35 risks in two categories and six sub-categories and
calculated their importance. Tu et al. (2014) used AHP to identify and evaluate risks of
emerging infectious diseases. AHP has been used for benefit-risk assessment of tissue
regeneration to repair small cartilage lesions in the knee (Hummel et al., 2014). Raka and
Liangrokapart (2017) applied AHP method to analyse risks of a new generic drug
development process. They categorised identified risks into the seven levels and proposed
some preventive actions. AHP was applied in the obstetrics and gynecology department
to prioritise risks of patient care activities to establish preventive actions (Moallem et al.,
2018). Lin (2019) proposed FAHP method to design a pillbox for patients with chronic
diseases to identify medical treatment to take the right amount of medicine timely.
Some researchers have proposed integration of MCDM methods to increase
robustness of results so hybrid decision-making frameworks have been applied to manage
potential risks in healthcare systems. In this section we mention some integrated methods

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which have been used in healthcare systems. Hsieh et al. (2018) used AHP and fuzzy
TOPSIS approach to identify and assess error factors in emergency departments. An
integrated approach includes AHP, DEMATEL and VIKOR method was proposed to
evaluate the risks of ‘adverse events’ in the hospital sector by Ortiz-Barrios et al. (2018).
In a recent study, an integrated ‘Delphi method’, ‘fuzzy analytic hierarchy process’ and
‘fuzzy technique for order of preference by similarity to ideal solution’ was used to
identify and rank workers’ safety risk factors in a hospital during the COVID-19 situation
(Rathore and Gupta, 2021).
Based on literature review, there are two main gaps in previous studies. Firstly, there
is no study to analyse electrocautery surgery risks. The second gap is to show limitations
of experts’ judgment which causes conflicts in final results. There is not a comprehensive
approach to determine all parameters affect risk assessment (Ilbahar et al., 2018).
Regards to different results which have been obtained by different methods, this study
aims to compare the results of two decision making methods based on experts’ judgment
to emphasise using effective methods in health care systems.
3 Research methodology
This research is a real case study in medical system. The participants in the study consists
of surgeons, head nurses, experts and operators who use electrocautery in operating
rooms. The research methodology has been shown according to Figure 1.
Figure 1 Methodology steps

390 S. Golrizgashti et al.
Figure 2 Failure modes structure (criteria and sub-criteria)

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3.1 Fuzzy Delphi method
At first, in-depth interviews were conducted to identify potential failure modes, the root
causes of the failures and potential effect of each failure. Documentations that show
patients’ injuries in operating rooms during several years were the secondary resource. So
the all possible failure modes, their probable effects and their root causes were listed. So
a questionnaire based on literature review, in-depth interviews and hospital’s
documentations was designed to conduct fuzzy Delphi method. The expert team included
three surgeons, an anesthetic expert and two nurses. Delphi questionnaire was designed
based on 5-point Likert scale include ‘strongly agree’ (5), ‘agree’ (4), ‘neutral’ (3),
‘disagree’ (2) and ‘strongly disagree (1). The experts’ opinions were gathered in three
steps and the experts’ disagreements in the second and third steps were less than the
threshold of 0.2 so the Delphi process was stopped in the third step. Finally 25 failure
modes with eight categories were identified based on experts’ opinions. The identified
failure modes are according to Figure 2.
3.2 FMEA (RPN method)
In this step, firstly the FMEA methodology was described for experts. Then failure
modes were categorised into the two levels includes main failure modes and sub-failure
modes. Each sub-failure mode is related to its potential effect and failure cause. So a
questionnaire was designed based on identified failure modes. In the FMEA technique,
the priority of failure mode is calculated by simply multiplying the three values named
risk factor [equation (1)] (Kumru and Kumru, 2013; Maheswaran and Loganathan, 2013).
**RPN O S D= (1)
where:
Occurrence (O) is the frequency of the failure mode
Severity (S) is the effect of failure mode on the system
Detection (D) is the probability of detecting the failure mode
Table 1 Scoring scale for severity, occurrence and detectability
Score Severity Occurrence Detectability
1 None Extremely remote Almost certain
2 Very minor Remote, very unlikely Very high
3 Minor Very slight chance High
4 Very low Slight chance Moderately high
5 Low Occasion Moderate
6 Moderate Moderate Low
7 High Frequent Very low
8 Very high High Remote
9 Hazardous with warning Very high Very remote
10 Hazardous without warning Extremely high Absolutely uncertainty
Source: Thomadsen et al. (2013) and Rah et al. (2016)

