control chart applications in healthcare: a literature review

Int. J. Metrol. Qual. Eng. 9, 5 (2018)© G. Suman and D. Prajapati, published by EDP Sciences, 2018https://doi.org/10.1051/ijmqe/2018003

International Journal ofMetrology and Quality Engineering

Available online at:www.metrology-journal.org

REVIEW

Control chart applications in healthcare: a literature reviewGaurav Suman and DeoRaj Prajapati*

Department of Mechanical Engineering Punjab Engineering College (Deemed to be University), Chandigarh 160012, India

* Correspo

This is an O

Received: 13 December 2017 / Accepted: 19 February 2018

Abstract.The concept of Statistical process control (SPC) was given by the physicistWalter Shewhart in orderto improve the industrial manufacturing. The SPC was firstly applied in laboratory and after then shifted topatient level in hospitals. As there is more involvement of human in healthcare, the chances of errors are alsomore. SPC i.e., control chart can help in determining the source of errors by identifying the special and commoncauses of variations. This paper presents the review of literature on the application of SPC and control chart inhealthcare sector. Forty articles are selected out of 142 potentially relevant searched studies. Selected studies arecategorised into eight departments. Literature survey shows that most of work on control chart applications inhealthcare is carried out in Surgery, Emergency and Epidemiology departments. US, UK and Australia are themain customers where maximum amount of work was done. The US is the country where control chart inhealthcare sector have been used at regular interval. This shows the gap of deploying control chart in differentdepartments and different countries as well. The CUSUM and EWMA chart came into picture in healthcaresector after 2008 and are used at regular interval.

Keywords: statistical process control / control charts / healthcare sector / countries / departments

1 Introduction

Quality in healthcare is always a big concern because badquality in healthcare canmake a difference in terms of life ordeath of patients. The investment is continuously growing inthis sector with little interest towards quality. Theimportance of building safe and effective healthcare systemwas realised in 2000 because several reports issued by USInstitute of Medicine as reported by Lazarus and Neely [1].In one of his report, released in November 1999, the authorsestimated that around 98,000 patients die each year due tomedicalerrors.According toDepartmentofHealth (2001) [2]& Milligan and Robinson [3], due to adverse incidents andclinical negligence claim, an estimated £400 million is beingpaid 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 permanentlybecause of medical mistakes;
–
fifteen diagnoses out of every 100 are incorrect; – twenty to fifty out of 100 diagnostic procedures shouldnever have been done because their results did not help todiagnose patients;
–
five to ten out of every 100 admitted patients get infectedduring treatment.
nding author: [email protected]

pen Access article distributed under the terms of the Creative Comwhich permits unrestricted use, distribution, and reproduction

These all data illustrate that there is a need to monitorand control the healthcare performance so that adverseevents can be minimized. Healthcare system needs bothinvestment as well as quality tomeet the ultimate objectiveof patient satisfaction. As healthcare is a service organisa-tion and every organisation wants to boost up their revenuewhile decreasing cost by providing appropriate quality inthe services. In order to attain the quality objectives,quality initiative like statistical process control (SPC) willbe very effective.

The aim of this paper is to provide the guidance in thedirection of implementing control charts in healthcare. Thevarious studies show applications of the control charts invarious departments. This paper provides the informationabout departments and countries; where these studies havebeen done. Statistical analysis shows the frequency of thesecharts used in healthcare.

1.1 Statistical process control

Statistical process control (SPC) techniques have playedan efficacious role in monitoring hospital performance suchas mortality rate, pre and post-operative complications,number of infections in hospital etc. as reported by Finisonet al. [5], Finison and Finison [6], Benneyan [7], Maccarthyand Wasusri [8], Clemente et al. [9]. The concept of SPCwas given by the Walter A Shewhart in order to improvethe industrial manufacturing processes. The SPC was

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Fig. 1. Sample u chart for No. of defects/unit, given by Finisonet al. (1993).

2 G. Suman and D.R. Prajapati: Int. J. Metrol. Qual. Eng. 9, 5 (2018)

firstly applied in laboratory and after that shifted topatient level in hospital. As there is more involvement ofhuman in healthcare, the chances of errors are also more.SPC can help in determining the source of errors byidentifying the special and common causes of variations.

SPC is a system of online quality control and can bedefined as a philosophy, strategy and methods for theimprovement of systems, outcomes and processes. The SPCis based on theory of variation i.e., common and specialcauses of variations. It involves the concept of processthinking, learning through data, analytical study, experi-mentation, measurement system and methods of datacollectionas reportedbyNing etal. [10].Themost commonlyused charts in SPC are run charts, mean chart, range chart,histogram, Pareto chart etc. One thing should be noted thatSPC and statistical quality control are often used recipro-cally but the later one is used to describe the extensivemanagement approach towards quality management.

