national emergency department overcrowding study tool is not useful in an australian emergency...

Emergency Medicine Australasia

(2006)

18

282ndash288 doi 101111j1742-6723200600854x

copy 2006 The AuthorsJournal compilation copy 2006 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine

Blackwell Publishing AsiaMelbourne AustraliaEMMEmergency Medicine Australasia1742-67312006 Blackwell Publishing Asia Pty Ltd2006183282288Miscellaneous

Web based ED overcrowding assessmentK Raj

et al

Correspondence Dr Kamini Raj 24 Gila Place Ipswich Qld 4300 Australia Email kamini_rajhealthqldgovau

Kamini Raj MB BS Emergency Registrar Kylie Baker MB BS FACEM Emergency Physician Stephan Brierley MB BS FRACGP FRACMADirector of Emergency Department Duncan Murray MB BS FACEM Emergency Physician

M

ANAGEMENT

amp Q

UALITY

National Emergency Department Overcrowding Study tool is not useful in an Australian emergency department

Kamini Raj Kylie Baker Stephan Brierley and Duncan Murray

Department of Emergency Medicine Ipswich General Hospital Ipswich Queensland Australia

Abstract

Objective

To determine the accuracy and usefulness of the National Emergency Department Over-crowding Study (NEDOCS) tool in an urban hospital ED in Australia by direct comparisonwith subjective assessment by senior ED staff

Method

A sample of simultaneous subjective and objective data pairs were collected six times aday for a period of 3 weeks All senior medical staff in the ED answered a brief question-naire along with the senior charge nurse for the ED Simultaneously the senior chargenurse also documented the total number of patients in the ED the number of patientsawaiting admission the number of patients on ventilators the longest time waited by anED patient for ward bed and the waiting time for the last patient from the Waiting Roomplaced on a trolley The objective indicators were entered into a Web-based NEDOCS tooland transformed scores were compared with the averaged and transformed subjectivescores for each sample time BlandndashAltmann and Kappa statistics were used to test theagreement between the objective and subjective measuring methods

Results

The mean difference between the subjective and objective methods was small (35 [95confidence interval

minus

0875ndash7878] ) however the 95 limits of agreement was wide(

minus

4652ndash5343) The Kappa statistic used to assess the extent of reproducibility betweencategorical variables was 031 (95 confidence interval 017ndash045)

Conclusion

The present study suggests that NEDOCS method of processing the objective overcrowd-ing data does not accurately reflect the subjective assessment of the senior staff workingat that time in the ED This might be because the assumptions of the original NEDOCSstudy are flawed

Key words

Australian emergency department

emergency department

National Emergency DepartmentOvercrowding Study tool

objective score

overcrowding

Web based ED overcrowding assessment


283

Introduction

ED overcrowding is a growing issue worldwide Overthe years various articles in the press

12

governmentreports and journals

34

have highlighted this issue Itwas first documented in Australia in a Sydney metro-politan hospital in late 1980

5

Overcrowding of the EDhas been demonstrated to have negative effect onpatient care

6ndash8

staff satisfaction and health

910

Early warning of overcrowding would allow an orga-nizational response to prevent any potential adverseoutcomes Strategies could include calling in of extrastaff

1112

changing management strategies

13ndash17

hospitalambulance bypass and notification of neighbouringhospitals There is need for a tool which can rapidlyand accurately forewarn of overcrowding

18

Arguably the most accurate indicators of overcrowd-ing are the adverse events and complaints

19

but thereis a delay in notification and correlation They are nothelpful as an early warning system

The computerization of hospital admissions opensthe possibility of a computer generated automatic andobjective warning system

20

Weiss and colleagues have explored the relationshipof the computer-generated indicators of overcrowdingwith the subjective assessment by senior staff atthe time of sampling This model is called NationalEmergency Department Overcrowding Study(NEDOCS) tool

21

The NEDOCS tool was produced to lsquoquantitativelydescribe the staffrsquos sense of overcrowdingrsquo in varioussized academic ED in the USA It was recognized thatthere is currently no gold standard measure of ED overcrowding so a qualitative measure of overcrowdingwas devised and described by same team

22

This qual-itative measure was transformed into the subjectivescore The NEDOCS Tool is a Web-based calculatorwhich converts a simple dataset into a number (objec-tive score) said to correlate accurately with degree ofovercrowding as perceived by senior staff working atthe time (subjective score) The NEDOCS tool has beendeveloped for the use in clinical setting It began as a23 site-sampling questionnaire reflecting the subjectiveassessment of ED physicians

