Benchmarking Cervical Cancer Care Pathways—Measuring Timelines and Identifying Factors Associated With Delay for Women Undergoing Definitive Radiotherapy in New South Wales, Australia

ABSTRACT

Background

Diagnosis and management of cervical cancer require multiple investigations and specialist reviews, with delays leading to increased mortality and morbidity. Australia's Optimal Care Pathways (OCPs) recommends that patients with suspected diagnosis of cervical cancer should have their diagnostic processes completed within certain timeframes. Despite these guidelines, there is currently no benchmarking of cervical cancer timelines.

Aims

This study aims to benchmark cervical cancer care and identify factors associated with delay.

Materials and Methods

Patients who underwent radiotherapy for Stage I‐III cervical cancer between 2015 and 2024 at a major tertiary hospital in New South Wales (NSW) were included in the study. This hospital provides centralised gynae‐oncology care for all women in three NSW local health districts. Patients were stratified based on age, stage of disease, level of rurality and level of socioeconomic advantage/disadvantage. Each step of the diagnostic timeline was benchmarked against the OCPs. Univariate and multivariate logistic regression were performed to detect factors associated with delays.

Results

The study cohort comprised 155 patients. Under 60% of women were managed in‐line with the OCPs recommended timelines. 86% (n = 19/22) of women from the least disadvantaged socio‐economic tertile saw a gynae‐oncology specialist within the recommended timeframe compared to 47% (36/76) from the most disadvantaged socio‐economic tertile (OR: 6.24, CI: 1.60–24.27, p = 0.008).

Conclusions

Recommended diagnostic timelines were not met in a significant proportion of women. Women from the greatest levels of socioeconomic disadvantage were more likely to face delays. Benchmarking processes need to be implemented in cancer care, to ensure equity in outcomes/processes for all patients.

1. Introduction

There are an estimated 1000 women diagnosed with cervical cancer annually in Australia, with over 200 deaths [1]. Cervical cancer diagnosis and commencement of treatment is time‐sensitive, with delays in diagnosis leading to increased morbidity and mortality [2]. To diagnose and commence treatment for women diagnosed with cervical cancer, multiple steps are required, including biopsy, review by a gynae‐oncologist, relevant imaging and discussion at a multi‐disciplinary meeting [3], with each step being time critical. As defined by the Cancer Australia Optimal Care Pathways (OCPs), implemented in 2021 for cervical cancer, each step within the diagnostic and treatment timeline, has a recommended timeframe [4]. This includes review by a gynaecological oncologist within 2 weeks of suspected diagnosis, diagnostic investigations to be completed within 2 weeks of suspected diagnosis and treatment commencement within 4 weeks of decision to treat. These recommendations have also been adopted into the Australian Cancer Plan [5], the overarching plan for improving cancer care throughout Australia, and endorsed by Cancer Australia. The plan stipulates 2‐year and 5‐year goals for the integration of the OCPs throughout the cancer care continuum.

Despite these guidelines, there is currently no benchmarking in Australia to see whether these targets are being reached, and what delays may be occurring within the healthcare system. Prior studies show that most diagnostic timeframes, suggested by the OCPs, are not being achieved [6]. This situation contrasts with other health care systems around the world, such as the United Kingdom and Denmark [7, 8], where the timeliness of cancer diagnosis is tracked, and publicly reported, aiming for over 75% compliance with national guidelines.

For women diagnosed with cervical cancer in New South Wales, the Gynae‐Oncology service at Westmead Hospital provides centralised care for women in three separate local health districts: Western Sydney Local Health District (WSLHD), Nepean Blue Mountains Local Health District (NBMLHD) and Western NSW Local Health District (WNSWLHD). Furthermore, Westmead Hospital also provides centralised brachytherapy for these local health districts, with this being the final component of curative intent treatment for women diagnosed with locally advanced cervical cancer. The centralisation of gynae‐oncology care is the model throughout Australia, with gynae‐oncology and brachytherapy services based at either major metropolitan or major regional centres across the country [9].