392 S. Golrizgashti et al.
Table 2 The application of traditional FMEA
Potential main failure mode Potential sub-failure mode Potential effects of the failure mode Potential causes of the
failure mode
Severity
Occurrenc
e
D
etection
R
PN
Metal existence (C1) Existence metal objects in the patient body (C11) Increasing burn risk Carelessness, nurse or
technician tiredness, high
workload, nurses
insufficient knowledge
7 3 8 168
Existence metal objects with the patient (C12) 7 4 3 84
Moisture existence (C2) Moisture on the patient skin (C21) Increasing burn risk 7 5 6 210
Wetness of surgical drapes (C22) 7 5 4 140
Wetness of patient mattress (C23) 7 5 4 140
Patient skin maceration during long operations (C24) 8 5 6 240
Incorrect situation of the
plate (C3)
Forgetting to close the plate (C31) Increasing burn risk 7 3 2 42
Fastening plate on large blood vess els(C32) 8 2 3 48
Wetness of patient mattress (C33) 8 4 3 96
Putting plate near leads (C34) 7 3 3 63
Low quality of plate (C35) 8 1 1 8
Incorrect situation of the
pencil (C4)
Pencil tip breakage (C41) Increasing burn risk 7 4 2 56
Pencil tip dirtiness (C42) 7 4 2 56
Disconnecting to the earth syst em (C43) 8 3 8 192
Using the electrocautery device as a desk (C44) 6 2 1 12
Water penetration into the device (C45) Short circuiting and increasing burn risk 8 3 7 168
Incorrect situation of the
cables (C5)
Defect of power connections (C51) Short circuiting and increasing burn risk 7 3 6 126
Defect of cabl es conn ections (C52) 7 3 6 126
Direct connection to
mattresses and metal
objects (C6)
Direct connection of patient body to mattress (C61) Increasing burn risk 8 2 1 16
Direct connection of patient body to metal objects (C62) 8 3 2 48
Patient inappropriate position (C63) 7 3 2 42
Misuse of electrocautery by
surgeon (C7)
Forgetting to shut down electrocautery device (C71) Short circuiting and increasing burn risk 7 6 2 84
Forgetting to check device mode (C72) Creating overcurrent electricity and increasing burn 7 6 4 168
Skin burns after surgery
(C8)
Forgetting to check patient skin after surgery (C81) Unaware of patient burn 8 7 2 112
Forgetting to announce fast (C82) Repeating burn risk for other patients 8 7 2 112

