The NHS waiting list in England must halve to reach waiting time targets

Highlights

• •The elective waiting list in England’s NHS has increased to over 7 million in recent years.
• •An NHS constitutional requirement is that 92% of patients must be waiting no longer than 18 weeks.
• •However, this figure is currently standing at approximately 60%.
• •Our modelling finds that the waiting list must reduce to 3.4 million to achieve the 92% target.
• •We also reveal an unequal distribution of this challenge among the various regions and clinical specialties.

Abstract

It is a constitutional requirement in England’s NHS that 92% of patients awaiting consultant-led elective treatment must be waiting no longer than 18 weeks. This target was last met in November 2015. Since then, waiting times have lengthened, with only 58.9% of the 7.5 million who were waiting at year-end 2024 waiting under 18 weeks. The government has pledged to restore the 92% standard by 2029 – the end of the current parliament. While it is known that the waiting list must reduce, the size that it must reduce to has not before been formally investigated. Leveraging a recently published mathematical framework for modelling NHS waiting lists, we find that the waiting list must be more than halved to 3.4 million to reach the 92% standard. Furthermore, we reveal the unequal distribution of this challenge, with different regions and specialties requiring disproportionately more or less resources than others to reach their required waiting list targets.

Background

The referral-to-treatment (RTT) metric is the principal barometer of waiting time performance for consultant-led elective treatment in England’s NHS. The metric, set up in 2012, measures the number of patients waiting under 18 weeks as a percentage of the total number waiting. An NHS constitutional requirement is that, at any time, 92% of waiting patients must be waiting under 18 weeks. We ask what size the waiting list needs to be to meet this target.

The national RTT standard was last met in November 2015, when the total waiting list stood at 3.5 million.¹ Since then, waiting times have lengthened significantly, with only 58.9% of the 7.5 million who were waiting at year-end 2024 waiting under 18 weeks. Long waits result in patient harm and additional healthcare demands,² worklessness and economic inactivity,³ and exacerbated inequalities, with poorer patients unable to afford private treatment.⁴ The government, elected in 2024, has pledged to restore the 92% 18-week RTT standard by the end of parliament.⁵ Clearly, this requires the waiting list to be reduced, but knowing by how much at a national, regional and specialty level could help signpost areas where the greatest efforts are required.

Methods

We leverage an equilibrium variant of a mathematical framework previously developed for modelling consultant-led elective waiting times in the NHS.⁶ This framework assumes that patients leave the waiting list either through treatment or ‘reneging’, ie, leaving before treatment due to, eg, going private, becoming inoperable, or death. We consider hypothetical scenarios in which treatment capacity is varied in order to achieve 92% 18-week RTT waiting time performance based on recent referrals and renege behaviour, calibrated from 2024 data.¹ The treatment allocated to different sections of the waiting list is permitted to vary within the range of profiles observed in the historical data from 2016 to 2024. For the results presented here, we consider three points from this range: those associated with the lowest and highest capacity requirements, and the midpoint. For each of these, the corresponding waiting list size is derived. Full methodological details are available in the Supplementary Material.

Results and implications

Our results show that restoring the 92% RTT target requires a substantial reduction in the total national waiting list to 3.4 million, which is a 54.9% decrease from the 7.5 million at year-end 2024 (Fig. 1). The last time the waiting list was at this size was over a decade ago, in April 2015, when referrals were considerably lower. A waiting list reduction of this magnitude represents a significant challenge for the NHS. Phased in over the remaining 4 years of the parliament, this corresponds to approximately 1 million additional waiting list removals per year, equating to a 5.5% uplift on 2024 activity. Providing this additional capacity will require further innovation. Despite greater use of the private sector and claims that ‘elective surgical hubs can significantly increase treatment volumes’,⁷ the waiting list has remained above 7 million since August 2022. And without improved surgical training, the Royal College of Surgeons of England (RCS) suggests that ‘the NHS will struggle to maintain a sustainable surgical workforce for future patients’.⁸

Fig. 1.

Waiting list size for elective treatment in England’s NHS, comparing actual data for year-end 2024 and modelled requirements for meeting the 92% 18-week RTT waiting time target. The vertical bars on the modelled estimates specify the feasible range, based on different levels of treatment capacity and how it is allocated to those on the waiting list. The implications of this are explained further in the Supplementary Material, alongside a tabulated version of the results.

