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All issues Volume 9 (2018) Int. J. Metrol. Qual. Eng., 9 (2018) 5 Full HTML
Open Access
Review
Issue
Int. J. Metrol. Qual. Eng.
Volume 9, 2018
Article Number 5
Number of page(s) 21
DOI https://doi.org/10.1051/ijmqe/2018003
Published online 18 May 2018

© G. Suman and D.R. Prajapati, published by EDP Sciences, 2018

Licence Creative CommonsThis is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

1 Introduction

Quality in healthcare is always a big concern because bad quality in healthcare can make a difference in terms of life or death of patients. The investment is continuously growing in this sector with little interest towards quality. The importance of building safe and effective healthcare system was realised in 2000 because several reports issued by US Institute of Medicine as reported by Lazarus and Neely [1].  In one of his report, released in November 1999, the authors estimated that around 98,000 patients die each year due to medical errors. According to Department of Health (2001) [2] & Milligan and Robinson [3], due to adverse incidents and clinical negligence claim, an estimated £400 million is being paid which caused approximately £2 billion per annum. Arthur [4] reported the following statistics:

  • one percent of patients will die because of medical errors;

  • six percent of patients will be disabled permanently because of medical mistakes;

  • fifteen diagnoses out of every 100 are incorrect;

  • twenty to fifty out of 100 diagnostic procedures should never have been done because their results did not help to diagnose patients;

  • five to ten out of every 100 admitted patients get infected during treatment.

These all data illustrate that there is a need to monitor and control the healthcare performance so that adverse events can be minimized. Healthcare system needs both investment as well as quality to meet the ultimate objective of patient satisfaction. As healthcare is a service organisation and every organisation wants to boost up their revenue while decreasing cost by providing appropriate quality in the services. In order to attain the quality objectives, quality initiative like statistical process control (SPC) will be very effective.

The aim of this paper is to provide the guidance in the direction of implementing control charts in healthcare. The various studies show applications of the control charts in various departments. This paper provides the information about departments and countries; where these studies have been done. Statistical analysis shows the frequency of these charts used in healthcare.

1.1 Statistical process control

Statistical process control (SPC) techniques have played an efficacious role in monitoring hospital performance such as mortality rate, pre and post-operative complications, number of infections in hospital etc. as reported by Finison et al. [5], Finison and Finison [6], Benneyan [7], Maccarthy and Wasusri [8], Clemente et al. [9]. The concept of SPC was given by the Walter A Shewhart in order to improve the industrial manufacturing processes. The SPC was firstly applied in laboratory and after that shifted to patient level in hospital. As there is more involvement of human in healthcare, the chances of errors are also more. SPC can help in determining the source of errors by identifying the special and common causes of variations.

SPC is a system of online quality control and can be defined as a philosophy, strategy and methods for the improvement of systems, outcomes and processes. The SPC is based on theory of variation i.e., common and special causes of variations. It involves the concept of process thinking, learning through data, analytical study, experimentation, measurement system and methods of data collection as reported by Ning et al. [10]. The most commonly used charts in SPC are run charts, mean chart, range chart, histogram, Pareto chart etc. One thing should be noted that SPC and statistical quality control are often used reciprocally but the later one is used to describe the extensive management approach towards quality management.

The present paper deals with department wise review of control charts applications in healthcare. Control chart is primary tool in SPC and is commonly used for monitoring and improvement of on-going process. The sample u control chart is shown in Figure 1.

In order to construct a control chart, first of all, there should be availability of data. Data can be of two types i.e., variable and attribute. The data which can be measured on some scale comes under the category of variable and on the other hand, the data which cannot be measured and represented on count basis comes under attribute. Figure 1 shows the data of attribute type control chart. There are three lines in control chart i.e., Centre line, upper control limit and lower control limit. Upper and lower control limits are at a distance of 3 standard deviations from the centre line. A set of decision rule was suggested in that a process is out of control and these are:

  • if any point lies beyond the specified three sigma control limits;

  • if two out of three consecutive points fall beyond the two sigma control limits;

  • if four out of five consecutive points fall beyond the one sigma control limits;

  • if the run of eight consecutive points fall on either side of centre line.

