The central challenge for healthcare: avoiding the avoidable

One of the key questions facing health services today can be summed up in three words: avoiding the avoidable.

This is an international problem. I’ve been working around the healthcare sector, mostly in England but also in France, Germany, Switzerland and Spain since the mid-1980s. Much of the time, I was working for American companies through which I also gained some knowledge of the US system.

The graph below shows how expenditure on healthcare has grown, decade by decade, in those countries over that period (based on figures compiled by the OECD). The rate of growth is not the same in all of them, and the starting and end points are different, but they all show the same inexorable climb. Nor, despite all the moves to reduce expenditure – the NHS in England is on a five-year plan to save £20bn – there’s no sign of the upward pressure ending any time soon.

Notice that back in the 1980s, the US was spending a little under 10% of its income on healthcare. Today, it’s one dollar in every six.

The inexorable upward pressure on healthcare expenditure
(source OECD)

The reasons for that pressure are not hard to find. It is becoming possible to treat a constantly increasing range of diseases, which is wonderful for the patient seeking relief. Financially, however, it is far more problematic, as all those treatments have to be paid for, and many are expensive.

At the same time, life expectancy continues to grow in the advanced economies, so far higher numbers are reaching the period in life when there’s a much-increased chance of acquiring long-term diseases such as arthritis, heart disease, diabetes and so on. Many of these conditions are also influenced by lifestyle: this is the case of obesity and heart disease, for instance.

More to the point, many of our ageing population find themselves suffering from more than one long-term condition (LTCs as they’re commonly called, in a field that just loves three-letter abbreviations or LTAs). Take obesity: as well as being a dangerous LTC itself, it can also lead to coronary heart disease (CHD, of course) and type 2 diabetes. The patient may have arthritis too and perhaps the beginnings of dementia. Suddenly, as well as being a human enjoying a longer life than used to be possible in previous centuries, he or she is also a mass of conditions each of which needs treating.

Thereby hangs another tale. Western medicine has grown up in specialties. But how do you handle a patient with a heart, endocrine and rheumatic condition simultaneously? We need new models of care, based on multi-disciplinary cooperation.

When talking about new models of care, however, one that has to be right at the top of the list is a model that focuses on keeping patients out of hospital where possible. Hospital care is the most expensive form. Where in the past it was perhaps not a major issue for a patient to show up at a hospital because they were worried and no GP was available, today with the system creaking under the strain, that’s really no longer viable.

Here’s how it works. In England, to visit a General Practitioner incurs a cost of roughly £80. If that patient went to an Emergency Department instead, the cost would be nearer £150. Now imagine the situation where in that Emergency Department, the patient, an old man running a high temperature and coughing uninterruptedly, is being seen by a relatively junior doctor on call late at night. Will he have the courage to issue some medications and tell the patient to go home and see his GP in the morning? Or will he admit him, just in case?

If he does admit him and the patient ends up staying over two nights, we’re talking about a cost in the region of £1500.

That’s what I mean about avoiding the avoidable. If the patient doesn’t really need the hospital care, you’re talking about saving 80-90% of the cost of the treatment if you can avoid the admission.

In the next in this series, I’m going to talk about some of the ways in which that can be achieved.

Clustering around in Mental Health

A great conference last week on the introduction of new payment arrangements for NHS Mental Healthcare! And it was all the more interesting because, far from being exclusively limited to Mental Health, much of what it had to say was relevant to significant areas of Acute care too.

For anyone who has not been following these developments closely, from 1 April it becomes mandatory in Mental Health to start using so-called ‘Clusters’ to classify cases, and include them within the process of contracting for care. There is general recognition that we are still a long way from being able to base budgets exclusively on Clusters, let alone set a national tariff for them: there is simply too much variation among cases within Clusters, or more precisely too much unexplained variation between them, to allow them to be used reliably in this way.

On the other hand, it is a breakthrough that large numbers of Mental Health Trusts are at least applying the Clusters to their records. One of the conference speakers mentioned that his Trust was achieving nearly 98% coverage, though he was candid enough to admit that he had his own doubts about the figure: in relation to just what should he be measuring the percentage? There is a grey area of definition between what should be included in Clustering and what should be excluded, so getting a precise percentage is difficult.

Even so, it’s clear that a high proportion of cases are now being Clustered, and that’s a major advance. It means that we can at last begin to see just how well Clustering is working, to assess the level of variability and, ideally, to work out what is acceptable variation and what is not. In particular, we need to find out where variation is simply down to the way Clustering is being applied. 