Given this background, this study had two main aims: (1) To assess the timeliness of cervical cancer diagnostic and management pathways for patients treated with curative intent definitive radiotherapy, benchmarked to the OCPs and (2) To identify whether there were any socio‐demographic factors associated with these timelines.

2. Methods

This was a retrospective cohort study, conducted at a single site within WSLHD. Eligible patients were women, aged 18 years and older, diagnosed with Stage I–III cervical cancer, as per International Federation of Gynaecology and Obstetrics (FIGO 2018), between 2015 and 2024 and completed a portion of their curative‐intent radiotherapy at the Crown Princess Mary Cancer Centre (CPMCC), Westmead Hospital. Patients diagnosed between 2015 and 2018 were retrospectively restaged with this updated staging system to ensure staging consistency throughout the study. This specific patient population was chosen given limitations in data accessibility to other health districts, private providers and surgical procedures. Patients diagnosed with Stage IV disease were excluded as their diagnostic and treatment timeline is not centralised to CPMCC at Westmead Hospital and thus undergo different processes compared to women diagnosed with Stage I–III cancer. This project was granted an ethics waiver and approved as a Quality Improvement Audit by the WSLHD Research Governance Office.

Data was extracted from two internal hospital electronic medical record systems, Powerchart (Electronic Medical Record (EMR) used for patients admitted within WSLHD) and Varian ARIA Oncology Information System (EMR for patients undergoing radiotherapy at CPMCC).

Three time‐intervals were analysed in line with the OCPs for Cervical Cancer: (1) Review by a gynaecological oncologist within 2 weeks of suspected diagnosis (2) diagnostic investigations to be completed within 2 weeks of suspected diagnosis and (3) treatment commencement within 4 weeks of decision to treat. To measure these time intervals, several assumptions were made to ensure consistency in analysis. The first time‐interval was measured as time from gynaecologist to gynae‐oncologist, the second was time from the first investigation to the last investigation, and the third, the time between the last investigation to commencement of treatment. Any missing data points were excluded from the analysis.

Socioeconomic demographic data extracted included; age, whether the patient is from an English speaking background or not, suburb of residence and stage of disease at diagnosis. Patients were grouped into 10‐year age groups to ensure that any age group‐specific outcomes could be adequately captured. Due to the limited number of patients between 18 and 30 years old, the younger women were combined into an 18–40 year age group. Similarly, patients above the age of 70 were also grouped together. Based on the suburb of residence, patients were categorised into one of the three local health districts (WSLHD, NBMLHD or WNSWLHD) as well as level of rurality, as defined by the Modified Monash Model (MMM) [10]. Using the MMM, patients were either categorised as Major City, if their suburb scored a 1 on the MMM Scale, or Non‐Major City if the suburb of residence scored a 2–7 on the MMM scale. Area‐level socioeconomic status was defined using the Socio‐Economic Indexes for Areas (SEIFA) Score 2021, as per the Australian Bureau of Statistics [11], with each suburb allocated a score, with a lower score indicative of greater disadvantage. Patients were then stratified into tertiles based on their SEIFA Score, in relation to other suburbs in NSW. Other data collected included; location of initial presentation (general practitioner vs. Emergency Department), health sector where patient was reviewed (public vs. private), location of external beam radiotherapy (EBRT) (WSLHD vs. outside WSLHD).

Variance inflation factor (VIF) was used to check for multicollinearity. The VIF values range between 1.1 and 2.0 indicate low collinearity between predictors of interest. Univariate logistic regression analysis was initially performed to assess if any factors listed above contributed to delays within the three measured time intervals. This was then compared with multivariate logistic regression to assess whether the same factors continued to contribute to delays within the three time intervals. Statistical significance was assumed if p‐value < 0.05. Results from logistic regression are presented as odds ratios (ORs) together with their 95% confidence intervals. To assess compliance, pre and post OCP implementation, a chi‐squared test was used, comparing patients treated between 2015 and 2020 versus patients treated between 2021 and 2024.

Interactions between level of socioeconomic disadvantage and sector of gynaecologist review (public vs. private) were assessed for the first two time‐intervals. Microsoft Excel Version 11, IBM SPSS Statistics Version 31 and SAS Version 9.4 were used for all analyses.