According to the types of data, there are two types of control chart i.e., control chart for variables and control chart for attributes. The data is arranged into subgroup in order to establish a set of reading in which process shows stable and controlled behaviour. For example, we can choose to group response time readings taken at regular intervals throughout the day into a subgroup which is then plotted as a single point on a control chart. In attribute chart, there is count of defects or defectives. In one defective unit, there may be several defects. Control charts can be classified as & S charts, & R charts, p chart and np chart etc. The details about construction and use of control chart are given by Kaminsky et al. [11] and Amin [12]. Thor et al. [13] discussed the variables used in clinical monitoring, benefits and barrier associated with control chart use. According to Koetsier et al. [14], the maximum numbers of charts are plotted on two phases of the PDSA cycle. Laney [15] and Mohammed & Laney [16] suggested control chart for attributes for very large sample sizes (in thousands or even millions). Prajapati [17] presented modified chart for autocorrelated observations. Depending upon the suitability of charts; they can be used for a particular situation. Figure 2 shows the detailed classification of control charts.

In addition to above charts, there are other charts like CUSUM (Cumulative Sum Chart) and EWMA (Exponential Weighted Moving Average Chart). These charts come under the category of Control chart for variables. These charts come into picture because the above mentioned control charts are not sensitive enough for the process measurement. The detail about construction and use of CUSUM and EWMA chart is given by Woodall et al. [18]. Pillet et al. [19] stated that multivariate chart is used when more than one variable are to be monitored. Most of studies on the application of control chart in healthcare are carried out in US as reported by Seddon et al. [20]. The present paper deals with department wise review of control charts applications in healthcare sector. First section deals with introduction to SPC and control charts while Section 2 describes the methodology. The detailed literature survey in Emergency, Surgery, Epidemiology, Radiology, Pulmonary, Cardiology, Administration and Pharmaceutical departments in tabular form are presented in Section 3. Section 4 provides the statistical analysis and discussion on the results of statistical analysis is given in Section 5. Limitations of studies are discussed in Section 6 and managerial and academic implications are presented in Section 7. The final conclusion of the paper provided in Section 8.

thumbnail Fig. 1

Sample u chart for No. of defects/unit, given by Finison et al. (1993).
thumbnail Fig. 2

Classification of control charts.

2 Methodology

The extensive research is done to find out the articles related to control charts applications in healthcare sector. The Pubmed, EBSCO, ResearchGate and Google Scholar databases have been used to find the studies; describing the use of control chart in the specific departments of the healthcare. The ‘SPC’, ‘Control chart’, ‘Application’ and ‘Healthcare’ are the key words in the search. The search excluded master and doctoral dissertation since there is greater possibilities of these studies to appear in academic and professional Journals.

The criteria for the inclusion of research papers is that control chart should be applied at departmental level. Figure 3 shows the flow chart for selection of studies. Initially 142 potentially relevant articles are identified. Out of which, 92 articles are removed by studying abstract because most of them were irrelevant, review papers and tutorials. The 50 research papers are selected for full study. Out of which 40 studies are included in the review as shown in Figure 3.

thumbnail Fig. 3

Flow chart for selection of studies.

3 Survey of literature

The literature survey is divided according to the departments in healthcare. Forty studies were found over eight departments i.e. Emergency, Surgery, Epidemiology, Radiology, Cardiology, Pulmonary, Administration and Pharmaceutical. The details about the included research papers is arranged in tabular form showing the details of work done, types of chart, variables used, country and types of study.

