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Received February 28, 2020, accepted December 9, 2020, date of publication February 3, 2021

Evaluation and Optimization of CES Performances: 
Application of the Pareto Principle to KPIs

By Alessia Cecchini, Grazia Maria Pia Masselli, Sergio Silvestri    

Clinical Engineering Service, University Hospital Campus Bio-Medico, Rome, Italy

ABSTRACT

In recent times the approach to health care has been mostly influenced by the growing quantity of biomedical equipment used 
in hospitals, which needs the support of the clinical engineering service (CES). 
This work aims to suggest a methodology to improve the performance of a CES through the application of Pareto principle to the 
leading Key Performance Indicators (KPIs). The methodology is applied by focusing on using KPIs that represent a quantifiable 
measure of achieving goals set by an organization. In this study, five KPIs are considered: Uptime, MTTR (mean time to repair), 
PPM (percentage preventive maintenance completion), MTBF (mean time between failures), and the COSR (cost of service ratio). 
The first three indicators express the measure of CES efficiency in ensuring regular maintenance. 
The first step consists of retrieving data related to work orders for 2015-2016 on 6000 installed devices, carried out by manage-
ment software. The second step is to get the results by using an environment for numerical calculation and statistical analysis. 
To identify the main critical issues that may be present, three indicators (Uptime, MTTR and MTBF) are analyzed by applying 
the Pareto principle (i.e., 20% of the causes produce 80% of the effects). Considering the totality of work orders, it is possible 
to concentrate on only 20% of them to focus on a small group to understand the correlations between them. Identifying these 
characteristics means identifying the main critical issues that are present, on which action must be taken, and which affect 80% 
of the overall behavior. Instead, the COSR and PPM indicators suggest distribution models that focus on the most critical devices. 
In conclusion, the way to analyze the results is obtained, when possible, by applying Pareto principle. Therefore, a CES will be 
able to focus on a few causes of poor performance. The achievement of these results could allow the standardization of the 
method used, enabling it to be applied to any healthcare system.
Keywords – CES, KPIs, UPTIME, MTTR, MTBF, COSR, PPM, UCBM.

Copyright © 2021. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY): Creative Commons - Attribu-
tion 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.

INTRODUCTION
• Key Performance Indicators (KPIs) represent a 

quantifiable measure of achieving goals set by an 
organization, both operational and strategic. Generally 
speaking, companies have different KPIs depending 

on their priority criteria. KPIs can also be established 
arbitrarily but, to be useful, they must meet the fol-
lowing requirements1:

• Quantifiability - KPIs must be presented in the form 
of numbers.

http://www.globalce.org
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http://globalce.org
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15 J Global Clinical Engineering Vol.4 Issue 1: 2021

Cecchini, Masselli, Silvestri: Evaluation and Optimization of CES Performances: Application of the Pareto Principle to KPIs

• Practicality - they integrate well with current busi-
ness processes.

• Directionality - they help to determine whether 
companies are improving.

• Operations - they can be related to the practical 
context to measure an effective change.

The four leading indicator typologies are1:
1. General indicators - measure the amount of work in 

the process.
2. Quality indicators - evaluate the quality of the process 

output based on certain standards.
3. Cost indicators.
4. Service or time indicators - they measure the response 

time from the start of the process until its conclusion.

Thanks to a set of KPIs, it is possible to evaluate the 
performance of a clinical engineering service (CES). 

This paper discusses the current status of benchmark 
indicators within the field of clinical engineering. The 
paper focuses on the evaluation and optimization of the 
medical equipment repair and maintenance activities of 
a CES by applying the Pareto principle to KPIs to focus 
on main critical activities.

STATE OF THE ART

According to Cohen et al., KPIs represent the process 
of comparing business performance levels to identify op-
portunities to improve. The results provided an idea of 
what should be changed and how. However, comparisons 
have met with limited success due to poor and inconsistent 
definitions of the parameters measured and lack of qual-
ity data available. In the first phase, it was necessary to 
identify basic indicators that are applied to any healthcare 
facility. They must be uniquely defined and consequently 
calculated by the same method so that they can be com-
pared. Afterward, it was possible to start to build other 
indicators that will be different depending on the specific 
needs and the technical-economic information that ev-
eryone possesses. For this reason, the primary purpose 
of this paper was to detail some of the quantitative per-
formance and cost benchmark indicators that have been 
historically used in medical equipment maintenance and 

repair and to make recommendations on how the clinical 
engineering profession can develop good quality, useful 
and meaningful benchmarks. The general characteristics 
of a useful benchmark indicator are2:

• well defined;
• objective;
• measurable;
• based on current knowledge experience; and
• valid.

