There are no magic bullets to cure the NHS. But better diagnosis of its problems and clearer focus on addressing their root causes are a good place to start.

There are a lot of symptoms the NHS is failing. But plans that address the symptoms are not going to fix the root causes of the problems. And a dilution of effort across too many improvement projects will yield little improvement. Accurate diagnosis and ruthless focus on tackling root causes rather than symptoms are essential.

The government knows the NHS has a problem but is confident that current plans can fix it. Campaigners disagree. To some this is a government conspiracy to underfund the systems as a prelude to privatisation; to many of the less conspiracy-minded  the primary problem is just underfunding. But those are not the only things said to be a major cause of the current crisis: not enough GPs, A&E doctors, beds, social care places. Too many patients, old people, worried well patients have also been suggested.

The NHS has a lot of very challenging problems. This, you might think, would be a strong case for very careful focus so the scarce resources available are not diluted so no problem gets enough attention to address it.

When organisations face complicated problem one of the secrets to successfully resolving them is focus. No organisation has enough people, skills or money to tackle every problem and attempting to address too many at once tends to ensure that every effort is so diluted none will succeed. This is an acute problem in the NHS which is very short of people with good problem solving skills and the money required to invest in improvements.

But the only focussed strategies for solving the NHS crisis come from campaigners and lobby groups and they have the disadvantage of being about as credible as a one legged man at an arse kicking contest.

In fact the magic bullets suggested by campaigners get in the way of effective action as they all claim to be the only significant thing that needs to be done, a claim that lacks credibility because it depends more on what they are lobbying for than on any actual analysis of the problem.

Inside the NHS the problem isn't any better. One hospital where I have been working was put into special measures after a bad CQC report. The report is problematic to start with as it is a long list of hundreds of symptoms that things are broken and dysfunctional. The hospital's response is worse: a list of nearly 100 projects designed to address the top symptoms raised by the CQC.

This programme of work is a serious problem itself. The hospital has so little management capacity that tackling just a handful of major projects would be a stretch. And, when the individual projects conflict with each other there is no overriding rationale for deciding which one gets priority.

There is an alternative that would help both this hospital and the NHS as a whole. The alternative is to focus on the handful of underlying problems rather than the scores of symptoms. But this involves developing an understanding of how the whole system fits together and doing the analysis to identify the causes of problems and not just their symptoms. This is both hard and rarely done.

But let's try anyway.

More than half the NHS budget is spent by acute hospitals. And there are many symptoms of failure manifest in current hospital performance. Most are running deficits; many have recently seen the worst A&E performance in decades; elective waiting lists are growing with many breaching the key waiting time targets; many face serious recruitment and staffing problems.

A&E performance attracts many of the headlines. The problem is often blamed on the relentless increase in demand. Hence 15 years of money spent trying to reduce that demand by investing in primary care or in diverting patients to other services. All of which has had no measurable impact on the levels of demand or the performance of the system. Current STPs continue this grand tradition of failure like an unlucky gambler who assumes his next hand will be a winning one. Others blame the problem on a lack of A&E medics or nurses. But staffing has increased faster than demand over the last decade and performance has continued to decline. Yet others blame the many patients who arrive at A&E but could have been treated elsewhere. More focussed analysis notices that this group isn't the one with long waits (that would be patients sick enough to need a bed) or that this group can be treated quickly and cheaply as long as the A&E organises itself effectively to do so (some have put GPs at the front door doing the quick, cheap and simple things that would have happened had they gone to a GP. This is often characterised as "diverting" patients from A&E even though it is actually just organising the work inside the A&E to better match the needs of the patients who arrive).

There is a diagnosis that explains the majority of the observed symptoms in A&E that has the advantage of also explaining problems with waiting lists. It even suggests that many of the other symptoms of acute failure may have the same root cause. The diagnosis is that most hospitals don't organise the flow of patients through their beds in an effective way. When flow is blocked, capacity to treat electives is lower, hitting the waiting list targets and the trust income. Bed occupancy is too high, lowering flexibility, increasing stress on staff, potentially damaging infection control and certainly causing knock-on delays in A&E admissions. When A&E is stuffed with patients waiting for a bed, its flow becomes problematic for even minor injuries leading to a crowded department with a lower capacity to treat patients. And one with a higher workload for staff and a more stressful environment. And, consequently, higher staff turnover and recruitment problems.

