A&E staffing and performance: don't mistake lobbying for accurate analysis of the problem

A shortage of A&E staff isn't the reason for poor performance in A&E. It never has been. Some A&Es have shortages of staff and that is a problem, but is clearly isn't the result of a national shortage of qualified staff. And the more the RCEM lobby for more staff the more they detract from accurate analysis of the real problems of A&E. Fix those and we might even fix the local staff shortages.

If you read the newspaper headlines or the press statements issued by the Royal College of Emergency Medicine (RCEM), you might believe that we are desperately short of qualified staff in A&E and that is the reason performance is current at dismal record-breaking lows. You would be wrong.

You might also believe that the reason why some A&E departments struggle to maintain safe levels of medical staffing is because there is a declining number of A&E doctors or because we are desperately short of A&E doctors. But this certainly isn't the cause of local shortages and whether we are short of the ideal number of A&E medics is certainly not the reason why performance is poor.

You might, in responding to one of my many complaints that misleading things are being said about A&E staffing which is growing faster than any other specialty, argue that attendance has outpaced growth. You would still be wrong.

Don't get me wrong, an understaffed A&E department is not good and is not safe. And there are several departments in England where staffing is far too low (they are the ones generating all the ink in headlines giving the RCEM an excuse to lobby for more doctors). But blaming those local shortages on a national shortage is misleading and distracts attention from the real problems. And blaming local shortages on "NHS cuts" or squeezed budgets is ludicrous.

For a start, the local departments who are severely understaffed have the budget for more A&E staff but they can't recruit them. That's a recruitment problem not a budget problem. You could plausibly argue that those local problems were the result of a national shortage. That's a reasonable argument but falls down because the majority of A&E departments don't have problems recruiting. People being unwilling to work for your department might be caused by purely local problems like the fact that the department is badly managed and is a really bad place to work. Arguing about the national numbers of doctors isn't going to fix that. And lobbying for more staff actively detracts from identifying the local issues that need to be fixed to actually address the problem.

And the national numbers don't show the things headline writers assume they show. Here is a chart (based on ESR data) showing the national number of doctors with an A&E specialty:

Total medical staff in A&E have increased by more than 20% over this period; consultant levels have risen by more than 50%. Over this time attendance at major A&Es has risen by about 12% (so staff are not being "overwhelmed by demand" as the common belief has it).

So we have more A&E doctors but do we have enough? The RCEM have a model that says we still don't. And their model might be right, but we are clearly closer to their recommended level of staffing that we have ever been so hinting that current problems in A&E are caused by an increasing shortage is just bollocks. We can have a rational debate about the right number of A&E doctors nationally but that debate has nothing to do with the local problems in some A&Es or the current performance of the system.

So why do perceptions differ so much?

One of the biggest reasons is a failure of many to grasp the distinction between demand and queues. In A&E the flow of patients into the department isn't the primary cause of the department being busy: that would be caused by the number of people waiting. But the queue of people waiting is a lot more sensitive to the speed of the flow than it is to the number turning up. When the flow slows down, the queue expands quickly and that is what the staff perceive as the workload. But they mistake this for an issue with demand or attendance (we know this isn't true as the we count the attendance numbers and they have not suddenly increased.)

There is a good reason why flow in A&E has got slower and it also explains why A&E staffing is irrelevant to the speed. Flow is slow because we can't find empty beds for patients who need to be admitted (go read some of my other analysis of this especially the one where I point out the key problem is beds). Even doubling the staff number in A&E would not make any more free beds appear in the hospital.

More significantly, the performance problems caused by poor flow through beds may go a long way to explaining why some departments struggle to find enough staff. The direction of causality is not poor staffing -> poor performance it is poor performance -> poor staffing. I've observed departments which are very crowded because of problems getting patients into beds. The A&E medics still try improvement initiatives but they don't make much difference. They can't as they don't address the bottleneck in the flow which is outside the A&E. This increases frustration and adds to the depressing environment of being in a crowded department where nothing you do makes things better. Eventually people don't want to work there any more. Hence: recruitment crisis and staff shortages. But the correct answer isn't more A&E staff: it is to address the bottlenecks in flow which are mostly outside the A&E. Improving flow makes the A&E less crowded and makes the work there less stressful.

