Open Access
Open Peer Review

This article has Open Peer Review reports available.

How does Open Peer Review work?

Is theatre utilization a valid performance indicator for NHS operating theatres?

  • Omar Faiz1Email author,
  • Paris Tekkis2,
  • Alistair Mcguire3,
  • Savvas Papagrigoriadis4,
  • John Rennie4 and
  • Andrew Leather4
BMC Health Services Research20088:28

https://doi.org/10.1186/1472-6963-8-28

Received: 06 April 2007

Accepted: 31 January 2008

Published: 31 January 2008

Abstract

Background

Utilization is used as the principal marker of theatre performance in the NHS. This study investigated its validity as: a managerial tool, an inter-Trust indicator of efficient theatre use and as a marker of service performance for surgeons.

Methods

A multivariate linear regression model was constructed using theatre data comprising all elective general surgical operating lists performed at a NHS Teaching hospital over a seven-year period. The model investigated the influence of: operating list size, individual surgeons and anaesthetists, late-starts, overruns, session type and theatre suite on utilization (%).

Results

7,283 inpatient and 8,314 day case operations were performed on 3,234 and 2,092 lists respectively. Multivariate analysis demonstrated that the strongest independent predictors of list utilization were the size of the operating list (p < 0.01) and whether the list overran (p < 0.01). Surgeons differed in their ability to influence utilization. Their overall influence upon utilization was however small.

Conclusion

Theatre utilization broadly reflects the surgical volume successfully admitted and operated on elective lists. At extreme values it can expose administrative process failure within individual Trusts but probably lacks specificity for meaningful use as an inter-Trust theatre performance indicator. Unadjusted utilization rates fail to reflect the service performance of surgeons, as their ability to influence it is small.

Background

Utilization has become the principal measure of NHS operating theatre service performance. In part, the current reliance on utilization has arisen from its historical use in foreign, often privatised, healthcare systems [15]. In addition however, major recent Audit Commission [6, 7] and Modernisation Agency [8] publications have served to enhance the profile of this performance indicator in the United Kingdom.

Nearly seven million operations are performed each year in the NHS [9]. In the 2002/03 financial period the annual budget for main theatre departments in acute Trusts in England and Wales exceeded £1 billion [10]. As such, hospital theatres represent a significant expense. Efficient use of this costly resource is therefore economically desirable. In addition to financial reasoning – the current political pressures on waiting lists serve to amplify the importance of effecting efficient theatre usage. At present, approximately 1 million people are awaiting NHS treatment [11]. In order to achieve the governments aim to progressively shorten total waiting times to less than 18 weeks by 2008 [12] – enhanced theatre capacity is required. To this end service change has involved various government initiatives including: a promotion of day case operating [1315] as well as the development of independent Treatment Centres [12, 16]. In addition to these measures however, a requirement to increase efficiency amongst theatre units within acute NHS Trusts is also recognized.

Despite the widespread use of utilization rates in the public setting there has been little research to date investigating its validity as a performance indicator. The purpose of this study was to investigate the factors that influence elective general surgical theatre list utilization within an NHS hospital. As such, the study sought to assess the validity of utilization as a performance indicator that could be used to benchmark theatre performance between Trusts as well as a tool that could be used by individual Trusts to facilitate managerial decision-making. In addition, this investigation aimed to explore the influence of individual surgeons on utilization and thereby assess its potential use as a marker of their service performance.

Methods

Data methods

The study data comprised all elective day case (DC) and inpatient general surgical operations performed at a Teaching Hospital between April 1997 and April 2004. Prospectively entered data relating to the: procedure type, timings and personnel involved in operations were retrieved from the hospital theatre database (Surgiserver © McKennon systems). Operations were aggregated into operating lists. Procedure durations were calculated through subtraction of the recorded time when anaesthetic administration was commenced from the time of surgical drape removal at the end of the procedure. Database variables were consequently recoded into: list, session and personnel factors (see below). The latter, in addition to operating list size, represented the utilization covariates investigated in this study.

Study endpoint

Operating list utilization rates represented the principal study outcome measure. These were calculated through division of the sum of total list procedure time by the allocated session duration. Utilization rates were expressed as percentages.

