A&E waits might be the biggest cause of avoidable death in England

 

New data from the ONS on their analysis of mortality and A&E waits has enabled new, better calculations on the magnitude of excess deaths related to long waits in A&Es in England. The results are unambiguously apocalyptic and suggest that long waits in A&Es now account for close to 1 in 10 of all annual deaths. In a rational world this would make fixing A&E waits the top NHS priority for improvement in the NHS.

In January the ONS published some new analysis of the relationship between long A&E waits and mortality. I explained their analysis here.

But they left out some important parts of the data that would enable more reliable estimates of the total number of excess deaths. Extra data released by the ONS now allows those calculations to be done.

By excess deaths I mean something specific: the number of deaths that would not have happened if A&E waits were mostly below 4hr as they once were. In 2010 the NHS had fewer than 2% of patients waiting more than 4hr, so we should have some confidence this could be achieved again. Now, more than 10% of patients wait more than 12hr and it does not seem to be a big priority for action. 

The new ONS data allows reliable estimates of the human cost of failing to tackle long waits. And we can estimate the the total deaths which is far more impactful and resonant than an abstract, hard to interpret and potentially misleading mortality rate.

I’m going to walk through some of their analysis to justify the claim that long A&E waits are one of the biggest causes of avoidable deaths in England.

Why mortality rates are not enough

The first release of the ONS data included the total number of patients with different waiting times. It showed analysis of the mortality rates and relative mortality rates for a variety of different subgroups of patients (discharge status, presenting condition, age and more). But, while the rates alone send a clear message that longer waits are bad, they can’t tell us how significant the bad is because they didn’t publish the size of the different subgroups. This prevents a good analysis of the number of patients suffering higher mortality and, therefore, the number of excess deaths. For example, if patients with eye conditions are 100 times more likely to die when waiting 12hr than they are at 4hr that sounds bad. But if mortality at 4hr is very low and only a small number of patients have eye conditions, the number of excess deaths might be trivial. 

The extra information now published shows the number of patients in each subgroup making further analyses far more useful. A good example of why this matters is given in one of the simpler tables in the released data. This table breaks down the mix of discharged and admitted patients waiting for different times.

A simple version is shown below:

The ONS tables include the total number of admitted and discharged patients for each waiting time (they show time bands from 0hr to over 40hr but I have grouped these for simplicity into all waits less than 4hr and over 12hr).

The important observation is that the mix of patients changes over time. Only 14% of the <4hr group are admitted; but 73% of the >12hr group are admitted. Importantly admitted and discharged patients have hugely different mortalities. In the under 4hr group the mortality for discharged patients is about 0.11% but admitted patients mortality is 1.86%, nearly 20 times higher. If we just averaged the mortality for waits under 4hr and ignored the admitted/discharged mix any estimate of the total mortality would be unreliable as it would ignore the changing mix of admitted and discharged patients over time. 

Now we can translate the published rates into total deaths, a number far more impactful than the mortality rates and a far better way to communicate the magnitude of the problem and compare it to other causes of death.

The new ONS data shows both mortality, waiting times and the number of patients for a variety of groupings: discharge status; age bands; acuity on arrival; presenting complaint; plus some demographic variables like patient deprivation and NHS region.

What the data represents

There are many possible criticisms of analyses that can be done with this data. But many fade away when you understand what the data is.

Statistical models, for example, can often contain dodgy assumptions or badly sampled source data so their conclusions can be very controversial. This data is harder to criticise on any of those grounds.

It is not a sample. It is a count of all unique patients attending english A&Es in the year to march 2022 (or, strictly speaking, it is a sample but one that includes about 95% of all patients because of data linking and missing data).

So when it reports that there were 88,657 deaths that year in 6.7m patients we don’t need large error bars on either number. They are reporting actual counts of deaths.

And when I reported above that the mortality for patients waiting below 4hr was 0.11% if they were discharged, that is a reliable fact not speculation. This distinction is worth bearing in mind for most of the results below.

It is also worth providing some context for the numbers based on other mortality data.

There were just over 540k deaths in England in 2022. 

The top 6  assigned causes of death were:

Dementia and alzheimers	65,697
Ischaemic heart disease	59,356
Chronic lower respiratory diseases	29,815
Cerebrovascular diseases	29,274
Malignant neoplasm of trachea, bronchus and lung (basically smoking-related)	28,571
covid	22,454

These numbers are worth bearing in mind for the analyses below. Though waiting in A&E is not a specific cause itemised separately in ONS death statistics–those deaths will be classified as caused by disease groups as in the table–the number of deaths caused by excessive waiting times should be considered in the context of major causes of death as they are largely avoidable and the numbers above provide some context for judging how significantly A&E performance might influence the total number of deaths.