Lobbying for more A&E doctors to fix this sort of problem is irrelevant, ineffective and a serious distraction from dealing with the real problem. That's why I object so much when the RCEM use local problems to lobby for more A&E doctors nationally.

And talking about a mythical national shortage detracts from other analysis the RCEM does, most of which is good and almost all of which is more important than the national number of A&E doctors.

Ad hoc analysis of big data sources is easy if you use the right tools: an example using English prescribing data

A recent newspaper story highlighted large price increases for some generic drugs prescribed by the NHS. I was able to replicate the information in the story and explore other issues with drug pricing in a single afternoon. Here I describe how I did it. There are valuable lessons here for anyone who needs to get rapid analysis from large datasets.

Two stories in The Times (the original story, the follow up) claimed that the NHS was losing £260m a year due to extortionate pricing of some generic drugs, some of which had seen price increases of > 1,000% as their licence holders exploited a loophole in the pricing rules. I wanted to check their facts and investigate price changes in all the drugs prescribed by the NHS.

This blog is (mostly) about how I did that and I'm telling it because the lessons about how get rapid answers from large datasets are very useful in healthcare. But the NHS tends to use its vast repositories of data badly and is slow to adopt technologies to make the task of searching for insights faster and easier. What I did to validate the stories from The Times shows that, if you use the right technology for storing and querying the data, you can get almost instant insight whenever a new question arises.

The data source

The reason the question can be answered is that the HSCIC NHS Digital has been releasing the data about all prescriptions issued in England every month since August 2010 (you can find the latest month here). This data describes every prescription (not just the drug but the specific formulation) dispensed in primary care, which GP practice or clinic prescribed it and how much the NHS paid for it.

It is a large data source. Each month the raw data has about 10m rows of data in a single CSV file of about 1.5 GB. The full dataset is about 700m rows of data and takes nearly 100 GB of space to store. This summarises about 5 billion individual prescriptions. Even a single month is too large for convenient analysis by spreadsheet and the full dataset is too large to fit easily on a laptop hard drive unless you have a very powerful laptop and don't want to do much else with it. In my case it was prescribing data or several editions of Football Manager: no contest.

But the raw data isn't enough for useful analysis. Individual items (which can be drugs or a range of devices or equipment) are coded using only a 15 character BNF Code which describes the item uniquely but doesn't give much information about what it is (for example what the key active ingredient is). Prescribers are coded by a unique code that doesn't tell who they are or where they are. Some of this information is released alongside the data: a mapping of prescribers to their address is provided as is a mapping of the BNF Codes to the key ingredient.

But for convenient analysis we need to group the items together in a hierarchical way. The BNF (the British National Formulary) does this by assigning each item to a chapter, a section and paragraph/subparagraph which groups things together into meaningful categories. For example, the Cardiovascular System constitutes a chapter, Diuretic drugs a section and different types of diuretics are grouped together in paragraphs (with similar groupings for other cardiovascular drugs).

Unfortunately, the HSCIC doesn't provide an up to date list of the BNF categories. The Business Services Agency, which collects this data as a side effect of the process for paying pharmacists to dispense the drugs, does but it is in an obscure place (see instructions here) and it isn't kept rigorously up to date (so every month you need to do some manual editing when new drugs are launched and, when the BNF reorganises the hierarchy, even more work is required to tweak the mappings between old codes and the new structure). Luckily, I've been keeping my data up to date.

Storing and managing large data sources

I've been keeping an up-to-date copy of the prescribing data since I was involved in developing a tool to help medicne managers exploit it several years ago. While building that tool I explored several ways to help store and manage the data and several tools to make it easy to analyse. The combination I ended up with is Google's BigQuery and Tableau. BigQuery is a cloud data warehouse optimised for fast queries on large datasets. Tableau is a desktop visual analytics tool that works well alongside BigQuery (or most other databases).