In fact problems with beds tie together an incredible number of other symptoms that a hospital is failing. But the problem with flow is rarely addressed as a core problem at all. Instead the symptoms are tackled in a siloed and incoherent way which wastes resources and dissipates motivation when the individual initiatives fail. And the programmes to tackle the symptoms conflict with each other further reducing their chances of success. This is inevitable when there is no coherent vision of the central root cause of the observed symptoms. Which is unfortunate because that is what most NHS plans at every level from STPs to hospital improvement plans look like.

It is not uncommon to have an improvement programme that contains separate projects to deal with A&E recruitment of nurses, A&E recruitment of consultants, staff retention, diverting patients to other services, improving compliance with agreed professional standards, reducing medical outliers in surgical beds and vice versa … all of which would be smaller problems if only the central underlying problem of poor flow were addressed.

Neither individual hospitals nor the small staffs developing STPs have much management capacity to start with. The only way any project will make progress is to focus the available effort on just a handful of goals. And it would help if the goals were based on a very solid understanding of the root causes of the symptoms and not just the symptoms themselves. It is quite possible to devote a great deal of effort, for example, into diverting patients away from A&E (certainly many have tried in the last handful of years) but but this has not worked and wouldn't impact performance even if it did. Wasting resources on doomed wishes is just plain stupid and counterproductive.

If the NHS wants to improve it needs to get better at both diagnosing the root causes of its problems and better at marshalling its resources to focus on those underlying issues. Nothing else matters more.

Another year, another A&E crisis, but the same dumb solutions

I was going to write another rant on the evidence-free stupidity of much of the current political and media commentary on the current NHS A&E crisis. Then I realised I'd written most of it before during a previous A&E crisis where the same evidence-free solutions were floated by commentators. It seems that our media and political leaders have learned little and still don't bother to check whether their solutions are compatible with the evidence.

So here is a bullet point summary of the things that we know to be true (from analysis of detailed public performance data and patient-level HES data) followed by an edited version of what I wrote in a BMJ response in 2015:

• It isn't an A&E crisis: that is just the symptom. It is a whole hospital crisis caused by a failure to manage effective, timely flow through beds.
• The volume of patients turning up at A&E is irrelevant: it isn't about "pressure" on the input side; it is about blockages on the flow from A&E to beds.
• More resources to A&E won't fix the problem: only solutions that improve flow across the whole hospital will help.

So, if you are still blaming GPs, patients with trivial problems, immigrants etc. you don't understand the problem and your solutions will just waste NHS resources and will deliver no actual benefit.

Anyway here is what I wrote in the BMJ in 2015 in response to a similar fact-free debate.

Yes, stop blaming patients, but start by identifying the root causes of problems

It is really worrying that so many system leaders think that the problem is caused because too many people are coming to A&E and that the solution is to encourage them to go somewhere else. The idea is superficially attractive as an explanation for problems but is clearly wrong for several reasons. Moreover there are no proven ways to drive patients elsewhere.

The data about A&E attendances in major A&E departments (type 1, 24hr, full service A&Es) shows a steady low rate of attendance growth over the last 20 years with no sudden surges (many people confusingly include the numbers from non-24hr minor injury units and walk-in centres which have expanded greatly over this time period without any notable effect on the numbers turning up at major A&Es). Staff numbers have grown faster than attendance.

More significantly, if we analyse the variation in attendance numbers and performance, there is no relationship at all. Higher attendance does not drive poorer performance. This is one of the clearest messages from the data.

Monitor recently published a very comprehensive review of the possible reasons for poor A&E performance ( https://www.gov.uk/government/uploads/system/uploads/attachment_data/fil... ) and concluded that the most significant problem was poor flow through the hospital's beds. This has been well known to experts for some time. In hospitals with poor internal coordination (which is many of them) this problem isn't within the span of control of the A&E department, so blaming the department for poor performance seems particularly unfair.

Why do leaders fail to identify this root cause or tackle it effectively? This seems to be a consequence of a failure to train medics or many managers in the science of how operational processes work. An effective understanding of how processes involving queues work is a significant part of the science Operational Research. And the results are often surprisingly at variance with a naive intuition.

To a naive observer untrained in operational research, it feels like the only reason why a queue is long is because the flow into the queue is high. "Too many people have turned up." The science recognises something more subtle. The speed that a queue is processed is usually far more important than the number of people joining it. And, importantly, the length of the queue will grow very quickly if the processing speed gets slightly slower even if the numbers joining the queue don't change at all. In A&E departments this means the crowding and the overall delay for patients is highly sensitive to the speed of the whole process (of which treatment and assessment are not the bottlenecks). So, if it takes a long time to find a bed when a patient needs it (which we know is a very common and significant problem) the number of patients waiting can grow very quickly indeed even if no more patients than normal arrive in the department. If the department becomes crowded, even the patients who don't need a bed get treated more slowly, compounding the problem and making the queue grow even more.