Because Clustering isn’t like HRG or American DRG grouping, and not just because there are only 20 or so Clusters as opposed to nearly 1200 HRGs. The biggest difference is that HRGs can be derived by an automated system based on data already recorded against patient records — diagnoses, procedures, lengths of stay, etc. — whereas Clusters are based on a professional’s assessment of the case.

This makes the extent of variation in treatment within a single Cluster unsurprisingly high. For example, another speaker reported on the results of a survey of over fifty Mental Health Trusts. Within Cluster 11 alone, the cost per day of treatment varies from Trust to Trust from a few pounds up to nearly £550:

Cost per day variation across Trusts — within a single Cluster

From the graph, it look as though between about Trust 11 and Trust 35, daily costs seem to be in the £20-£30 range, suggesting a reasonable level of consistency. Outside that range, however, variability is so high as to undermine the system: at the ends of the distribution, it is of the order of 100:1 or more.

There are at least three possible explanations of this variation:

1. Wrong Clusters: the clinician’s assessment is incorrect. In this context, a speaker at the conference mentioned the ‘Richmond-Lambeth’ syndrome: mental health problems tend to be far more pronounced in the under-privileged London borough of Lambeth than in relatively well-heeled Richmond; will that lead psychiatrists in Richmond to include in the more severe Clusters service users who may be more seriously ill than many others of their case mix, but far less than those included in similar Clusters in Lambeth? Even without such general trends, it is of course possible that individual cases can slip into an inappropriate Cluster.


2. Poorly-defined Clusters: some of the Clusters may be too broadly defined and therefore cover cases that are not entirely homogeneous, but include service users whose condition differs too much in severity for their treatment to be comparable.


3. There are genuine variations in clinical practice within a Cluster of reasonably homogeneous service users.

In the longer run, it is only the third type that is of interest: we want to identify, analyse and take action over variations in practice that can’t be justified by any specific characteristic of the service user.

On the other hand, in the short term it is certainly the first two that are going to attract most of our attention. Until that kind of problem can be ruled out as a possible cause of the differences we see between cases and between providers, we can’t really use the data for analysing the third type of variation. And, above all, we certainly can’t use the Clusters as a reliable guide to cost.

So the first stage of the exercise is going to be looking into what exactly lies behind each of the Clusters and what is causing the observed differences. As we do that, we shall start to build a picture of what we would normally expect to see in the way of a mix of treatment types within a Cluster: between so many and so many outpatient appointments or community visits, between so many and so many admissions or days of inpatient care.

In other words, we shall start to build definitions of the packages of care that are associated with Clusters. When we have those, we shall be able to identify treatment profiles that differ significantly from the norm.

Now there’s nothing exclusive to Mental Health in this approach to defining bundles or packages of care. We can build them for Mental Health care clusters, but why not for somatic diseases too? Why not for congestive heart failure, diabetes or even different types of cancer? Indeed, for any long term condition?

Because this kind of work is breaking down another of the deeply-established barriers in healthcare, between somatic and psychiatric care. Anything that requires treatment over a long period, in different care settings, perhaps by different providers, lends itself to this kind of package of care approach. It’s by no means limited to Mental Health only.

Nor is it limited to British healthcare only: much of the thinking behind the launching of Accountable Care Organisations in the United States is also concerned with having a single body responsible for care in a variety of settings by a range of different institutions. There is nothing surprising about this convergence: it is a piece of increasingly accepted wisdom that anything up to 40% of what we previously thought of as acute care is evolving into chronic condition management (with Cancer as perhaps the most striking example). Inevitably, that is driving us all, everywhere, to undertake this kind of work.

So again it was interesting to discover that many of the people attending the Mental Health conference also had responsibility for helping to manage Long Term Conditions. The need to think in terms of packages of care spans traditionally distinct fields.

Interesting times ahead. And, not for the first time, I was struck by how Mental Health is showing the way.

There are some interesting challenges ahead. Not least is what lay behind what several speakers pointed out: they didn’t like talking about Payment by Results. They felt that the initials ‘PbR’ should be viewed as standing for ‘Payment by Recovery.’

A refreshing view, and one that fits well with the package of care approach. After all, how do you know a package is complete except when the patient has recovered? 


But can you imagine the impact on healthcare if remuneration started to be based on outcome?