Estimates of excess deaths caused by long waits based on admission/discharge status

Because of the huge difference in mortality between discharged and admitted patients the first improved estimate of “excess” deaths can be based on that ONS table.

It is worth clarifying what I mean by “excess” deaths. I start by assuming that long waits in A&E are avoidable. In 2010 fear than 2% of all attendances spent more than 4hr in A&E. In recent years between 40 and 50% wait longer. Worse, in 2010 there were, perhaps, only a few thousand 12hr waits in A&E; each of the last three years have seen more than 1.7m 12hr waits. This is not an inevitable consequence of far higher attendance (this is up only about 20% since 2010). Those of us who worked to achieve the 4hr target in the early 2000s and remember how it was done believe it is still achievable and the problem has been misguided policy not an inevitable consequence of demand.

On that assumption, we can estimate the number of deaths associated with longer waits by comparing the mortality for waits less than 4hr to longer waits. To do that calculation we need to know the mortality for different types of patient at different waiting times and the number of patients suffering those waits.

Here is a table showing the raw data and the simple steps to make that estimate of excess deaths:

The first three columns are facts from the ONS tables separating the numbers for admitted and discharged groups: total patients, total deaths and mortality rates. The other two columns show the comparison of the mortality rates in each waiting time group to the mortality rates for those waiting <4hr and calculate the implied excess deaths from the cohort size of the same waiting time and discharge status and that excess mortality rate.

There were a total of 88,657 deaths. 11,626 of those deaths occured in patients waiting <4hr; 77,031 deaths occurred in patients waiting >4hr. This isn’t an indication of excess deaths as it ignores the base mortality those longer waiting patients would have incurred had they waited <4hr. So the excess mortality rate column makes that adjustment so that the excess deaths can be counted in each group.

On this calculation 51,147 patients died who would not have died had they waited less than 4hr. This adjusts for the changing mix of admitted and discharged patients (which was impossible in the original ONS publication).

Here is what that data looks like in a chart:

This also highlights another important feature of the extra data. While mortality for discharged patients is consistently much lower than for admitted patients (as intuition should expect), the mortality for discharged patients rises far more steeply. It is over 20 times higher at 12hr than it is at <4hr. Mortality for admitted patients starts high but is less than 4 times higher at 12hr.

It could still be argued that other factors in the changing mix of patients could account for some proportion of that excess mortality, so the wait times don’t explain it all (eg the age mix or the mix of specific conditions). More on that later. But we don’t have the details of the sophisticated ONS logistic regression model that tries to decompose the component contributions to mortality. We don’t have the cross-tabs as some analysts would say. But the waiting time is a major factor in every analysis and the changing patient mix is often an indirect consequence of the process that causes long waits so it should be reasonable to assign time in A&E as the major contributor.

So the first cut suggests that long A&E waits were the third biggest cause of avoidable deaths in England, higher than smoking and covid in 2022 with more than 50k excess deaths.

Does accounting for acuity change the picture?

Patients arriving at A&E are assessed on a 4-point acuity scale on arrival at A&E. The ONS data looks a little like this:

NB the lines are annotated with the mortality rates at <4hr and >12hr to indicate the degree of mortality increase associated with longer waits.

The raw data looks like this:

As intuition would expect, the base mortality is higher for higher acuity, with very urgent starting at just over 2% but with low and standard being just over 0.1% each. All mortality rates rise rapidly with waiting times. Less intuitively, mortality for the very urgent group is about 4 times higher for >12hr than the base level but the standard group sees a near 40-fold increase in the rate as does the low group. The relative worsening of mortality with long waits is larger for lower acuity patients than for urgent ones. This empahsises the importance of keeping waits low even for those patients who do not appear so acute on arrival.

Also the urgency with which A&Es handle each group does not match their acuity classification. About 60% of low and standard patients leave in under 4hr (see the % of total in this group column) but only 36% of the urgent and 32% of the very urgent group do. Nearly 10% of the very urgent group wait >12hr. 

Applying the same simple calculation used in the admitted/discharged table will give an excess deaths estimate of about 58k, with about 18k of those accounted for by waits >12hr. As with most analyses here, the >12hr waits account for a disproportionate number of the extra deaths.