Study covariates

database variables were recoded into: operating list size as well as session, personnel and list factors.

a) Calculation of "operating list size"

A scoring system was developed from all operative procedures to quantify the size of general surgical operating lists. This system that was developed we termed the Operative Score of Complexity index. It has been applied to the measurement of workload and productivity in inpatient and outpatient theatres separately (In Press). Specifically, a numerical case-score (measured in units) was assigned to each Office of Population Census and Statistics-4 (OPCS-4) code on the basis of the historical median case duration of all operative procedures that had been assigned to the corresponding code. The actual numerical score represented the procedure median duration (in seconds)/30. The latter calculation was performed to simplify the numerical score to a tangible figure. For example, the case-score of a day surgery primary inguinal hernia repair was 106 units. This numerical value represented the median duration (in seconds)/30 of all procedures that had been performed in the day surgery department during the study period and coded to the 'Primary Repair of Inguinal Hernia' OPCS-4 code. Case-score indices were calculated separately for the main theatre (MT) and day surgery (DS) databases to account for differences in complexity between operations performed in the respective departments. The sum of the case-scores of constituent list procedures derived the output of individual operating lists (i.e. list-scores). In MT's an adjustment was made to list-scores (i.e. list-score/hour of allocated session time) in order to overcome heterogeneity of session duration. As 99.2% of all day surgery cases were performed on 4-hour operating lists, output was recorded as the list-score without adjustment.

b) Session factors
Operating lists were recoded according to whether they took place on 'morning', 'all-day' or 'afternoon' sessions. In addition, lists were classified according to the departmental theatre suites where surgery was undertaken (see Table 1).
Table 1

A summary of general surgical operating list characteristics in the DS and MT departments between 1997 & 2004.

Operating list factors

Day Surgery (DS)

Main Theatres (MT)

Operating list volume

  

   Mean list-score in units per hour (SD)

70.3(26.0)

86.86 (38.29)

Session factors

  

   Session type

  

Percentage of operations performed on Morning lists (n)

38.2%(3226)

15.9%(1156)

Percentage of operations performed on Afternoon lists (n)

61.1%(5083)

17.4%(1265)

Percentage of operations performed on 'All-day' lists (n)

-

66.8%(4862)

   No. of theatre suites

5

10

Personnel factors

  

   Surgeons

  

Total number of surgeons coded on database

133

125

No. of surgeons with >100 operative procedures

16

16

Percentage of total cases performed by surgeons with>100 cases (n)

79.3% (6594)

78.7% (5732)

   Anaesthetists

  

Total no. of Anaesthetists' coded on database

246

238

No. of anaesthetists with >100 operative procedures

10

14

Percentage of total cases performed by anaesthetists with>100 cases (n)

23. 9% (1983)

65. 6%(5290)

List factors

  

   Overruns

  

Overrunning operating lists (%)

627/2092 (30.0%)

1079/3234 (33.3%)

Median list overrun (Q1-Q3,n) in minutes

50 (24 – 84, n = 627)

-

Median list overrun (Q1-Q3,n) as a percentage of session duration (%)

-

13.1(5.5–26.2, n = 1079)

Number.(%) of MT lists where no overrun occurred

-

2262 (69.9%)

Number(%) of MT lists where overrun : session length = 0–0.1

-

403 (12.5%)

Number(%) of MT lists where overrun : session length = 0.11–0.2

-

215 (6.6%)

Number(%) of MT lists where overrun : session length = 0.21–0.3

-

150 (4.6%)

Number(%) of MT lists where overrun : session length >0.31

-

204 (6.3%)

   Late-starts

  

Median (Q1-Q3, n) late-start in minutes

32 (17–48, 2087)

65 (41–90, 3229)

%(n). DSC operations on lists where Late start <30 minutes

996 (47.61%)

-

%(n). DSC operations on lists where Late start is 30–60 minutes

870 (41.59%)

-

%(n). DSC operations on lists where Late start is > 60 minutes

221 (10.56%)

-

Number (%) of MT lists where no Late-start occurred

-

103 (3.18%)

Number(%) of MT lists where Late-start: session length = 0–0.1

-

730 (22.57%)

Number(%) of MT lists where Late-start : session length = 0.11–0.2

-

1564 (48.36%)

Number(%) of MT lists where Late-start : session length = 0.21–0.3

-

522 (16.14%)