Patient confidentiality: opening a gateway

As the potential to provide better and better healthcare keeps growing, with new techniques and drugs constantly arriving on the market, so does the pressure to control healthcare costs. 

Understandably. No-one has ever worked out the maximum a society can spend on healthcare. With the United States nudging towards 20% of GDP, it is reasonable to wonder at what point will its expenditure be so high that it can no longer spend sensible amounts on other key areas, whether it’s education or even roads - or, and this is particularly sensitive in the US, defence.

But there's nothing simple about controlling healthcare expenditure. Not if we are also going to keep taking advantage of the latest advances and maintaining the best quality of care possible.

From the point of view of the information professional, both imperatives offer opportunities: any drive in either area depends on having access to reliable information. 

Take an obvious way of cutting out waste and improving care: eliminating unnecessary treatment. 

Why is a patient who has been put on a course of drugs by a GP on Tuesday turning up at Accident and Emergency in the local hospital on Thursday? Is he just expressing his lack of confidence in the GP? Or was the drug regime taking so long to improve his condition that he felt the need for hospital care?

Either way, what has happened has been a waste of resource.

What makes the information angle interesting, however, is the question of how the GP finds out in the first place. Somehow we need to alert her that the patient she saw one day turned up two days later at the hospital. That means marrying the record in the Primary Care system with another in the Hospital’s A&E system.

Which means sharing information by which the patient can be identified.

Healthcare information professionals have wanted to do just that for many years. Unfortunately, such information sharing conflicts with the justifiable anxiety of patients over different bodies swapping identifiable information about them. And the process of linking the information may even involve a non-NHS organisation, such as the company I happen to work for.

The concern is understandable because there have been such scandalous breaches of confidentiality of patient information. Lost USB keys, disks going astray, stolen laptops with unencrypted data. As a simple citizen and potential patient, I’m not happy that data about me may be floating around in this uncontrolled way.

So a series of legislative initiatives have made it increasingly difficult to share healthcare information. The Data Protection Act, the European Convention on Human Rights, the Statistics and Registration Services Act, even the confidentiality provisions of Common Law, mean a veritable thicket of legal restrictions makes it practically impossible in Britain to construct a service which would tell the GP about the possibly unnecessary double treatment of her patient.

All this is symptomatic of what happens when there’s an over-reaction to a scandal (or several scandals). And it has led to a conflict between principles: on the one hand, the entirely commendable protection of patient confidentiality, on the other, the legitimate use of data to inform necessary actions in healthcare.

Now it is when such conflicts arise that political and moral debates become the most interesting. To take another topical example, which has precedence, freedom of speech or the right to privacy? The trick is to get the balance right: protection of necessary privacy without excluding legitimate public information.

So I was fascinated to attend a recent meeting hosted by the NHS Information Centre and attended by representatives of the Office of National Statistics (ONS), who hold information about deaths which many of us have wanted to tie up with healthcare data for years. 

It was at this meeting that I heard for the first time of a ‘Gateway’ through the confidentiality regulations.

How do we you access to that gateway? You have to complete applications, naturally - where would we be without bureaucracy? By the way, that’s not a reflection on the NHS, rather on the whole of humanity. 

The application has to make it absolutely clear that you are going to use the patient identifiable data you want to handle for a specific purpose; that you will take only as much as you need for that purpose; and you will keep only for as long as strictly necessary for that purpose.

If your purpose is deemed to be legitimate, then your application will be approved and the gateway will open to you. 

Now this strikes me as immensely sensible. What can one object to? As a citizen, I don’t want my personal information abused. I don’t want it held any longer than it needs to be. And I don’t want it collected for one objective and used for another.

As an information professional, I want to be able to get hold of patient identifiable data, but only to provide a specific service. How can I object to an outside body ruling on whether my purpose is reasonable? In any case, if I’m setting out to provide information to help maintain quality and control costs, that’s a double objective that we all want to achieve - as I said at the beginning -  so my application is likely to be approved.

And if I’ve been given access to the data for the purpose stated, by what right can I expct to use it for any other? Or to retain it any longer than necessary?

Strikes me that we’ve found just what I said we needed when principles conflict: a point of balance.