22

which was then statis-tically reduced using mixed effect linear regression todevelop a 5-questionnaire model The validity of thereduced model was tested using a bootstrap techniqueand was found to reflect the full model with 88 accu-racy The NEDOCS tool was also found to correlate wellwith the number of patients who left without beingseen

23

in further trials by the inception team

There has been an interest in implementing theNEDOCS tool in Australia

The objective of the present study was to assess theusefulness and accuracy of the Web-based model of theNEDOCS tool in an Australian ED compared with sub-jective clinician assessment

Method

Ipswich Hospital is a 317-bed non-tertiary urban hos-pital located in Ipswich Qld Australia with an annualED attendance of over 42 000 The ethics committee forIspwich Hospital approved the study Patient consentwas not required

The present prospective pilot study covered a sampleperiod of 3 weeks from 20 December 2004 to 10 January2005 Samples were collected at four hourly intervals(100 500 900 1300 1700 and 2100 hours) Themethod duplicated the original paper by Weiss

21

exceptthat sample times were not randomized and the studywas undertaken in a single hospital The samples werecollected six times a day although the study by Weisscovered each sample time twice only in the 3 weekperiod

The composite outcome variable score (subjectivescore) was calculated as follows On each shift betweenone and four senior medical officers present completeda short survey consisting of two Likert type scales(Appendices 12) rating the lsquoDegree of overcrowdingrsquoand lsquoFeeling of being rushedrsquo No instructions or defini-tions other than the terms presented were given Thesenior charge nurse of the ED was asked independentlybut simultaneously to rate the degree of overcrowdingusing the same scale The senior charge nurse was notasked to rate lsquoFeeling of being rushedrsquo The even Likertscale was used so that a break point for lsquonot over-crowdedrsquo versus lsquoovercrowdedrsquo fell between 3 and 4Each timed and dated survey form was placed in asealed box in ED In keeping with the Weiss

21

study forease of interpretation the six-point scale was convertedto a scale ranging from 0 to 200 (0

=

not busy40

=

busy 80

=

extremely busy but not overcrowded120

=

overcrowded 160

=

severely overcrowded 200

=

dangerously overcrowded)The survey results of lsquoDegree of overcrowdingrsquo and

lsquoFeeling of being rushedrsquo were averaged for all seniorphysicians and charge nurse for the ED working at thetime of data collection These averaged results formedthe lsquooutcome variablersquo or lsquocomposite scorersquo This scorecomprised the subjective rating of each paired sample

K Raj

et al

284


Interrater agreement was tested retrospectively onthe composite outcome data collected in the first weekThe intraclass correlation coefficient was calculated forthe responses to the lsquofeeling rushedrsquo question askedfrom the doctors and then again for the lsquoovercrowdingrsquoquestion asked from the doctors and the charge nurse

The objective rating in each sample pair was calcu-lated by the Web-based NEDOCS Calculating Tool

24

using the following objective measurements1 Total number of patients in ED occupying beds

(Patients in waiting area were not included)2 Total number of patients on ventilators3 Total number of patients awaiting admission4 Waiting time for the last patient called in from wait-

ing room (Patient occupying ED bed waiting to beseen by the physician)

5 Longest time patient waiting for admission6 Number of beds in ED7 Number of total beds (occupied and vacant) in

hospitalThe first five variable objective measures were collectedsimultaneously and prospectively using a prepareddata sheet subsequently placed in a sealed box in EDThe ED attendance screen was printed at the same timeto check for the accuracy of data collection

The NEDOCS Calculating Tool amalgamated theseven objective data points for each time of data collec-tion and rated the data for that sample time on follow-ing scale 0ndash20

=

not busy 20ndash60

=

busy 60ndash100extremely busy but not overcrowded 100ndash140

=

overcrowded 140ndash180 severely overcrowded180ndash200

=

dangerously overcrowdedBeing a pilot study we aimed to duplicate Weissrsquos

sample method for the same time duration as in originalstudy

21

rather than calculate a new sample size How-ever we used different statistical analysis as we hadconcerns about the product moment correlation (

r

) coef-ficient used by Weiss The valid use of correlation coef-ficient requires a null hypothesis stating that there isno relationship between the two statistics This wouldnot be the case as both subjective and objective scoresare being collected at the same time to assess the samething that is lsquoovercrowdingrsquo In our pilot study theBlandndashAltman