What is particularly fantastic about BigQuery is that it allows superb analytics query speed for no upfront investment. To achieve similar speeds on in-house systems you would have to spend tens of thousands on hardware and software: BigQuery give you almost interactive analytics performance as soon as you have loaded the data. And you only pay for storage and the volume of data returned by queries, neither of which are expensive. No database tweaking or maintenance are required. And, if you drive you analytics from Tableau, you don't even have to write any SQL to get results: it is all driven by visual actions inside Tableau.

In fact the hardest and most time consuming parts of the process of managing the prescribing data is maintaining the metadata which requires sourcing and manipulation of data from a variety of other sources.

Some basics about the dataset using Tableau

My core data and metadata are already stored in BigQuery so all I need to do to analyse it is to connect Tableau to the data sources and define the relationships between the core data table and the metadata tables. Tableau has a neat visual window for defining these relationships. The only other steps required for interactive analysis is to define some additional calculations for convenience. This is as easy as writing formulae in a spreadsheet. In this case I had to convert the dates in the data source (YYYYMM format) into dates Tableau can understand via simple string manipulation and I had to define some new formulae to calculate things like the average cost per prescription.

The hardest part of doing the analysis relevant to drug prices was remembering how to calculate changes in price from a fixed date in Tableau (it is easy: see here for a guide to doing it).

Once Tableau is plugged into BigQuery and those additional calculations are set up, everything else is a matter of a few minutes away. Query results from BigQuery can take ~30s on a bad day and Tableau will return tables or charts straight away which can all be modified or adjusted visually without the user needing to go to SQL or some other query language. The interactive and visual approach used by Tableau allows the user to focus on getting the right question, the right analysis and the right presentation of the data.

The analyses

The table below, for example, took a couple of minutes to generate even though it summarises all of the drugs issued since 2010 in several different ways (part of the process was to summarise everything and then eliminate the devices and dressings categories).

It is easy to create visual analysis as well as tables. This summary of monthly spend and prices took a few more minutes to generate:

This is very high level summary analysis, but Tableau makes it easy to drill down to the lowest levels of detail available. For addressing the question posed by the stories in the times I needed to look at the changes in prices of individual drugs (or chemicals) over time.

It is easy to set up the analysis. I just had to create a table showing the volume and average price of all chemicals by date (this is a big table as there are nearly 2,000 unique chemicals though not all the ones in 2016 were also used in 2011). I sorted the table and selected all the examples where the price was more than 400% higher in january 2016 than it was in january 2011 (my threshold is slightly different than the times). Then I grouped the low volume examples together and got this table:

This gives a good general overview of the places to look for big price hikes.

We can also do specific analysis of individual chemicals. The times mentioned 4 in particular. Their volume and price history is shown in the chart below.

Again, this took only a few minutes to generate. Note the log scales used to fit the enormous price increases into a scale you can read.

Of course, we can also do ad-hoc analysis of things that the Times Didn't ask, like which drugs have seen the biggest price decreases because of the benefits of generic competition. That table is below:

The NHS is saving >£70m every month for this list alone (and if we took rising volume into account the savings would be even bigger).

If you pick the right tools for analysing big data, you can spend time focussing on the questions

It took me an afternoon to replicate the Times analysis and to go much further. Admittedly, I already had the data and platform for analysing it. But this is much faster than can be achieved by any tool provided by the NHS. This is strange because a number of key NHS improvements depend on good analysis of this dataset. The Times highlighted where it is useful in controlling spending by highlighting excessive pricing based monopolistic positions from suppliers. But the same data can highlight who prescribes too many antibiotics, those who don't give their patients the right mix of diabetes medication or where modern alternatives to warfarin are being used.