Number(%) of MT lists where Late-start : session length > 0.31

-

315 (9.74%)

c) Personnel factors

Surgical and anaesthetic practitioners were included in day surgery and main theatre analyses on an anonymous individual basis if they had performed more than 100 operative procedures (see Table 1). Practitioners that had performed less than 100 cases were pooled into separate surgical and anaesthetic personnel categories respectively.

d) List factors

List factors describe the extent to which operating sessions started late or overran the allocated session time. An overrun was defined to have occurred when the last procedure on an operating list finished beyond the scheduled finish time. A binary approach to day surgery overruns (i.e. overrun, no-overrun) was used because even minor time infringements in this setting may have adverse staffing consequences. Late-starts in the day surgery setting were however categorised according to the time delay incurred. Overruns and late-starts in MT's were categorised according to the proportion of time infringement as a function of session length. The latter was necessary to compensate for varying session length in MT's. The specific definitions of late-start and overrun categories for the DS and MT data are described in Table 1.

Statistical Analysis

Unifactorial regression analysis was used to identify risk factors related to theatre utilization. Utilization outcome measures assumed a normal distribution and no data transformation was required. Operating list size (i.e. list-score units) was entered into the models as a continuous variable as it demonstrated a clear linear relationship with utilization in both DS and MT models. Other independent risk factors (i.e. list, session and personnel factors) were entered into the models as categorical variables.

In order to determine the adjusted relationship between list utilization and other variables including: the size of the list, list factors, (overruns and late-starts), personnel and session factors (session type and theatre suite), multiple linear regression models were constructed for the DS and MT departments respectively by entering influential univariate risk factors. Stepwise regression was used to evaluate individual predictors. Criteria were set so that variables were excluded from model if their probability of influence was low (p > 0.1). The mean ± Standard Deviations (SD) and median (interquartile range, n) values were recorded for outcomes as appropriate. For all tests of significance, P < 0.05 was considered statistically significant.

Results

Operating list characteristics

Throughout the study period 7283 operations were performed on 3234 general surgical operating lists in the MT department. Over the same period 8314 operations were carried out on 2,092 lists in the DS centre. Nearly all (97.6%) patients that were operated in MT's were performed under general anaesthesia (GA) whereas in the DS centre 61.6%, 29.8% and 7.7% operations were performed under GA, 'Local Infiltration' and 'Sedation' respectively.

The descriptive characteristics of the operating lists performed in the DS and MT departments throughout the study period are described in Table 1. The sub-categories of list, session and personnel factors are described in accordance with the categories entered in the regression analyses.

Theatre list utilization rates

Throughout the study period the mean theatre list utilization rate was 73.2% (SD: 27.5%, n = 3234) and 68.2% (SD: 21.4, n = 2087) in the MT and DS departments respectively. Over the same time period 30% (n = 627) of day surgery lists and 33% (n = 1079) of main theatre lists overran. Figures 1 &2 demonstrate the annual mean theatre utilization rates and the corresponding annual overrun rates. An association between utilization rates and overruns was observed in the MT and DS departments.
Figure 1

Mean utilization (+SD) and annual overrun rates for main theatre general surgical lists between 1997 & 2004.

Figure 2

Mean utilization (+SD) and annual overrun rates for day theatre general surgical lists between 1997 & 2004.

The results of the constructed regression models are shown in Tables 2 (DS) and 3 (MT). The latter tables can be used to predict list utilization rates by extrapolation from the regression equation y = a+b(x), where y = the predicted utilization rate, a = the model constant (or intercept) and b = the regression (Beta) coefficient of covariate(x). For example, a list utilization rate prediction can be made for a hypothetical scenario where a day surgery list with 300 list-score units are operated upon by surgeon 12 and all other session variables correspond to reference categories (i.e. the list does not overrun and starts promptly and is carried out by anaesthetist 1). The predicted utilization rate for this scenario equates to the model constant (38.1%) + (300 list-score units × Beta coefficient for list size i.e. 0.093)% + (1 × 3.8% i.e. the Beta coefficient for surgeon 12) + nil else (as all other covariates were the reference categories). Therefore the predicted utilization rate for this list scenario = 69.8%.
Table 2

Multiple regression model for Day Surgery list utilization. (only reference categories and retained model variables listed).