2526

plot was chosen for statistical analy-sis to compensate for this error Differences betweenmeasurements within each pair were plotted on the

Y

-axis The averages of the two measurements were plot-ted on the

X

-axis The average was used as this wasthe closest estimate of the true result in the absence ofa gold standard for lsquoover-crowdingrsquo The mean differ-ence of all pairs was also calculated reflecting the

lsquoestimated biasrsquo or the systematic difference betweenthe two methods

Kappa statistics were used to analyse agreementbetween subjective and objective measures as well asassessment of interrater agreement on subjectiveresponses

Results

During the 3 week study period 2293 patients attendedthe ED an average of 109 patients per day For theentire 2004 calendar year the Ipswich Hospital ED aver-aged 115 patients per day

One hundred and twenty-eight sample times weredescribed by two scores (subjective and NEDOCS)over 3 weeks The datasets for the 3 week period werecomplete

Figure 1 shows a scatter diagram of transformedsubjective and objective (NEDOCS) scores Figure 2shows that the mean difference between the methods ofmeasurement is small (347 95 confidence interval[CI]

minus

0875ndash7878) Figure 2 also illustrates that therange of the 95 limits of agreement are wide (

minus

4652ndash5343)

The BlandndashAltman plot shows that in 5 of themeasurements one method of measurement is likely tobe more than 100-point raw score or 25 categoriesbeyond the other In the present study 16 pairs (125[95 CI 78ndash193]) had scores more than 40 points (onewhole category) apart and 49 pairs (38 [95 CI 30ndash469]) had scores in adjacent categories The kappavalue of 031 (95 CI 017ndash045) suggests poor agree-ment between the categorical scores

Interrater agreement of the subjective responses usedin the composite outcome score was high The intraclasscorrelation coefficient was 087 (95 CI 071ndash095) forthe question on lsquofeeling rushedrsquo For the lsquodegree of over-crowdingrsquo the intraclass correlation coefficient was 094(95 CI 082ndash097)

Discussion

The present study demonstrates that the NEDOCS toolcorrelated poorly with subjective clinician assessmentof overcrowding in our ED The poor correlation is veryobvious looking at Figure 2 The 95 limits of agree-ment are wide (more than two categories apart) makingthe NEDOCS tool imprecise when used in clinical set-ting Such imprecision is unacceptable if one is



285

considering up scaling hospital responses based purelyon the NEDOCS score In addition Figure 1 shows usthat there is no specific relationship between the NEDOCSand subjective scores These findings indicate that theNEDOCS tool is not ready to be used in Australia

Our findings might differ from Weissrsquos original studyfor several reasons The NEDOCS tool was developed

in the USA and might not be applicable in a regionalAustralian hospital with different patient flow andstaffing profile

The difference in findings may be partly explainedby our decision to use different statistical methodologyto Weiss and to interpretation of data results Weissused product moment correlation (

r

) coefficient for

Figure 1

Scatter diagram comparison of both raw scores (

) National Emergency Department Overcrowding Study (NEDOCS) andcomposite score

150

125

100

75

50

Com

posi

te_O

utco

me_

Var

iabl

e_S

core

25

00 25 50

NEDOCS_Score

75 100

Figure 2

BlandndashAltman plot

50

75

25

0

minus25

Diff

eren

ce b

etw

een

met

hods

minus75

minus50

0 25 50Average of both scores

75 100 125

95 confidence interval formean difference= minus0875 to 7828

95 limits of agreement= 5343 to minus4652

upper limit of agreement

mean diff =3476

lower limit of agreement

standard deviation = 2498

K Raj

et al

286


statistical analysis in their original study and the basisof its calculation may be questioned They concludedthat the NEDOCS score accurately reflects staffrsquos senseof lsquoovercrowdingrsquo quoting R

2

049 for the full model TheNEDOCS tool reflects the full model with 88 accuracyso it is even less accurate

Weiss quoted a high interrater agreement (correlationcoefficient 086ndash093) between physiciansrsquo and nursesrsquoLikert results and between each result and the averageas a justification for their use as composite outcomescore The Likert like scale used for subjective scoreswere developed from a preliminary survey of EDdirectors

