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?

Indicators are only useful if they’re useful indicators

We’ve seen that pulling healthcare data from disparate sources, linking it via the patient and building it into pathways of care are the essential first steps in providing useful information for healthcare management. They allow us to analyse what was done to treat a patient, how it was done and when it was done. Paradoxically, however, we have ignored the most fundamental question of all: why did we do it in the first place?

The goal of healthcare is to leave a patient in better health at the end than at the start of the process. What we really need is an idea of what the outcome of care has been.

The reason why we tend to sidestep this issue is that we have so few good indicators of outcome.

In this post we’re going to look at the difficulties of measuring outcome. In another we'll review the intelligent use that is being made of existing outcome measures, despite those difficulties, and at initiatives to collect new indicators.

The first thing to say about most existing indicators is that they are at best proxies for outcomes rather than direct measures of health gain. They're also usually negative, in that they represent things that one would want to avoid, such as mortality or readmissions.

Calculating them is also fraught with problems. Readmissions, for example, tend to be defined as an emergency admission within a certain time after a discharge from a previous stay. The obvious problem is that a patient who had a perfectly successful hernia repair, say, and then is admitted following a road traffic accident a week later will be counted as a readmission unless someone specifically excludes the case from the count.

At first sight, it might seem that we should be able to guard against counting this kind of false positive by insisting that the readmission should be to the same speciality as the original stay, or that it should have the same primary diagnosis. But if the second admission had been as a result of a wound infection, the specialty probably wouldn’t have been the same (it might have been General Surgery for the hernia repair and General Medicine for the treatment of the infection). The diagnoses would certainly have been different. However, this would certainly have been a genuine readmission, and excluding this kind of case would massively understate the total.

It’s hard to think of any satisfactory way of excluding false positives by some kind of automatic filter which wouldn’t exclude real readmissions.

Another serious objection to the use of readmission as an indicator is that in general hospitals don’t know about readmissions to another hospital. This will depress the readmission count, possibly by quite a substantial number.

Things are just as bad when it comes to mortality. Raw figures can be deeply misleading. The most obvious reason is that clinicians who handle the most difficult cases, precisely because of the quality of their work, may well have a higher mortality rate than others. Some years ago, I worked with a group of clinicians who had an apparently high death rate for balloon angioplasty. As soon as we adjusted for risk of chronic renal failure (by taking haematocrite values into account), it was clear they were performing well. It was because they were taking a high proportion of patients at serious risk of renal failure that the raw mortality figures were high.

This highlights a point about risk adjustment. Most comparative studies of mortality do adjust for risk, but usually based on age, sex and deprivation. This assumes that mortality is affected by those three factors in the same way everywhere, and there's no really good evidence that they really do. More important still, as the balloon angioplasty case shows, we really need to adjust for risk differently depending on the area of healthcare we’re analysing.

This is clear in Obstetrics, for instance. Thankfully, in the developed world at least, death in maternity services is rare these days, so mortality is no longer a useful indicator. On the other hand, the rate of episiotomies, caesarean or perineal tears are all relevant indicators. They need to be adjusted for the specific risk factors that are known to matter in Obstetrics, such as the height and weight of the mother, whether or not she smokes, and so on.

Mental Health is another area where mortality is not a helpful indicator. Equally, readmission has to be handled in a different way, since the concept of an emergency admission doesn't apply to Mental Health, and generally we would be interested in a longer gap between discharge and readmission than in Acute care.

Readmission rates and mortality are indicators that can highlight an underlying problem that deserves investigation. They have, however, to be handled with care and they are only really useful in a limited number of areas. If we want a more comprehensive view of outcome quality, we are going to have to come up with new measures, which is what we’ll consider when we look at this subject next.

Mental Health may show the way to Healthcare sanity

Although I’ve been active in healthcare information, mostly in Britain, for a quarter of a century, it’s only in the last couple of years that I’ve had much to do with Mental Health.

To my shame, I have to admit that I was surprised by what a pleasure it’s been. I was expecting something far less enjoyable. As in most countries, Mental Health has tended to be the poor cousin when it comes to healthcare information systems, if not the poor cousin of healthcare generally. In recent times, however, that has been changing rapidly.

Healthcare in Britain has been the subject of an apparently unending succession of organisational reforms, by governments of all hues. The latest wave is under way right now and promises to be particularly painful. In passing, let me say that it’s incomprehensible to me why governments think that by constantly reorganising the way healthcare’s managed, they are helping it be more efficient, more effective or less expensive.

One initiative a few years ago was the introduction of Foundation Trust status for hospitals. This gives them far greater autonomy, in the way they manage not just their work but also their finances. A large number of Mental Health hospitals applied for and were granted that status. One of the results was that they suddenly needed to become far better equipped in information systems to support decisions by their managers, including clinical managers.

This came on top of a brave and highly effective reform that they had themselves driven through over 25 years, as they moved away from being a strongly hospital-based service to delivering far more care in the community. This was particularly difficult to achieve as Mrs Thatcher’s government in the early eighties, at the start of the process, only saw care in the community as a way of saving money. At the time I lived in Hastings where a local Mental Health hospital had recently thrown out a lot of its former inmates. I remember groups of sad individuals moping around as they experienced the joys of being cared for in the community by being left on street corners.

Since then, however, there has been serious investment in Mental Health. Today, therefore, there is real care in the community, allowing people to live at home, with their families and friends and even jobs, rather than being shut up in hospitals out of sight. The possibility of inpatient care is available to those who really need it, either for extended periods or for a briefer time until they are well enough to return to the community. All this has added up to a dramatic improvement in the quality of Mental Healthcare over the time that I have been working with the NHS.

But the final aspect that completes this picture is the way that Mental Healthcare, instead of being little more than an also-ran in healthcare generally, is beginning to emerge as a model. This is because a lot of healthcare, of the kind that used to be provided by acute (short-stay) hospitals is becoming long-term chronic care. Diabetes, cancer, certain types of heart disease, obesity, infections like HIV among many other conditions, are not treated by spectacular actions at a specific point in time – say a massive and complex operation – but by careful management over long periods, with regular interventions by many different types of staff (doctors, nurses, therapists, counsellors) who have to work together as a team.

That is precisely the way that Mental Health functions. Treatments can take months, years or even an entire lifetime. They involve many different types of professionals working in different contexts – in a hospital, in an outpatient clinic, in a peripheral clinic or health centre, in the patient’s home – and having to coordinate their activity. Why, the concept of the multi-disciplinary team meeting, now increasingly widespread across different types of hospitals, is central to the way Mental Healthcare is delivered.

So suddenly it may be Mental Health that can teach the rest of Healthcare a thing or two.

All these things make the Mental Health sector vibrant and exciting. Long may it remain so – and survive the ravages of next wave of cuts.