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

In order to construct a control chart, first of all, thereshould be availability of data. Data can be of two types i.e.,variable and attribute. The data which can be measured onsome scale comes under the category of variable and on theother hand, the data which cannot be measured andrepresented on count basis comes under attribute. Figure 1shows the data of attribute type control chart. There arethree lines in control chart i.e., Centre line, upper controllimit and lower control limit. Upper and lower controllimits are at a distance of 3 standard deviations from thecentre line. A set of decision rule was suggested in that aprocess is out of control and these are:
– if any point lies beyond the specified three sigma controllimits;
–
if two out of three consecutive points fall beyond the twosigma control limits;
–
if four out of five consecutive points fall beyond the onesigma control limits;
–
if the run of eight consecutive points fall on either side ofcentre line.
According to the types of data, there are two types ofcontrol chart i.e., control chart for variables and controlchart for attributes. The data is arranged into subgroup inorder to establish a set of reading in which process showsstable and controlled behaviour. For example, we canchoose to group response time readings taken at regularintervals throughout the day into a subgroup which is thenplotted as a single point on a control chart. In attributechart, there is count of defects or defectives. In onedefective unit, there may be several defects. Control chartscan be classified asX & S charts,X &R charts, p chart andnp chart etc. The details about construction and use ofcontrol chart are given by Kaminsky et al. [11] and Amin[12]. Thor et al. [13] discussed the variables used in clinicalmonitoring, benefits and barrier associated with controlchart use. According to Koetsier et al. [14], the maximumnumbers of charts are plotted on two phases of the PDSA

cycle. Laney [15] and Mohammed & Laney [16] suggestedcontrol chart for attributes for very large sample sizes (inthousands or even millions). Prajapati [17] presentedmodifiedX chart for autocorrelated observations. Depend-ing upon the suitability of charts; they can be used for aparticular situation. Figure 2 shows the detailed classifica-tion of control charts.

In addition to above charts, there are other charts likeCUSUM (Cumulative Sum Chart) and EWMA (Exponen-tial Weighted Moving Average Chart). These charts comeunder the category of Control chart for variables. Thesecharts come into picture because the above mentionedcontrol charts are not sensitive enough for the processmeasurement. The detail about construction and use ofCUSUM and EWMA chart is given by Woodall et al. [18].Pillet et al. [19] stated that multivariate chart is used whenmore than one variable are to bemonitored. Most of studieson the application of control chart in healthcare are carriedout in US as reported by Seddon et al. [20]. The presentpaper deals with department wise review of control chartsapplications in healthcare sector. First section deals withintroduction to SPC and control charts while Section 2describes the methodology. The detailed literature surveyin Emergency, Surgery, Epidemiology, Radiology, Pulmo-nary, Cardiology, Administration and Pharmaceuticaldepartments in tabular form are presented in Section 3.Section 4 provides the statistical analysis and discussion onthe results of statistical analysis is given in Section 5.Limitations of studies are discussed in Section 6 andmanagerial and academic implications are presented inSection 7. The final conclusion of the paper provided inSection 8.

2 Methodology

The extensive research is done to find out the articles relatedto control charts applications in healthcare sector. ThePubmed, EBSCO, ResearchGate and Google Scholar data-bases have been used tofind the studies; describing the use of

Fig. 2. Classification of control charts.

G. Suman and D.R. Prajapati: Int. J. Metrol. Qual. Eng. 9, 5 (2018) 3

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 masteranddoctoral dissertation since there is greaterpossibilities ofthese studies to appear in academic and professionalJournals.

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

3 Survey of literature

The literature survey is divided according to the depart-ments in healthcare. Forty studies were found over eightdepartments i.e. Emergency, Surgery, Epidemiology,Radiology, Cardiology, Pulmonary, Administration andPharmaceutical. The details about the included researchpapers is arranged in tabular form showing the details ofwork done, types of chart, variables used, country andtypes of study.

3.1 Studies done in Emergency department

Emergency department is always associated with longerwaiting time, length of stay, longer turnaround time andovercrowded room. So there is always scope for theimprovement in Emergency department. Table 1 shows thedetails of work done, types of charts, variables used, typesof study and country along with authors’ name and year.Seven numbers of studies have been included in Emergencydepartment. It is clear from Table 1 that EWMA is themostly used chart and ‘Mortality rate’ is mainly usedvariable in Emergency department.

3.2 Studies done in Surgery department

The Surgery department is often overburdened witherrors and inefficiencies like surgical site infections, preand post-operative complications etc. Table 2 showsthe details of work done, types of chart, variablesused, types of study and country along with authors’name and year. Nine numbers of studies have beenincluded in the Surgery department. It is clear fromTable 2 that run chart, CUSUM chart and p charthave been mostly used in surgery department.Mortality rate, length of stay and complications areoften used variable.

Fig. 3. Flow chart for selection of studies.


3.3 Studies done in Epidemiology departments

The Epidemiology takes into consideration the effects ofdisease and health condition of particular patients in thedefined population. In simple terms, this department takecare of infections related to particular disease or healthcondition. The numbers of cases with infections perthousand patients’ days are generally very less. This isthe reason that p chart is most commonly used inEpidemiology departments. Table 3 shows the details ofwork done and types of charts used along with authors’name and year. Nine numbers of studies have been includedin 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 ofhospital acquired infections in Epidemiology department.