22

who identified lack of ED beds ED closureand ambulance diversion patients managed in hall-ways full waiting rooms feeling lsquorushedrsquo and a wait ofgreater than 1 h to see a physician as evidence of EDovercrowding

The statistical analysis of the data where one mea-surement method (subjective score) is compared withanother measurement method (objective score) can befraught with danger When there is no gold standard tocompare the new measuring tool the statistical analysismust take into consideration the possible error of eachmethod For that reason we chose the BlandndashAltmanplot and subsequently demonstrated poor agreement

A review of the literature highlights the difficulty indefining overcrowding Hwang

et al

27

reviewed the lit-erature over 26 years and found a total of 230 articlesrelating to overcrowding in the ED including originalarticles reviews and editorials Only 23 of these hadexplicit definitions of overcrowding There was sub-stantial variation in the definition of lsquoovercrowding inthe EDrsquo within these articles The definitions focused onspecific issues like waiting time hospital-related butnon-ED factors or factors external to the hospital suchas ambulance diversions to describe overcrowding

There is little literature documenting quantitativemeasurements of overcrowding in the ED

2021

Bernstein

et al

developed the lsquoEmergency Department WorkIndexrsquo (EDWIN index) which included (i) number ofpatients in triage category (ii) triage category (iii)number of attending physicians on duty (iv) numberof treatment bays and (v) number of admitted patientsin ED The limitation of EDWIN index is that it is nota computer-based model It does not take into accountvarious other issues such as the role of a resident inED and also does not have any input from nursingstaff

Weiss

et al

2122

recognized this deficit in knowledgeand undertook the difficult but important task of devel-oping a computer-based objective model In its current

form this computer-based model NEDOCS tool has cor-related poorly with staffrsquos sense of overcrowding in ourhospital For the moment subjective assessment seemsto be the best method of assessing overcrowding untilwe find a better tool or refine the NEDOCS scoringmethod

The NEDOCS scoring method is easy simple andquick to use and could become a more important anduseful tool with further refinement A refined NEDOCStool may provide us with the definitions and scoringtool we need As Weiss suggests measurements thatinclude case mix within ED triage category and skillmix of staff may improve agreement between NEDOCSscore and staffrsquos perception of lsquoovercrowdingrsquo

There are several limitations to the present study Atno time during the study was the department severelyor dangerously overcrowded Correlation might bebetter or worse at these extreme times The study wasconducted at only one urban hospital ED with mixedchildren and adult presentations and might not be appli-cable to other systems or private ED The biggest lim-itation of the present study however is the lack of alsquogold standardrsquo by which to define ED overcrowdingWe chose to use subjective physiciannurse assessmentas the gold standard by which to measure the NEDOCStool It is possible that the NEDOCS tool might actuallybe very good and that staff subjective assessment isactually poor

Conclusion

The NEDOCS tool was not valid in our setting and hasbeen inconsistent in reflecting staffrsquos sense of lsquoover-crowdingrsquo for which it was primarily designedNEDOCS is still a potentially important tool worthy ofrefinement

Acknowledgements

I would like to acknowledge the assistance of all staffof the ED Ipswich General Hospital for helping to col-lect the data

Author contributions

KR conceived and designed the study collected the dataand wrote the manuscript KB assisted with some sta-tistics and manuscript editing SB and DM edited andassisted in writing the manuscript



287

Competing interests

None declared

Accepted 13 February 2006

References

1 Eisenberg D Woodbury R Willwerth J Brice LE Sieger MDonely M Critical condition

Time

2000

155

0040781X

2 Gibbs N Browning S Do you want to die

Time

1990

135

0040781X 90

3 Adrulis DP Kellrmann A Hintz E Hackman BB Weslowski VBEmergency departments and crowding in United States teachinghospitals

Ann Emerg Med

1991

20

980ndash60

4 Committee on Pediatric Emergency Medicine American acad-emy of pediatrics overcrowding crisis in our nationrsquos emergencydepartment is our safety net unraveling

Pediatrics

2004

114

878ndash88

5 Access block and overcrowding in emergency department Posi-tion paper Australian College for Emergency Medicine down-loaded on 2005 Available from URL httpwwwacemorgauopendocumentsaccessbookbackpdf [Accessed December2004]

6 Derlet RM Richards JR Overcrowding in the nations emergencydepartment complex causes and disturbing effects

Ann EmergMed

2000

35

63ndash8

7 Miro O Antonio MT Jimenez S

et al

Decreased health carequality associated with emergency department overcrowding

Eur J Emerg Med

1999

6

105ndash7

8 Fatovich DM Nagree Y Sprivulis P Access block causes emer-gency department overcrowding and ambulance diversion inPerth Western Australia