So a naive manager identifies that the department is crowded and assumes that is because too many people have turned up when the real problem is that there is a bottleneck in the process that means patients can't be moved quickly from the A&E department. The manager might argue more staff are required to cope with the extra demand, but, if the problem is finding a bed, more staff will do nothing to make the discharges faster and actually won't help the crowding problem at all.

The consequence of a naive understanding of how queues work and a failure to analyse the data about the key causes of A&E crowding is a large amount of effort and money spent on the wrong problems. Adding staff in A&E won't magic up more free beds; diverting patients (even if we knew any way to do it) won't actually reduce the crowding in A&E.

So let's stop blaming patients. But, more importantly, let's analyse the data to identify the real causes of A&E delays and let's train NHS medics and managers in how operational processes work so they know where to focus their improvement efforts instead of naively wasting time, effort, and money on the wrong problems.

The way the NHS measures average bed occupancy doesn't support effective solutions to the shortage of beds

News headlines today (the BBC's on increasing occupancy of beds and The Times on nighttime discharges) reflect a real problem with bed occupancy in NHS hospitals. But the metric on average bed occupancy doesn't measure what it claims to measure and actively distracts from practical solutions that would improve the system.

The NHS has been collecting data about bed occupancy and availability for a very long time. But just because the statistic has been around for a long time doesn't mean it is useful. Sure, it measures something, but whether that something helps the NHS do a better job is highly questionable.

I first came across the metric in the early 2000s when I was working on problems in A&E departments and realised that finding a free bed was one of the biggest barriers to quick treatment. It still is. What I discovered was that the way the metric is measured is about as useless as it is possible for a metric to be. It not only doesn't help solve real problems, it actively drives people to suggest the wrong solutions.

The trouble is that what we need to understand is why beds are hard to find at the particular time of day and day of the week when they are needed. Peak arrivals at A&E, for example, usually occur between 9am and 10am. So the demand for beds for the 1 in 4 patients in A&E who need to be admitted peaks sometime before lunchtime. That's when we need the beds at least for the uncontrolled flow of emergencies. (In principle, hospitals can control the timing of the flow into elective beds though many don't.)

But the bed occupancy metric doesn't tell us about the availability of beds at the point when they are needed. Nor does it tell us about the average occupancy across the day or the week. It tells us about the number occupied on a particular day of the week at midnight. When I first started working for the NHS I expressed astonishment that the statistic was so irrelevant to the real problem of finding beds when you needed them. I was told that such a long standing practice could not be changed.

The reason why the metric is so useless in practice isn't hard to understand. In a typical DGH with 500 beds, each day will see somewhere between 75 and 100 discharges. In many hospitals those discharges typically happen in the afternoon, often late in the afternoon. This doesn't match the demand for beds which is dominated by emergency admissions which peak in the morning. It isn't helpful to know how many beds are free at midnight: we need to know how many are free every hour of every day.

If we focus purely on the published metric the only way to fix a lack of availability is to add more beds and hope discharges don't become any less disciplined (unfortunately there is plenty of evidence that things will get more relaxed and the beds will fill up with patients who should have been discharged more quickly). If we focus on the pattern of arrival and departure across each day we can see better ways to create space for emergencies. I supported the Department of Health to develop a Bed Management Toolkit in 2007 that recommended a focus on doing as many discharges in the morning as possible (this is still part of good practice recommendations now). If a good proportion of the 75-100 patients are discharged in the morning, there will be plenty of free beds for the emergency admissions. If they stay in their beds all day awaiting slow processes to get them out (like prescriptions for take home medication or discharge notes) then the hospital may well find itself running out of free beds early in the afternoon even though it will have free beds later in the day. Patients in A&E will spend a long in an environment that isn't the best place for their care. There is plenty of evidence that small changes in discharge practices can make big differences to bed availability at the times of day when beds are needed.

In the hospitals who do collect real time bed utilisation, this pattern can be seen and managed. Surprisingly, many can't even collect this data and many who can do nothing to ensure that the data is collected reliably. Others do collect it and do nothing with it.