3.1 Studies done in Emergency department

Emergency department is always associated with longer waiting time, length of stay, longer turnaround time and overcrowded room. So there is always scope for the improvement in Emergency department. Table 1 shows the details of work done, types of charts, variables used, types of study and country along with authors' name and year. Seven numbers of studies have been included in Emergency department. It is clear from Table 1 that EWMA is the mostly used chart and ‘Mortality rate’ is mainly used variable in Emergency department.

Table 1

Literature survery in Emergecny department

3.2 Studies done in Surgery department

The Surgery department is often overburdened with errors and inefficiencies like surgical site infections, pre and post-operative complications etc. Table 2 shows the details of work done, types of chart, variables used, types of study and country along with authors' name and year. Nine numbers of studies have been included in the Surgery department. It is clear from Table 2 that run chart, CUSUM chart and p chart have been mostly used in surgery department. Mortality rate, length of stay and complications are often used variable.

Table 2

Literature survery in Surgery department

3.3 Studies done in Epidemiology departments

The Epidemiology takes into consideration the effects of disease and health condition of particular patients in the defined population. In simple terms, this department take care of infections related to particular disease or health condition. The numbers of cases with infections per thousand patients' days are generally very less. This is the reason that p chart is most commonly used in Epidemiology departments. Table 3 shows the details of work done and types of charts used along with authors' name and year. Nine numbers of studies have been included in Epidemiology department. Sellick [21] and Morton et al. [22] used various types of control charts like CUSUM, EWMA, p, c, u charts etc. for detection and monitoring of hospital acquired infections in Epidemiology department.

Table 3

Literature survery in Epidemiology department

3.4 Studies done in Radiology departments

The use of X-rays and other high energy radiation for treatment and diagnosis of disease comes under the Radiology department. Table 4 shows the details of work done, types of chart, variables used, types of study and country along with authors' name and year. Five numbers of studies have been included in Radiology department.

Table 4

Literature survery in Radiology department

3.5 Studies done in Pulmonary Departments

The branch of medicine that deals with causes, diagnosis, prevention and treatment of various diseases that affects the lungs comes under Pulmonary. Table 5 shows the details of work done, types of chart, variables used, types of study and country along with authors' name and year. Two numbers of studies have been included in Pulmonary department.

Table 5

Literature survery in Pulmonary department

3.6 Studies done in Cardiology departments

The Cardiology department deals with diseases and an abnormality of human's heart. Table 6 shows the details of work done, types of chart, variables used, types of study and country along with authors' name and year. Two numbers of studies have been included in Cardiology department.

Table 6

Literature survery in Cardiology department

3.7 Studies done in Administration departments

Table 7 shows the details of work done, types of chart, variables used, types of study and country along with authors' name and year. Three numbers of studies have been included in Administration department.

Table 7

Literature survery in Administration department

3.8 Studies done on Pharmactuel department

Table 8 shows the details of work done and types of charts used along with authors' name and year. Three numbers of studies have been included in Pharmactuel department.

Table 8

Literature survery in Pharmactuel Department

4 Statistical analyses

As stated earlier, 40 suitable research papers out of 142 potentially relevant searched articles have been identified for this study. Generally the statistical analysis can be defined as collection, examination and interpretation of quantitative data in order to find trends, relationships and underlying causes. The bar chart and Matrix plot are utilized for the statistical analysis. Analyses of studies are categorised into following sub-sections.

4.1 Analysis of studies using bar chart

The range of number of studies included from different years is shown in Figure 4. It is clear from  Figure 4 that 25 out of 40 studies are from year 2009 onwards. Figure 4 shows bar chart for range of number of studies. Figure 5 shows the number of included studies from different departments. Most of the work is carried out in Emergency, Surgery and Epidemiology department. A little amount of work is done on Cardiology and Pulmonary departments. Figure 6 shows the number of studies selected from different countries. It is found that most of work on control chart applications in healthcare is carried out in US, UK and Australia.