3.4 Studies done in Radiology departments

The use of X-rays and other high energy radiation fortreatment and diagnosis of disease comes under theRadiology department. Table 4 shows the details of workdone, types of chart, variables used, types of study andcountry along with authors’ name and year. Five numbersof studies have been included 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 affectsthe lungs comes under Pulmonary. Table 5 shows thedetails of work done, types of chart, variables used, types ofstudy and country along with authors’ name and year. Twonumbers of studies have been included in Pulmonarydepartment.

3.6 Studies done in Cardiology departments

The Cardiology department deals with diseases and anabnormality of human’s heart. Table 6 shows the details ofwork done, types of chart, variables used, types of studyand country along with authors’ name and year. Twonumbers of studies have been included in Cardiologydepartment.

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 withauthors’ name and year. Three numbers of studies havebeen included in Administration department.

3.8 Studies done on Pharmactuel department

Table 8 shows the details of work done and types of chartsused along with authors’ name and year. Three numbers ofstudies have been included in Pharmactuel department.

4 Statistical analyses

As stated earlier, 40 suitable research papers out of 142potentially relevant searched articles have been identifiedfor this study. Generally the statistical analysis can bedefined as collection, examination and interpretation ofquantitative data in order to find trends, relationships andunderlying causes. The bar chart and Matrix plot areutilized for the statistical analysis. Analyses of studies arecategorised into following sub-sections.

Table 1. Literature survery in Emergecny department

Authorsand Year

Details of work done Types of chart Variables Types of study Country

Kaminskyet al. [23]

Recommended the use of SPC toanalyse the quality indicators inhealthcare organizations. The useand interpretation of p chart waspreferred. The examples of use ofSPC for number of patientsleaving Emergency departmentsand number of birth withcaesarean were shown.

p chart Number ofpatients leavingED, Number ofbirth withcaesarean

RetrospectiveStudy

US

Callahanand Griffen [24]

Recommended the use of SPC toreduce door to reperfusion time ofpatients. After the successfulintervention, it was found thatthere was run of 14 data pointsbelow the centre line whichshowed huge improvement in theprocess. The efforts were in rightdirection and helpful in reducingthe door to reperfusion times.Initially the upper control limit ofdata was 206 minutes which wasreduced to 140 minutes.

X bar chart Door toreperfusion time

LongitudinalStudy

US

Gilliganand Walters [25]

Reduced the hospital mortalityrate by reducing medical errorsand timely interventions tofacilitate flow of patient. Afterintervention, there was punctualityin daily senior medical review andin planning of discharge. The earlywarning training allowed reducingthe mortality rate in Emergencydepartment which is reflected inhospital standardised mortalityrate.

Run chart andCUSUM

Mortality rate,Medical outliers

LongitudinalStudy

UK

Minne et al. [26] The authors made use of SPC inorder to validate a classificationtree model for estimatingmortality rate. It was investigatedthat proposed tree model did notprovide the reliable results andhence could not be used forpurpose of benchmarking. Itshould be noted that in order toidentify the high risk subgroup, atleast two out of three pre-identified subgroups could beused.

EWMA Mortality rate RetrospectiveStudy

Netherlands

Moran et al. [27] Implemented Statistical ProcessControl (SPC) to control themortality in the intensive caresociety of Australia and NewZealand. Initially the average rawmortality was 14.07%. The rawaverage and standard deviationfor mortalities ranged from 0.012and 0.113 to 0.296 and 0.457respectively whereas the expectedmortality average and standarddeviation ranged from 0.013 and0.045 to 0.278 and 0.247respectively. Out of control

EWMA Mortality rate RetrospectiveStudy

Australia


Table 1. (continued).

Authorsand Year


signalling was shown for rawmortality series in risk adjustedexponential weighted movingaverage chart. It wasrecommended to hospital toinvestigate the assignable causesof variations.

Harrouet al. [28]

Decreased the time for detectionof abnormal daily patient arrivalsat the Paediatric Emergencydepartment. Auto regressivemoving average model was usedto analyse the data collectedduring January–December 2011.The used model provided positiveresults in terms of detection ofabnormal patient arrivals. Thebenefit of early detection ofabnormal situations was toencourage the control of theseconditions.

EWMA Daily attendance RetrospectiveStudy

France

Pagelet al. [29]

The authors found the initial andfinal point of winter surge inintensive care unit. Initially theoptimal Bollinger band thresholdswere found to be 1.2 and 1standard deviation above andbelow of 41 day demand movingaverage respectively. In the end,positive results were obtained assurge was identified from 18thNovember 2013 to 4th January2014.

Run chart Demand forretrieval

RetrospectiveStudy

UK


4.1 Analysis of studies using bar chart

The range of number of studies included from different yearsis shown inFigure 4. It is clear fromFigure 4 that 25 out of 40studies are from year 2009 onwards. Figure 4 shows barchart for range of number of studies. Figure 5 shows thenumber of included studies from different departments.Most of the work is carried out in Emergency, Surgery andEpidemiology department. A little amount of work is doneon Cardiology and Pulmonary departments. Figure 6 showsthe number of studies selected from different countries. It isfound that most of work on control chart applications inhealthcare is carried out in US, UK and Australia.

