NHS gooroo blog

Posts Tagged ‘referrals’

Well, does it? Here’s the evidence. You can see on the chart the steady reduction in outpatient and inpatient long-waits in the early noughties, followed by the dramatic cut in referral-to-treatment long-waits from 2007 to 2009.

GP referrals are used to indicate the level of demand, and they rise in two steps over the period. The first step doesn’t line up with the long-wait events, but the second step does coincide quite nicely with the achievement of 18 weeks.

Although it doesn’t look like much on the chart, the increase from mid-2007 is a 22 per cent step up in GP referrals over two years, which is not to be sneezed at (but hardly represents a spiralling out of control). Was it caused by the improvement in waiting times? Who knows; this is perhaps one for the health economists (who have various views on the subject).

Data sources: RTT data, inpatient and outpatient waits, GP referrals (recent), GP referrals (older) 

Over the weekend, a local paper in North Wales reported:

Shock rise in North Wales hip and knee surgery waiting times

THE number of patients waiting more than nine months for hip and knee replacements has rocketed by 11,700%.

In March last year just 16 people in North Wales had to wait more than 36 weeks for orthopaedic treatment such as joint replacement surgery. But that number saw an astonishing jump to 991 as of March this year. There has been a similar surge nationwide over the same period, from 38 to 4,466.

The shadow health minister, Clwyd West Tory AM Darren Millar, says the increase is “shocking” and “beggars belief”.

It’s a good thing they didn’t go back another couple of months for their percentages, or it would have been “over 40,000%” and Darren Millar might have stopped breathing altogether.

Digging beneath the figures at StatsWales confirms the story, and shows that The Daily Post picked out the right specialty from all the data: nearly all of Wales’s very-long-waiting patients are in Orthopaedics.

What is happening? Is the whole waiting list going up, or are the figures being skewed by local pressures in a few places? Bad news: this is happening right across Wales, with large increases in every Local Health Board.

The Welsh waiting list has grown by nearly 50 per cent in just 14 months, both for Orthopaedics (from 41,876 to 61,986) and for all specialties combined (from 231,947 to 345,554). So although Orthopaedics is worst for long-waiters, other specialties are heading for trouble too.

How do we explain this? In the news article, the Welsh Government pointed to increasing demand for orthopaedics. So perhaps GP referrals have been soaring? (If you’re digging up the figures yourself, unfortunately there seems to be a gremlin in the StatsWales database for GP outpatient referrals, but the obviously-wrong figures can be filled in from the statistics releases for the all-specialties figures.)

The upward trend across all specialties is 1,730 extra referrals a year, which doesn’t explain a waiting list that is shooting up at nearly 100,000 a year. Nor does the upward trend for orthopaedics, at 177 a year, explain the 17,000 a year rise in the waiting list. According to the referrals figures, demand doesn’t seem to explain the rise.

Have the hospitals slammed the brakes on treating patients then?

 

 

No sign of any dramatic slowdowns. In fact, the trend is slightly up.

So how can we explain the dramatic and continuing increase in Welsh waiting times? On the strength of these figures, sadly, we can’t. We need to dig even deeper.

But something needs to be done in Wales, and quickly, to turn this around. On the current spending plans, that “something” will need to be more imaginative than buying more activity. If anyone in Wales knows how to explain the increase, or better still how to reverse it, then comments are welcomed below.

 

(This post first appeared at HSJ blogs)

The “Liberative” Government’s health reforms started life with a light and permissive vision of GP commissioning. But now they are mired in confusion. What happened? In short, the new vision collided with the old. Last week the Health Select Committee sided firmly with the old vision, calling for Consortia to be renamed as Commissioning Authorities with formal governance structures and stakeholder representation.

New vision or old, everybody wants commissioning to be done well. But what does commissioning mean, and how should it change?

In the conventional vision, commissioning starts with the carefully-assessed healthcare needs of your local population. Then you compare this against the services actually provided. Inevitably, you find plenty of areas where needs are not being met at all, or where provision could be improved, or where there is over-provision and ineffectiveness. Starting with the biggest mismatches, you work with other stakeholders to design new and better pathways, and then you seek providers to deliver them (or work with existing providers to improve things).

Conventionally, you manage “your” providers through the annual contracting process. You estimate the amount of activity to be done, and then apply the tariff price (if there is one) or negotiate a price (if there isn’t). You manage quality using Key Performance Indicators (KPIs). If quality falls short or activity is at variance with the contract volumes, then you apply the remedies specified in the contract.