3. Results

The study population included 155 patients, with a median age of 53 years. 44% (n = 68) were from within WSLHD. 47% (n = 73/155) of patients were diagnosed with stage I/II cervical cancer. 74% (n = 115/155) of patients commenced their diagnostic journey initially following general practitioner referral to a gynaecologist, whilst all other patients were referred to a gynaecologist following initial review in an Emergency Department.

For the first time‐interval, 59% (n = 76/128) of patients were seen by a gynae‐oncologist within 2 weeks of gynaecologist review. 27 patients were excluded from this analysis (n = 27/155) due to missing data, with no referral information available. Of these patients, 81% (n = 22/27) were referred following general practitioner (GP) referral, and 59% (n = 16/27) were diagnosed with Stage I/II cervical cancer. These excluded patients were from more favourable socioeconomic distributions compared to the included patients (Table 1), with 33% (n = 9/27) from the most disadvantaged tertile, 37% (n = 10/27) from the middle tertile and 30% (n = 8/27) from the least disadvantaged tertile. 41% (n = 11/27) patients were from within WSLHD and 70% (n = 19/27) were seen via a private gynaecology service, in contrast to the included patients, where 35% (n = 47/128) were seen via a private gynaecology service (Table 1).

TABLE 1.

Patients' sociodemographic characteristics and univariate logistic regression assessing adherence to Optimal Care Pathways recommendations split by each of the three diagnostic time‐intervals.

	First time period: gynaecologist review to gynae‐oncologist review					Second time period: time for all investigations					Third time period: completion of investigation to commencement of treatment
	Total (%)	Yes (%)	UOR	95% CI	Type 3, p	Total (%)	Yes (%)	UOR	95% CI	Type 3, p	Total (%)	Yes (%)	UOR	95% CI	Type 3, p
Total	128	76 (59)				148	38 (26)				155	87 (56)
Location of residence
Within WSLHD	57 (45)	37 (65)	1	1	0.254	64 (43)	15 (23)	1	1	0.589	68 (44)	38 (56)	1	1	0.956
Outside of WSLHD	71 (55)	39 (55)	0.66	0.32–1.35		84 (57)	23 (27)	1.23	0.58–2.61		87 (56)	49 (56)	1.02	0.54–1.93
Age groups
18–40	29 (23)	19 (66)	1	0.24–1.31	0.887	28 (19)	7 (25)	1	1	0.959	30 (19)	15 (50)	1	1	0.584
41–50	31 (24)	19 (61)	0.83	0.29–2.39		36 (24)	11 (31)	1.32	0.44–4.01		39 (25)	25 (64)	179	0.68–4.71
51–60	27 (21)	14 (52)	0.57	0.19–1.66		28 (19)	7 (25)	1	0.30–3.35		30 (19)	19 (63)	1.73	0.62–4.85
61–70	22 (17)	13 (59)	0.76	0.24–2.39		30 (20)	7 (23)	0.91	0.27–3.04		30 (19)	14 (47)	0.88	0.32–2.41
> 70	19 (15)	11 (58)	0.72	0.22–2.38		26 (18)	6 (23)	0.9	0.26–3.14		26 (17)	14 (54)	1.17	0.41–3.34
English speaking
Yes	88 (69)	53 (60)	1		0.771	103 (70)	1			0.299	107 (69)	51 (48)	1		0.003
No	40 (31)	23 (58)	0.89	0.42–1.91		45 (30)	0.64	0.27–1.49			47 (31)	35 (74)	3.20	1.50–6.83
Stage
Stage I/II	57 (45)	34 (60)	1	1	0.955	67 (45)	14 (21)	1	1	0.228	73 (47)	38 (52)	1	1	0.335
Stage III	71 (55)	42 (59)	0.98	0.48–1.99		81 (55)	24 (30)	1.59	0.75–3.40		82 (53)	49 (60)	1.37	0.72–2.59
Route of initial referral
Via emergency department	35 (27)	21 (60)	1	1	0.93	38 (26)	12 (32)	1	1	0.444	40 (26)	26 (65)	1	1	0.191
Via GP	93 (73)	55 (59)	0.96	0.44–2.13		110 (74)	26 (24)	0.67	0.30–1.51		115 (74)	61 (53)	0.61	0.29–1.28
Sector of gynaecologist
Private health sector	47 (37)	34 (72)	1	1	0.025	65 (37)	20 (31)	1	1	0.211	66 (35)	35 (53)	1	1	0.503
Public health sector	81 (63)	42 (52)	0.41	0.19–0.89		83 (47)	18 (22)	0.62	0.30–1.31		89 (48)	52 (58)	1.25	0.66–2.36
Socioeconomic status
Most disadvantaged	76 (59)	36 (47)	1	1	0.004	81 (55)	22 (27)	1	1	0.901	85 (55)	48 (56)	1	1	0.937
Moderate disadvantaged	30 (23)	21 (70)	2.59	1.05–6.39		38 (26)	9 (24)	0.83	0.34–2.04		40 (26)	23 (58)	1.04	0.49–2.23
Least disadvantaged	22 (17)	19 (86)	7.04	1.92–25.78		29 (20)	7 (24)	0.85	0.32–2.28		30 (19)	16 (53)	0.88	0.38–2.03
Rurality (as defined by ARIA score)
Major City	113 (88)	67 (59)	1	1	0.958	129 (87)	32 (25)	1	1	0.529	133 (86)	75 (56)	1	1	0.703
Outside of Major City	15 (12)	9 (60)	1.03	0.34–3.09		19 (13)	6 (32)	1.4	0.49–3.99		22 (14)	12 (55)	0.93	0.37–2.30
Location of external beam radiotherapy
Outside WSLHD											53 (34)	26 (49)	1	1	0.202
Within WSLHD											102 (66)	61 (59)	1.55	0.79–3.01