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

5 Analysis of studies using matrix plot

Matrix plot is a graph that can be used to find therelationship among different pairs of variable at the sametime. It can also be defined as set of individual scatter plots.

These are two types: matrix of plots and each Y verses eachX.

Figure 9 shows the distribution of number of studies indifferent departments across the time line starting from1996 to 2017. The different rows in Figure 9 show thedifferent departments. X axis shows the year of studies;while Y axis shows the number of studies in a particularyear.

Figure 10 shows the distribution of number of studies indiffernet countries across the time line. It can be concludedfrom Figure 10 that conrol charts have been used in USregularly. The number of studies employing different typesof chart across the time line are shown in Figure 11. It isevident from Figure 11 that p chart, run chart and X barchart in healthcare have been used very frequently.

6 Results and discussions

The control charts have been proved to be magnificenttools in improving the quality of healthcare industry sincelast three decades. The control charts were firstly applied inlaboratory and after then shifted to patient level inhospitals. There are astounding results in terms of decreasein mortality rate, door to reperfusion time, door to needle

Table 2. Literature survery in Surgery department

Authorand Year


Levettand Carey [30]

Presented various examples of SPCto monitor the coronary arterybypass grafting mortality, length ofstay and admission rate in intensivecare unit. The authors ended upwith conclusion that control chartsallow making the process stable bydetecting and eliminating the specialor assignable causes of variations.

X Chart Mortality rate,length of stayand admissiontime

Longitudinalstudy

US

Mohammed [31] Recommended the use of statisticalprocess control to improve thequality of healthcare. The examplessurgeon specific mortality ratefollowing colorectal cancer surgerywas presented with the help of Xchart. In addition, backgroundfeature of control chart and how ittransformed to healthcare were alsoprovided.

X Chart Mortality rate Retrospectivestudy

UK

Duclosand Voirin [32]

Provided essential points on how todevelop and interpret SPC forhealthcare practice. Theimplementation of p chart intohealthcare practice required thethorough investigation of the rootcauses of the variations identified.The every quality initiative wasmust supported by the topmanagement to be successful.Implementation of p chart alsoencouraged the healthcarepractitioner to do self-examination ofthe care delivered.

p chart Number ofcomplication

Retrospectivestudy

France

Jonesand Steiner [33]

Determined the performance of riskadjusted CUSUM chart in thepresence of estimation error.Estimation error effect on riskadjusted CUSUM chart performancewas evaluated and found to havesignificant effect in terms ofvariation of true average run length(ARL). It is estimated that mainsource of estimation error was theoverall adverse event.

CUSUM Chart Mortality rate of30 days

Longitudinalstudy

Australia

Smith et al. [34] The authors checked the dailymorbidity and mortality review ofsurgical process at a single site. Thevariables like length of stay orreadmission less than 28 days,bypass duration, acute pre and post-procedural complications etc. werefound to be suitable for monitoringhealthcare outcomes. The specialcauses of variation were evaluateddeeply and assigned to variations inhealthcare practice as well asindividual performance.

CUSUM and EWMA Length of stay,complications.

Longitudinalstudy

Australia

Keller et al. [35] Initiated the SPC in order to identifythe length of stay (LOS) outliers andevaluate the effect of processimprovement. The out of control

Run Chart Length of Stay Retrospectivestudy

US



Authorand Year


points were identified and thesepoints indicated longer operatingtimes and temporary nursing atdischarge. Higher readmission ratewas identified for outliers asemergent open, emergent lap andelective lap.

Oguntundeet al. [36]

Identified the importance of SPC tohealthcare with application of realdata set. The XMR control chartfrom the previous study showed thatonly one sample point was outsidethe upper control limit (i.e SurgeonH) while the p-chart in this articlereveals that three sample points (i.e.,Surgeons E, H and J) were outsidethe upper control limit. Surgeonscorresponding to sample pointsoutside the control limits werehazardous to the patients as well asto the health institution.

p chart Proportions ofdeath

Retrospectivestudy

Nigeria

Woodallet al. [37]

The authors made use of SPC toreduce surgical site infections andmortality. The positive results wereobtained as the reduction inmortality rate from 3.7% to 1.8%which shows approximately 50%decline in mortality rate. Accordingto the hospital administrativedepartment, approximately 20,000surgical procedure were carried outin a year and decrease in mortalityrate caused approximately saving of300 lives.

Run Chart Rate of surgicalsite infectionsand 30 daymortality

Longitudinalstudy

US

Schremet al. [38]

Determined the utility of G chart forhealthcare process control. G chartexamined the changes in detectionrate of cancer during aftercare andno significant relation was foundwith identified risk factor for cancerfree survival i.e., showing p valueless than 0.05. There was incrementin SIR for renal cell carcinoma(SIR=22.46) and post-transplantlymph proliferative disorder(SIR=8.36).