My main point is that the national bed occupancy metric tells us nothing useful about the problems many have finding beds at the time of day when they are needed. Worse, it tends to lead commentators to demand a major increase in bed numbers, which is both unrealistic and could only happen slowly, rather than a focus on the effective management of discharges, which could yield benefits tomorrow at minimal cost. There are hospitals who genuinely need more beds in the medium term, but a failure to manage discharges effectively makes their problem much worse right now.

The NHS could argue that the problem is outside its control because many patients can't be discharged because of a lack of social care capacity. It is true that this is a big and growing problem. But it isn't the biggest problem. Audits of clinical notes of patients currently in beds usually show that between a quarter and a half are fit to leave hospital. And while perhaps a third of those are stuck because of external problems the rest are stuck because the hospital hasn't got its discharge act together. But, what the hell, it is far easier to be able to simply blame others than it is to do the hard work required to redesign processes inside the hospital.

But back to my main point. Average occupancy isn't very much help and the nationally reported metric doesn't even measure average occupancy. Hospitals need to understand real-time occupancy every hour of every day if they are to have any hope of managing the availability of beds at the times of day when beds are needed. Good systems to manage bed occupancy can lead to major improvements in bed occupancy at the points of the day when it matters and, as a direct result, will dramatically reduce long waits in A&E. This involves understanding of the pattern of demand across the day and a disciplined approach to discharges that achieves much better coordination of departures with the pattern of demand.

If the NHS continues to focus on a bad way to measure the wrong thing about beds it won't get the insight it needs to drive real improvement.

The NHS isn't very good at driving operational improvement: the data it collects could help it get better

The NHS collects a large volume of administrative data. It could use that for driving operational improvement but mostly doesn't.

The central NHS collects patient-level data about what is happening in its hospitals. Since 2007 the major datasets have collected more than a billion records of admissions, outpatient appointments and A&E attendances. These datasets are collectively known as "administrative data" and are used for a variety of internal purposes including paying hospitals for the activity they do.

The primary reason why they are collected isn't operational improvement. Arguably, it should be, though, if it were, we might collect the data differently (we might also disseminate if more speedily and collect additional things).

The controversial care.data programme (which is an attempt to join-up data collected by GPs with hospital data) was promoted as a way to enhance economic growth by exploiting the data for medical research even though it is probably far more useful for driving improvement in the existing care offered by the system. But improvement is the neglected orphan child of NHS data collection and is barely mentioned in any of the arguments about care.data or any other NHS data collections. It should be the primary reason why we bother with this data not least because making NHS care better is easy for patients to understand (and harder to object to) than, for example, helping the pharmaceutical industry make even more otiose margins.

Even though the big data collections are not optimised for supporting improvement, they are still useful. I'm going to illustrate this with a few examples from analysing the HES (hospital episodes statistics) A&E dataset. HES is one of the ways the central data is disseminated back to the system.

What we collect in A&E data and why it is relevant

Since 2007 the English NHS has collected a range of useful data about every A&E attendance (which includes attendance at minor injury units as well as attendance at major 24hr, full service A&E departments). It took several years before that collection achieved nearly complete coverage of all departments in England, but it has mostly been complete for the last 5 years.

The data contains basic information about each attendance such as what time the patient arrived and left A&E plus some other timestamps during their stay (eg when first seen by a professional, time treatment finished and time patient departed the A&E). Basic demographics about the patient are recorded and some data about where they came from.  Data about the where the patient came from and where they went after the visit are also collects as well as information about investigations diagnoses and treatments (though these are often not collected reliably).

This is a rich source of data for identifying why A&E departments struggle to treat patients quickly, which is currently a major concern in many hospitals.

So here are a few examples of how the data can be used.

Local operational insights are available in the data

How well organised you are matters. If you have a grip on the operational detail you will be constantly identifying where things can be improved. One of the key tasks is to identify whereabouts in the process things are broken. We might identify a department that has a problem with one type of patient, or one time of day or one particular step in the process. If we know where the problem is,we can focus improvement effort in one place which is much more effective than wasting effort on a wide range of interventions most of which will have no effect.

I'm going to show two ways the patient-level dataset can support such focus. I'm only going to show how to isolate performance issues with the type of patient and the time of the week. But I hope this illustrates how effective use of the data can support improvement.