The study by Bassem et al. aims to evaluate CES's 
performance at the University of Cairo, Egypt, using 
quantitatively measured parameters to allow comparison 
and improvement objective.3 In addition to the param-
eters proposed by Cohen et al., considered insufficient, 
Bassem proposed new indicators. Data were collected by 
10 hospitals, corresponding to different healthcare orga-
nizations.2,3 These data were subsequently analyzed by 
a software tool, providing a score for each CES. The first 
step was to decide what exactly to evaluate and monitor. 
Some of the studies reported by Bassem adopted a survey 
technique as CES directors were asked to select from a 
list of proposed performance indicators that could be 
used for performance measurement benchmarking. Their 
response revealed three mainstream performance indica-
tors. Other indicators had to be added and measured to 
evaluate the performance of the other services. They used 
additional essential indicators that should be involved to 
get an increased accurate evaluation. The results indicated 
an average gap of 67% between the performance of the 
CES and the reference they have identified, considered 
the ideal target.3

According to the Tiwari study, service performance 
on medical equipment serviced by external suppliers is 
assessed.4 The performance indicators of CES are first 
defined according to the needs and benefits required in 
the specific hospital structure and are then categorized 
and finally measured as indicated below.
1. The definition of KPIs considering the opinion of 

experienced staff.
2. The categorization of KPIs into four groups.
3. The measurement of KPIs.



Cecchini, Masselli, Silvestri: Evaluation and Optimization of CES Performances: Application of the Pareto Principle to KPIs

J Global Clinical Engineering Vol.4 Issue 1: 2021  16

The Tiwari study is an example of an outsourced CES, 
and the key to success is the measurement of performance 
to quantify the expected benefits.4

METHODOLOGY

This study, conducted by the University Campus Bio-
Medico of Rome (UCBM), where there are about 6,000 
pieces of biomedical equipment for the year 2016. Before 
going into the explanation, and then in the calculation of 
the KPIs, it is vital to understand what type of data have 
been used. The CES utilizes an equipment management 
software, in which all the data are collected and related 
to the inventory number of biomedical equipment: the 
description, the serial number, the manufacturer, the 
purchase value, the intervention priority, therefore any 
information useful to characterize a specific piece of equip-
ment. From the mentioned software, further information 
can be obtained relating to work orders and the schedule 
for preventive maintenance. In the first case, the CES takes 
care of entering all the work orders executed. In the second 
case, the CES takes care of inserting, within a schedule, 
all the equipment and the corresponding deadlines for 
preventive maintenance, in order to record the periodicity 
with which it is required to carry out maintenance. This 
approach maximizes effectiveness and efficiency in tech-
nical management and ensures economic and technical 
control of maintenance. It has the objective of providing 
operational and decision-making support for further 
optimizing the processes related to registry/inventory 
management. In this way, from this software, it is possible 
to obtain categorized data, from which it is possible to 
calculate the KPIs. Starting from these data, the results 
are obtained through the use of "Matlab," an environment 
for numerical calculation and statistical analysis, which 
also includes the programming language. It allows the 
calculation of the KPIs considered here. The following 
paragraph will report the explanation, and the subsequent 
calculation, of the identified and measurable KPIs.

The following KPIs are used and calculated:
1. Uptime. This denotes the time the biomedical equip-

ment has been working for over one year; downtime 
is its complementary statistic and denotes the state 
of a not operational system. This may be due to fail-
ure, preventive maintenance, or other causes. The 

measurement is carried out in absolute values or 
percentage. Uptime is particularly important for all 
machines where stability and availability are funda-
mental. Through Uptime, efficiency can be deduced: 
a high Uptime indicates that the equipment is well 
configured, while a low Uptime could mean instability 
of the equipment. To get more evidence on the critical 
issues, this KPI calculation involves the initial use of 
data from all the equipment from which one or more 
work orders have taken place. Also, all devices that 
have not undergone a work order are then considered 
and has always been functional; a maximum Uptime 
value will appear. The Uptime calculation, represented 
by a percentage, is carried out by first calculating the 
downtime: the work orders corresponding to each in-
ventory number of the equipment are considered and, 
consequently, the duration given by the sum of all the 
work orders for that specific inventory is calculated. 
It is then divided by the number of days within a year 
to indicate, the percentage of the number of days that 
a specific piece of equipment remained inoperative 
relative to the total period.4 The formulas used are 
the following: 