Abbreviation: UOR, unadjusted odds ratio.

For the first time‐interval, 86% of patients (n = 19/22) from the least disadvantaged socioeconomic area were adherent with this timeframe compared to 46% (n = 30/76) of patients from the most disadvantaged socioeconomic area as shown in Table 1. The unadjusted odds of being seen by a gynae‐oncologist within 2 weeks were lower for patients initially seen by a public gynaecology service (OR = 0.41, 95% CI = 0.19–0.89), and increased as level of socioeconomic disadvantage decreased, compared to the most disadvantaged tertile (moderate disadvantaged: OR = 2.59, 95% CI = 1.05–6.39; least disadvantaged: OR = 7.04, 95% CI = 1.92–25.78). Similar findings were found in the multivariate logistic regression as shown in Table 2 (moderate disadvantaged: OR = 2.81, 95% CI = 1.05–7.53; least disadvantaged: OR = 6.45 95% CI = 1.65–25.20, p = 0.008). In the multivariate model, patients initially seen by a public gynaecology service had no statistically significant delays (OR 0.41, 95% CI = 0.17–1.02, p = 0.05) (Table 2) contrary to the univariate model.

TABLE 2.

Multivariate logistic regression assessing adherence to Optimal Care Pathways recommendations split by each of the three diagnostic time intervals.