Making the data easy to work with is an essential part of making it useful. Platforms that allow rapid answers to be derived from the complete dataset are far better than tools that allow only local or partial analysis. Many smart people are currently wasting weeks of their valuable time wrangling parts of this dataset to answer smaller, less useful questions. They should be applying their brains to bigger questions and the NHS should be giving them the tools to make them more productive.

This isn't happening. Ben Goldacre, for example, struggled to get NHS funding for an online tool that allows this sort of analysis to be done by anyone (this now exists in beta form but has mostly be funded by charities not the NHS).

And the NHS has many other large datasets it needs to make use of. Patient level data for outpatient, A&E and inpatient activity all exist but are significantly underused, partially because finding interesting patterns is hard and slow.

But the world of data analytics has changed in the last five years. The tools exist to take the time and the drudgery out of big data analyses. The NHS should be using them.

Gresham's Law works in health policy: bullshit squeezes out honest truthful analysis

Gresham's law is an economic idea that states that counterfeit money drives real money out of circulation. The same thing seems to happen in healthcare policy: good analysis of problems and good policy is squeezed out of the debate by bad analysis and bad policy that sounds good but won't work. This tendency has to be fought vigorously or it will become impossible to improve the NHS except by accident.

"Bullshit is a greater enemy of truth than lies are…" Tim Harford

Gresham's law is a very old principle in economics (it has been known since Aristophanes) which states that bad currency drives good currency out of circulation. I won't say more about the economic mechanisms here as this is an article about health policy where I've noticed that the same sort of problem appears to be happening there.

In short, dumb analysis of what the problems in the NHS are is starting to dominate intelligent, reliable analysis in debate and in policy making; dumb policies are driving out the good ideas that might make things better.

There are a number of reasons for this problem. One was dissected masterfully by Tim Harford in a recent FT article:

This is the real tragedy. It’s not that politicians spin things their way — of course they do. That is politics. It’s that politicians have grown so used to misusing numbers as weapons that they have forgotten that used properly, they are tools.

But we should not rush to blame politicians for dumb analysis of problems in the NHS. Many in the commentariat and many workers in the NHS are keen to shortcircuit good focused analysis and substitute attractive but dumb policy ideas.

There are, unfortunately, a large number of plausible analyses of key problems are that are just wrong. And, if you leap from bad analysis to policy, an equal number of solutions that sound good but will simply waste time and resources because they won’t work. (I would say obviously won’t work but that is apparent only to those who do the detailed, dirty work of actual analysis and the bad ideas are often attractive only because nobody has stopped to do any actual analysis or because nobody has paid any attention to the analysis that has been done). Leaping from symptoms to treatment with no intervening effort on generating a correct diagnosis is bad in medicine and just as bad in management.

One major cause of the Gresham effect here is that many proposed solutions sound good. So they take up space in newspaper headlines and discussion and thereby exclude the more nuanced solutions that require some explanation of why things are a bit more complicated than that. Hence Jeremy Hunt’s repeated assertion that death rates are higher at the weekend. It is a nice, simple idea that sounds plausible and backs up one of his favourite policies: a 7-day NHS. But the reality is the analysis is complex; we probably can’t be certain that the mortality really is worse at the weekend; and we certainly don’t know what causes it even if it is true. So using it as a crutch to support an attractive policy (who doesn’t want the NHS to work the same at weekends?) is deeply misleading. It is particularly misleading because even if we need an NHS that works the same way at the weekend it is far from obvious that changing doctor’s contracts will make any difference. We have plenty of operational evidence that the NHS doesn't work well at weekends but it doesn't point the finger at medical staff as the key problem.

Another zombie idea that wasted space in policy and newspaper headlines was the idea that problems in A&E were caused by changes in the GP out-of-hours contract. Superficially it looked like the numbers attending A&E grew strongly after the contract was changed. But that was coincidence: England started counting attendance at minor injury units (and the number of such units grew rapidly) around the same time as the GP contract was altered. Core attendance at major A&Es (which is where all the problems with treatment speed are) didn’t change from its long term trend. And those who know the statistics also pointed out that few people attend A&E at night; volume is far higher during the day and peaks when GPs are still open.