A word of warning though: getting agreement on that balance point isn’t always easy. At the meeting I attended, the ONS representatives weren’t at all happy about releasing their mortality data. It seems that though the dead can’t be libelled - you can say what you like about them, they have no right to protect their reputation - they do have a right to confidentiality beyond the grave. And the ONS wasn’t convinced that the NHS was doing enough to protect identifiable data. They weren’t that keen on the gateway.

But I think we’ll get there. And my GP will get the information she needs. And maybe we’ll be able to do what's necessary to hold healthcare expenditure at a level which won't impact on our capacity to repair our roads and educate our kids. 

While still providing adequate levels of care.

Frailty, thy name is more and more of us

Daniel Dreuil, a geriatrician, and Dominique Boury, a Medical Ethics specialist, both from Lille in Northern France, gave a paper to the Fourth International Congress on Ethics in Strasbourg in March 2011. They started by talking of the case of Mrs B., an 87-year old recently admitted to an Accident and Emergency department:

She had spent the night on the bathroom floor following a fall. She had suffered three other falls in the previous six months, two of which had led to hospital admissions. Her poor eyesight and arthritis meant that she was at risk of falling again. On arrival at A&E, Mrs B was suffering from confusion: she no longer knew where she was , was unaware of the date or time, dozed during the day and tossed and turned at night, was suffering from anxiety and cried out when she was not depressed, complaining that money was being stolen from her and that hospital staff were trying to harm her. Though she had been widowed two years earlier, she claimed that her husband was going to fetch her and take her away. Her clinical, lab and radiological examination suggested early stage dehydration.

She was referred to Care of the Elderly where the dehydration was treated; the confusion increased and lasted a week, before receding rapidly. She had fortunately avoided a fracture on this occasion, but had to be referred for rehabilitation since the fall had affected her ability to walk: she was displaying symptoms of ‘post-fall syndrome’, specifically retropulsion when walking – she would lean backwards – which threw her off balance and made her very apprehensive as soon as she stood up. It would take her several weeks of rehabilitation to become a little more sure of herself.

During her six-week stay, a memory assessment revealed incipient Alzheimer’s disease. On her return home, a new treatment regime was put in place for Mrs B., an intensified programme of domiciliary care and home-based rehabilitation to master walking again. She is being monitored by a home care network coordinated by her GP and is due to see a neurologist. In retrospect, Mrs B talks of her fall and her admission to hospital as a traumatic event, as a ‘collapse’.

Dreuil and Boury gave this case study as a striking example of a condition known as frailty. It was an eye-opener to me, as I hadn't previously come across it, though it has been known about for decades and has been attracting increasing attention in recent years. Intermediate between good health and incapacity, it is a state in which a person is coping reasonably well with life but can be plunged through a relatively insignificant event into a state, to use Mrs B.’s own word, of collapse, characterised by multiple simultaneous pathologies: Mrs B had multiple physical conditions, some related to her fall, some to other diseases such as arthritis or the incipient Alzheimer’s, but was also suffering from mental difficulties, specifically confusion and depression.

The event that had precipitated her difficulties was a fall, from her own height. In a completely healthy individual, that is unlikely to have any serious consequences – bruising or simply a little pain, at worst a sprain – perhaps the most serious consequence would be the hurt pride caused by the laughter and mockery or our so-called loved ones. But in a frail individual, the effect can be devastating.

From being able to cope, Mrs B. was plunged into a condition where she could no longer manage her life at home. As well as healthcare, she needed social services far more intensively: domiciliary visits for now, but with the prospect of residential care clearly on the horizon and increasingly imminent.

This is a French example, but precisely the same type of case is common, and indeed increasingly common, in Britain and other nations. And certainly frailty is a condition that is being met throughout the wealthier nations more and more frequently – and for the very best of reasons: although it can affect people of any age it is much more likely to afflict the old, as is the case of Mrs B., and more and more of us are living to increasing old age. That great success of nutrition, of social care and above all of healthcare, is creating new healthcare challenges – and frailty is one of the most significant.

Now let us look back at my previous post in this series. It compared two women of 61 and 62, both of whom had suffered strokes, but one of whom had been discharged from hospital very quickly. I focused on the other, and by looking at her earlier record of treatment in both healthcare and social care, saw that she was suffering from multiple conditions that had caused her to be treated repeatedly in hospital and to require significant levels of domiciliary and residential care.

Doesn’t that sound similar to Mrs B.’s case? Though the conditions were different and the 62-year old stroke patient was far younger, don’t we again see many of the symptoms of frailty? Ill in multiple ways, undergoing repeated treatment of many different kinds. This feels like a woman who had been in a frail condition and has now been precipitated into a state of collapse.