In healthcare applications, p chart, run chart, CUSUM and EWMA are mostly used as shown in Figure 7. Figure 8 shows the longitudinal and retrospective types of studies. It is clear from the figure that the most of the work is done on retrospective studies.

thumbnail Fig. 4

Number of studies performed in various years.
thumbnail Fig. 5

Number of studies performed in various departments of hospitals.
thumbnail Fig. 6

Number of studies included from different countries.
thumbnail Fig. 7

Number of studies employing different types of charts.
thumbnail Fig. 8

Bar chart for longitudinal and retrospectives studies.

5 Analysis of studies using matrix plot

Matrix plot is a graph that can be used to find the relationship among different pairs of variable at the same time. It can also be defined as set of individual scatter plots. These are two types: matrix of plots and each Y verses each X.

Figure 9 shows the distribution of number of studies in different departments across the time line starting from 1996 to 2017. The different rows in Figure 9 show the different departments. X axis shows the year of studies; while Y axis shows the number of studies in a particular year.

Figure 10 shows the distribution of number of studies in differnet countries across the time line. It can be concluded from Figure 10 that conrol charts have been used in US regularly. The number of studies employing different types of chart across the time line are shown in Figure 11. It is evident from Figure 11 that p chart, run chart and X bar chart in healthcare have been used very frequently.

thumbnail Fig. 9

Year wise matrix plot for number of studies in various departments (EG = Emergency, SG = Surgery, RL = Radiology, PL = Pulmonary, CL = Cardiology, ED = Epidemiology, AS = Administration, PM = Pharmactuel).
thumbnail Fig. 10

Year wise matrix plot for number of studies in various Countries (US = United States, UK = United Kingdom, Aus = Australia, FR = France, TN = Taiwan, SL = Switzerland, SA = Saudi Arabia, PG = Portugal, NG = Nigeria, NL = Netherlands, IL = Israel, GM = Germany, BZ = Brazil, Ind = India, Rom = Romania, KR = Korea, TL = Thailand).
thumbnail Fig. 11

Year wise matrix plot for number of studies employing different types of charts (MV = Multivariate chart).

6 Results and discussions

The control charts have been proved to be magnificent tools in improving the quality of healthcare industry since last three decades. The control charts were firstly applied in laboratory and after then shifted to patient level in hospitals. There are astounding results in terms of decrease in mortality rate, door to reperfusion time, door to needle time, length of stay, processing time, admission time, complications, surgical site infections, percentage of errors etc. in almost every department of the hospital. One thing should be noted that implementation of control charts does not automatically leads to process improvement. There is responsibility of top management along with staff associated with the process to find the special causes of variation and rectify them. In this way, the management system needs to be flexible for making the required changes.

The present paper deals with control chart applications in healthcare. The extensive research is done to find out the articles related to the control chart in healthcare. Out of 142 relevant articles identified, 40 have been found to be more relevant for the study. The criteria for the inclusion of research papers are that control charts should be applied at departmental level. Most of the included studies i.e. 25 out of 40 are from year 2009 onwards as indicated in Figure 4. This shows that application of control charts in healthcare is continuously increasing from nineties to 2017.

It is evident from Figure 5 that most of work on control chart applications in healthcare is carried out in Surgery, Emergency and Epidemiology departments. Little work is done in other departments like Pulmonary, Radiology, and Cardiology etc. Another thing should be noted that the control charts applications in healthcare at departmental level are very limited i.e. out of 142 articles; only 40 have been selected. These data also show great opportunities for control chart to be applied in other departments like Pathology, Pharmacy, Inpatient, Outpatient departments etc. Similarly from Figure 6, it is clear that most of work on control chart is carried out in developed countries like US, UK and Australia. That shows the huge gape of deploying control charts in healthcare at departmental level in different countries. The countries like India, China and Russia etc. have great opportunity to apply control charts in healthcare sector.