In fact policy about A&E is littered with dumb ideas that simply can’t be reconciled with the actual data. The idea that A&E performance is declining because of too many people turning up is attractive. So there are repeated discussions about policies to respond to this: diverting patients somewhere else; massively increasing staffing in A&E; putting GPs at the front door… They all sound like they might do something. But every dataset we have says the performance problems have nothing to do with volume. In fact the best analysis says the biggest cause of slow A&Es is nothing to do with the A&E department at all: it is about the inability of hospital wards to accommodate the flow from A&E admissions. Spend all you want on the other policy ideas, but, if you ignore that bottleneck, you are wasting your money. Sadly, every time A&E performance deteriorates, we get a torrent of bullshit policies and almost no commentary that tries to identify (or points out that we have already identified) the most important problem and can do something about it.

One of the most important areas where bad ideas squeeze good ones from the arena is money. The NHS as a whole could probably use more money and probably should get it. Many parts of the system have been campaigning to get more of the budget for their activity. GPs complain that their share of the NHS budget has been falling (and then describe this as "cuts" when it isn't). They claim they are swamped by patient demand and can't cope without vastly more investment. The problem here typifies the way bad ideas squeeze out good ones. The bad idea is that all the problems are caused by lack of (or will be solved by more) money. Nothing else matters. There is therefore almost no discussion of whether anything other than an increased budget could make the life of a GP better or help the GP do a better job for patients.

Yet there are concrete examples that show GPs who pay attention to how they match their capacity to the things patients actually want (the demand) can dramatically lower their workload at the same time as improving patient satisfaction. Flexible attitudes to how patients needs are met and wider use of modern technology can more than fill the perceived gap in capacity that GPs campaign about. But this gets almost no attention in the debate as operational ideas that work are squeezed from the arena by demands for more money.

The idea that the only problem is money is insidiously dangerous across the whole NHS. I'm sure the system could do better with more. But to focus on campaigning for more money and forget all the other things that could be done to improve things is disastrous for several reasons. One is that getting more money is unrealistic in the short term; we should be seeking improvements that can make a difference right now. Another is that getting more money without fixing some of the current problems is a likely to guarantee that the money will deliver far less benefit than expected should it ever arrive. If we lack good management systems that create an awareness of where the real problems are, we will spend extra money on things that don't address the problems and have little impact on the actual problem. A belief that money is the only problem pushes out any thinking on the problems we could fix right now without any extra money and prevents us acting now so we spend any future money on the areas that will yield the largest benefits.

Another attractively populist idea is encapsulated in the slogan "more resources to the front line". It is attractive because it makes a good slogan. It feeds the popular myth that bureaucracy consumes too much money for no useful purpose. It panders to the idea that every problem is solved by having more front-line staff. Sadly every analysis suggests the NHS is extraordinarily undermanaged (see my comments here). While it is possible to be too bureaucratic and undermanaged at the same time, cutting the budget for management is not exactly an effective response. One of the biggest problems in the NHS is a failure to coordinate care and that is a management and information problem that gets harder not easier when you have more medical staff to coordinate. And not just across organisations but inside them. In many hospitals settings the biggest failures in both quality and productivity come because the activity of different people is not well coordinated. This affects how we discharge patients in a timely way; it damages the throughput of operating theatres; it hurts patients because their medication is screwed up; it guarantees long waits in A&E because it is hard to find free beds (we don't coordinate the discharge process with the demand pattern for emergency beds).

So what?

The battle against statistical bullshit and Gresham's law must be fought. The more bad ideas are allowed to dominate debate and policy making, the less improvement will actually happen in the NHS.