So far, so familiar. But this is all office-based activity. What are the chances of it making a real difference to patients?

You hope to reach a position where need and provision roughly match. But your experience shows that anything you measure in healthcare displays huge and unexplained variations; if you do find a match between need and provision, it is only by chance. And if you achieve a match today, then it probably won’t match tomorrow. So trying to match need with provision is going to be highly inexact at best.

0.5% of the population consumed over 20% of acute spend

Patients also show great variety even within a single pathway, and the sickest patients usually have multiple conditions. The harder you try to tailor a pathway to a particular condition, the more you find there are exceptions to the rule. Do these exceptions matter? Yes, because they are your most expensive patients. Data from one PCT shows that a mere 0.5 per cent of the catchment population (about 1,000 people) accounted for over 20 per cent of acute expenditure. So good judgement by GPs trumps good pathway specification when it comes to handling the sheer variety of patients presenting.

What about quality? You hope that quality and performance can be managed with KPIs and contractual sanctions. But “quality” is too rich a concept to be described in even the most comprehensive list of KPIs. The harder you try to specify everything, the more you lock yourself into the status quo. Moreover, anything that isn’t in the KPIs is simply driven out: the effort of monitoring everything else in the contract takes over. So quality needs to managed through dialogue, not specification, and the organised concerns of GPs are a better guide to quality than words in a contract.

Even activity – the crunchiest of numbers – is hard to control in the standard contract. You can try to limit elective activity if the waiting list isn’t rising. You can try to throttle cost by using activity caps and restrictions on “procedures of limited clinical effectiveness”. However, most contractual changes need to be implemented with the agreement of the provider (which may not be forthcoming), and in any case tactics such as banning procedures tend to be blunt and limited instruments that displace or defer the problem rather than solving it.

Finally, awarding contracts only to selected providers (especially if the contracts specify guaranteed volumes) involves saying “no” to other potential providers. The argument is that this helps to control expenditure, but again there is a lot of hoping going on: you hope that, by restricting the availability of providers, you will reduce demand. As Don Giovanni said in a different context:

Wer nur einer getreu ist,
Begeht ein Unrecht an den andern;

If I am faithful to one,
I am unfaithful to all the others;

Mozart

So the old vision of commissioning falls short on a number of counts. How could a new vision improve on it?

In commissioning, as with everything else in healthcare, real life happens in the consulting room not in the office. So better commissioning needs to happen in the consulting room too: if individual GPs manage their referrals and patient pathways well, then quality and budgets will follow. So the Consortium should focus its attention “downwards” to practices, rather than “upwards” to the Commissioning Board or “across” to providers.

That way, the life of a commissioner no longer revolves around the annual contracting round or the enforcement of KPIs. Instead, it revolves around helping GPs manage value, by:

• monitoring and escalating quality concerns raised by GPs;
• providing a “bank manager” function to GPs;
• peer-reviewing GP referral patterns and pooling risk;
• providing back-office, scheduling, and financial services to GPs;
• calling for new and better services, and helping prospective providers with their market research;
• ensuring that GPs are aware of the services and drugs available to them.

This moves decisively away from the adversarial contract-driven approach of the past. But one major step needs to be taken to make it work, a step that is not taken in the Health and Social Care Bill. Consortia need to be able to enforce budgetary limits at practice level, which is something that politicians (understandably) have tended to shy away from.

However, there is nothing to prevent GPs from opting to accept practice-level budgetary limits within their Consortium, or even formalising this rule in their Consortium’s constitution. After all, many GPs are pretty fed up with having their referrals interfered with, and their choice of providers restricted from on high, whenever PCTs are struggling to achieve their statutory duties because they cannot control demand.

So GPs and their Consortia are faced with a choice: genuine freedom to refer within a limited budget that they control; or a continuation of the imposed and inconsistent restrictions that face them now. What will they do? Perhaps the best outcome would be for different Consortia to make different choices. That would truly test the two visions of commissioning.

A new video shows the power of better patient scheduling to reduce waiting times. It shows waits falling from 20 weeks to 14, just by managing bookings better.

What made the difference?