	First time period: gynaecologist review to gynae‐oncologist review			Second time period: time for all investigations			Third time period: completion of investigation to commencement of treatment
	Adjusted OR	95% CI	Type 3, p	Adjusted OR	95% CI	Type 3, p	Adjusted OR	95% CI	Type 3, p
Location of residence
Within WSLHD	1	1	0.414	1	1	0.872	1	1	0.142
Outside of WSLHD	0.71	0.31–1.62		1.07	0.47–2.44		1.95	0.80–4.75
Age groups
18–40	1	1	0.914	1	1	0.978	1	1	0.479
41–50	0.83	0.25–2.73		1.15	0.36–3.69		1.30	0.45–3.80
51–60	0.58	0.18–1.93		1.08	0.30–3.84		1.55	0.50–4.77
61–70	0.72	0.20–2.67		0.79	0.22–2.85		0.59	0.19–1.80
> 70	0.93	0.24–3.56		0.93	0.25–3.48		0.86	0.27–2.71
English speaking
Yes	1		0.512	1		0.469	1		0.003
No	0.74	0.30–1.82		0.71	0.28–1.79		3.73	1.55–8.99
Stage
Stage I/II	1	1	0.881	1	1	0.272	1	1	0.370
Stage III	1.06	0.48–2.37		1.58	0.70–3.55		1.39	0.68–2.83
Route of initial referral
Via emergency department	1	1	0.060	1	1	0.101	1	1	0.108
Via GP	0.50	0.19–1.32		0.43	0.16–1.18		0.47	0.19–1.18
Sector of gynaecologist
Private health sector	1	1	0.054	1	1	0.067	1	1	0.636
Public health sector	0.41	0.17–1.02		0.42	0.17–1.06		0.83	0.37–1.83
Socioeconomic status
Most disadvantaged	1	1	0.008	1	1	0.908	1	1	0.934
Moderate disadvantaged	2.81	1.05–7.53		0.86	0.33–2.27		0.91	0.38–2.17
Least disadvantaged	6.45	1.65–25.20		0.80	0.27–2.37		0.84	0.32–2.21
Rurality (as defined by aria score)
Major city	1	1	0.785	1	1	0.505	1	1	0.803
Outside of major city	1.19	0.33–4.00		1.46	0.48–4.39		0.88	0.32–2.44
Location of external beam radiotherapy
Outside WSLHD							1	1	0.137
Within WSLHD							2.00	0.80–5.00

For the second time interval, 26% (n = 38/148) completed all their diagnostic interventions within 2 weeks. 5% patients were excluded from this analysis (n = 7/155) due to missing data regarding dates of diagnostic investigations. These patients were all diagnosed prior to 2021, with 57% (n = 4/7) from WSLHD. The associations between patients' sociodemographic and clinical factors were found to not be statistically significant in the unadjusted logistic regression model (Table 1) and multivariate regression model (Table 2).

For the final time interval, 56% (87/155) of patients commenced treatment within 4 weeks of completing all required investigations. 59% (n = 61/102) of patients who completed EBRT within WSLHD, met the 4‐week recommendation compared to 49% (n = 26/53) of patients that commenced their EBRT outside of WSLHD as shown in Table 1 (OR = 1.55, 95% CI = 0.79–3.01, p = 0.202). Table 2 shows that after adjusting for other sociodemographic and clinical factors, the odds of commencing treatment within 4 weeks was significantly higher for patients from non‐English speaking backgrounds (OR: 0.3.73, 95% CI: 1.55–8.99, p = 0.003) (Table 2). There were no other factors in both the univariate and multivariate analysis that were statistically significantly associated with adherence to the OCP recommended timeframes.

The wide confidence intervals at all three time‐intervals indicate a high degree of uncertainty, reflecting the small sample group within this retrospective study. Thus, the findings should be viewed with caution.

There was no statistically significant change in compliance with the implementation of the OCPs for any of the time intervals (first time interval p = 0.132, second time interval p = 0.297, third time interval p = 0.538). Note that though not significant, decreases in the percentages of compliance were observed between the pre and post OCP implementation for each of the time intervals (first time interval: 66% pre OCP vs. 53% post OCP; second time interval: 30% pre OCP vs. 22% post OCP; third time interval: 59% OCP vs. 54% post OCP).

The interactions between gynaecologist sector (public vs. private) and socioeconomic status were not significant for the first (p‐value = 0.390) and second time periods (p‐value = 0.305); however the effects of gynaecologist sector on timeline compliance showed a consistent pattern across socioeconomic status (Table 3). For patients who lived in more disadvantaged areas, if seen by a public gynaecology service their care was less likely to meet the benchmark for first time period (most disadvantaged areas AOR: 0.58, 95% CI = 0.21–1.60; moderate disadvantaged areas: AOR = 0.21, 95% CI = 0.04–1.29) (Table 3). However, for patients living in the least disadvantaged areas, if seen via a public gynaecology service, this was associated with lesser delays over both the first time period (AOR = 1.80, 95% CI = 0.14–23.37) and second time period (AOR = 1.61, 95% CI = 0.28–9.21).