2. MTTR (Mean Time To Repair). This denotes the Time 
to Restore (TTR) expected value, where the TTR is the 
time interval where the equipment is unavailable due 
to a failure. The MTTR includes the time for diagnosis, 
the arrival of the maintenance technician, the arrival of 
the component(s) to be replaced, and the actual repair. 
It is a useful parameter for evaluating the effectiveness 
of the CES in terms of the logistic organization. The 
calculation of this coefficient involves data from all 
equipment on which a work order has occurred involv-
ing corrective maintenance or functional verification. 
The MTTR is calculated according to the work orders 
corresponding to the equipment’s inventory and the 
duration given by the sum of the times to repair in all 
the work orders, or that specific item is calculated. 
This is referred to as TTR and it is then divided by 



17 J Global Clinical Engineering Vol.4 Issue 1: 2021

Cecchini, Masselli, Silvestri: Evaluation and Optimization of CES Performances: Application of the Pareto Principle to KPIs

the total number of work orders within a year.4 The 
formula used is:

3. MTBF (Mean Time Between Failures). This term cor-
responds to the average time interval between two 
successive failures (TBF) and indicates the frequency 
with which a failure can occur. This is mainly a reli-
ability parameter used to indicate the probability 
that equipment operating under certain conditions 
will retain, after a predetermined time, the functional 
capacities for which it was built. The calculation of this 
coefficient involves data from all equipment on which 
a work order has occurred that involved corrective 
maintenance or functional verification. The work orders 
correspond to each inventory number of the equipment 
and consequently the total time that elapses between 
the start date of one work order and the start date 
of another. This would be the TBF. The TBFs from all 
work orders are added together and then divided by 
the total number of work orders within one year.4 The 
inverse parameter, defined as “frequency of failure,” 
indicates the rate at which technicians must carry out 
maintenance. The formulas used are the following:

4. COSR (Cost Of Service Ratio). COSR is an economic 
parameter that represents the sustainability of costs. 
It is calculated as the ratio between the overall main-
tenance cost and the purchase cost, assessed through 
a percentage measure.4 The calculation of the overall 
COSR is carried out by proceeding in three phases 
outlined below.
1. Equipment with maintenance contracts, to which 

maintenance cost (if any) has been added the cost 
of the pieces of spare parts.

2. Equipment with only spare parts and without 
maintenance contracts.

3. Company cost of all the personnel working in the 
CES.

The formula used is the following:

5. PPM (Percentage Preventive Maintenance). This term 
expresses the overall number of preventive maintenance 
events or carried out within the deadline, divided by 
the total planned preventive maintenances within a 
year expressed as a percentage.4 This calculation is 
made for each piece of equipment based on the future 
expiration date of preventive maintenance and the 
scheduled maintenance frequency. This makes it pos-
sible to derive the previous preventive maintenance 
expiration date, which is compared with the date of 
the beginning of the maintenance carried out on each 
piece of equipment, allowing us to understand if the 
maintenance has been carried out before or after 
the deadline. The PPM calculation is also necessary 
in light of the accreditation manual of the Hospitals 
of the Joint Commission International.5 According to 
this manual, “all medical equipment and technologies 
are regularly subjected to inspections, maintenances 
and calibrations and these activities are documented 
in the appropriate registers. The staff ensure that all 
medical equipment and technologies operate at ac-
ceptable levels and safely for operators.” When there 
is a need to manage the maintenance of many pieces 
of equipment, it is necessary to adopt criteria allow-
ing priority. The equipment is not all critical in the 
same way, so it is necessary to distinguish the critical 
equipment on which the patient's life depends from the 
less critical ones for which the priority level is lower. 
There is, therefore, an issue to solve: if the technician 
receives two maintenance requests at the same time, 
the technician must be able to evaluate what the prior-
ity request is. To do this, a risk assessment is carried 
out, which is done with objective criteria and not left to 
free interpretation. In our case, the criticality analysis 
is carried out based on the assignment of five scores, 
respectively relating to five categories of equipment 
criticality (Equipment Management Program Mayo 
Clinic6 has taken up this method). According to these 
criteria, equipment that should be excluded from the 