Part of the problem would be addressed if the NHS collected better data about what actually happens on the shop floor. Too much of the data currently collected is focussed on top-down performance management rather than identifying and fixing operational problems. The central management style that demands ever more performance reporting (as criticised in this excellent rant by Nigel Edwards) drives out intelligent thinking about the root causes of problems (not least because it consumes so much of the scarce management time available for the operational managers who should be problem solving). Worse, the senior management of hospitals have a worrying tendency to collect data just for performance reporting while neglecting to collect the data they should acquire so they can understand the causes of their operational problems.

Even when we do collect useful information we tend to collect it slowly and make shamefully little use of it to derive operational insights. Patient-level data on A&E performance, for example, has had almost no influence on where money is directed in attempts to solve the persistent decline in A&E performance (see my argument here). We need to make more use of the big datasets for improvement and we need better tools to enable managers to get to those insights more quickly.

Having good analysis of the problem isn't enough. We also need to communicate those answers in ways that actually influence people. Data isn't convincing by itself: it needs to be turned into a message that works for the different audiences that can make a difference to what gets done. Partly this is about paying attention to how data is communicated: good data visualisation is an often neglected first step. But we also need to tell convincing stories. Bad ideas propagate not just because they are often unchallenged but because they are encapsulated in convincing, plausible stories. Gresham's law applies because the bad ideas sound more plausible than the good ones and attract more attention in the commentariat, the policy makers and the operational managers. Counteracting this with analysis isn't enough: we need better stories about what works as well.

The fight against Gresham's law must be fought. If it isn't, the NHS will continue to waste effort on initiatives that won't help it improve. Even if it eventually gets more money, much of that will be wasted because it won't be focussed on addressing the real bottlenecks to better performance. Britain's most loved public institution can't afford that.

The debate on BREXIT illustrates how little evidence determines major public policy decisions

People don't decide their position on BREXIT by looking at the numbers: they choose their position and then seek numbers to justify it. Most of the numbers quoted on either side can't be trusted. This is corrupting public discourse by damaging the credibility of numbers that do tell a clear story.

The ferocity of debate in politics is often inversely proportional to the amount of actual hard evidence available on the topic. Whether the UK should stay in the EU is a typical example. Many decide they don't like the loss of freedom required when you are a member of the club and call for exit; others, like me, decide that the compromises required and the bureaucratic overhang of membership are worth it because the gains from collaboration are worth it. Some choices are more atavistic: many presume (probably incorrectly) that uncontrolled immigration is caused by EU membership (and they also believe the populist myth that immigrants are the cause of many other problems in society). Neither side reaches their conclusion because they have done some calculations: the conclusions come from deep emotional value choices not statistics. But the debate pretends otherwise and dredges up volumes of statistics to confirm the emotionally reached decision.

The numbers thrown around in the debate are proxies for that emotional choice. Most people can't admit that they didn't reason their way to their choice and they seek what look like the rational arguments that got them there. But this process suffers from all of the cognitive biases that so beset much thinking (especially confirmation bias where people are more likely to believe numbers supporting the position they already hold). People seek the numbers that confirm their side of the debate.

Nothing illustrates this better than the meme of European bureaucracy. Even EU supporters often complain about bureaucracy and many even quoted this tweet as an example:

Luckily for rational thinkers the BBC's More or Less programme (see this BBC article) decided to test the assertion. Their conclusion: there are no EU regulations specifically about cabbage sales and the 26,911 number originated in a complaint in the post-war USA about government regulations (and was probably mythical even then). Amazingly the same number of words has been repeated by many other objectors to bureaucracy without ever being validated in even the most cursory way against real documents.

The meme of EU bureaucracy is so strong that even pro-EU campaigners didn't think to challenge the basic facts in the assertion.

My main point, though, is about the harder economic numbers bandied about by the two sides and the extent to which they corrupt public discourse when we are debating significant issues. Both sides in the debate, for example, agree that leaving the EU would be disruptive. I agree: that is one of the few things that is clear. But there is a lot of disagreement about how disruptive leaving would be and even more on the economic benefits of staying or leaving. The leave campaign tend to assume that new trade treaties would be easy to negotiate quickly (not that there is any evidence to support this) and they assume that the benefits of an independent UK would be large when freed from the dead hand of EU regulation. The stay camp assume that renegotiation is a long and costly process and that many current jobs in the UK that depend on EU trade would be lost.