Emerg Med J

2005

22

351ndash4

9 Henson VL Vickey DL Patient self discharge from the emer-gency department who is at risk

Emerg Med J

2005

22

499ndash501

10 Fernandes CM Daya MR Barry S Palmer N Emergency depart-ment patients who leave without seeing a physician the TorontoHospital experience

Ann Emerg Med

1994

24

1092ndash6

11 Shaw KN Lavelle JM VESAS a solution to seasonal fluctuationsin emergency department census

Ann Emerg Med

1998

32

698ndash702

12 Saint Lamont S lsquoSee and Treatrsquo spreading like wildfire A qual-itative study into factors affecting its introduction and spread

Emerg Med J

2005

22

548ndash52

13 Anonymous To ease overcrowding delay elective surgeries

EdManag

2005

17

29ndash31

14 Anonymous Three strategies to reduce overcrowding

EdManag

2004

16

1ndash16

15 Anonymous Itrsquos not business as usual you can fight patientsurges with an aggressive plan

Ed Manag

2003

15

121ndash4

16 Kelen GD Scheulen JJ Hill PM Effect of an emergency depart-ment (ED) managed acute care unit on ED overcrowding andemergency medical services diversion

Acad Emerg Med

2001

8

1095ndash100

17 Lynn SG Kellermann A Critical decision making managing theemergency department in an overcrowded hospital

Ann EmergMed

1991

20

287ndash92

18 Reeder TJ Garrison HG When the safety net is unsafe real-timeassessment of the overcrowded emergency department

AcadEmerg Med

2001

8

1070ndash4

19 Wolff AM Bourke J Detecting and reducing adverse events inan Australian rural base hospital emergency department usingmedical screening and review

Emerg Med J

2002

19

35ndash40

20 Bernstein SL Verghese V Leung W Lunney AT Perez I Devel-opment and validation of a new index to measure emergencydepartment crowding

Acad Emerg Med

2003

10

938ndash42

21 Weiss SJ Derlet R Arnold J

et al

Estimating the degree ofemergency department overcrowding in academic medical cen-ters result of the National ED Overcrowding Study (NEDOCS)

Acad Emerg Med

2004

11

38ndash50

22 Weiss SJ Amdhal J Ernst AA Derlet R Richards J Nick TGDevelopment of site sampling form for evaluation of ED over-crowding

Med Sci Monit

2002

8

CR549ndash53

23 Weiss SJ Ernst AA Derlet R King R Bair A Nick TG Rela-tionship between the National ED Overcrowding Scale and thenumber of patients who leave without being seen in an academicED

Am J Emerg Med

2005

23

288ndash94

24 NECDOCS Calculating Tool Available from URL httphscunmeduemermednedocs_finshtml

25 Martin Bland J Altman DG Comparing methods of measure-ment why plotting difference against standard method is mis-leading

Lancet

1995

346

1085ndash7

26 Martin Bland J Altman DG Statistical method for assessingbetween two methods of clinical measurements

Lancet

1986

i

306ndash10

27 Hwang U Concato J Care in the emergency department howcrowded is overcrowded

Acad Emerg Med

2004

11

1097ndash101

Appendix I

Survey form for ED physician for NEDOCS tool

DatePlease circle the timeTime 1 AM 5 AM 9 AM 1 PM 5 PM 9 PM

Please circle the opinion on lsquoDegree of Overcrowdingrsquo1 2 3 4 5 61 = not busy2 = busy3 = extremely busy but not overcrowded4 = overcrowded5 = severely overcrowded6 = dangerously overcrowded

Please circle the opinion on lsquoFeeling rushedrsquo in ED1 2 3 4 5 61 = not rushed6 = rushed

K Raj et al

288 copy 2006 The AuthorsJournal compilation copy 2006 Australasian College for Emergency Medicine and Australasian Society for Emergency Medicine

After completing the survey form please place it intosealed box provided in Emergency DepartmentED emergency department NEDOCS National Emer-gency Department Over Crowding Study

Appendix II

Survey form for the charge nurse in ED for NEDOCS tool


Please circle the lsquoDegree of Overcrowdingrsquo1 2 3 4 5 61 = not busy2 = busy3 = extremely busy4 = overcrowded5 = severely overcrowded6 = dangerously overcrowded