Risk Factor

Model coefficient

Beta coefficent

Beta Standard Error

p-value

Change in R-Square

Model R-Square

Model Constant

38.1

-

0.6

<0.01

-

0.603

List volume (1 (unit)

 

0.093

0.0

<0.01

0.394

 

List factors

      

   List Overruns

 

19.2

0.4

<0.01

0.152

 

   Late-starts

      

<30 mins

 

Ref

    

30–60 mins

 

-3.1

0.3

<0.01

0.007

 

>1 hr

 

-10.2

0.6

<0.01

0.014

 

Personnel factors

      

   Surgeon

      

Surgeon 1

 

Ref

    

Surgeon 2

 

-13.2

0.9

<0.01

0.004

 

Surgeon 3

 

-3.8

0.5

<0.01

0.002

 

Surgeon 5

 

-5.4

1.1

<0.01

0.001

 

Surgeon 7

 

-8.7

0.6

<0.01

0.005

 

Surgeon 8

 

-12.8

1.3

<0.01

0.004

 

Surgeon 9

 

-7.3

1.2

<0.01

0.001

 

Surgeon 10

 

-4.8

0.9

<0.01

0.001

 

Surgeon 11

 

-7.4

0.6

<0.01

0.004

 

Surgeon 12

 

3.8

0.7

<0.01

0.007

 

Surgeon 14

 

-2.7

1.4

0.05

0.000

 

Surgeon 'others'

 

-4.4

0.5

<0.01

0.002

 

   Anaesthetist

      

Anaesthetist 1

 

Ref

    

Anaesthetist 2

 

3.6

1.2

<0.01

0.000

 

Anaesthetist 5

 

4.6

0.9

<0.01

0.001

 

Anaesthetist 9

 

-3.4

1.1

<0.01

0.000

 

Anaesthetist 10

 

3.5

0.9

<0.01

0.000

 

Session factors

      

   Session type

      

AM list

 

Ref

    

   Theatre

      

Day Theatre 2

 

Ref

    

Day Theatre 4

 

2.4

0.8

<0.01

0.000

 

Day Theatre 5

 

-1.5

0.4

<0.01

0.002

 
Table 3

Multiple regression analysis model and list utilization in Main Theatres (only reference categories and retained model variables listed).

Risk factor

Model coefficient

Beta coefficient

Beta Standard Error

p-value

Charge in R Square

Model R Square

Constant

46.3

-

0.7

<0.01

-

0.621

List volume (list-score units/hour)

 

0.252

0.007

<0.01

0.334

 

List factors

      

   Adjusted overruns

      

No overrun

 

Ref

    

Overrun 1 (0–0.1)

 

17.9

0.6

<0.01

0.051

 

Overrun 2 (0.11–0.2)

 

25.2

0.8

<0.01

0.038

 

Overrun 3 (0.21–0.3)

 

33.7

1.0

<0.01

0.038

 

Overrun 4 (>0.31)

 

54.0

1.0

<0.01

0.118

 

   Adjusted late-starts

      

No late start

 

Ref

    

Late-start 3 (0.21–0.3)

 

-2.9

0.6

<0.01

0.001

 

Late-start 4 (>0.31)

 

-12.0

0.9

<0.01

0.010

 

Session factors

      

   Session type

      

AM list

 

2.8

0.7

<0.01

0.001

 

All-day list

 

Ref

    

   Theatre

      

MT2

 

Ref

    

MT3

 

2.8

0.9

<0.01

0.000

 

MT6

 

3.1

0.6

<0.01

0.001

 

MT8

 

7.7

3.3

0.02

0.000

 

MT10

 

2.3

0.8

<0.01

0.000

 

Other 'theatres

 

5.7

0.9

<0.01

0.000

 

Personnel factors

      

   Surgeon

      

Surgeon 1

 

Ref

    

Surgeon 2

 

-4.3

1.4

<0.01

0.000

 

Surgeon 8

 

-8.1

0.8

<0.01

0.007

 

Surgeon 9

 

-4.4

2.1

0.03

0.000

 

Surgeon 10

 

-4.1

1.2

<0.01

0.000

 

Surgeon 12

 

-5.0

0.9

<0.01

0.000

 

Surgeon 13

 

2.5

0.8

<0.01

0.001

 

Surgeon 14

 

-4.4

1.3

<0.01

0.000

 

Surgeon 'Others'

 

-8.2

0.6

<0.01

0.009

 

   Anaesthetist

      

Anaesthetist 1

 

Ref

    