So splitting the analysis by acuity does not fundamentally change the idea that long waits kill as the excess deaths estimate is in the same ballpark as the estimate based on admission status.

What does the analysis look like by age group?

Another way to group patients is by their age. Older people are expected to have higher mortality than younger people. That analysis looks like this:

In table form:

This is a cruder breakdown than some others as the ONS have used broad age groups and broad groupings of waiting times. Mortality, even with long waits, is very low for ages under 40 and those groups tend to depart A&E much faster than the older groups. Even so a quick excess deaths analysis suggests about 45k deaths caused by waits over 4hr and 15k of those occurring in the group waiting >12hr. Again, in the same ballpark as the result from other group breakdowns. Again, deaths in the group waiting >12hr account for about a third of all excess deaths, about 15k of about 45k.

What about the breakdown by presenting condition?

Again, intuitively, different underlying conditions are expected to have different underlying mortalities. The overall picture looks like this (but I’ve eliminated some presenting conditions to keep the chart clearer:

The full table looks like this:

Note the very large difference in the sizes of the groups. 

There are also big differences in the base mortalities for the different groups. The groups eye, head and neck, obstetrics and gynecology, skin and trauma and orthopaedics all being below 0.1%. And airway/breathing, neuro, genitourinary, and (surprisingly) general/minor/admin with base rates over 0.5%. 

All groups show major relative elevation in mortality rates for long waits but some start so low the rates are not significant even at 12hr. 

The total excess deaths implied using this breakdown is about 58k with about 18k of those coming from waits >12hr. This is again in the same ballpark as other estimates and suggests that 12hr waits are disproportionately bad.

Conclusion: excess deaths associated with long waits are the third largest cause of avoidable deaths in England.

The above analysis shows that the majority of the 89k deaths in A&E (technically within 30 days of discharge after an A&E attendance) are associated with waits longer than 4hr in A&E. The excess deaths associated with long waits calculated using different patient subgroups cluster around 50k in 2022 which would make A&E waits the third biggest cause of deaths in the UK that year, exceeding both covid and smoking (obviously those deaths are all included in other ONS categories in the official tables reporting total deaths but the comparison is helpful for understanding the overall magnitude of the problem.

Worse, the problem might be even worse in recent years. For example, in 2022 only a little over 5% of patients waited more than 12hr to leave A&E. Waits over 12hr have been over 10% since 2023 hinting that excess deaths from extreme waits might be twice as bad now as they were then. Even on a crude extrapolation from the numbers provided by the ONS for 2022 this might imply an extra 15k-20k more deaths which might push deaths from long A&E waits to the top position on the ONS causes of death table if they separated these deaths from other assigned causes.

If the calculations above are correct waits of over 4hr in A&E might be accounting for more than 10% of all annual deaths in England. This is not a small problem the NHS can ignore. Waiting times in A&E might be the biggest cause of avoidable harm in the NHS.

Another way to put the numbers into perspective is to compare them with other huge scandals. The NHS contaminated blood scandal was estimated to have led to about 3,000 deaths but that was over more than a 20 year period. The novel painkiller vioxx was estimated to have caused about 50k deaths in the 5 years it was on the market in the USA before its withdrawal. Neither of these comes close to the number of deaths associated with long waits in England’s A&E departments. 

There are two possible defences against making this an urgent NHS priority. 

One is that the problem is not caused by the NHS. NHSE spent a lot of time claiming the problem was the result of excess demand, not a dysfunctional NHS. But demand isn’t the problem and never has been.

The other defence is that the NHS has no known strategies that work to reduce long waits. This might actually be true for the NHSE’s outgoing leadership who have persistently pursued bad strategies. But it is a little hard to sustain if your corporate memory extends beyond the last decade. Fewer than 2% of attendances waited over 4hr in 2010. A&E performance had seen huge improvements from 2002 to 2005 when the 98% target was first achieved. Good performance was sustained for 9 years after 2005 despite Andrew Lansley’s poor decision in 2010to relax the standard to 95% (which caused a decline in performance by signalling that A&E speed was no longer a priority). It isn’t that A&E performance can’t be fixed, it has been fixed but the NHS forgot how it was fixed.

It needs to rediscover how it was once fixed as current performance is causing too many deaths for the problem to be ignored.

Notes and caveats

There are some important points to note when looking at the ONS analysis.