G chart Site Specificstandardizedincidence ratio(SIR)

Retrospectivestudy

Germany


time, length of stay, processing time, admission time,complications, surgical site infections, percentage of errorsetc. in almost every department of the hospital. One thingshould be noted that implementation of control charts doesnot automatically leads to process improvement. There isresponsibility of top management along with staffassociated with the process to find the special causes ofvariation and rectify them. In this way, the managementsystem needs to be flexible for making the required changes.

The present paper deals with control chart applica-tions in healthcare. The extensive research is done to findout the articles related to the control chart in healthcare.

Out of 142 relevant articles identified, 40 have been foundto be more relevant for the study. The criteria for theinclusion of research papers are that control charts shouldbe applied at departmental level. Most of the includedstudies i.e. 25 out of 40 are from year 2009 onwards asindicated in Figure 4. This shows that application ofcontrol charts in healthcare is continuously increasingfrom nineties to 2017.

It is evident from Figure 5 that most of work on controlchart applications in healthcare is carried out in Surgery,Emergency and Epidemiology departments. Little work isdone in other departments like Pulmonary, Radiology, and

Table 3. Literature survery in Epidemiology department

Authorand Year

Details of work done Types ofchart

Variables Type of study Country

Quesenberry [39] Recommended the use of controlcharts i.e., Q charts to detectchanges in nosocomial infection ratesin hospitals. The ultimate aim of thearticle was to aware the readersabout the modern statistical processcontrol methods. With the help ofthese methods, nosocomial infectionsurveillance program became moreeffective because of time warning ofthe onsets of epidemics.

Q chart Infections rate Retrospectivestudy

US

Gustafson [40] The author reduced infection usingrisk adjusted quality control chart.The results showed that unadjustedcontrol charts (c, p and u charts)were not effective in healthcare asthese charts with 3 sigma limits hadsensitivity less than 50% and for 2sigma limits had specificity less than50%. On the other hand, riskadjusted control chart performedmuch better based on standardizedinfection ratio. The author foundthat XMR Chart were most suitableas having sensitivity and specificitygreater than 80%. So in this way,these findings suggested specifictechniques and statistics that couldmake control charts more suitableand vtable and valuable for infectioncontrol.

p chart, Runchart,XMRchart

Standardizedinfection ratio

Retrospectivestudy

US

Hansliket al. [41]

The authors performed a pilot studyduring 1998 World Football Cupwith the help of 553 sentinel generalpractitioners. The data related to thenumber of persons refereed tohospital, average number of cases ofenvironmental, communicable andsocial diseases were collected dailyby general practitioners and plottedon u chart. The parameters selectedfor the study remain under controlwith the use of control chart.

u chart Number ofpatients

Longitudinalstudy

France

Aranteset al.[42]

Recommended the use of SPC chartsfor the close monitoring ofnosocomial infections (NI) inhospital. Results showed that meanNI incidence was 20 per 1000 patientdays. The one out of control pointwas identified in July 2010 with aninfection rate 63 per 1000 patientdays which showed the period ofepidemic. Finally, it can beconcluded that use of SPC forcontrolling infection allowed foridentification of uncommonvariations in infection rate. Thisthing was made possible without theneed of hypothesis testing andcomplicated calculations.

p chart Number ofnosocomialinfections perthousandpatient days

Longitudinalstudy

Brazil



Authorand Year



Grantand Kim [43]

The authors explored the variousways to measure the importantfunction of infection control process.The retrospective study wasconducted for seven years. The chartutilized were XMR along withPearson’s correlation coefficient. As aresult of seven years of study,consultation process became moreefficient. The reason for this increasein efficiency could be attributed toincrease in number of questionswhich lead to decrease in duration toaccomplish closure.

XMR chart Number andduration ofinfectioncontrolconsultations

Retrospectivestudy

US

Limaye et al. [44] Identified the utility of control chartfor monitoring infections associatedwith hospital and recommendationon how to use chart for infectionsurveillance. They recommended theuse of u chart for infection control asu chart was simple to construct, easyto interpret. It could also help todeal with condition like varyingnumber of patient days, ventilatordays because this chart took intoaccount variable sample size.

CUSUM,g chart,u chart

Number ofHospitalassociatedinfections

Longitudinalstudy

US

Curran et al. [45] The authors made use of SPC chartsin order to reduce meticillin resistantstaphylococcus aureus. The authorsdivided seventy five wards in twentyfour hospitals into three categories.First category included wardreceiving SPC chart feedback, secondcategory included wards with SPCchart feedback with structureddiagnostic tools and finally thirdcategory included wards whichreceived neither type of feedback.The authors found that there wasdecrement of 32.3% and 19.6% and23.1 for ward receiving SPCfeedback, SPC & diagnostic feedbackand no feedback respectively.Finally, the authors concluded thecontrol charts as a valuable tool fordisseminating hospital infection data.

p chart Percentage oferrors

Retrospectivestudy

UK

Harbarth et al. [46] Investigated the effect of an earlyMethicillin-Resistant Staphylococcusaureus (MRSA) on the patient’sinfection rate. The study wasconducted between July 2004 andMay 2006 among 21,754 patients.The two MRSA control strategiesi.e., standard infection control aloneand rapid screening on admissionwith standard infection control werecompared. Finally it was concludedthat rapid MRSA admissionscreening strategy does not reducesurgical site infection with low ratesof MRSA infection