One way to get a quick overview of how the complete process functions is to look at the histogram of patient waiting times (ie toting up how many patients wait different lengths of time before leaving A&E). In this case a useful way to do this is to use counts of waits in 15 minute blocks. A typical chart is shown below:

This plot summarises the experience of every patient (in this case over a whole year, but it works well for smaller numbers and time periods). It is common to see a peak in the waits in the 15 minute interval before the 4hr target time. This is a useful indicator of a last minute rush to meet the target (which is bad). But the other features are also useful indicators. We can see at a glance for example the total waits of >12hr (this is the last peak on the right of the chart). We can tell in this case that a lot of patients leave before they get to even 1.5hr (which is good).

Experience shows that we can diagnose many problems in A&E from the shape of this curve.

Some of those are easier to spot if we look at how different types of patient wait. The next chart shows the histogram broken down by 4 categories of patient: admitted patients, discharged patients, discharged patients with a referral and transferred patients (patients admitted to another hospital usually for specialist treatment).

We can instantly see that the shapes are different for different types of patient. And we can see that nearly half of all patients being admitted get admitted in the 15 minute interval before they have waited for 4hrs. Other patient types show a similar but much less strong peak just before 4hr.

This 4hr peak is a sign of bad things in the process. Are doctors making rushed last minute decisions to admit patients? Do they know the patient needs to be admitted earlier but can get access to a bed unless a breach of the target is about to occur? Neither of these are good for the patient. But knowing where the problem is is the first step in fixing it.

To show that not every trust is the same, here is the same analysis for a different (much better) trust. They still have a peak at 4hr for admitted patients. But it is only 15 % of all patients not 50 %: most admissions are spread over the 3hr period before 4hr not the 15 minute period before 4hr.  Other types of patient show only a tiny 4hr rush and the majority are dealt with well before they get close to a 4hr wait.

Analysis of these patterns can tell us a lot about the underlying quality of the process for treating patients. One particular insight found in most trusts is the apparent problems admitting patients quickly when they need to be admitted. The shapes of the admitted patient curve often show a last minute rush to admit just before 4hr. This isn't usually because sick patients need more care in A&E; it is often obvious from the moment they arrive that they will need a bed but free beds are often hard to find. The contrasting pattern for transferred patients is a strong confirmation of this idea. Transferred patients also need a bed, but are often transferred because they need a specialty unavailable in that hospital. Most hospitals achieve that transfer much more quickly than they achieve admission to their own beds. The clock stops when they leave the A&E and they leave faster than admitted patients and often in much less than 4hr. Finding a bed for them is another hospital's problem.

Admitted patients wait until the priority of not breaching the target triggers some action to free up beds. This is bad for patients, who wait longer, and staff, who could be treating other patients instead of searching for free beds.

The insight that the problem is associated with beds is well-known but often neglected in improvement initiatives (not least because it is not really an A&E problem and it is A&E who get the blame for the delays). But A&E departments don't control the flow through beds. Adding more A&E staff or facilities won't fix waits caused by poor bed flow. Nor will diverting patients to other services (you can only divert the minors who are often treated quickly even in departments with bad problems with their beds.)

These sorts of insights should be a crucial part of deciding what initiatives to focus improvement programmes on. But far too much effort is actually spent on non-problems that will have no impact. Sorting out flow in beds is a hard problem; but much harder if you don't even recognise that it is the most important problem.

We can also do other analyses that localise where in the process the problems occur. For example, some departments have problems at particular times of day or particular days of the week. If you know, for example, that some days are usually good and others are usually bad, you can ask what is different on the good days and, perhaps, find ways to improve the bad ones.

Here are some examples.

This shows the average performance for one trust on different weekdays:

There is no huge insight here except that performance at weekends is better than on weekdays. This might reveal some important issues with matching staffing to the volume of attendance or it could be caused by different admission practices at weekends.

But we can drill further into the data and get more detailed insights. Here is the volume and performance by hour of week for the same trust:

We can tell from this that although volume at the weekends is a little lower, performance is better and more consistent. We can also tell that performance falls off a cliff at 8am every weekday but just for that hour, just  when it starts to get busy but no such effect is seen at weekends.

We can drill deeper into the data and look at performance by different types of patient. The chart below is the same as the one above but we have broken out performance and volume by patient type.

In this chart we can see that the unusual performance collapse at 8am occurs only for the discharged patient group (normally considered to be the easiest to deal with). The most likely explanation for this is some major problem with shift handovers at that time in the morning. We can't prove this from the data but we can certainly trigger some careful local analysis to explore the cause. I'm guessing this has not happened since the same pattern is seen over several years since the merger that created this trust. We also can't tell whether this problem is localised to one site (this trust runs several major A&E sites) because this trust doesn't report site-specific data nationally (unhelpfully site-specific reporting is not mandatory). I know they have recently recruited a new data team so I hope they are addressing the problem now.