Cecchini, Masselli, Silvestri: Evaluation and Optimization of CES Performances: Application of the Pareto Principle to KPIs

J Global Clinical Engineering Vol.4 Issue 1: 2021  18

plan can still be included if requested by a certification 
body or if scheduled by the manufacturer for periodic 
maintenance or calibrations. The formulas of PPM used 
are the following:

RESULTS AND DISCUSSION

Firstly, the first three indicators are analyzed, where 
possible, by applying the Pareto principle, one of the most 
used tools in quality management. The Pareto principle, 
also known as the 80/20 rule, took its name from Vilfredo 
Pareto, an Italian engineer who, in 1906, observed that 
the distribution of wealth in his country had an unequal 
distribution: 20% of the population owned 80% of the 
wealth.7 By expanding this idea to other areas and con-
cepts, an empirical theory has been formulated which 
is respected in the majority of cases (this means that 
the distribution referred to it must be a very numerous 
distribution). The Pareto diagram's construction, based 
on this principle, shows that 20% of the causes produce 
80% of the effects.

The advantages that derive from the application of the 
Pareto principle and diagrams are:

• to help to break down big problems into smaller 
problems and to establish what are the main factors 
causing them;

• to help to focus on the most important causes in 
order to establish priorities, using the available time 
more effectively;

• to help to link causes with effects; and
• to support in evaluating the improvement based 

on an analysis of the situation before and after the 
application of the corrective action.

To identify critical issues, the data relating to the first 
three indicators, namely Uptime, MTTR and MTBF, are 
analyzed by applying the Pareto diagrams, showing that, 
where applicable, 20% of the causes produce 80% of 
the effects. Considering all the medical equipment, it is 

possible to concentrate efforts only on 20% of causes to 
obtain a significantly better result. However, the analysis 
of data through the Pareto law is not always possible, but 
to extend its application, it expanded to 30% of causes. 
However, where the percentage is more than 30%, its ap-
plication is not considered significant. This analysis makes 
it possible to focus on a small group of medical devices 
and understand the correlations between them (type of 
equipment, manufacturer). Identifying these characteristics 
allows the discovery of the main critical issues present 
within the health structure and what action is required. 
This will pertain to 80% of the overall behavior.

For the construction of the Pareto diagram, a combina-
tion of a bar chart can be considered showing the data in 
order of decreasing KPIs (Uptime, MTTR, MTBF), and of a 
cumulative curve, constructed by adding the i-th value to 
the previous values. This allows for immediate identifica-
tion and effect of the relevant elements.

On the other hand, the COSR and PPM indicators help 
analyze the data and suggest distribution models that 
enable focus on the most critical equipment.

The graph of Uptime only considered those devices on 
which one or more work orders took place, which involved 
a value of Uptime <100%. It is precisely on these param-
eters that the analysis of Pareto is carried out. Focusing 
will be on the downtime rather than on the Uptime to 
immediately highlight any critical issues. Overall, we note 
distribution of data mainly concentrated around 100%, 
while only a smaller percentage corresponds to a value 
of Uptime less than 100%, mainly distributed between 
20% and 30%.  Figure 1 shows the average value calcu-
lated, which is very high and aligned well with similar 
measurements noted in the bibliography.

The MTTR graph shows a data distribution mainly 
concentrated in a range between 0 and 1000 hours (42 
days), while only a smaller part, the initial one, shows 
an increase in the number of hours that goes up to 1800 
hours. As shown in Figure 2, this reflects the average 
value calculated.

Since MTBF is the time between failures, the calcula-
tion is performed if there are, for each inventory item, at 
least two failures, therefore two work orders within a year. 



19 J Global Clinical Engineering Vol.4 Issue 1: 2021

Cecchini, Masselli, Silvestri: Evaluation and Optimization of CES Performances: Application of the Pareto Principle to KPIs

The data analysis focuses on the frequency of faults, and 
it is carried out using Pareto diagrams. The MTBF graph 
(Figure 3) shows data distribution mainly concentrated 
in a range between 0 and 832 hours (35 days), while only 
a smaller part, the initial one, shows an increase in the 
number of hours that rises to 1354 hours.