Understanding her case, as we saw, meant bringing information together from many different sources: admitted patient care, outpatient attendances, inpatient stays, community treatment in or out of hospital, domiciliary care provided by social services or residential care.

The concept of frailty has been a bit of an eye-opener to me. But the message I take it from it is one that I’ve stressed again and again in these occasional posts: we need to monitor patients over the long term and we need to do it across care settings, so that we understand what is happening to the patient in the many different areas of care he or she encounters.

A frail person suffering a collapse will need care provided by many different specialties and professions. If the patient is to get the most of out of it, and society is to deliver care in the most cost-effective way, we need to understand what they are all doing and to ensure that their efforts are coordinated as fully as possible.

Frailty: in information terms it just means that it is more urgent than ever to break down data silos.

A tale of two stroke patients

It’s been a while since I last put up a blog here. My only excuse is that I’ve been so heavily involved in doing healthcare information that I’ve not had enough time to talk about it.

In particular I’ve been working on what remains as much as ever my hobby horse, pathways. So I thought it might be interesting to give an example of one. Or rather two. 

Two women, one aged 61 and the other 62, both had emergency hospital admissions for strokes. The first woman’s hospital stay only lasted a night, which meant it incurred just a short stay emergency charge of about £1400, but the second stayed four nights and cost £4400.


So the obvious issue is – why was there such difference between them?

The first place to look is among the secondary diagnoses recorded for both women.

The short-stay case, primary and secondary diagnoses

Code	Diagnosis
I639	Cerebral infarction, unspecified
I251	Atherosclerotic heart disease
I248	Other forms of acute ischaemic heart disease
G409	Epilepsy, unspecified
Z867	Personal history of diseases of the circulatory system

Diagnoses for the four-day case

Code	Diagnosis
I639	Cerebral infarction, unspecified
I678	Other specified cerebrovascular diseases
F329	Depressive episode, unspecified
Z870	Personal history of diseases of the respiratory system

To a non-clinician like me at least, nothing springs out from this to explain the differences between the two cases. And that’s the problem with focusing exclusively on a single event in this way, in this case on the hospital spell: it gives much too limited a view of the patients’ real experience.

The picture changes fundamentally if we take a longer view. We don’t have information about the GP care of these two women, but we do know about all their treatment in acute hospitals, in community hospitals, in community health services, even in social care. So let’s take a look at what happened to them both in the period leading up to their strokes.

For the patient who was in for a day after the stroke, the only care we know about over the previous eighteen months were two outpatient attendances in Cardiology. It seems that she must have shown symptoms of a developing heart problem, but nothing serious enough to justify further hospital treatment. Ten months after the second outpatient clinic, she attended A&E followed her stroke and was admitted for emergency treatment.

With the other patient, on the other hand, the picture could hardly be more different. Below is the pathway of just six months before her stroke (drawn to the scale of the lengths of each event):

The poor woman has been through a real catalogue of misfortunes:

1. She was admitted for an acute myocardial infarction five months before the stroke
2. A month later she was in for a pulmonary embolism
3. She had a great deal of care in the community, including physio, occupational health as well as district nursing
4. She was taken into residential care
5. Despite the care she was receiving, she had four more emergency admissions for respiratory or suspected cardiac symptoms over a period of about a month some three months before the stroke.
6. She then had her stroke

All we need is to move away from our focus on a single acute event and look instead at the whole pathway of her care, to understand that we are talking about two profoundly different cases. This woman is simply far more ill, in a state similar to what is referred to as ‘frailty’ in older patients: any problem, even a small one, can lead to a string of others, some far more serious.

So there’s absolutely nothing surprising about the fact that she needed a longer stay in hospital after the stroke. In fact, it’s now clear that while the stroke was a major event in the history of the other woman, for this one it was just the latest in a series of severe problems. If we wanted to take a look at ways of making her care more effective, or more cost-effective, it might not even be with the stroke event that we’d start (after all, she was in hospital for 25 days after the myocardial infarction).

All it takes to get this much richer and, I’m sure you’ll agree, much more valuable view of the patient’s healthcare is to take a pathway view. And all that needs is to get hold of the data and string it together...

Counting the deaths that count

According to the American columnist H L Mencken, ‘there is always a well-known solution to every human problem – neat, plausible, and wrong.’