Anaesthetist 4

 

-4.8

0.9

<0.01

0.007

 

Anaesthetist 5

 

-4.4

1.7

<0.01

0.000

 

Anaesthetist 6

 

3.2

0.9

<0.01

0.000

 

Anaesthetist 8

 

-4.3

0.9

<0.01

0.003

 

Anaesthetist 10

 

6.6

1.1

<0.01

0.000

 

Anaesthetist 14

 

-7.9

1.6

<0.01

0.000

 

Anaesthetist 'Others'

 

-1.5

0.5

<0.01

0.000

 

The relative influence of the individual predictors within the model are summarised as the change in R-Square statistic. This statistic represents the impact that exclusion of the considered cofactor has upon the models overall explanatory capability. In both the day surgery and main theatre models the principal determinants of theatre list utilization were: the size of the operating list (p < 0.01) and whether or not the list overran (p < 0.01). Specifically, in the DS department the change in R Square statistic associated with 'operative list size' and overruns were 0.394 and 0.152 respectively. Other DS model cofactors including; late starts > 1 hour (p < 0.01, change in R Square statistic = 0.014), as well as individual surgeons and anaesthetists demonstrated a significant, but small, independent influence on the models explanatory power (see Table 2). In the DS model, session type demonstrated no independent relationship with utilization rates once adjusted for other factors. Similarly, only two of the five theatre suites used for surgery in the day unit demonstrated a small independent influence on utilization (see Table 2). In the MT model, operating list size (p < 0.01, change in R Square statistic = 0.334) and overrun categories (p < 0.01, change in R Square statistic 0.038 – 0.118 for categories 'Overrun 1–4') demonstrated the greatest independent influence on list utilization rates of all model covariates (see Table 3). By comparison, the relative influence of other covariates, including session type, individual theatre suites, as well as specific surgical and anaesthetic practitioners, was significant but modest (see Table 3).

Discussion

Theatre utilization represents a qualitative measure of theatre time usage. Since the publication of the 'STEP Guide to Improving Operating Theatre Performance' by the Modernisation Agency [8] and two national Audit Commission reports on Operating Theatres [6, 7], utilization has become the principal managerial measure of theatre performance across Trusts in the United Kingdom. Little investigation has however hitherto been conducted to determine the validity of theatre utilization, as a marker of theatre performance, in the public sector setting.

The results of this study pertain to a single centre. Direct extrapolation of the study results to other Trusts, or even other specialties, is not possible. Many problems within NHS hospitals are however shared between centres. Although only data from one centre was used, the results and conclusions of this study are therefore, by proxy, of relevance to other units. The principal driver of theatre list utilization within this study was operating list size in both the DS and MT departments. In reality, the size of operating lists is often determined by the availability of resources such as ward or high dependency beds. MT utilization in a public sector hospital is therefore possibly determined largely by bed capacity. Importantly, this cannot be directly substantiated in the current study as bed capacity data was not a collected variable. If however a relationship between bed capacity and main theatre utilization is accepted – then, in the context of declining numbers of ward beds in NHS hospitals [17], utilization of MT units may decline also. In the DS department low operating list volumes frequently arise due to late 'patient' or 'hospital' cancellations. As such, low theatre utilization rates in this context may require specific corrective measures to ensure that all list patients attend, and are fit, for their operations. To this end the Modernisation Agency has issued specific practical advice on administrative and clinical measures aimed at reducing cancelled operations [18]. Despite this, using measures of surgical workload to measure: intended admissions, patient cancellations and eventual operative list volume might represent more useful managerial data than theatre utilization rates.

In our study a strong association between theatre utilization and list overruns was observed in both the DS and MT departments. This is understandable as, 'allocated session time' was used to calculate list utilization rates. The rationale for not adjusting the session time to include overtime is that it is not current standard managerial practice to do so in NHS hospitals. Although dangers can arise from the extrapolation of the findings of a single centre study the relationship demonstrated here between utilization and overruns is logical when the basis of the equation used to calculate utilization is considered. Hence, overrunning lists probably serve to inflate utilization rates reported by NHS Trusts. List overruns are however a significant source of inefficiency in NHS theatres as they are costly in terms of overtime payments and staff morale. Confusingly therefore, Trusts where theatre overruns occur commonly are likely to report high utilization rates also. Although there is evidence that some researchers investigating theatre time usage have adjusted utilization methodology to account for overtime [19] there is little evidence that this is being performed in NHS Trusts. In fact, the inclusion of units with reported utilization rates in excess of 100% in the latest Audit Commission report suggests that adjustment was not made by at least some centres.