Run chart MRSAinfections per1000 patientsdays

RetrospectiveStudy

Switzerland



Authorand Year



Wiemken et al. [47] Suggested the development of theapplication and demonstrateapplication using simulated data forinfection prevention. It wasconcluded that process control chartscan easily be developed based onindividual facility needs using freelyavailable software. They outlined theimportance of implementing an easyto use and free available softwarepackages for measuring qualityimprovement in healthcare.

p chart Hand hygienecompliant per100observations

Retrospectivestudy

US

Table 4. Literature survery in Radiology department

Author andYear

Detail of work done Types of chart Variables Types of study Country

Waterhouseet al. [48]

Provided guidance on how totackle the situation when yourdata are incomplete and non-normal and providerecommendations on how to selecta particular control chart forhealthcare sector. The resultshowed that MEWMA andMCUSUM are more effectivecompared to the T2 chart as theMEWMA and MCUSUM are ableto detect small to moderate shiftsvery quickly irrespective ofcorrelated or uncorrelated data. Itcan be concluded that wheneverthere is need for quick detection oflarge shift, T2 is preferred.

Multivariatechart (T2,MEWMA,MCUSUM)

Dose areaproduct,fluoroscopy timeand Number ofdigital images

Longitudinal

Study AustraliaSanghangthumet al. [49]

Established the control limits forpercentage gamma pass of patientspecific intensity modulatedradiotherapy (IMRT) andvolumetric modulated arc therapy(VMAT) quality assurance (QA).The process capability index wasused to evaluate the efficiency ofprocess. The average percentagegamma pass was calculated to be93.7%±3.7% for IMRT and96.7%±2.2% for VMAT. Theprocess capability index values forIMRT QA and VMAT QA was1.60 and 1.99 respectively. It wasfound that there is lessersystematic errors in VMAT QAthan IMRT QA because there wasmore complicated setup in case ofIMRT QA.

X bar chart Percentagegamma passes

RetrospectiveStudy

Thailand



Author andYear

Detail of work done Types of chart Variables Types of study Country

Mezzenga et al.[50]

Examined the static and dynamicoutput of helical tomo therapytreatment system using SPC. Thebinding of acceptable range ofdaily output checks was givenimportance and recommendationswere given for detailedinvestigation of daily outputchecks for Helical Tomo Therapytreatment system.

X bar chart,Moving rangechart, EWMA

Static anddynamic output

RetrospectiveStudy

Italy

Fuangrod et al.[51]

Provided recommendation forquality evaluation of individualpatient and identify the gap forcontinuous quality improvement.The process capability indiceswere evaluated for rectum andpelvis cancer treatments andfound to less than one. Thecontrol limits were identified as62.5 and 70.0% of the x pass-ratefor intensity-modulatedradiotherapy and deliveriesrespectively.

Moving rangechart

x pass-rate Longitudinal

Study AustraliaLee et al. [52] Applied the SPC charts for head,

neck, breast and prostate cases ofintensity modulated radiationtherapy (IMRT) or volumetric arcradiation therapy (VMAT). Thenumber of patients data utilizedfor head, neck, prostate and breastwere 73, 68, 49 and 152respectively. Finally it wasconcluded that VMAT qualityassurance has better processcapability than IMRT. Theauthors suggested paying moreattention for planning and qualityassurance before breastradiotherapy treatment.

XMR chart Number ofpatient

RetrospectiveStudy

Korea


Cardiology etc. Another thing should be noted that thecontrol charts applications in healthcare at departmentallevel are very limited i.e. out of 142 articles; only 40 havebeen selected. These data also show great opportunities forcontrol chart to be applied in other departments likePathology, Pharmacy, Inpatient, Outpatient departmentsetc. Similarly from Figure 6, it is clear that most of work oncontrol chart is carried out in developed countries like US,UK and Australia. That shows the huge gape of deployingcontrol charts in healthcare at departmental level indifferent countries. The countries like India, China andRussia etc. have great opportunity to apply control chartsin healthcare sector.

Figure 7 shows the types of control charts used inhealthcare in the included studies. It is clear from Figure 7that p chart, X bar chart and run chart are the mostlyimplemented in the healthcare applications. That showsthe great applicability of these charts in healthcare. TheCUSUM, EWMA and XMR charts are also usedfrequently. There is very little use of c chart, g chart,and u charts in healthcare sector. The use of multivariatechart is also very less but its use should be increased in thefuture. The reason for less use of multivariate chart may bedue to complications in the design of chart and it is alsovery difficult to identify which factor contributes to theassignable causes of variations.

Table 5. Literature survery in Pulmonary department

Author andYear


Correia et al.[53]

Provided the timely treatment tochronic obstructive diseasepatient by monitoring andcontrolling of complications. Itwas concluded that when theobjective was to find out changesin mean only then one sidedcontrol charts were more suitable.So in order to detect previouslyunidentified out of control points,one side multi and univariatecharts were constructed. It wasrecommended that one sidedcontrol charts were valuable toolsfor monitoring healthcare and inthis way contributing to medicaldecision making.