For example, in assessing efficiency of healthcare, nothing is simpler or more plausible than to measure length of stay. So we’ve had countless studies comparing hospitals on the basis of ‘average’ (i.e. mean) length of stay. A particular hospital may have a mean value of, say, 4.8 against 4.5 for a peer group. That difference becomes the basis for the conclusion that if there are 80,000 inpatient stays, the hospital could save 24,000 days and close some eye-watering number of beds. All this is advanced without a thought as to whether a mean value is even appropriate for a measure like length of stay, which is usually distributed with a very long tail (small numbers of patients with massively long stays, usually due to the complexity of their condition), or whether a difference of 0.3 is even significant.

In case anyone thinks this is a wild exaggeration, we’ve seen a hospital rebuilding programme that took this kind of analysis as the basis for calculating its required number of beds, and paid the price when it discovered that the new building had far too few. 

In addition, we need to ask whether this kind length of stay analysis even compares like with like. We’ve seen comparisons with peer groups which confidently predict efficiency savings, only to find on closer examination that the hospital treats a sub-group of complex patients that the peer group doesn't. Careless analysis can lead to bad and costly conclusions.

If length of stay taken in isoloation is the simple, plausible and wrong measure of efficiency, the equivalent in the field of care quality is mortality. Now, there’s no denying that the patient’s death is not a desirable outcome. Keeping mortality down is an obvious step in keeping quality up. There are however two problems with the measure.

The first is that there are huge areas of hospital care in which mortality is simply too low to be useful as a blunt comparative measure. Mortality in obstetrics has now fallen to such a level, for example, that it would be perfectly possible to find just two deaths in an entire year in one hospital, and one in another. To conclude that the first delivers care that is 100% poorer than the second would be a conclusion that can only really be described as rash. Or, as Mencken would no doubt have told us, plain wrong.

That is not to say that these individual deaths shouldn’t be monitored and investigated: rare events such as a maternal mortality or, say, death following a straightforward elective procedure should be thoroughly investigated. It's simply that they cannot in isolation form the basis of an overall assessment of one hospital's care quality compared to another.

Again, don’t think that this is a wild exaggeration – we know of reports suggesting poor performance by a clinician, based on comparisons as meaningless as these. And we’ve argued before that the use of crude mortality figures in analysing Mid Staffs hospital distorted the debate. We don't of course mean that there were no quality problems at Mid Staffs: there were and it was appropriate to address them.

Interestingly these problems were highlighted by patients and relatives some time before various organisations began to raise any issues. Unfortunately, once information analysis began to appear, it focused on mortality data and drew conclusions from the figures which they couldn't properly support. Many of the problems at mid-Staffs were on wider quality issues that the patients identified but weren't measured or when highlighted did not appear to be investigated. 

The temptation to make mortality a focus is understandable. It's a measure that's easy to obtain because hospitals routinely record their deaths. So it's natural to want to tot them up and convince ourselves that we then have a valid measure of comparison.

Well, do we? Here we come up to the second objection to mortality as an idicator. Let's start by taking another look at length of stay. If a hospital keeps patient stays short, might that not reflect a lot of early discharges, including perhaps a number of patients who go home and die there, with the result that they’re not included in the hospital’s death figures?

And what about transfers? If one hospital is transferring a high proportion of particularly ill patients to a tertiary referral centre, won’t its own mortality figures be artificially reduced while the receiving institution’s are inflated?

That’s why if you’re going to use mortality as a measure of quality, you need firstly to ensure that you’re applying it to specialties, conditions or procedures where it makes sense, and secondly that you’re measuring not just in-hospital mortality but also mortality after discharge, choosing a period beyond discharge that is appropriate to the patient's condition.

Now HES data has been analysed with Office of National Statistics death records linked to them, on an annual basis, for some years now – since about 2002. This means that since then it has been possible to take a look at mortality following hospital treatment in a much more comprehensive and useful way. What’s surprising is how few NHS and commercial providers have taken advantage of this information.

It’s not as though there haven’t been innovative thinkers who’ve used this kind of data to produce interesting conclusions. For example, we have the National Clinical and Health Outcomes Base (NCHOD) studies on 30-day mortality following emergency admissions for stroke. This has all the characteristics you’d want: an area of care – emergency strokes – for which mortality is a useful indicator, and the right measure, taking in much more than deaths in hospital.