The analysis differs from previous analyses of A&E mortality in looking at the mortality by patient not attendance. The previous EMJ analysis used mortality rates per attendance for admitted patients only and is not directly comparable. The ONS looked only at mortality per unique patient (presumably on their last visit to A&E that year). This also means that the mortality rates cannot be directly applied to current attendance to estimate current mortality levels (unique patient statistics are not normally part of the A&E statistics that get published).

The assertion that longer waits cause extra deaths might upset some purist statisticians who demand randomised controlled trials to confidently assert causality. But those are clearly unethical. But even if we don’t make a confident claim of causality, the numbers estimated from a range of different analyses of comprehensive historic data are consistent in shining a very bright flashing red light in the direction of the claim that long waits cause a large number of deaths that would not happen with shorter waits.

Another possible objection is that the ONS have not released the cross-tabs (eg the breakdown of deaths by discharge status and age and acuity etc.). They have promised some of this data and analysis in a future publication. This might allow some separation of the interactions between different factors which might modify the calculation of excess deaths by a small amount. Though the results above suggest this would not be by much. Time in A&E seems to independently relate to higher rates of death for every subgroup studied and the excess death calculations are all in the same ballpark. 

And, don’t forget, the factual reporting of deaths in 2022 show that the majority of all deaths related to A&E attendance were associated with waits longer than 4hr. That’s a basic fact, not speculation. Further analysis might highlight the relative contributions to each link in the chain of cause and effect but they won’t alter the overall picture. Long waits kill patients.

And the mortality rises with time, sometimes steeply. So the first priority will have the biggest impact if it tackles very long waits. Nobody argues that 12hr waits are acceptable but recent policy has tried to reduce the waits >4hr as the major metric of success at the cost of allowing 12hr waits to rise. While having few waits over 4hr is a desirable goal, improvement programmes should target a rapid reduction in the very long waits over 12hr first. This policy focus would lead to a bigger reduction in excess deaths and might trigger the sort of notable improvement that would create the momentum so completely lacking in current plans.

Making sense of the new ONS estimates on A&E waiting times and mortality

Long waits in A&E kill patients. A new analysis of mortality and A&E waits by the ONS–despite issues in the analysis and presentation of the results–makes this look like an even bigger problem than previous analyses.

In January 2025 the Office of National Statistics (ONS) released a new analysis of the relationship between A&E waiting times and mortality. 

This is an important study because understanding when NHS performance is killing patients unnecessarily is a major indicator of where the system’s biggest and most important problems are.

But the results will be more contended and confusing than they needed to be because the ONS have presented them badly and have omitted some key data that make the importance of the results harder to judge and harder to compare with previous analysis.

This note is an attempt to explain the significance of the ONS results while also suggesting some of the improvements that could be made to make the results more useful.

The background to this analysis

The ONS are not the first to attempt to estimate the excess mortality caused by long waits. A previous analysis (of which I was a co-author) was published in 2022 in the Emergency Medical Journal and also used NHS patient-level data to derive reliable estimates of the relationship between long waits and mortality. 

This work was partly inspired by a previous Canadian study which also concluded that longer waits substantially increase mortality but with less reliable data on length of wait (the UK studies use data that contains the wait for individual patients).

The EMJ study, which has been extensively used in campaigns by the Royal College of Emergency Medicine (RCEM) to highlight the apocalyptic state of English A&E departments, used comprehensive data from april 2016 to march 2018 but only for admitted patients. Conservative estimates based on this study suggest that long waits cause between 10,000 and 20,000 extra deaths every year. The EMJ study did not estimate mortality for waits longer than 12hr due to the small numbers (which were below 2% of attendance in the period; they are over 10% now). The RCEM derived the excess deaths estimates using total published numbers for 12hr waits and the EMJ estimate of mortality for shorter waits of 8-12hr. Plus, recent estimates by the RCEM of excess deaths number were smaller than their original estimates as the EMJ data only covered admitted patients but recent NHS data says about one third of 12hr waits were discharged (an astounding statistic by itself) and the EMJ analysis did not estimate mortality for discharged patients.

It is notable that NHSE leadership’s response to the original publication was to dismiss the results. It is well worth reading the evidence session to the House of Commons Health Committee where Adrian Boyle of the RCEM presented the case and NHSE leaders dismissed it.

One argument too easily used to dismiss the importance of waiting times is that many other factors also influence mortality and some of them also increase waiting times, making describing the part of the excess mortality attributable just to long waits complex (though the EMJ paper went to great length to do adjustments and still concluded that waits were a big factor.