In various units individualised theatre utilization rates are routinely sent to surgical and anaesthetic staff as a marker of service performance. The results of this study question the validity of this exercise. Specifically, surgeons displayed significant independent differences in the determination of list utilization in both the DS and MT settings where coefficients ranged from 3.8 to -13.2% and 0 to -8.2% between them in these differing contexts respectively. Although differences between individuals were significant their overall influence on utilization was modest compared to that of operative volume and whether, or not, list overruns occurred. As such, unadjusted individualised utilization rates are more likely to represent the influence of the latter factors rather than the specific performance of theatre personnel. For this reason the use of unadjusted utilization rates could be misrepresentative if used for service activity monitoring of surgical personnel.

For the reasons cited above an optimal level of theatre utilization that is appropriate to NHS theatres is difficult to define. The Audit Commission reported that the average Trust utilizes 73% of their total planned session time but theatre utilization across Trusts varied between 41 per cent and 103 per cent. These figures compare broadly to estimates of utilization detected in other investigations into theatre time usage across a variety of specialties in the United Kingdom [1922]. The Audit Commission methodology incorporates however the attrition of theatre time brought about by cancelled operating lists and national estimates suggest that these comprise approximately 10% of all planned Trust sessions [23]. As such, utilization rates that do not account for cancelled sessions will overestimate utilization. The extent to which this methodology has previously been applied by Trusts is uncertain. In the short-term however, the Toolkit devised by the Modernisation Agency [8] should facilitate standardisation of theatre utilization calculation. Overall, the Audit Commission suggests an optimal 'end utilization' performance target of 77% [6]. Presently however, this study suggests that – whilst unaccounted discrepancies exist between Trusts': overrun rates, inpatient bed facilities and their methodology used to calculate utilization – some scepticism regarding the validity of a 'target' utilization rate for NHS theatres should be maintained. In the future, quantitative measures of surgical service workload, such as Human Resource Group (HRG) tariffs, are likely to predominate over theatre utilization. Definition of an actual service 'output' in NHS Trusts has facilitated political, strategic as well as operational decision-making. A possible extension of this to the operating theatre environment may be to use 'HRG output per theatre per time-period' as an efficiency measure. Irrespective however of the validity of a specific tool that quantifies theatre effectiveness; improving elective theatre efficiency demands a broad perspective over the entire surgical pathway.

Conclusion

Maximising theatre usage is obviously desirable in the NHS. Variation between Trusts, in terms of overrun rates and inconsistent methodologies used to calculate utilization, impede its meaningful use as a tool that can benchmark theatre performance. Extreme utilization rates do however merit managerial investigation. Quantitative measures of theatre workload and efficiency are likely to be used for decision-making in the future.

Declarations

Authors’ Affiliations

(1)
Department of Surgery, St Mark's Hospital
(2)
Imperial College London, Department of Surgical Oncology and Technology, St Mary's Hospital
(3)
Department of Health and Social Care, Cowdray House, London School of Economics and Political Science
(4)
Department of General Surgery, Kings College Hospital