At a regional level, the East of England requires the highest percentage reduction (60.0%) in waiting list size, while the North East and Yorkshire require the lowest (43.2%). In absolute terms, the South West requires the least reduction (318,000), with the Midlands needing the greatest (764,000). This will be a particular challenge for the Midlands, owing to current workforce shortages. NHS data for year-end 2024 shows the Midlands having the highest vacancy rate in acute medical staff, at 6.3%.⁹ On the demand side, renewed efforts to promote patient choice⁵ may lead patients to travel to reduce waiting time, thereby improving the most pressured waiting lists and balancing load.

Regarding clinical speciality, the required waiting list reductions are reasonably proportionate to current waiting list sizes. Accordingly, the largest absolute reduction is required for trauma and orthopaedic, which is estimated to require a 519,000 reduction to 334,000 to meet the 92% RTT target. One notable exception is ophthalmology, which requires a 36.8% reduction from 590,000 to 373,000. This is the lowest percentage reduction required of all specialties, with at least 100,000 currently waiting. Longer-term disparities may be addressed by ensuring that surgical training is ‘matching service requirements by specialty’, as advised by the RCS.¹⁰ In the short term, extra priority could be given to those specialties where waiting appears to result in greater patient harm. With no national data or target set for priority groups, research in this area is lacking. However, recent work suggests that those in the cardiothoracic surgery specialty are most severely affected, while those awaiting trauma and orthopaedic treatment are less severely affected than some other specialties.²

Discussion and conclusion

Past behaviour is no guarantee of the future, and, as with any modelling study, there may be limiting assumptions to acknowledge in this regard. First, we assume the wait-based probabilities of reneging – a key model parameter – are equivalent to those observed in 2024, whereas people’s capacity to wait is complex and multifactorial (and currently lacks conclusive research). It cannot be dismissed that the two resident doctor strikes in 2024 had some particular influence on reneging rates. Second, we assume that referral rates are consistent with year-end 2024 levels. With rising demographic pressure, it would be expected that referrals would increase slightly year-on-year, and in this respect, the required waiting list sizes estimated here serve as lower bounds (see the Supplementary Material for a sensitivity analysis on referrals). Third, with regard to the proportionate allocation of treatment capacity to different waiting times, while we have acknowledged that this cannot vary without constraint (eg, it would be practically impossible to treat everyone in the first month of waiting, due to patient availability, requiring weight loss, etc), it is perhaps insufficiently ambitious to necessitate similarity to any observed behaviour in recent times, given that more radical solutions may be required. Each of these potentially limiting assumptions is explored further in the Supplementary Material. Finally, it should be noted that, fundamentally, this modelling is conducted at aggregated levels and does not explicitly appreciate heterogeneity in the inputs (eg, different geographical casemixes) and outputs (eg, performance being met at an aggregated national level, but not necessarily within all trusts or specialties).

In summary, our modelling finds that, at a national level, the elective waiting list needs to be more than halved in order to achieve the government’s ambition to restore 92% performance. The demand–capacity imbalance of recent years must now be addressed with an overcompensation of treatments, to the scale of 1 million extra waiting list removals each year until the end of the parliament. Furthermore, we reveal the unequal distribution of this challenge, with different regions and specialties requiring disproportionately more or less resources than others to reach their required waiting list targets.

Ethical approval and consent to participate

Ethical approval was not required for this study, as no patient-identifiable data were used. All data were obtained from publicly available sources.

Data availability

All data used in this article are publicly available: https://www.england.nhs.uk/statistics/statistical-work-areas/rtt-waiting-times/ (noting that these data are accredited as ‘Official Statistics’).

Funding

The research of Neil Walton was funded by the EPSRC INFORMED-AI project EP/Y028732/1. No other funding was received by the other authors.

CRediT authorship contribution statement

Richard M Wood: Writing – original draft, Methodology, Formal analysis, Conceptualization. Sebastian S Fox: Writing – review & editing, Formal analysis, Data curation, Conceptualization. Lucy E Morgan: Writing – review & editing, Conceptualization. Neil S Walton: Writing – original draft, Validation, Formal analysis, Conceptualization.

Declaration of competing interest

The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: Neil Walton reports financial support was provided by Engineering and Physical Sciences Research Council. If there are other authors, they declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.