• the right number of slots was reserved for urgent patients
• urgent patients waited as long as was clinically safe for them
• only genuinely urgent patients were declared urgent

The video tells the story by itself. So let’s look instead at something the video doesn’t cover…

Follow-up outpatients

Imagine this: You are booking referrals into outpatient slots. Every routine patient gets the next available slot. You’re busy, and all slots are fully booked up for the next few weeks. Then an urgent referral arrives, and the patient needs to be seen quickly. But all the early slots are full. The only way to squeeze them in is to cancel someone else. But who?

In the video we assumed that a routine first appointment would be cancelled – whoever is least inconvenienced by the delay. But many hospitals prefer to cancel follow-up patients instead (usually two or three, in fact, because follow-up appointments tend to be shorter than new appointments). This lets them make space for the urgent patient, without putting extra pressure on their 18-week waiting time target by delaying a first appointment.

So which is better? If you have to cancel patients, should you cancel one routine first appointment, or two (or three) follow-ups?

To answer this, we need to look at follow-ups more closely. A follow-up patient should (if referred appropriately) be someone who needs to be seen during a specified window of time: for instance 2-4 weeks, or 5-8 months, after their previous appointment.

The first thing to point out is that some patients are followed-up unnecessarily, so the first priority should be to ensure that patients are only followed-up for the right reasons. Clogging up the clinic with unnecessary follow-ups is a waste of clinicians’ time, and the patients’.

Now let’s look at this time window. If the patient could just as well be followed-up after 8 months as 5, then it makes sense to go for 8 months. Why? Because it reduces the number of follow-up appointments in any given year, releasing capacity for other work. Otherwise you end up seeing patients more often than necessary, which again wastes everybody’s time.

But there is a consequence. Once the patient reaches the 8 month mark, they really do need to be followed-up now. In effect, they are clinically urgent. So we can’t cancel them. We should cancel a routine first appointment instead, if we have to.

So the story told by the simulation video is the right one. We should cancel routine first appointments, not follow-ups, if we need to make space for urgent referrals. As the simulation shows, it is possible to do this and keep waiting times to a minimum.

We talk about the demand for healthcare all the time, but sometimes the talk is loose. If you hang around NHS offices for long enough you might hear statements like:

• Demand can’t be that high – the contract doesn’t provide that much.
• Last year we did 1,000, add 3% growth in demand, so that makes 1,030 next year.
• I’ve got hips coming out of my ears.

…and so on.

This kind of talk confuses demand and activity. More accurately, we might say things like this:

• Demand is likely to grow, but we don’t know exactly why or by how much.
• The waiting list is the accumulated mismatch between demand and activity.
• If we want to control waiting lists, we have to at least keep up with demand.
• Historic demand is activity plus the growth in the waiting list (adjusted for removals).

This is the sort of thing that is built into good planning models, and it allows us to make other useful distinctions, like:

• recurring activity is the activity required to keep up with demand; and
• non-recurring activity is everything else, and it brings down the waiting list.

So far so good. But behind all this, we are making a big assumption that won’t spring out of a planning model: that all our “demand” represents real work that we need to do. For instance:

• a patient is seen in outpatients by the wrong consultant and has to be rebooked with the right one; is the first appointment “demand”?
• a patient is referred for unnecessary follow-up by a junior who is not confident enough to discharge; is this follow-up “demand”?
• a patient is seen in outpatients, but the necessary test results aren’t ready so they have to be rebooked; is this “demand”?
• a one-stop clinic replaces an outpatient-diagnostic-outpatient sequence; does demand fall by two-thirds?

And on the inpatient side, are any of the following “demand”?

• a patient remains in an acute bed for a couple of days longer than necessary, waiting for a ward round and then drugs;
• a patient arrives for surgery, but is sent home and rebooked because they had toast for breakfast;
• a patient is admitted to avoid breaching the 4 hour A&E target, even though they don’t meet any AEP criteria.

These examples of “demand” are not caused by unmet healthcare needs in the population. Rather, they are artefacts of the system. How much of our total demand is created like this? 3 per cent? 10 per cent? 30 per cent? Do we have the faintest idea?

If it’s a sizeable proportion, and I suspect it probably is, then reducing it could substantially offset the (apparently) growing genuine demand for healthcare. Which would be handy at a time of near-frozen real-terms funding.

Garbage in, garbage out.

When you’re planning for next financial year, you don’t want your modelling to be shot through with data errors. But neither do you want to have to pick through your data, line by line, looking for errors and fixing them manually (and perhaps inconsistently).

So how can you detect and fix common data errors automatically? Naturally, it depends on what’s wrong, and the difficulties comes in several flavours.