References

  1. O' Donnell DJ: Theatre utilization analysis. Med J Aust. 2 (17): 650-1. 1976 Oct 23Google Scholar
  2. Cranfield J, Soljak M: Use of time information to maximise theatre utilization. Aust Health Rev. 1989, 12 (3): 5-15.PubMedGoogle Scholar
  3. McQuarrie DG: Limits to efficient operating room scheduling. Lessons from computer-use models. Arch Surg. 1981, 116 (8): 1065-71.View ArticlePubMedGoogle Scholar
  4. Docherty J, McGinnes M: Optimised theatre utilization. NATNEWS. 1987, 24 (9): 25-8.PubMedGoogle Scholar
  5. Barr A, Rogers S: Operating theatre use. NATNEWS. 1983, 20 (3): 14-7.PubMedGoogle Scholar
  6. The Audit Commission, 2003. Operating Theatres. [Accessed on 14th August 2007], [http://www.audit-commission.gov.uk/reports/NATIONAL-REPORT.asp?CategoryID=&ProdID=6CDDBB00-9FEF-11d7-B304-0060085F8572]
  7. The Audit Commission, 2002. Operating Theatres. [Accessed on 14th August 2007], [http://www.audit-commission.gov.uk/Products/NATIONAL-REPORT/49419FE0-6E68-11D6-80C3-00C04F240EA2/district-audit-operating-04.pdf]
  8. The Modernisation Agency, 2002. Step guide to improving operating theatre performance. [Accessed on 13th July 2005], [http://www.modern.nhs.uk/theatre/6547/6706/Complete%20Step%20Guide.pdf]
  9. Hospital Episode Statistics (HES) data 2003/4. [Accessed on 14th August 2007], [http://www.dh.gov.uk/PublicationsAndStatistics/Statistics/HospitalEpisodeStatistics/fs/en]
  10. NHS Estates, Healthcare Capital Investment Supplement for Quart Volume 1, No.1 2002/NHS Estates 2003.Google Scholar
  11. Department of Health. Waiting list falls below 1M for first time in a decade. 2003, London: Department of Health, [Accessed on 14th August 2007], [http://www.dh.gov.uk/en/Publicationsandstatistics/Pressreleases/DH_4046840]
  12. The Department of Health. The NHS Plan: a plan for investment – a plan for reform. 2000, London: Department of Health, [Accessed on 14th August 2007], [http://www.dh.gov.uk/assetRoot/04/05/57/83/04055783.pdf]
  13. Department of Health. Thousands of NHS patients to benefit from day surgery expansion – Hutton. 2002, London: Department of Health, [Accessed on 14th August 2007], [http://www.dh.gov.uk/en/Publicationsandstatistics/Pressreleases/DH_4014344]
  14. Department of Health. Drive to boost day surgery in the NHS. 2002, London, Department of Health, [Accessed 14th August 2007], [http://www.dh.gov.uk/en/Publicationsandstatistics/Pressreleases/DH_4013040]
  15. Department of Health. Priority Areas: First Round. Implications of Day Case Surgery. 2003, London, Department of Health, [Accessed on 14th August 2007], [http://www.dh.gov.uk/en/Policyandguidance/Researchanddevelopment/A-Z/Primaryandsecondarycareinterface/DH_4015535]
  16. Department of Health. Treatment Centres: Delivering Faster, Quality Care and Choice for NHS Patients. 2005, London, Department of Health, [Accessed on 14th August 2007], [http://www.dh.gov.uk/en/Publicationsandstatistics/Publications/PublicationsPolicyAndGuidance/DH_4100523]
  17. The Department of Health. Shaping the Future NHS: Long Term Planning for Hospitals and Related Services: Consultation Document on the Findings of The National Beds Inquiry. 2000, London: Department of Health, [Accessed on 14th August 2007], [http://www.dh.gov.uk/assetRoot/04/02/04/69/04020469.pdf]
  18. The NHS Modernisation Agency. Tackling Cancelled Operations. Published December 2001. 2001, London: Department of HealthGoogle Scholar
  19. Durani P, Seagrave M, Neumann L: The Use of Theatre Time in Elective Orthopaedic Surgery. Ann R Coll Surg Engl. 2005, 87 (Suppl): 170-172.View ArticleGoogle Scholar
  20. Iyer RV, Likhith AM, McLean JA, Perera S, Davis CH: Audit of operating theatre time utilization in neurosurgery. Br J Neurosurg. 2004, 18 (4): 333-7. 10.1080/02688690400004852.View ArticlePubMedGoogle Scholar
  21. Haiart DC, Paul AB, Griffiths JM: AN audit of operating theatre time in a peripheral teaching surgical unit. Postgrad Med J. 1990, 66 (778): 612-5.View ArticlePubMedPubMed CentralGoogle Scholar
  22. Cole BO, Hislop WS: A grading system in day surgery: effective utilization of theatre time. J R Coll Surg Edinb. 1998, 43 (2): 87-8.PubMedGoogle Scholar
  23. Eaton L: Trusts cancel 10% of operating theatre sessions. BMJ. 324 (7347): 1174-10.1136/bmj.324.7347.1174/b. 2002 May 18Google Scholar
  24. Pre-publication history

    1. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1472-6963/8/28/prepub

Copyright

© Faiz et al; licensee BioMed Central Ltd. 2008

This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Advertisement