The COSR is calculated by paying attention to equip-
ment with COSR> 0% to highlight any critical issues more 
efficiently. A histogram has been constructed (Figure 4) 
from these devices: the COSR trend has been highlighted 
through 0.5% intervals and the number of devices is then 
reported, having the corresponding COSR. The COSR 
trend resembles a Poisson distribution, in fact the data 

are distributed bell-shaped around a value belonging to 
the COSR range between 1.5 and 2%, corresponding to 
which we have 115 devices in 2015 and 124 devices in 
the year 2016. The trend over the two years is, in fact, the 
same. However, the highest histogram bar remains the 
one with COSR> 10% and will be analyzed later in detail 
for the analysis of critical issues. As can be seen, the COSR 
values are quite low at around 1%, but personnel costs 
must be added to this value, so overall it is around 4%.

The data analysis for the PPM is carried out through the 
use of histograms (Figure 5) that highlight the equipment 
on which preventive maintenance took place in advance 

FIGURE 1. Pareto diagram for calculating downtime for equip-
ment in 2015.
x axis = number of devices; y-axis = percentage of time out of 
service (Downtime).  

FIGURE 2. Pareto diagram for calculating MTTR for equipment 
in 2015. 

FIGURE 3. Pareto diagram for calculating the frequency of 
failures for equipment in 2015. 

FIGURE 4. Histograms for calculating COSR interspersed by 
0.5% in 2016. 



Cecchini, Masselli, Silvestri: Evaluation and Optimization of CES Performances: Application of the Pareto Principle to KPIs

J Global Clinical Engineering Vol.4 Issue 1: 2021  20

(negative numerical value of the days) and late (positive 
numerical value of the days) concerning the scheduled 
deadline. If the number of days is 0, the preventive main-
tenance took place on the same day as the planned date. 
It should be noted that the maintenance carried out in 
advance is greater than the delayed since the initial sec-
tion of the graph is greater than the final one.

If we want to summarize in a single value what has just 
been shown, the value of the Uptime, MTTR, MTBF, COSR, 
and PPM is reported expressing the measure according 
to its mean value and standard deviation. Each KPI is 
calculated for 2015 and 2016, as follows (Equipment that 
does not have work orders is also included in the Uptime 
calculation, resulting in 100% Uptime).

As shown earlier, the Tiwari study, which shows numeri-
cal calculations and graphs of identified KPIs, assesses the 
performance of medical equipment serviced by external 
suppliers; the CES examined in this work, on the other 
hand, is a predominantly internal CES, so a first difference 
is immediately apparent. In detail, however, we note that 
the numerical value of Uptime and COSR obtained from 
the two studies is comparable; the calculation of MTTR 
was carried out individually for each month by Tiwari, so 
it is clear this type of comparison is inconsistent. Also, 
because some work orders last more than one month; 
finally, in order to be able to compare the PPM, a clarifica-
tion is needed, that is, it is necessary to take into account 
that, in the UCBM Polyclinic, the system revolves around 
a risk classification that guides the professionals of the 
CES to act according to different priorities. It is clear that 
comparing the two numerical values, they are different, 

FIGURE 5. Histograms of the number of days in advance and 
delay concerning the scheduled deadline from the next preven-
tive maintenance date.

but, taking this into account, it would then have been more 
significant to take as a reference the value of the calculated 
PPM for priority equipment, at 93.7%. Comparison with 
the study by Tiwari et al is shown in Table 1.

Starting with the results obtained critical issues are 
analyzed. In the case of the Uptime, MTTR, and MTBF, this 
analysis is conducted globally as there is a correlation 
between them and the level of the numerical calculation 
performed. They are calculated starting from the durations 
of the work orders that are carried out during a year. It 
is therefore important to concentrate on the equipment 
for which this phenomenon is most evident and this is 
possible with the analysis carried out using the Pareto 
diagrams, which are found to be applicable only in those 
cases where 20% or 30% of the causes have produced 
80% of the effects. Therefore, making a detailed analy-
sis for each of these KPIs, a global analysis is deduced, 
identifying the equipment that more frequently falls into 
20% or 30% of the causes. Also, in the case of COSR, the 
critical issues present are analyzed and made possible by 
focusing on the equipment for which COSR is more than 
10%. Finally, the same reasoning is carried out for the 
PPM, which, regarding preventive maintenance, focuses 
on the type of intervention priority, such as equipment 
of priorities I, II, III.  