Both sides quote specific estimates for the economic benefit of their position. This is where the problem comes. Although specific numbers are quoted, the estimates have no credibility. John Kay, the economist and FT columnist addressed some of the problems with these kinds of estimates in a column in 2011. His concerns, though originally made about the economic rationale for big government projects, apply equally to any analysis of the case for staying or leaving:

...Because so many inputs to the analysis are invented, they can be chosen with a view to the desired result...

...The only information exercises such as these convey is the limits of the imagination of the people who have undertaken them….

...Yet the mistaken belief persists that these procedures provide an objective basis for decision making...

...We do great damage by claiming to know things that are not known, by asserting certainty in the face of uncertainty and ambiguity, and by attaching a veneer of rationality to decisions that have in fact been made on other, rarely articulated, grounds. The paradoxical result is all too obvious. The public sector and large bureaucratic organisations appear as paragons of good decision making process and exemplars of bad decisions.

I would add an extra concern. The specific nature of the forecasts made by each side damage the credibility of almost any analysis intended to illuminate a major public decision. This is bad because, for example, when we have to decide how much to spend to mitigate global warming, the numbers we are shown will have no credibility and we will, most likely, make poor decisions as a result. Sometimes the numbers do point one way on a major decision. If we pollute public discourse with spurious, over-precise analysis, we will undermine our ability to make good decisions when the evidence is clear.

When deciding whether to stay in the EU or to invest in major infrastructure projects we can never have precise estimates of the costs and benefits. We should not deny the uncertainty by pretending that we do. We can't decide on whether to stay in the EU by purely objective analysis: the uncertainties are too large. We should admit that our choice is based on values not numbers. I believe that international cooperation is a better way to conduct our affairs than standing independent and alone, despite the cost in bureaucracy. I don't pretend I can prove it with economic models.

But, if you care about honesty in public debate, there is something you can do whatever position you hold on the EU. Donate to Full Fact's campaign to fact-check the debate. They don't care how you vote but they do care whether the debate is conducted honestly.

The government approach to cutting costs is the worst way to achieve lower costs

Governments love to distribute the pain of budget cuts evenly by slicing a percentage from every department's budget. This seems fair but is the worst way to achieve sustainably lower spending or to minimise the damage caused by the cuts.

The trouble with government is that it is run by politicians. And politicians usually care more about image than they do about substance when it comes to making decisions. So when public finances are squeezed they tend to focus on the fairness of their budget cuts rather than the effectiveness of their budget cuts and this sometimes leads to really dumb decisions.

To do a good job of cost cutting or improving efficiency you need to know where and how the money is being spent now. For example, if you run a factory and its costs are way higher than the competition, it helps to know in detail why that is the case. It could be you have a serious problem with overmanning; or it might be that the skill mix is wrong and you need higher quality, more productive people; or, it might be that you need more people because the machines they work with are unreliable and old; or perhaps you buy all the raw materials from low quality, expensive suppliers. If you don't know which problem you have, cutting the budget might make things worse not better. If your problem is obsolete machinery, for example, the best fix is to invest in new machinery (which involves spending more); short term cuts to the maintenance budget will, ultimately, lead to less reliability and higher costs.

If the first thing you cut is the accounting and analysis department (they don't produce anything, do they?) then you will not be able to diagnose why your costs are high and therefore tell which action is most important for the long term future of your factory. You will most probably make the wrong decisions.

This is a pretty good analogy for how governments cut public spending.

The ONS, for example, is responsible for gathering and analysing the statistics that tell us what is happening in our economy, but they haven't been doing a good enough job (see BBC story or this story in Public Finance ). But government has been cutting their budget and doing crazy things like moving their headquarters to Newport from London (which saved costs by ensuring that most of their experienced staff resigned to stay in London leaving the organisation with a huge experience deficit and a much reduced capacity to do its job).