After completing the survey form please place it in thesealed box provided in Emergency DepartmentED emergency department NEDOCS NationalEmergency Department Over Crowding Study



283

Introduction

ED overcrowding is a growing issue worldwide Overthe years various articles in the press

12

governmentreports and journals

34

have highlighted this issue Itwas first documented in Australia in a Sydney metro-politan hospital in late 1980

5

Overcrowding of the EDhas been demonstrated to have negative effect onpatient care

6ndash8

staff satisfaction and health

910

Early warning of overcrowding would allow an orga-nizational response to prevent any potential adverseoutcomes Strategies could include calling in of extrastaff

1112

changing management strategies

13ndash17

hospitalambulance bypass and notification of neighbouringhospitals There is need for a tool which can rapidlyand accurately forewarn of overcrowding

18

Arguably the most accurate indicators of overcrowd-ing are the adverse events and complaints

19

but thereis a delay in notification and correlation They are nothelpful as an early warning system

The computerization of hospital admissions opensthe possibility of a computer generated automatic andobjective warning system

20

Weiss and colleagues have explored the relationshipof the computer-generated indicators of overcrowdingwith the subjective assessment by senior staff atthe time of sampling This model is called NationalEmergency Department Overcrowding Study(NEDOCS) tool

21

The NEDOCS tool was produced to lsquoquantitativelydescribe the staffrsquos sense of overcrowdingrsquo in varioussized academic ED in the USA It was recognized thatthere is currently no gold standard measure of ED overcrowding so a qualitative measure of overcrowdingwas devised and described by same team

22

This qual-itative measure was transformed into the subjectivescore The NEDOCS Tool is a Web-based calculatorwhich converts a simple dataset into a number (objec-tive score) said to correlate accurately with degree ofovercrowding as perceived by senior staff working atthe time (subjective score) The NEDOCS tool has beendeveloped for the use in clinical setting It began as a23 site-sampling questionnaire reflecting the subjectiveassessment of ED physicians

22

which was then statis-tically reduced using mixed effect linear regression todevelop a 5-questionnaire model The validity of thereduced model was tested using a bootstrap techniqueand was found to reflect the full model with 88 accu-racy The NEDOCS tool was also found to correlate wellwith the number of patients who left without beingseen

23

in further trials by the inception team

There has been an interest in implementing theNEDOCS tool in Australia

The objective of the present study was to assess theusefulness and accuracy of the Web-based model of theNEDOCS tool in an Australian ED compared with sub-jective clinician assessment

Method

Ipswich Hospital is a 317-bed non-tertiary urban hos-pital located in Ipswich Qld Australia with an annualED attendance of over 42 000 The ethics committee forIspwich Hospital approved the study Patient consentwas not required

The present prospective pilot study covered a sampleperiod of 3 weeks from 20 December 2004 to 10 January2005 Samples were collected at four hourly intervals(100 500 900 1300 1700 and 2100 hours) Themethod duplicated the original paper by Weiss

21

exceptthat sample times were not randomized and the studywas undertaken in a single hospital The samples werecollected six times a day although the study by Weisscovered each sample time twice only in the 3 weekperiod

The composite outcome variable score (subjectivescore) was calculated as follows On each shift betweenone and four senior medical officers present completeda short survey consisting of two Likert type scales(Appendices 12) rating the lsquoDegree of overcrowdingrsquoand lsquoFeeling of being rushedrsquo No instructions or defini-tions other than the terms presented were given Thesenior charge nurse of the ED was asked independentlybut simultaneously to rate the degree of overcrowdingusing the same scale The senior charge nurse was notasked to rate lsquoFeeling of being rushedrsquo The even Likertscale was used so that a break point for lsquonot over-crowdedrsquo versus lsquoovercrowdedrsquo fell between 3 and 4Each timed and dated survey form was placed in asealed box in ED In keeping with the Weiss

21

study forease of interpretation the six-point scale was convertedto a scale ranging from 0 to 200 (0

=

not busy40

=

busy 80

=

extremely busy but not overcrowded120

=

overcrowded 160

=

severely overcrowded 200

=

dangerously overcrowded)The survey results of lsquoDegree of overcrowdingrsquo and

lsquoFeeling of being rushedrsquo were averaged for all seniorphysicians and charge nurse for the ED working at thetime of data collection These averaged results formedthe lsquooutcome variablersquo or lsquocomposite scorersquo This scorecomprised the subjective rating of each paired sample