Figure 7 shows the types of control charts used in healthcare in the included studies. It is clear from the Figure 7 that p chart, X bar chart and run chart are the mostly implemented in the healthcare applications. That shows the great applicability of these charts in healthcare. The CUSUM, EWMA and XMR charts are also used frequently. There is very little use of c chart, g chart, and u charts in healthcare sector. The use of multivariate chart is also very less but its use should be increased in the future. The reason for less use of multivariate chart may be due to complications in the design of chart and it is also very difficult to identify which factor contributes to the assignable causes of variations.

The included studies are divided into two categories on the basis of process of data collection. If the data is taken directly from the hospital staff, the study is called retrospective study. On the other hand, if close watch is done on the process in order to take the data, the study comes under the category of longitudinal study. Out of 37 included studies, 25 are retrospective studies and rest are longitudinal study as shown in Figure 8. Researchers found that, longitudinal study is better than retrospective study because it gives the idea about current situation of the process. So in future, number of longitudinal studies should be increased.

It is found in the literature survey that the mostly used variables for the construction of control charts are mortality rate, number of complications in particular number of cases, Rate of surgical site infections, length of stay, door to needle time etc. Since healthcare involves lot of complications, so selection of variable is very important.

Figure 9 shows the distribution of number of studies in different departments across the timeline. The matrix plot is two dimensional. Firstly, it tells about number of studies in different departments and secondly, it shows the timing of studies. It is evident from the Figure 9 that, control charts in Emergency, Surgery and Epidemiology departments are used at regular intervals. Literature survey shows that Emergency department is always associated with longer waiting time and overcrowded room. So there is always space for the improvement in Emergency department. On the other hand, Surgery and Epidemiology department is overburdened with errors and inefficiencies. These are the possible reasons for regular use of control charts in these departments. The control charts are slowly expanding to Pulmonary, Cardiology, Radiology and Pharmactuel as it is depicted from the Figure 9. So there is lot of scope of implementing control charts in these departments in future.

Similarly, it is depicted from the Figure 10 that only US shows regular interval use of control chart in healthcare. In case of UK, Australia and France, there is also a better distribution of studies throughout the years but not as uniform as in United States of America. Other countries like Portugal, Nigeria, Israel, Saudi Arabia, Italy, India and Germany start implementing control chart in healthcare from 2009 onwards and there is great opportunity for regular interval use in the future. Figure 11 shows the distribution for number of studies employing different types of charts across the time line. It is clear from the Figure 11 that p chart, X bar chart and run charts show regular interval use starting from 1996 to till 2017. That shows the great applicability of these charts in healthcare. CUSUM and EWMA charts come into picture after 2008 and show continuous use after that. Multivariate charts are rarely used in healthcare. There is great possibility in future to come up with.

One thing should be noted that during construction of control chart, the performance of individuals in hospitals is direct under observation. So, some chances of errors in implementing the control charts are possible. This is called Hawthorne effect. The Hawthorne effect, also known as the observation bias, refers to alteration of an individual's behaviour as a result of being observed and therefore improvements in productivity may be as a consequence of the observation rather than the efficacy of a specific intervention. So Hawthorne effect or observation bias needs to be minimised during the observations.

7 Limitations

There are various benefits of applying SPC in healthcare like its simplicity, improvement of process, identification of areas of improvement, investigating the impact of changes to the process, prediction of the future process performance etc. But there are some inherent limitations. The difficulty in obtaining the baseline data for process performance is one of the major challenges while applying control charts in healthcare sector. The plotting of performance data do not automatically lead to improvement of the process. It needs the strong top management commitment in order to make the required changes. The behaviour of the employees during collection of data for control chart may be biased.

Another limitation is that statistical control cannot equate with clinical control. The chart under statistical control tells only the absence of special cause(s) of variation. Even if the chart shows the process under statistical control, it does not give the guaranty for risk of other kinds of infections to the patients. Similarly; the conditions of patients admitted to the hospitals vary according to severity of their illness. So this also limits the appropriateness of combining data into one control chart.