When rumours emerged that the ONS were doing an updated version of the analysis, there was some hope that it might swing the debate so the NHS would pay more attention to the problem. But the way the ONS chose to present their results blunted some of their possible impact as we shall see.

What the ONS did

The ONS created a cohort to analyse based on data from three datasets: the 2021 census (for demographic information); the ONS death registration data; and the complete NHS patient level data about all attendances to major (type 1) A&E departments for the financial year ending in march 2022.

The link to census data allows adjustments to expected death rates based on factors recorded in the census. The death registration data allows actual death rates to be analysed (the specific mortality metric is deaths within 30 days of hospital discharge). The A&E attendance data allows the analysis to cover all A&E attendances in the data. 

One important fact to note (in principle) is that this analysis is not based on sample data but on actual data. The total number of deaths is not an estimate but a count of actual deaths (so removing a major potential source of statistical uncertainty). This is also true of the EMJ analysis. There can be minor data quality issues because of poor data recording. For example, not all the patients in the A&E data can be matched to the other datasets (but this misses fewer than 5% of the total so should not be a big issue).

In short this should be a very high quality dataset leading to very reliable results.

In presenting the evidence the ONS chose to mostly present the adjusted data (so the mortality differences are adjusted to take account of multiple factors other than waiting times that also influence mortality). The EMJ paper also did this adjustment but using a completely different method.

Only one dataset in the ONS release does not adjust mortality for other factors. But they did not describe their adjustments in detail or how many patients were omitted from the final cohort totals (this will be a big issue as described later). This lack of detail does not mean their results are not notable or important but it does create some opportunities to cast doubt on the conclusions (many of which will be unfair or downright wrong but the ONS could have avoided the potential for criticism if they had provided more detail).

What were the key results?

I’m going to go through the key results and present many of them as charts which are a lot easier to understand than the raw tables released by the ONS. In the next section I will describe some of the issues that could have been avoided if the ONS had released additional data they must have to have been able to derive the analysis they have done.

The first chart here presents the raw data in the cohort they used for analysis:

The bar chart shows the total number of patients who waited different amounts of time to leave the A&E. The data counts the total attendances and the total 30-day deaths for each waiting time. The chart shows the raw analysis as the proportion of people in each waiting time group who died. Below 4hr the raw mortality rate is <0.5% for all arrivals. By the time waits are 12hr long that number is about 5%. That’s a big increase but hard to interpret because, for example, perhaps the cohort waiting over 12hr contains far more old people who are far more likely to die. Other analyses have adjusted for many such effects.

The total number of patients covered in the chart is about 6.7m.

This, as will be discussed later, is less than half the number recorded as attending major A&Es in the same time period which was about 16.1m. Where did the missing patients go? The ONS don’t explain. So, is the cohort representative of the attendance? Probably. This table shows the stats on grouped waiting times for the ONS cohort:

So the reported public waiting time statistics for this year are close to the ONS cohort despite the cohort being less than half the size of the total attends.

It is also worth noting that the reported proportion of wais over 12hr is close to the official annual reported number (close to 5.8%). This proportion has more than doubled since the year this analysis was done (december 2024, for example, had more than 10% of all attendance waiting more than 12hr.

All the other tables reported by the ONS don’t cite raw numbers but, rather, odds ratios after extensive adjustments to account for confounders. The details are not given (which may cause some complaints).

In most cases the odds ratio describes the probability of mortality for a particular waiting time group relative to the chosen comparison waiting time (which, I think, means the group labelled 2hr). The waiting groups, technically, mean all the waits that round down to the number. So the group labelled 2hr means all patients waiting between 2hr and 2hr 59mins.

The important message which stands out–whatever the method–is that long waits are bad for mortality in every subgroup even after extensive adjustment for other factors. Sometimes the effect of long waits is very bad. This is a stronger result than the EMJ analysis. 

So what do those results look like?

This is the analysis by admission status:

This shows the different effect of long waits on mortality for admitted patients and discharged patients. Mortality for admitted patients clearly rises with longer waits and rises by about 30-40% for waits of 8-12hr (not grossly different from the analysis in the EMJ paper).

The mortality rate rises far faster for discharged patients, with the rate nearly doubled for 8hr waits and tripled for 12hr waits. This is important as previous estimates of excess deaths ignored mortality in discharged patients.