K Raj

et al

284




24







=

not busy 20ndash60

=


=


=



21


r


2526


Y


X




Results




minus


minus

4652ndash5343)



Discussion




285





r

) coefficient for

Figure 1



150

125

100

75

50

Com

posi

te_O

utco

me_

Var

iabl

e_S

core

25

00 25 50

NEDOCS_Score

75 100

Figure 2


50

75

25

0

minus25

Diff

eren

ce b

etw

een

met

hods

minus75

minus50


75 100 125




mean diff =3476



K Raj

et al

286



2



22




et al

27



2021

Bernstein

et al


Weiss

et al

2122





Conclusion


Acknowledgements






287

Competing interests

None declared


References


Time

2000

155

0040781X


Time

1990

135

0040781X 90


Ann Emerg Med

1991

20

980ndash60


Pediatrics

2004

114

878ndash88



Ann EmergMed

2000

35

63ndash8


et al


Eur J Emerg Med

1999

6

105ndash7


Emerg Med J

2005

22

351ndash4


Emerg Med J

2005

22

499ndash501


Ann Emerg Med

1994

24

1092ndash6


Ann Emerg Med

1998

32

698ndash702


Emerg Med J

2005

22

548ndash52


EdManag

2005

17

29ndash31


EdManag

2004

16

1ndash16


Ed Manag

2003

15

121ndash4


Acad Emerg Med

2001

8

1095ndash100


Ann EmergMed

1991

20

287ndash92


AcadEmerg Med

2001

8

1070ndash4


Emerg Med J

2002

19

35ndash40


Acad Emerg Med

2003

10

938ndash42


et al


Acad Emerg Med

2004

11

38ndash50


Med Sci Monit

2002

8

CR549ndash53


Am J Emerg Med

2005

23

288ndash94



Lancet

1995

346

1085ndash7


Lancet

1986

i

306ndash10


Acad Emerg Med

2004

11

1097ndash101

Appendix I





K Raj et al



Appendix II





K Raj

et al

284




24







=

not busy 20ndash60

=


=


=



21


r


2526


Y


X




Results




minus


minus

4652ndash5343)