Multivariatechart

Body massindex, Pao2,PaCo2

Longitudinal

Study PortugalAlsadat et al.[54]

Reduced ventilator associatedpneumonia (VAP) rate for allmechanically ventilated patients.It was found that VAP bundlecompliance rate was graduallyincreased in all the hospitals. Nodata was available for hospital 4as the VAP bundle was notapplied in it. Only hospital 3 hadachieved a target of 95%. Thesuggested multidimensionalstrategy from top management isneeded in order to prevent VAPrate which included interventionsfor infection control, processmonitoring and outcomesurveillance.

Run Chart VAP rate Longitudinal

Study Saudi Arabia


The included studies are divided into two categories onthe basis of process of data collection. If the data is takendirectly from the hospital staff, the study is calledretrospective study. On the other hand, if close watch isdone on the process in order to take the data, the studycomes under the category of longitudinal study. Out of 37included studies, 25 are retrospective studies and rest arelongitudinal study as shown in Figure 8. Researchers foundthat, longitudinal study is better than retrospective studybecause it gives the idea about current situation of theprocess. So in future, number of longitudinal studies shouldbe increased.

It is found in the literature survey that the mostly usedvariables for the construction of control charts aremortality rate, number of complications in particularnumber of cases, Rate of surgical site infections, length ofstay, door to needle time etc. Since healthcare involves lotof complications, so selection of variable is very important.

Figure 9 shows the distribution of number of studies indifferent departments across the timeline. The matrix plotis two dimensional. Firstly, it tells about number of studiesin different departments and secondly, it shows the timingof studies. It is evident from Figure 9 that, control charts inEmergency, Surgery and Epidemiology departments areused at regular intervals. Literature survey shows thatEmergency department is always associated with longerwaiting time and overcrowded room. So there is alwaysspace for the improvement in Emergency department. Onthe other hand, Surgery and Epidemiology department isoverburdened with errors and inefficiencies. These are thepossible reasons for regular use of control charts in thesedepartments. The control charts are slowly expanding toPulmonary, Cardiology, Radiology and Pharmactuel as itis depicted from Figure 9. So there is lot of scope ofimplementing control charts in these departments infuture.

Table 6. Literature survery in Cardiology department

Author andYear


Bonetti et al.[55]

The authors used SPC to reduce thedoor to needle time for patients ofacute myocardial infraction. In thestarting, the door to needle time forpatients was found to be 57 minuteson average basis. After finding andeliminating the root causes of theproblem, time was reduced to 24minutes. It was concluded theformal process analysis technique issuitable to improve the processes inintensive care unit.

Run Chart Door to needletime

Longitudinalstudy

Switzerland

Coory et al.[56]

Monitored the quality of hospitalcare by using administrative data.CUSUM charts were utilized and nooutliers were found on cross-sectional funnel plot, using threesigma limits for either of two years.It was concluded that control chartswere valuable tools in the sense thatthese provide an early detection ofgood and bad result. So thathospital could trace out the areaswhich need more examining andmonitoring.

CUSUM Mortality rate Retrospectivestudy

Australia

Table 7. Literature survery in Administration department

Author andYear


Variables Types of study Country

Gabbay andBukchin [57]

Monitored whether the numbers ofstaff nurses meets the daily workloadrequirements. The results showedthat 42% of the total days fell withinthe acceptable control limits and onthe other hand, 71% fell withintolerable control limits. The authorsfound that only 33% of the wardsshowed acceptable nurse workload.

Run Chart Nurseattendance andward workload

Retrospectivestudy

Israel

Canel et al. [58] Analysed and reduced the time formaking medical record. The hospitalimproved the efficiency andeffectiveness of assemble recordprocess as there was significantreduction in the medical recordsassembly time. The authorsimplemented a new procedure, as aresult of which medical record stayedtime reduced from 9 to 7.6 days.

c chart TotalCompletiontime forassembly record

LongitudinalStudy

US

Chien et al. [59] Examined the quality performance ofhospital using intraclass correlationcoefficient. In order to annually assessa country’s year based convergentpower to constrain medical services

Regressionchart, XMRchart

Intraclasscorrelationcoefficient

Retrospectivestudy

Taiwan



Author andYear


Variables Types of study Country

across hospitals, the authorsdeveloped the intraclass correlationcoefficient. The authors found that allhospitals groups presented a slowlywell control supply of services thatdecrease from 0.772 to 0.415. Finally,authors preferred intraclasscorrelation coefficient to annuallyassess a country’s year basedconvergent power.

Table 8. Literature survery in Pharmactuel Department.

Author andYear

Details of work done Type of chart Variables Type of study Country

Spiridonica[60]

The author analysed the process ofampicillin bottles, of 1000mg, 500mg and250mg. It was concluded that thisapplication is very useful and easy to applyfor all specialists working in industrialquality assurance.