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Experts provided their opinions based on a simple 1–10 scoring scale for O, S and D
(Table1). The average for each risk factor was used to calculate the final RPNs score. The
RPN number shows the importance of risks. The higher RPN value shows higher
significant risk so has the higher priority and vice versa.
RPN values for all 25 sub-failure modes were calculated. The obtained results have
been shown in Table 2. The potential effects of failure modes and their potential causes
were defined by experts during several meetings (Table 2).
3.3 Fuzzy AHP method (FAHP)
To compare the obtained results of two methods, FMEA and FAHP, the weights of
identified failure modes were estimated by using fuzzy logic and AHP (Saaty, 1980). The
first step in AHP is to construct a hierarchy of failure modes (Figure 1). Then a
questionnaire based on identified failure modes was designed and experts were asked to
compare the failure modes based on linguistic scale (Table 3). The linguistic preference
of experts was converted to triangular fuzzy numbers. Then a pairwise comparison matrix
was made based on triangular fuzzy numbers and pairwise comparison matrix was
formed. Finally the weights of identified risk criteria and sub-criteria were calculated.
Table 3 The fuzzy AHP scales
Linguistic variables Triangular fuzzy scale Triangular fuzzy reciprocal scale
Equally preferred (1, 1, 1) (1/1, 1/1, 1/1)
Equally to moderately preferred (1, 2, 3) (1/3, 1/2, 1)
Moderately preferred (2, 3, 4) (1/4, 1/3, 1/2)
Moderately to strongly preferred (3, 4, 5) (1/5, 1/4, 1/3)
Strongly preferred (4, 5, 6) (1/6, 1/5, 1/4)
Strongly to very strongly preferred (5, 6, 7) (1/7, 1/6, 1/5)
Very strongly preferred (6, 7, 8) (1/8, 1/7, 1/6)
Very strongly to extremely preferred (7, 8, 9) (1/9, 1/8, 1/7)
Extremely preferred (8, 9, 9) (1/9, 1/9, 1/8)
Source: Chan et al. (1999) and Sofyalioglu and Özturk (2012)
To calculate crisp matrix, defuzzification method of triangular fuzzy numbers was used.
In the next step the consistency index (CI), and the consistency ratio (CR) were
calculated according to equations (2) and (3). The CR was calculated to determine the
consistency of pairwise comparison matrix (Saaty, 1977; Kwong and Bai, 2003). To
obtain the incompatibility rate of the pairwise comparison matrix (CR) it is necessary to
calculate CI, eigenvalue and determine random consistency index (RI) by using
equations (2) and (3).
()n
CI
CR RI
= (2)
max max
() ;
1
n
CI A A W W
n
λ−
=×=λ
− (3)

394 S. Golrizgashti et al.
In the equation (3), ‘λmax’ is maximal positive real eigenvalue of the comparison matrix,
‘n’ is dimension of the matrix and ‘RI(n)’ is random consistency index based on ‘n’
according to Table 4 (Saaty, 1980; Kwong and Bai, 2003).
Table 4 Average random consistency (RI)
n 1 2 3 4 5 6 7 8
RI(n) 0 0 0.58 0.9 1.12 1.24 1.32 1.41
n 9 10 11 12 13 14 15
RI(n) 1.45 1.49 1.51 1.48 1.56 1.57 1.59
Source: Saaty (1980), Golden et al. (1989) and Kwong and Bai (2003)
The threshold 0.1 is acceptable for CR and it shows reliability for calculations and
pairwise comparison matrices with inconsistency rate more than 0.1 require experts’
judgment revision (Saaty, 1977). According to the obtained results, the inconsistency rate
of pairwise comparison matrices were less than 0.1 so the reliability of the calculations
was confirmed. The pairwise judgment matrix is according to Table 5.
Table 5 The pairwise judgment matrix
C1 C
2 C
3 C
4
C1 1.00 1.00 1.0 0.33 0.5 1.00 1.00 2.00 3 1.00 2.00 3
C2 1.00 2.00 3.0 1.00 1.00 1.00 2.00 3.00 4 2.00 3.00 4
C3 0.33 0.5 1.0 0.25 0.33 0.5 1.00 1.00 1 1.00 1.00 1
C4 0.33 0.5 1.0 0.25 0.33 0.5 1.00 1.00 1 1.00 1.00 1
C5 1.00 1.00 1.0 0.33 0.5 1.00 1.00 2.00 3 1.00 2.00 3
C6 1.00 2.00 3.0 1.00 1.00 1.00 2.00 3.00 4 2.00 3.00 4
C7 0.2 0.35 1.0 0.17 0.22 0.33 0.25 0.41 1 0.25 0.41 1
C8 0.2 0.28 0.5 0.17 0.22 0.33 0.25 0.41 1 0.25 0.41 1
C5 C
6 C
7 C
8
C1 1.00 1.00 1.0 0.33 0.5 1.00 2.00 3.46 5 2.00 3.46 5
C2 1.00 2.00 3.0 1.00 1.00 1.00 3.00 4.47 6 3.00 4.47 6
C3 0.33 0.5 1.0 0.25 0.33 0.5 1.00 2.45 4 1.00 2.45 4
C4 0.33 0.5 1.0 0.25 0.33 0.5 1.00 2.45 4 1.00 2.45 4
C5 1.00 1.00 1.0 0.33 0.50 1.00 2.00 3.46 5 2.00 3.46 5
C6 1.00 2.00 3.0 1.0 1.00 1.00 3.00 4.47 6 3.00 4.47 6
C7 0.2 0.29 0.5 0.17 0.22 0.33 1.00 1.00 1 0.33 1.00 3
C8 0.2 0.29 0.5 0.17 0.22 0.33 0.33 1.00 3 1.00 1.00 1
As an example, the largest eigenvalue of matrix C1 is obtained as shown below:
max
λ8.099=
(n)
n 8, so according to Table 4, RI 1.41==
CI 0.0142=
CR 0.0106 0.1=≤