But we can’t judge from this data whether mortality is worse for discharged patients because the base mortality isn’t shown for either group (this observation applies to all the odds-ratio data presented by the ONS and is a big issue when trying to judge the importance of some of the results). We know that admitted patients are perhaps 10 times more likely to die than discharged patients so a small increase in their mortality means more deaths than a similar increase in the mortality of discharged patients. 

The message would be far stronger if the ONS released the additional data they must have for the base mortality rates and the number of patients in each wait group (then we could calculate the total excess deaths easily as has been done with the EMJ analysis). 

But I don’t want to undermine the message that still stands out in this analysis: long waits are bad for patient mortality even when you adjust for all the possible confounding factors.

The ONS also analysed the effect on the mortality in different age groups:

Again, the mortality mostly rises with waits over 4hr, sometimes by a lot. Again it is hard to judge how many deaths this adds up to because we don’t know the base rates for any group. And there is the strange pattern for waits for the young (I think the band labelled “20” means anyone under 20). But this might be a product of having very few long waits in that cohort. Also, children’s A&Es have far better waiting performance than others.

This possible explanation is reinforced by showing the age odds ratios with the statistical confidence intervals:

Note that in this chart the scales for each cohort are different to accommodate the large 95% confidence intervals and very different odds ratios for some groups. Those intervals are strongly driven by the sample size so very wide intervals imply a small and potentially unreliable sample.

The ONS also analysed the odds ratios by the primary complaint at arrival. This is that chart:

There are some odd anomalies here, mostly in groups which probably have small sample sizes. 

The results are clearer if we stick to a single time cohort and compare the odds ratios for each condition. 

The highest risk increases are in the groups with the widest error bars so might be unreliable (again if the ONS gave us the raw cohort sizes we could make a better judgement).

But the key message remains unchanged. At 12hr the mortality risk is between 50% and 400% higher than at 2hr even for conditions where the confidence intervals are tight.

Problems with the ONS data

While the key message of the analyses are clear, there are problems in how the ONS have chosen to present the results and one major potential issue with the data. Both might be used to undermine the results but are also easy for the ONS to fix without doing any more analysis.

The biggest issue is the size of the total cohort. The ONS claims to have a near-comprehensive dataset of people attending A&E. They claim to have omitted some data but hint that this didn’t cause large numbers of omissions. But their complete cohort only has 6.7m patients when about 16.1m attended A&E in the period. That’s a big gap.

It may not matter as the 6.7 m seems to be fairly representative of all attendances. But the failure to report where the missing records went is annoying. It might even be an error caused by their unfamiliarity with A&E data. Good A&E analysts will have approximate numbers of total attendance in their heads and will instantly wonder, as I did, why the sample is only 6.7m big. It is possible that the ONS accidentally omitted a big chunk of their data and nobody noticed the gap and, therefore, didn’t see that there was anything to explain. They claim to have collaborated with the RCEM, DHSC and NHSE but someone there should have noticed this gap (though, cynically, I might question the motivation of NHSE to correct errors given their track record in downright denying the EMJ analysis).

The other problems with the analysis as presented is that it omits the information needed to translate the analysis into simpler, starker counts of the number of excess deaths (a number that seems to have been very effective in getting journalistic and public attention).

This could be easily fixed without further analysis. The ONS could simply provide the actual base rates and cohort sizes for each analysis. As the analysis currently stands we don’t know, for example, the number of 80 year olds in the sample or the proportion of the attenders who were admitted.

So we can tell that the rate of death rises with longer waits but not the number of deaths that leads to. 

Conclusion

The key message should not be ignored. Long waits kill patients. 

This is particularly important given that the number of long waits has risen rapidly and is still rising. The annual total waiting more than 12hr is ten times longer than when the EMJ analysis was done and has more than doubled since the time covered in the ONS analysis. 

But A&E performance is not a current top NHSE priority. And what is being set as the performance target by NHSE is based on an unambitious goal for 4hr performance. Some of us and the RCEM have suggested that A&E performance should be as important, if not more important, an improvement goal as elective waits. And that the first target for A&E performance should be to eliminate 12hr waits not to make minor improvements in 4hr waits.

I’m sure that the response to these results will contain a phrase something like “long A&E waits are completely unacceptable” perhaps accompanied by “everyone is trying extremely hard to improve A&E performance”. This is the stock answer when the consequences of A&E crowding hit the headlines. But, as Yoda said in Star Wars: ”There is no try: there is only do or not do”. Right now there is a lot of trying but not much doing.