Missing data

The data you get directly from your activity database is likely to be pretty complete (accuracy is a different matter), so that takes care of completeness for activity counts, actual lengths of stay, urgency rates (which are essential for calculating waiting times), and so on. You are more likely to find gaps in data that comes from elsewhere, such as:

• demand growth assumptions
• waiting time targets
• waiting list sizes
• removal rates
• bed occupancy and bed utilisation assumptions
• bed, theatre and clinic performance assumptions

The important thing is to be explicit about the assumptions you want to make when any of these data items are missing. In many cases zero will be an acceptable default when data is missing, but sometimes it won’t and it’s dangerous to assume.

For instance if you know the waiting list size at the start, but not the end, of your historical data period, then zero would not be a safe default because your model would then be based on a rapidly shrinking list size (and therefore a high level of historical non-recurring activity). So it would be more sensible to assume the waiting list had remained a constant size, and populate the missing end list by copying the start list. Or vice versa, if it’s the start list size that’s missing.

For demand growth (or waiting time targets) you may have a standard set of assumptions, such as 3% growth for non-electives (or 90% admitted within 8 weeks), in which case you just need to ensure that your standard assumption is used wherever it is needed (but without over-writing any exceptions).

For removals, zero is often a good-enough default because any systematic errors in the removal rate should be second-order in a well-constructed demand calculation.

Capacity performance figures are proportional to the capacity being calculated, and so it is important to get these numbers right. However it is common for very broad assumptions to be made without proper consideration. For instance, 85% bed occupancy is often assumed to be a suitable buffer against fluctuations, but for this figure to be arbitrarily raised to 95% when the calculations show that a bigger hospital would be needed! This is a big subject in its own right, but the broad message is that Trusts would benefit from closer attendance to capacity assumptions.

Unreliable data

Low-volume services

Even if the raw data in a low-volume service is accurate, it can still be misleading for modelling purposes because the data is “noisy”. For instance, in a service that is provided only occasionally by one consultant, demand might fluctuate wildly:

• 10 in 2007-08
• 5 in 2008-09
• 18 in 2009-10

You can see the danger. If you used only the years from 2008-09 to 2009-10 when calculating the trend, you might conclude that demand was growing at 2,600% per year and so future demand would be:

• 65 in 2010-11
• 233 in 2011-12
• 840 in 2012-13

Which would be ridiculous, but not necessarily easy to spot if you’re crunching dozens (or even hundreds) of services automatically in a giant spreadsheet.

Instead, you need to cap demand growth within sensible limits. It is also sensible to avoid conducting detailed waiting time modelling on very small services (again because of noisy data leading you astray), and instead assume simply that demand must be met.

Additions data

Additions data is used in waiting time calculations, but this data source is notoriously unreliable. The standard check for data quality (in the absence of suspensions and deferrals) is the reconciliation formula:

start list + additions = end list + activity + removals

So in most cases you can cross-check additions data against the other data, and if it lies outside a defined tolerance then you can cap it. So far, so simple.

However there is a complication when it comes to admitted patients. Daycases who stay overnight automatically become inpatients. So when you use the formula, you might find that it is out of balance because some of the daycase additions ended up as inpatient activity.

The solution is to consider daycases and inpatients together when detecting errors in the additions figures for a given specialty. If the reconciliation works out well in total, then the separate additions figures do not need adjusting.

Waiting list data

Sometimes there is a delay between receiving an elective referral (or making a decision to admit) and logging that patient onto the IT system as an addition to the outpatient (or inpatient/daycase) waiting list. So if you were to extract yesterday’s waiting list you would miss any patients that haven’t been keyed-in yet.

For planning purposes, this problem can usually be avoided just by using older data. When planning for next financial year, some months before it even starts, it makes little difference whether you rely on data up to the end of last month or the month before.

Gooroo Planner has comprehensive error detection and correction built-in, under the control of settings tables that are pre-set with defaults and editable by the user. This ensures that automated error handling is performed consistently and under user control.

So here’s the situation. Your clinics are completely booked up with routine referrals for the next six weeks. But now an urgent referral comes in; the patient has something potentially nasty and you need to see them within two or three weeks. How do you squeeze them in?

If urgent referrals are very rare events, then you might force this patient onto a clinic by over-running the session, or cancelling another patient to make space. But let’s say urgent referrals are a normal part of your practice – what then?