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The CRs of all comparison matrices were calculated. All calculated CRs were less than
0.1 so they were acceptable. The weight vectors of failure modes were calculated in the
next step. Fuzzy numbers were used to estimate the weight vectors of failure modes
hierarchy. Priority normalised vector of failure modes was obtained based on the extent
analysis method (Chang, 1996).
Let X = {x1, x2, ..., xn} is an object set and U = {u1, u2, ..., un} is a goal set. According
to this method, “each object is taken to perform extent analysis for each goal
respectively” (Chang, 1996; Kwong and Bai, 2003; Kahraman et al., 2003). Therefore, m
extent analysis values for each object is obtained, according to follows:
12
,,,,1,2,,
m
gi gi gi
M
MMi n=
where all the (1,2,,)
j
gi
M
jm=are triangular fuzzy numbers. The value of fuzzy
synthetic degree with respect to the ith object is defined as:
1
11
mm
jj
igi gi
jj
SM M
−
==

=⊗


 (4)
()
()
12
12
sup min ( ), ( )
MM
xy
VM M x y
μμ
≥

≥=

(5)
when a pair (x, y) exists such that
()
12 12
and ( ) ( ) so 1,
MM
xy x y VM M
μμ
≥= ≥= where
M1 and M2 are convex fuzzy numbers.
()
12 12
1ifVM M m m≥= ≥ (6)
()()
12 12
V M M hgt M M≥=  (7)
1()
Md
μ
=
In this equation, d is the ordinate of the highest crossover point’s D for 12
and .
mm
μμ
() ( )
()()
()()
1111 2 222
21 12
12
22 11
,, and ,,
M
lmu M l m u
V M M hgt M M
lu
mu ml
==
≥=
−
=−−−
 (8)
The degree of possibility for a fuzzy number to be greater than k fuzzy numbers Mi(i = 1,
2, …, k) can be defined as follow:
()
()
()
()
12 1 2
,,, and andand
min , 1, 2, 3, ,
kk
i
VMMM M VMM MM MM
VM M i k

≥…=≥ ≥…≥

=≥=… (9)
() ( )
Let min and 1, 2, , ; .
iik
dA VS S k nk i
′=≥= ≠ (10)

396 S. Golrizgashti et al.
The weight vector is calculated by:
() ( )
()
()
()
12
,,, , 1,2,,
T
ni
WdAdA dA Ai n
′′ ′ ′
== (11)
After normalisation, the final normalised weight vectors are obtained by:
() ( )
()
()
12
,,, T
n
WdAdA dA=
The weight and priorities for main risk criteria and sub-criteria risks (failure modes) were
provided based on above equations (Table 6 and Table 7).
Table 6 The weight and priorities for main risk criteria
Failure mode criteria C1 C
2 C
3 C
4 C
5 C
6 C
7 C
8
Weights 0.16 0.213 0.098 0.098 0.16 0.213 0.033 0.025
Priorities 2 1 3 3 2 1 4 5
Table 7 The weights and priorities of main failure mode criteria and sub-criteria
Main failure
mode
Fuzzy
weights Failure mode sub-criteria Relative
fuzzy weights Priorities
Metal
existence (C1)
0.16
(2)
Existence metal objects in the patient
body (C11)
0.988 1
Existence metal objects with the patient
(C12)
0.012 2
Moisture
existence (C2)
0.213
(1)
Moisture on the patient skin (C21) 0.346 1
Wetness of surgical drapes (C22) 0.154 3
Wetness of patient mattress (C23) 0.154 4
Patient skin maceration during long
operations (C24)
0.346 2
Incorrect
situation of the
plate (C3)
0.098
(3)
Forgetting to close the plate (C31) 0.129 3
Fastening plate on Great blood
vessels(C32)
0.129 4
Wetness of patient mattress (C33) 0.307 1
Putting plate near leads (C34) 0.129 5
Low quality of plate (C35) 0.307 2
Incorrect
situation of the
pencil (C4)
0.098
(3)
Pencil tip breakage (C41) 0.163 3
Pencil tip dirtiness (C42) 0.121 4
Disconnecting to the earth system (C43) 0.385 1
Using the electrocautery device as a desk
(C44)
0.07 5
Water penetration into the device (C45) 0.261 2
Incorrect
situation of the
cables (C5)
0.16
(2)
Defect of power connections (C51) 0.5 1
Defect of cables connections (C52) 0.5 1