TABLE 3.

Effect of gynaecologist sector on compliance to each time period by socioeconomic status.

Sector of Gynaecologist	Most disadvantaged			Moderate disadvantaged			Least disadvantaged
	Adjusted OR	95% CI	Type 3, p	Adjusted OR	95% CI	Type 3, p	Adjusted OR	95% CI	Type 3, p
First time period: gynaecologist review to gynae‐oncologist review
Private Health Sector	1			1			1
Public health sector	0.58	0.21–1.60	0.29	0.21	0.04–1.29	0.09	1.80	0.14–23.37	0.653
Second time period: time for all investigations
Private health sector	1			1			1
Public health sector	0.59	0.21–1.62	0.30	0.23	0.04–1.31	0.100	1.61	0.28–9.21	0.594

4. Discussion

To our knowledge this is the first study of its kind in Australia to assess diagnostic and treatment timelines for patients diagnosed with cervical cancer. Our results demonstrate poor compliance with OCPs at all three steps of the diagnostic and treatment timeline. Overall, there was 26% adherence to recommended timelines, irrespective of sociodemographic factors, with particularly low rates of completion of required investigations within 2 weeks. The implementation of the OCPs in 2021 has had no impact on compliance rates at any stage of the diagnostic and treatment timeline.

These low adherence rates to optimal diagnostic timelines are consistent with other parts of Australia and are also reported for other cancer types as well [6]. Delayed cancer management pathways are particularly an issue for patients from areas of socioeconomic disadvantage, who face inequity with timeliness of diagnosis, also demonstrated in our study. This is not surprising given the fragmented nature of diagnostic processes in Australia with patients often needing to move between public and private sectors or between different institutions, with data collected/stored in different databases. Currently there is no one system capturing all these components and associated delays. International studies have demonstrated that with the implementation of real‐time benchmarking processes, there are subsequent system wide improvements in the timeliness of cancer care [7, 8, 12, 13]. As a means to improve this in Australia, there has been the recent implementation of the National Cancer Data Framework [14], as well as the development of the Victorian cancer services performance indicator program [15], both set up with the aim to benchmark cancer care.

In terms of the first step of the diagnostic timeline, there are delays faced by women residing in regions of socioeconomic disadvantage, compared to those from areas of socioeconomic advantage and those accessing their initial care through a public gynaecology service, compared to a private gynaecology service. These findings are unlikely to be skewed by the excluded 27 patients, given that these patients had more favourable socioeconomic factors compared to the included population, with higher rates of private gynaecology referral and better socioeconomic distribution. Delays through the public sector have been reported in other studies and can contribute to increased mortality [16]. These delays may be secondary to higher reliance on public healthcare services for patients experiencing socioeconomic disadvantage [17]. This may be driven by the cost of seeking private healthcare in Australia, leading to inequity in access for those unable to pay for these services [16].

Internationally, socioeconomic disadvantage is a known barrier to cancer care, with lower rates of screening and delays in symptom presentation [18]. These delays may continue to be driven by potential differences in health literacy and barriers to access care [19], and thus additional resources are likely to be required in this component of the diagnostic timeline. Of note, the final two steps of the diagnostic timeline are not influenced by level of socioeconomic disadvantage. This indicates that once the patient's care is within the remit of the cancer care system, socioeconomic background does not influence patient care. Thus, additional solutions are required to improve socioeconomic disadvantage at a community level, to improve equity in cancer care.