8 Managerial and academic implications

The finding of this review paper is valuable to the researchers and practitioners; who seek to improve the quality of healthcare by applying control charts. The paper provides the departments which are responsible for maximum studies and in addition also provides the gaps for further research in departments; which accounts for minimum studies. This review also provides the information to the researchers about the control charts which have rarely been used in healthcare.

The control charts in healthcare industry provides the necessary information to the management regarding process capability. The failure of control charts implementation is generally due to lack of management support, lack of training and other social and human factors. So there is strong need of top management commitment before starting SPC project. The top management should incorporate the quality initiatives in their business strategy in order to improve the healthcare quality.

9 Conclusions

Healthcare is always overburdened with errors, infections, pre and post-operative complications, longer waiting time, length of stay, complications etc. As there is more involvement of human, the chances of errors are also more. SPC i.e., control charts can help in determining the source of errors by identifying the special causes of variations in healthcare sector.

Literature survey shows that most of work on control charts applications in healthcare is carried out in Surgery, Emergency and Epidemiology departments. US, UK and Australia are responsible for maximum amount of work. Matrix plots show that there is regular interval use of control chart in Emergency, Surgery and Epidemiology department and only US shows regular interval use of control chart in healthcare. This shows the huge gap of deploying control chart in other departments and others countries as well. It is also found that p chart, X bar chart and run chart have been used regularly across the time line. That shows the great applicability of these charts in healthcare. CUSUM and EWMA charts are also being used since 2008. Multivariate charts are rarely used in healthcare.

SPC is multifaceted tool which enable the staff and physicians in healthcare to continuously monitor and improve the patients' health. A strong evaluation of control chart is required to apply control chart in other departments like Pathology, Pharmacy, Inpatient, Outpatient etc. The limitation of this study is that numbers of included studies are less. So in future, statistical analysis can be performed on larger scale in order to generalize the results.

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Cite this article as: Gaurav Suman, Deo Raj Prajapati, Control chart applications in healthcare: a literature review, Int. J. Metrol. Qual. Eng. 9, 5 (2018)

All Tables

Table 1

Literature survery in Emergecny department

In the text
Table 2

Literature survery in Surgery department

In the text
Table 3

Literature survery in Epidemiology department

In the text
Table 4

Literature survery in Radiology department

In the text
Table 5

Literature survery in Pulmonary department

In the text
Table 6

Literature survery in Cardiology department

In the text
Table 7

Literature survery in Administration department

In the text
Table 8

Literature survery in Pharmactuel Department

In the text

All Figures

thumbnail Fig. 1

Sample u chart for No. of defects/unit, given by Finison et al. (1993).
In the text
thumbnail Fig. 2

Classification of control charts.
In the text
thumbnail Fig. 3

Flow chart for selection of studies.
In the text
thumbnail Fig. 4

Number of studies performed in various years.
In the text
thumbnail Fig. 5

Number of studies performed in various departments of hospitals.
In the text
thumbnail Fig. 6

Number of studies included from different countries.
In the text
thumbnail Fig. 7

Number of studies employing different types of charts.
In the text
thumbnail Fig. 8

Bar chart for longitudinal and retrospectives studies.
In the text
thumbnail Fig. 9

Year wise matrix plot for number of studies in various departments (EG = Emergency, SG = Surgery, RL = Radiology, PL = Pulmonary, CL = Cardiology, ED = Epidemiology, AS = Administration, PM = Pharmactuel).
In the text
thumbnail Fig. 10

Year wise matrix plot for number of studies in various Countries (US = United States, UK = United Kingdom, Aus = Australia, FR = France, TN = Taiwan, SL = Switzerland, SA = Saudi Arabia, PG = Portugal, NG = Nigeria, NL = Netherlands, IL = Israel, GM = Germany, BZ = Brazil, Ind = India, Rom = Romania, KR = Korea, TL = Thailand).
In the text
thumbnail Fig. 11

Year wise matrix plot for number of studies employing different types of charts (MV = Multivariate chart).
In the text

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