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g
ment methods 397
Table 7 The weights and priorities of main failure mode criteria and sub-criteria (continued)
Main failure
mode
Fuzzy
weights Failure mode sub-criteria Relative
fuzzy weights Priorities
Direct
connection to
mattresses and
metal objects
(C6)
0.213
(1)
Direct connection of patient body to
mattress (C61)
0.331 2
Direct connection of patient body to
metal objects (C62)
0.338 1
Patient inappropriate position (C63) 0.331 2
Misuse of
electrocautery
by surgeon
(C7)
0.033
(4)
Forgetting to shut down electrocautery
device (C71)
0.308 2
Forgetting to check device mode (C72) 0.692 1
Skin burns
after
surgery(C8)
0.025
(5)
Forgetting to check patient skin after
surgery (C81)
0.686 1
Forgetting to announce fast (C82) 0.314 2
Calculated priorities based on the two different methods have been shown in Table 8.
Table 8 List of failure modes prioritised by FMEA and FAHP methods
Mian failure
modes Failure modes sub-criteria
FMEA FAHP
R
P
N
R
anking
Weight
Weight
R
ankin
g
Metal existence Existence metal objects in the patient
body
168 4 0.16 0.988 1
Existence metal objects with the
patient
84 9 0.012 2
Moisture
existence
Moisture on the patient skin 210 2 0.213 0.346 1
Wetness of surgical drapes 140 5 0.154 3
Wetness of patient mattress 140 5 0.154 4
Patient skin maceration during long
operations
240 1 0.346 2
Incorrect
situation of the
plate
Forgetting to close the plate 42 13 0.098 0.129 3
Fastening plate on large blood vessels 48 12 0.129 4
Wetness of patient mattress 96 8 0.307 1
Putting plate near leads 63 10 0.129 5
Low quality of plate 8 16 0.307 2
Incorrect
situation of the
pencil
Pencil tip breakage 56 11 0.098 0.163 3
Pencil tip dirtiness 56 11 0.121 4
Disconnecting to the earth system
(C43)
192 3 0.385 1
Using the electrocautery device as a
desk
12 15 0.07 5
Water penetration into the device 168 4 0.261 2