To reduce this inequity, particularly in the first step of the diagnostic process, rapid access diagnostic clinics have been found to be an effective manner in streamlining investigations and processes [20, 21], with subsequent improvements in OCP compliance noted. Additionally, the implementation of nurse navigators, to assist patients in organising appointments at this early stage of the diagnostic process can also bridge the gap between these outpatient clinics and the cancer care system, reducing wait times and also improving the patients overall experience [22, 23]. Furthermore, studies have demonstrated that the implementation of web‐based referral pathways, between primary health care and specialist services within the local health district, can also improve the time for cancer diagnosis and treatment, and reduce any socioeconomic inequity [24, 25]. Given our findings that socioeconomic factors do not affect diagnostic and treatment timelines when patients are within the remit of the cancer centre, by extending cancer care into the community with nursing staff and rapid access clinics, this may be effective in improving timelines and inequity.

Our study demonstrates a higher proportion of patients from non‐English speaking backgrounds commencing treatment within 4 weeks, compared to those with English as a first language. This finding is contrary to other studies which have shown suboptimal care for patients of non‐English speaking background [26]. WSLHD is the most multicultural local health district within NSW, with over 45% of residents born overseas [27]. Several initiatives have focussed on improving accessibility and equity of health care within the district. These include the development and implementation of a Multicultural Health Plan in 2022 [27], aimed at improving health care access and outcomes for patients of culturally and linguistically diverse backgrounds. However, this implementation was toward the end of our study period and is unlikely to have had a major impact on results of this study.

Although this did not reach statistical significance, a smaller proportion of patients commencing EBRT at another health facility commenced treatment within 4 weeks of completing investigations (49% vs. 59%) compared to those whose treatment was wholly at WSLHD. This is likely secondary to the additional referral required to these health facilities, adding an additional step within the diagnostic pathway. To reduce these delays, ongoing cancer care coordination, with implementation of statewide cancer care coordinators, has been found to be an effective method in reducing referral wait times and improving patient/clinician satisfaction with their cancer diagnostic journey [28]. Additionally, centralised multidisciplinary meetings, with clinicians across multiple local health districts, may improve the referral pathways between the centralised gynae‐oncology service provided within WSLHD and other local health districts.

We aim to use this data to drive change both within our local health district and from external referrers. Locally, these findings have been presented to clinicians, with the aim to improve triaging services within the public system to reduce any incidence of delays. There is consideration for the use of a UK style model, with rapid triaging and early assessment of patients with suspected malignancy [29]. These findings have also been presented to consumer stakeholders, with additional formalised qualitative analysis to be conducted on an individual level to assess whether the findings demonstrated in this study align with the perceptions of clinicians and patients. It is important to note that the OCP recommendations are consensus guidelines, with no clear evidence demonstrating that abiding by these recommendations improves morbidity or mortality outcomes.

Our study is limited by the lack of availability to all relevant data. For instance, lack of general practitioner referral dates prevents assessment of the entire diagnostic/treatment timeline. Given the fragmented nature of Australia's healthcare system, with no linkage between private and public healthcare providers, a single institution retrospective study such as the one conducted is unable to access all components of the diagnostic timeline. This process may be resolved with the implementation of the Single Digital Patient Record system in New South Wales [30] over the coming years. Alternatively, accessing large datasets such as Medicare Benefits Schedule/Pharmaceutical Benefits Schedule may allow for assessment of the entire diagnostic timeline, providing additional validation to the results from this study; however this also poses additional issues.

From a population perspective, large‐scale population analysis is required to assess the entire diagnostic timeline and determine whether delays experienced by patients in this study are comparable to those faced across the state/country and assess for inequity throughout the population. This needs to be combined with evidence‐based interventions such as rapid access clinics and cancer care coordinators to improve adherence to OCPs. This multidimensional analysis and intervention can further drive improvements in patient equity in cancer care, aligning with the goals of the Australian Cancer Plan [5].

5. Conclusions

Our study demonstrates that for a significant proportion of women undergoing definitive radiotherapy for cervical cancer, their care is not meeting timeliness benchmarks. Inequity of care was noted based on socioeconomic status, route of diagnosis (public vs. private) and location of their radiotherapy.

Ethics Statement

An Ethics waiver was provided by the Western Sydney Local Health District—Research Governance Office, as this project qualified as a Quality Improvement Audit.

Conflicts of Interest

The authors declare no conflicts of interest.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author. The data are not publicly available due to privacy or ethical restrictions.