X Chart Weight ofbottles

Retrospectivestudy

Romania

Prajapati[61]

The author countered the autocorrelationby presenting the new X chart based onChi-squares theory. The readings weretaken for the quantity of syrup and level ofcorrelation was found and compared withnew Xchart for sample size of four. Thelevel of correlation was found to be 0.50among the industry data. Finally it wasconcluded that wherever there isautocorrelation in the industries, it is moreeconomical to use new X chart thantraditional X bar chart for sample size offour.

X Chart Quantity ofsyrup

Retrospectivestudy

India

Prajapatiand Singh[62]

The level of correlation for the weights oftablets was computed in pharmaceuticalindustry by using X chart. The design ofchart was modified based upon the Chi-square theory. The parameter selected inorder to evaluate the performance of chartis the average run length (ARL). It wasconcluded that modified X chart havebetter performance than traditional Xchart.

X Chart Weight oftablets

Retrospectivestudy

India


Similarly, it is depicted from Figure 10 that only USshows regular interval use of control chart in healthcare. Incase of UK, Australia and France, there is also a betterdistribution of studies throughout the years but not asuniform as in United States of America. Other countries likePortugal, Nigeria, Israel, Saudi Arabia, Italy, India andGermany start implementing control chart in healthcare

from 2009 onwards and there is great opportunity for regularinterval use in the future. Figure 11 shows the distribution fornumber of studies employing different types of charts acrossthe time line. It is clear from Figure 11 that p chart, X barchart and run charts show regular interval use starting from1996 to till 2017. That shows the great applicability of thesecharts in healthcare. CUSUM and EWMA charts come into

Fig. 4. Number of studies performed in various years.

Fig. 5. Number of studies performed in various departments of hospitals.


picture after 2008 and show continuous use after that.Multivariate charts are rarely used in healthcare. There isgreat possibility in future to come up with.

One thing should be noted that during construction ofcontrol chart, the performance of individuals in hospitals isdirect under observation. So, some chances of errors inimplementing the control charts are possible. This is calledHawthorne effect. The Hawthorne effect, also known as theobservation bias, refers to alteration of an individual’sbehaviour as a result of being observed and thereforeimprovements in productivity may be as a consequence ofthe observation rather than the efficacy of a specificintervention. So Hawthorne effect or observation biasneeds to be minimised during the observations.

7 Limitations

There are various benefits of applying SPC in healthcarelike its simplicity, improvement of process, identification

of areas of improvement, investigating the impact ofchanges to the process, prediction of the future processperformance etc. But there are some inherent limitations.The difficulty in obtaining the baseline data for processperformance is one of the major challenges while applyingcontrol charts in healthcare sector. The plotting ofperformance data do not automatically lead to improve-ment of the process. It needs the strong top managementcommitment in order to make the required changes. Thebehaviour of the employees during collection of data forcontrol chart may be biased.

Another limitation is that statistical control cannotequate with clinical control. The chart under statisticalcontrol tells only the absence of special cause(s) ofvariation. Even if the chart shows the process understatistical control, it does not give the guaranty for risk ofother kinds of infections to the patients. Similarly; theconditions of patients admitted to the hospitals varyaccording to severity of their illness. So this also limits theappropriateness of combining data into one control chart.

Fig. 6. Number of studies included from different countries.

Fig. 7. Number of studies employing different types of charts.


8 Managerial and academic implications

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

The control charts in healthcare industry provides thenecessary information to the management regardingprocess capability. The failure of control charts imple-mentation is generally due to lack of managementsupport, lack of training and other social and humanfactors. So there is strong need of top managementcommitment before starting SPC project. The topmanagement should incorporate the quality initiativesin their business strategy in order to improve thehealthcare quality.

Fig. 8. Bar chart for longitudinal and retrospectives studies.

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).


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 moreinvolvement of human, the chances of errors are alsomore. SPC i.e., control charts can help in determining thesource of errors by identifying the special causes ofvariations in healthcare sector.

Literature survey shows that most of work on controlcharts 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 ofcontrol chart in Emergency, Surgery and Epidemiologydepartment and only US shows regular interval use ofcontrol chart in healthcare. This shows the huge gap ofdeploying control chart in other departments and otherscountries as well. It is also found that p chart, X bar chartand run chart have been used regularly across the time line.That shows the great applicability of these charts inhealthcare. CUSUM and EWMA charts are also being usedsince 2008. Multivariate charts are rarely used in health-care.

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=Nether-lands, IL= Israel, GM=Germany, BZ=Brazil, Ind= India, Rom=Romania, KR=Korea, TL=Thailand).

Fig. 11. Year wise matrix plot for number of studies employing different types of charts (MV=Multivariate chart).


SPC is multifaceted tool which enable the staff andphysicians in healthcare to continuously monitor andimprove the patients’ health. A strong evaluation of controlchart is required to apply control chart in other depart-ments like Pathology, Pharmacy, Inpatient, Outpatientetc. The limitation of this study is that numbers of includedstudies are less. So in future, statistical analysis can beperformed 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)

control chart applications in healthcare: a literature review

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