398 S. Golrizgashti et al.
Table 8 List of failure modes prioritised by FMEA and FAHP methods (continued)
Mian failure
modes Failure modes sub-criteria
FMEA FAHP
R
P
N
R
anking
Weight
Weight
R
ankin
g
Incorrect
situation of the
cables
Defect of power connections 126 6 0.16 0.5 1
Defect of cables connections 126 6 0.5 1
Direct
connection to
mattresses and
metal objects
Direct connection of patient body to
mattress
16 14 0.213 0.331 2
Direct connection of patient body to
metal objects
48 12 0.338 1
Patient inappropriate position 42 13 0.331 2
Misuse of
electrocautery
by surgeon
Forgetting to shut down electrocautery
device
84 9 0.033 0.308 2
Forgetting to check device mode 168 4 0.692 1
Skin burns after
surgery
Forgetting to check patient skin after
surgery
112 7 0.025 0.686 1
Forgetting to announce fast 112 7 0.314 2
4 Discussion
Regards to importance of managing potential risks in healthcare system the aim of this
study is to compare the results of two experts’ judgment methods includes FMEA and
FAHP. In both two methods risks were identified and prioritised. It seems that the
obtained results have to be similar but we showed that in some cases all obtained
priorities are not the same in both FMEA and FAHP methods. According to results which
have been mentioned in Table 8, ‘moisture existence’ as main failure mode and ‘patient
skin maceration during long operations ‘as its sub-criteria are the most important risks
which have been obtained based on two methods so it’s needed to apply preventive
solutions to enhance patient’s safety. But according to results, some priorities are
completely different. For example, ‘direct connection to mattresses and metal objects’ in
FAHP method is ranked as the first failure mode and has the highest weight but based on
FMEA method it has the lowest priority with low RPN score.
There are some studies that mentioned to the different results by different methods.
Sofyalioglu and Özturk (2012) compared the results of simple RPN method, Grey RPN
(risk factors had equal weights) and Grey RPN (risk factors had different weights) in their
study. They showed that the obtained results by two first methods are the same but the
results of third method are completely different. Rah et al. (2016) analysed the results of
traditional FMEA and modified healthcare FMEA (m-HFMEA) to compare ‘congruency’
of potential risks in surface image guided, linac-based radiosurgery. They showed
different risk scores in applying two methods. They believed that implementation of
experts’ judgment methods such as FMEA or m-HFMEA are not sufficient to identify
and prioritise risks in healthcare services and needs more accurate methods to enhance
patient safety.

A
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g
ment methods 399
Some researchers proposed different solutions to improve experts’ judgment methods.
Using FAHP to calculate weights of risk factors to cover traditional FMEA shortages is
one of these solutions (Sofyalioglu and Özturk, 2012). Öhrn et al. (2018) mentioned that
HFMEA based on ‘fewer team leaders with more experience’ can be more effective
because it can decrease experts’ disagreements. There are limited studies to compare
obtained results under different experts’ judgment methods so there is not definite answer
which confirms results reliability.
The main result of this study is to explore limitations of qualitative methods based on
experts’ judgments. By using a real case study in healthcare system, results showed that
the priority of failure modes can be changed by using different methods and also can be
changed with different decision makers too (Sofyaliglu and Ozturk, 2012). Some
researchers mentioned that different knowledgeable decision makers and also considering
real observations can be useful to decrease errors in qualitative methods such as FMEA
(Sofyaliglu and Ozturk, 2012). So regards to importance of patients’ safety in healthcare
systems, it seems that applying accurate quantitative methods is requirement to decrease
potential errors in healthcare systems.
5 Conclusions
Identifying the main risks in healthcare systems is a challenging issue. Beside of lacking
studies which focus on risk identification in healthcare system, there are some differences
in ranking high risk failure modes based on experts’ judgment methods. So this research
was conducted to explore probable risks and determine their causes in electrocautery
surgery process based on two experts’ judgment methods to illustrate differences in risk
scoring. Regards to high importance of patient safety and also existing probable
differences between results obtained by using different qualitative methods, in this study
two methods includes traditional FMEA and fuzzy AHP were applied to compare. There
are many studies to discuss implementations of TQM tools in service systems such as
healthcare effectively (Talib and Rahman, 2010) but it seems comparing obtained results
with other data driven methods can be useful.
This paper showed that obtained results can be different by using different experts’
judgments methods and these methods may not be reliable to manage healthcare system
risks and enhance paints’ safety. According to existing limitations and high risk injuries
in healthcare systems, analysing probable risks and also comparing obtained results by
applying two approaches are the advantages of this study. Based on obtained results, this
study emphasises designing effective methods in healthcare systems based on real data
and mathematical methods to overcome experts’ judgment shortages. Quantitative
methods based on big data have key role in healthcare system management effectively
(Chinnaswamy et al., 2019). Future researches can apply quantitative methods such as
data science, image processing and, etc. to analyse healthcare failure modes and compare
obtained results.

400 S. Golrizgashti et al.
Acknowledgements
The authors would like to thank expert team in the studied hospital to participate and
spend much time in this research. They also acknowledge editor and reviewers to express
their valuable comments to improve quality of this paper.
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