The NHS as a whole has done relatively well compared to most other government departments with its total budget. But inside the NHS a similar pattern emerges.

NICE is responsible for evaluating the quality and cost effectiveness of drugs and procedures in the NHS. But it is facing significant budget cuts even though spending more might identify more opportunities to save costs and improve quality across the whole NHS. But we can't have more spent on analytics when there are squeezes on providers, can we? That would be unfair.

The new NHS Improvement is supposed to work alongside NHS providers to help them do a better and more efficient job of caring for patients. There is a strong case for doing more to identify good practice and help spread it across the system. But one of the barriers to achieving improvement is a serious lack of reliable data about how the money is spent now and, in many providers, how the staff are deployed. The Carter review of the opportunities for NHS savings was very clear on this (see this analysis). And it isn't as if the current archaic infrastructure of collecting data in the NHS is good enough to support improvement at any level (see this comment). But NHS Improvement is going to have to live with significant headcount cuts when it is finally officially established. Apparently it needs to send a signal to providers about the fairness of the NHS financial squeeze. And that is, apparently, more important than its ability to do a good job of supporting the NHS to improve.

And it isn't as if hospitals will be expanding their information departments in the current climate even if doing so might help them achieve improvement elsewhere. Cutting already inadequate information teams is easy as it doesn't affect patient care tomorrow (and if it is catastrophic for care in a couple of years, who is going to care as the Chief Executive will have moved on by then?)

If you don't understand how things work, arbitrary cost cutting will lead to long term damage

If your approach to cost cutting involves slicing the ends of everything that looks like a salami then you will also cut the ends off a lot of fingers.

In one of the very few good books on business strategy, Richard Rumelt argues that the first step in any effective strategy is a good diagnosis of the problem you are trying to solve (see this interesting analysis of how his thinking applies to the NHS). The current symptom we observe in the NHS is large financial deficits. But this isn't the problem any more than a fever is a problem for a patient with malaria. To understand what the actual problem in the NHS is we need to know where and how the money flows. If we don't understand what is happening to create deficits then we can't develop a coherent plan to create an NHS that can sustainably treat patients without lapsing into periodic financial catastrophe. We can treat a fever with an ice bath, but if we don't understand what caused it (was it malaria or was it viral pneumonia?) the patient's recovery will be brief and the fever will soon return.

I don't want to try and diagnose the underlying problems of the NHS in a short blog article; my point is merely that a salami slicing approach to budget cuts damages the system's ability to do diagnosis. This is especially true when the cuts affect the flow of information or the bodies with the expertise to analyse problems and develop improvements. The NHS has tended to grossly undervalue good, timely operational information and is severely short of the analytical capacity to make sense of that information (for examples see this on A&E data and this on the information infrastructure).

The Carter report on hospital productivity argued (see my analysis) that the biggest barrier to improvement was a lack of good information about operational performance. But the we-must-share-the-pain-equally, salami-slicing model of addressing the current deficits is cutting the capacity to collect and analyse that information even though that capacity was inadequate to start with.

We won't build a sustainable NHS without a good diagnosis of the underlying problems. That requires better information about what is happening inside hospitals. A good strategy for achieving a sustainable NHS would, therefore, focus on getting better information at the start so subsequent decisions could address the most important underlying problems.

What we are likely to get is more salami-slicing which damages our ability to understand the problem and exacerbates the failure to focus on which problems matter most. In fact NHS strategy is still in an era equivalent to medicine when bleeding patients with leeches was still thought to be a good universal cure. As in medicine, repeated application of the leeches will fail to cure the patient. Whatever the apparent short term gain, in the long term the patient will be sicker.

The problem generalises across all of government. Salami-slicing cuts to everyone's budget damages the government's very ability to know what is actually happening and therefore its ability to make good decisions about what really ought to be done.

It looks like the patient with the fever is stuck in the ICU until someone comes up with a better diagnosis.