For example, it is reported as an average across the 
fleet of pressure such as therapy units for that particular 
manufacturer of equipment, considered from the critical-
ity analysis of the Uptime, MTTR and MTBF (Table 2). 

TABLE 1. Comparison with the Study by Tiwari and Tiwari.



21 J Global Clinical Engineering Vol.4 Issue 1: 2021

Cecchini, Masselli, Silvestri: Evaluation and Optimization of CES Performances: Application of the Pareto Principle to KPIs

This paper summarizes medical equipment repair 
and maintenance benchmark indicators that can be used. 
Therefore, the clinical engineering profession must develop 
and use indicators that accurately reflect the true costs 
and quality of medical equipment repair and maintenance. 

The way to analyze the results obtained is, when pos-
sible, using Pareto diagrams. They help to break down 
the big problems into smaller problems and to determine 
which are the main factors that cause them; to help to focus 
on the most important causes and to set priorities, using 
the time available more effectively help to link the causes 
with the effects. This methodology makes it possible to 
have precise information on the critical equipment that 
will then be replaced or repaired correctly, which will be 
taken through work experience and information.

TABLE 2. Analysis of Critical Issues through the Pareto Diagram 
for Uptime, MTTR and MTBF: Types of Equipment that Fall 
Within 20% or 30% of the Causes

Performance measurement of clinical engineering 
departments in hospitals using these indicators will get 
more accurate and fairer performance evaluation. We will 
be able to find the real reasons for failure and improve 
performance. Further analysis may be required to better 
define creating a standard and substantive performance 
evaluation benchmarks and solve it. 

REFERENCES
1. Project Management Europa. Executive Dashboards 

and KPI: To Know to Decide. Available at: http://www.
projectmanagementeuropa.com/cruscotti-direzionali-
e-kpi-conoscere-per decidere/

2. Cohen T, Bakuzoinis C, Friedman SB, and Roa RL. 
Benchmark indicators for medical equipment re-
pair and maintenance. Biomed Instrument Technol 
1995;308–21.

3. Ouda BK, Ayman M. An integrated evalutation for 
the performance of clinical engineering department. 
IEEE Engineering in Medicine and Biology Society 
2014;1:3110–3. 

4. Tiwari A and Tiwari A. Performance evaluation of out-
sourced medical equipment maintenance service in a 
tertiary care hospital. Int J Sci Res Pub 2014;4(9):1–9. 

5. Joint Commission International. Joint Commission 
International Accreditation Standards for Hospitals; 
2017. Available at: https://www.jointcommissioninter-
national.org/-/media/jci/jci-documents/accreditation/
hospital-and-amc/jci-standards-only_6th-ed-hospital.pdf

6. Mayo Clinic. Equipment Management Program Rev 
6-08. Rochester: Au.

7. whatissixsigma.net. Pareto Chart and Analysis. 
Available at: http://www.whatissixsigma.net/
pareto-chart-and-analysis/

http://www.projectmanagementeuropa.com/cruscotti-direzionali-e-kpi-conoscere-per
http://www.projectmanagementeuropa.com/cruscotti-direzionali-e-kpi-conoscere-per
http://www.projectmanagementeuropa.com/cruscotti-direzionali-e-kpi-conoscere-per
https://www.jointcommissioninternational.org/-/media/jci/jci-documents/accreditation/hospital-and-amc/jci-standards-only_6th-ed-hospital.pdf
https://www.jointcommissioninternational.org/-/media/jci/jci-documents/accreditation/hospital-and-amc/jci-standards-only_6th-ed-hospital.pdf
https://www.jointcommissioninternational.org/-/media/jci/jci-documents/accreditation/hospital-and-amc/jci-standards-only_6th-ed-hospital.pdf
http://whatissixsigma.net
http://www.whatissixsigma.net/pareto-chart-and-analysis/
http://www.whatissixsigma.net/pareto-chart-and-analysis/