As it happens, this analysis itself needs to be taken further. Mortality, like length of stay, is only one measure and can still mislead when used in isolation. It really needs to be supplemented by looking at indicators concerning the quality of the care itself. Useful measures have been proposed and are being used by the Royal College of Physicians, including the type of facility that treats the patients, the provision of thrombolysis and the effective monitoring of patients in the first few days of admission, all factors which improve the outcome of care. This is a subject to which we might return in a future post.

Using the Royal College of Physician indicators would improve the analysis. However, at least the figures published by the Department of Health back in 2002/3 showed a way forward towards a more rational use of mortality figures themselves. It's disappointing that eight years on so few have followed that promising lead. 

Perhaps we can start to catch up before the decade is over.

Sometimes we get it right – but we don’t make it easy

At the cost of sounding like a health information geek, I have to say it’s been fascinating to get to know the Sixth National Adult Cardiac Surgical Database Report 2008.

What makes it so interesting is that it’s clearly designed to deliver to clinicians exactly the information they need to be able to compare their own performance in cardiac surgery against national benchmarks. The report, one of several produced by e-Dendrite Clinical Systems Ltd on behalf of different clinical associations and based on one of the many data registries they hold, shows indicators defined by clinicians and calculated to their specifications.

This is diametrically opposed to the approach adopted by the national programmes that have dominated health informatics in England over the last ten years, apparently about to vanish without trace and without mourners. They based themselves on datasets that used at one time to be called ‘minimum’. The word has been dropped but it still applies: these dataset represent the least amount of data that a hospital can sensibly be expected to collect without putting itself to any particular trouble. Essentially, this means an extract from a hospital PAS alone, with none of the work on linkage between different data sources that I’ve discussed before.

The indicators that can be produced from such minimal information are necessarily limited. Usually we can get little more than length of stay, readmissions and in-hospital mortality. We’ve already seen how misleading the latter can be when I talked about the lurid headlines generated over Mid Staffordshire Trust.

The contrast with the Cardiac Surgery Database could hardly be more striking.

Clinicians have defined what data they need, and have made sure that they see just that. If it’s not contained in an existing hospital system, they collect it specifically. The base data collection form shown in the report covers six pages. There are several other multi-page forms for specific procedures.

An automatic feed from Patient Administration System can provide some of the patient demographic data, but apparently in most contributing hospitals, the feed is minimal. All the other data has to be entered by hand. It must be massively labour-intensive, but clinicians ensure it’s carried out because they know the results are going to be useful to them.

An example of the kind of analysis they get is provided by the graph below, showing survival rates after combined aortic and mitral valve surgery, up to five years (or, more precisely, 1825 days) following the operation. What’s most striking about this indicator is that it requires just the kind of data linkage that we ought to be carrying out routinely, and this case with records from outside the hospitals: patient details are being linked to mortality figures from the Office of National Statistics, meaning that we’re looking at deaths long after discharge and not just the highly limited values for in-hospital deaths that were used in the press coverage about Mid Staffordshire.

Less obvious but at least as significant is the fact that the figures have been adjusted for risk – and not using some general rule for all patients, but on a series of risk factors relevant to cardiac surgery: smoking, history of diabetes, history of hypertension, history of renal disease, to mention just a few.

Looking at the list of data collected, it’s clear that more automatic support could be provided. For instance, it should be possible to provide information about previous cardiac interventions or investigations, at least for work carried out in the hospital. Obviously, this would depend on the hospital collecting the data correctly, but a failure in data collection is surely something to be fixed rather than an excuse for not providing the information needed.

It is unlikely that the hospital could provide the information if the intervention took place at another Trust, so cardiac surgery staff would still have to ask the question and might have to input some of the data themselves. Automatically providing whatever data is available would, however, still represent a significant saving of effort.

The converse would also be invaluable: if cardiac surgery staff are adding further data, surely it should be uploaded into the central hospital database or data warehouse? That would make it available for other local reporting. It seems wasteful to collect the data for one purpose and not make it available for others.

Of course, all this depends on linkage between data records. It’s becoming a recurring refrain in these posts: linkage is something that should be a key task for all Trust information departments. What we have here is another powerful reason why it needs to be done systematically.

And while we’re thinking about data linkages, let’s keep reminding ourselves that this Report uses links to ONS mortality data. Doing that for hospital records generally would provide far more useful morality indicators. So what’s stopping us doing it?