Discussion




285





r

) coefficient for

Figure 1



150

125

100

75

50

Com

posi

te_O

utco

me_

Var

iabl

e_S

core

25

00 25 50

NEDOCS_Score

75 100

Figure 2


50

75

25

0

minus25

Diff

eren

ce b

etw

een

met

hods

minus75

minus50


75 100 125




mean diff =3476



K Raj

et al

286



2



22




et al

27



2021

Bernstein

et al


Weiss

et al

2122





Conclusion


Acknowledgements






287

Competing interests

None declared


References


Time

2000

155

0040781X


Time

1990

135

0040781X 90


Ann Emerg Med

1991

20

980ndash60


Pediatrics

2004

114

878ndash88



Ann EmergMed

2000

35

63ndash8


et al


Eur J Emerg Med

1999

6

105ndash7


Emerg Med J

2005

22

351ndash4


Emerg Med J

2005

22

499ndash501


Ann Emerg Med

1994

24

1092ndash6


Ann Emerg Med

1998

32

698ndash702


Emerg Med J

2005

22

548ndash52


EdManag

2005

17

29ndash31


EdManag

2004

16

1ndash16


Ed Manag

2003

15

121ndash4


Acad Emerg Med

2001

8

1095ndash100


Ann EmergMed

1991

20

287ndash92


AcadEmerg Med

2001

8

1070ndash4


Emerg Med J

2002

19

35ndash40


Acad Emerg Med

2003

10

938ndash42


et al


Acad Emerg Med

2004

11

38ndash50


Med Sci Monit

2002

8

CR549ndash53


Am J Emerg Med

2005

23

288ndash94



Lancet

1995

346

1085ndash7


Lancet

1986

i

306ndash10


Acad Emerg Med

2004

11

1097ndash101

Appendix I





K Raj et al



Appendix II







285





r

) coefficient for

Figure 1



150

125

100

75

50

Com

posi

te_O

utco

me_

Var

iabl

e_S

core

25

00 25 50

NEDOCS_Score

75 100

Figure 2


50

75

25

0

minus25

Diff

eren

ce b

etw

een

met

hods

minus75

minus50


75 100 125




mean diff =3476



K Raj

et al

286



2



22




et al

27



2021

Bernstein

et al


Weiss

et al

2122





Conclusion


Acknowledgements






287

Competing interests

None declared


References


Time

2000

155

0040781X


Time

1990

135

0040781X 90


Ann Emerg Med

1991

20

980ndash60


Pediatrics

2004

114

878ndash88



Ann EmergMed

2000

35

63ndash8


et al


Eur J Emerg Med

1999

6

105ndash7


Emerg Med J

2005

22

351ndash4


Emerg Med J

2005

22

499ndash501


Ann Emerg Med

1994

24

1092ndash6


Ann Emerg Med

1998

32

698ndash702


Emerg Med J

2005

22

548ndash52


EdManag

2005

17

29ndash31


EdManag

2004

16

1ndash16


Ed Manag

2003

15

121ndash4


Acad Emerg Med

2001

8

1095ndash100


Ann EmergMed

1991

20

287ndash92


AcadEmerg Med

2001

8

1070ndash4


Emerg Med J

2002

19

35ndash40


Acad Emerg Med

2003

10

938ndash42


et al


Acad Emerg Med

2004

11

38ndash50


Med Sci Monit

2002

8

CR549ndash53


Am J Emerg Med

2005

23

288ndash94



Lancet

1995

346

1085ndash7


Lancet

1986

i

306ndash10


Acad Emerg Med

2004

11

1097ndash101

Appendix I





K Raj et al



Appendix II





K Raj

et al

286



2



22




et al

27



2021

Bernstein

et al


Weiss

et al

2122





Conclusion


Acknowledgements






287

Competing interests

None declared


References


Time

2000

155

0040781X


Time

1990

135

0040781X 90


Ann Emerg Med

1991

20

980ndash60


Pediatrics

2004

114

878ndash88



Ann EmergMed

2000

35

63ndash8


et al


Eur J Emerg Med

1999

6

105ndash7


Emerg Med J

2005

22

351ndash4


Emerg Med J

2005

22

499ndash501


Ann Emerg Med

1994

24

1092ndash6


Ann Emerg Med

1998

32

698ndash702


Emerg Med J

2005

22

548ndash52


EdManag

2005

17

29ndash31


EdManag

2004

16

1ndash16


Ed Manag

2003

15

121ndash4


Acad Emerg Med

2001

8

1095ndash100


Ann EmergMed

1991

20

287ndash92


AcadEmerg Med

2001

8

1070ndash4


Emerg Med J

2002

19

35ndash40


Acad Emerg Med

2003

10

938ndash42


et al


Acad Emerg Med

2004

11

38ndash50


Med Sci Monit

2002

8

CR549ndash53


Am J Emerg Med

2005

23

288ndash94



Lancet

1995

346

1085ndash7


Lancet

1986

i

306ndash10


Acad Emerg Med

2004

11

1097ndash101

Appendix I





K Raj et al



Appendix II







287

Competing interests

None declared


References


Time

2000

155

0040781X


Time

1990

135

0040781X 90


Ann Emerg Med

1991

20

980ndash60


Pediatrics

2004

114

878ndash88



Ann EmergMed

2000

35

63ndash8


et al


Eur J Emerg Med

1999

6

105ndash7


Emerg Med J

2005

22

351ndash4


Emerg Med J

2005

22

499ndash501


Ann Emerg Med

1994

24

1092ndash6


Ann Emerg Med

1998

32

698ndash702


Emerg Med J

2005

22

548ndash52


EdManag

2005

17

29ndash31


EdManag

2004

16

1ndash16


Ed Manag

2003

15

121ndash4


Acad Emerg Med

2001

8

1095ndash100


Ann EmergMed

1991

20

287ndash92


AcadEmerg Med

2001

8

1070ndash4


Emerg Med J

2002

19

35ndash40


Acad Emerg Med

2003

10

938ndash42


et al


Acad Emerg Med

2004

11

38ndash50


Med Sci Monit

2002

8

CR549ndash53


Am J Emerg Med

2005

23

288ndash94



Lancet

1995

346

1085ndash7


Lancet

1986

i

306ndash10


Acad Emerg Med

2004

11

1097ndash101

Appendix I





K Raj et al



Appendix II





K Raj et al



Appendix II





national emergency department overcrowding study tool is not useful in an australian emergency...

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