One way or another, you need to make some allowance for them; the question is how. When a clinic is “completely booked for the next six weeks”, does that really mean that every minute for every doctor in every session is booked? Or do you only book 3 hours of each session, in the knowledge that a bit of time will probably be needed to squeeze in an urgent referral or two? Or do you regard follow-up patients as cancellation-fodder whenever extra time is needed? Or do you book the clinic not the doctors, so that each doctor is under-utilised?

None of these options looks very palatable in cold print. Wasting doctor time is the worst of all sins, because it inevitably means less-productive consultants, less-experienced juniors, longer waiting lists, and longer waiting times. Regularly cancelling follow-ups is either clinically risky for the cancelled patients (if the follow-ups are necessary) or a sign that too many patients are being followed-up (if they aren’t); in any case those follow-ups will only need to be rebooked later on, so the problem is deferred not solved.

The best approach is surely to plan for all doctor time to be fully utilised, and then carve out enough time for follow-ups (who are often entirely predictable well into the future: “come back in six months” means the need for that follow-up slot is known six months in advance). The remaining capacity is then available for new patients; some should be booked up with routine referrals, but some should be reserved for urgent referrals. So the question is: how much?

It turns out that the worst number of urgent slots is somewhat too many. On the assumption that patients are being booked using the best booking rules (you can learn all about those using SimTrainer), the following chart shows how performance varies with the number of urgent slots. In this chart, demerit points are awarded for different kinds of undesirable event: delayed urgent patients, delayed routine patients, rebooking patients, and booking patients at very short notice.

A lower demerit score means a better performance, so in this example (where 40% of referrals are urgent), the best performance comes from having 43% of slots reserved for urgent referrals.

You can use the booking rules calculator on your Gooroo dashboard (the page you see just after logging in) to work out the right number of urgent slots for your clinic. You’ll see that it depends on more than the number of urgent patients: the amount of other disruption such as cancellations matters too, as does the choice of a fully-booked or partially-booked clinic.

Happily, other factors such as the number of patients waiting, how quickly urgent patients need to be seen, whether waiting lists are pooled between consultants or not, and the amount of random variation in the referrals rate, turn out to make no significant difference to the number of urgent slots required. Some of these do affect overall performance though, but we will have to leave that to a further post.

For full details of our research into reserving urgent slots, see Research White Paper 2.

The King’s Fund has just published a new report on referral management, and delivered a cold shower to the referral management centres that some PCTs have created to weed and redirect GP referrals. It concludes:

the greater the degree of intervention, the greater the likelihood that the referral management approach does not present value for money.

Or, as one triaging GP put it:

It gets back to individuals making decisions on other people’s decisions.

Not that everything is rosy in the world of GP referrals. When GPs were allowed to review their colleagues’ referral letters they were not shy about saying what they thought:

When we first started, some of the referrals were absolutely appalling, dreadful. Two lines, referrals of two lines, please see this patient with headaches, and we automatically rejected all of those…

and

I mean, I just couldn’t believe my eyes initially, the quality of referrals was just dire

Well, criticisms are always fun to read. But what did work? In the words of the King’s Fund:

A referral management strategy built around peer review and audit, supported by consultant feedback, with clear referral criteria and evidence-based guidelines is most likely to be both cost- and clinically-effective. …

Practice-based commissioning clusters and their successors, the GP commissioning consortia, are the obvious conduit and driver for peer review and audit.

In other words, don’t second-guess the referring GPs; but do work at a doctor-to-doctor level on improving their referral skills. This makes perfect sense. At the time of referral, nobody knows the patient’s condition better than the referring GP. If some GPs aren’t very good at referrals, then the problem is unlikely to be solved by inserting a layer of second-guessers (who have only the inadequate referral letters to base their decisions on). As the King’s Fund says:

any intervention to manage referrals cannot look at the referral in isolation but needs to understand the context in which it is being made

So full marks for the King’s Fund report, then? Very nearly. My slight disagreement is when they say:

any referral management strategy needs to include a robust means of managing the inherent risks at the point when clinical responsibility for a patient is handed over from one clinician to another (so-called clinical hand-offs)

I would argue that they accept the concept of the “clinical hand-off” too readily. Referrals should not be fire-and-forget, rather the GP should remain available as the patient’s advisor after the referral has been made. After all, patients must give their informed consent to every step of their treatment, and both the consultant and the GP have a role to play in informing them.