Just for reference here is the same plot for one of the top A&E performers.

Note that this trust achieves consistent and very good performance for all patient groups almost all the time.

This sort of analysis should be routine when trying to improve A&E performance

A large part of improving performance is knowing where to focus the improvement effort. I hope that these relatively simple examples show that there are plenty of simple analytical tools that can provide that focus. These tools should be available to any competent analyst. Trusts already have the data that feeds them and the national data is available to qualified analysts who want to benchmark their hospitals with others.

Unfortunately this is far from standard practice. Many trusts, even troubled ones being hounded to improve by their management or by external regulators produce analysis that never seems to ask the important questions that would create some focus for improvement. No national body routinely produces tools to enable this sort of analysis even though the data has been available for years.

The NHS has a huge challenge ahead in driving up the rate it can improve. Many large national datasets exist that contain (like the A&E data here) major insights that can help to focus that improvement effort. It is critical that analytical skills are focussed on identifying where problems occur so we can spent improvement effort in the right place. Sadly too many competent analysts in the NHS spend all their time doing routine reports which contain no useful insights for driving improvement. Many of the bodies who could have access to this sort of data don't exploit it for improvement. And many of the national bodies who do have the data never do this sort of analysis. Most surprisingly, perhaps, even the hospitals who could use this data in real time (national bodies only get their data several months after the activity occurs) mostly don't, even the troubled ones who really need to improve.

This has to change or improvement will remain impossible.

It's not the doctors: it's the beds...

People admitted to hospital are more likely to die if the admission happens at the weekend. The government thinks this is because hospitals don't really work 7-days a week. So they are engaged in an attempt to rewrite doctors contracts so they can't opt-out of weekend work. This is the wrong focus.

People don't stop getting sick at the weekend. So hospitals really shouldn't provide a worse service then. But the evidence suggests they do (though it should be admitted that working out the weekend mortality is both hard and controversial). The government thinks this is because consultants can opt-out of weekend working (though how many actually do this is unclear and a subject of significant controversy).

While it is obvious in activity statistics that hospitals function very differently at the weekend, it is a lot less obvious that the doctors are to blame. And focusing on them may be a mistake. A recent study (reported in the BMJ here) casts some new light on the problem that suggests the focus of government policy may be wrong.

The study reported a clear reduction in mortality in a hospital when bed occupancy was reduced. The text below is a version of what I said in a BMJ rapid response and on LinkedIn when I first saw the study.

I'm puzzled that more commentary has not noted the relationship between this study and the current topic of weekend mortality in NHS hospitals.

The government has focussed on trying to force changes to medical contracts to eliminate the ability for doctors to opt-out of weekend work in the hope that this will fix the problem of excess mortality at weekends. But, by focussing on the doctors, they miss the more general point that just having more doctors won't fix broken operational processes at the weekend. This study points to a much broader problem that links mortality to those processes.

The missing link is the fact that the processes for discharging patients and therefore keeping bed occupancy down are widely broken at the weekend. While admissions are lower as few elective patients are admitted, discharges are muchlower. Emergencies, of course, continue to arrive. So the beds fill up as the overall process for discharging patients is usually dysfunctional at the weekend. This leads to very clear patterns (easily observable in activity statistics and for length of stay). In many hospitals beds fill up at the weekend.

The presence of doctors at the weekend isn't (or shouldn't be) the critical factor here. Most of the discharges that should happen are probably routine and could happen automatically without medical supervision. But they often don't because the process for discharge has been poorly designed (this is sometimes because it has an unnecessary requirement for consultant sign off).

We have known for some time that the dysfunctionality of processes associated with the flow through beds is the dominant cause of delays in A&E (which are bad for patients). And we know that A&E delays are bad for mortality and outcomes. Monitor's recent report adds further weight to this hypothesis. This study now reports a direct link to mortality when beds are crowded.

The high bed occupancy is directly bad for patients and is caused by poor operational management of discharge processes. Those processes are much poorer at weekends than they are during the week. This alone may explain the weekend mortality effect.

The weekend mortality problem is not primarily a medical problem, it is a management problem. The lesson for policy is that a focus on medical contracts is a distraction. If we really want to fix weekend mortality we should focus on improving the way hospitals manage the flow through their beds, especially at weekends. As a bonus, this would also lead to major improvements in hospitals' ability to treat patients quickly in A&E. This should be a double win for patient outcomes.