Posts Tagged ‘modelling’

The whole point of developing a plan for the coming financial year is to resolve, at the outset, the tensions that are pulling your organisation in opposite directions.

So on the one hand you have demand to keep up with, and 18-week waiting times to sustain. On the other hand you have limited money, capacity and staff to do it with. Somehow your plan needs to reconcile those opposing forces.

What if it doesn’t?

Then the problem is effectively handed on for operational managers to resolve. They end up in meetings where the first half is about laying on costly extra capacity to clear the backlog that’s building up, and the second half is about how they need to slow down activity because they’re “over-performing” and the money’s running out.

That agenda, of course, doesn’t solve anything, so the problems fester. In the autumn, following a series of difficult meetings about demand management, some extra money is somehow identified to patch over some of the gaps. But everyone knows that nothing fundamental has really changed.

If life without a proper plan is so unappealing, why do so many NHS organisations begin the financial year without one?

Let’s try this scenario for size: The planning process begins in good time, but it quickly gets complicated. A lot of people need to be involved: general managers, finance, contracting, information, and that’s just from the hospital side. Different people approach the task in different ways, so there is a mix of methods and not all of them are valid. New assumptions are constantly thrown in to try and close the gap, and the model gets ever more complex. A planning analyst gamely tries to hold it all together in a spreadsheet, but it’s massive and people tire of looking at subsequent versions of it. The detail becomes unwieldy and time is running out. Something high-level has to be hammered out at the last minute, just to make the money balance. The detail is then retrofitted pro-rata and the “plan” signed-off.

In short, inclusive bottom-up planning is overwhelmed by complexity, and a top-down settlement has to be imposed instead. If complexity is the enemy, how could the process be simplified and streamlined, so that the bottom-up process can succeed?

Here is how Gooroo Planner solves the problem:

Firstly, we recognise that much of the data going in is a matter of simple historical record (recent activity levels, for instance). These facts can be agreed early on, and there is no need to discuss them further.

Secondly, we’ve taken all those complex calculations and developed them into a single model, based on principles that are widely-accepted across the NHS, fair to all sides, and transparent. So precious negotiating time is not taken up with detailed discussions about method. The calculations cover the whole of the planning period, and also break the plan down week by week so you can meet your objectives continuously through the seasons, and keep your plan up-to-date with events.

Thirdly, all the performance, demand and activity assumptions are laid out clearly and openly for discussion. Ultimately the key to reaching a settlement lies in successfully negotiating these assumptions, so that resources can be released from some areas to relieve pressures in others. So we’ve made it easy to test different scenarios, either item-by-item or by throwing in whole tables of alternatives.

Finally, we provide collaboration tools to get away from those giant emailed spreadsheets. Managed online collaboration means that participants can all see (and where necessary work on) the same plan, in real time, with full audit trails of any changes.

If you’d like to work that way, either to revise your plans for this year or start getting ready for next winter, then get in touch and we will be happy to visit and show you more. Just email info@nhsgooroo.co.uk for a free on-site demo.

It was only a few years ago that software upgrades were (shall we say) ever so slightly painful. You had to get hold of an upgrade disk, and then go round installing it on every computer that you needed to run the software on.

How much effort is it nowadays? Wait for it… there. That was it. You don’t have to do anything. At all. You just let us put the upgrade on our server and then you get it automatically from any computer. That’s the beauty of being cloud-based: we just keep on upgrading Gooroo and you keep getting better and better software.

So in case you haven’t used them yet, here’s a quick round-up of the upgrades we’ve implemented in recent weeks:

Major upgrades

Profiling: Week-by-week profiling trajectories for your activity, beds, clinics, theatres and waiting times. They’re all interactive and editable on-screen, and designed to be easy for operational managers to use. You can run Profiling via a link from the Reports view. There’s a worked example here; and a ‘how to use’ here.

Editing: After running the main analysis, now you can edit and tweak the data and even keep an audit trail of any changes. Ideal for tracking negotiations with commissioners. You can run Editing via a link from the Reports view. Details are here.

Smaller improvements

You can now see the Dataset Settings and Calculation Settings that were used to create any Report; available as a link from the Reports view.

We’ve tidied up the Templates you use to create new datasets. The Template Wizard now has just two options: “Statistical data” replaces the old “Advanced” and (recognising that it’s the most-used template of all) this is now the default. In the Template Manager we’ve provided two “Getting Started” templates with the most common data items for first-time users. There’s more detail on all the data and results fields in this document (which you can also navigate to from our Publications page).

In the Dataset Manager, the datasets are now sorted automatically with the newest dataset first. So you don’t need to go hunting through the list to find the dataset you just created, or have to click the headers to get it sorted right.

Over in the patient scheduling side of things, you can now download your SimActive files and file them on your own computer, with all the meta-data (addition rates etc) preserved. This makes it easy to switch from one waiting times analysis to another; just upload the new file and it’s all there.

You can also see how your waiting lists would look if they were booked according to Gooroo’s scheduling rules. In SimActive, click Save and then OK, and then you can let the software take over the patient bookings and bring down waiting times. There’s a worked example here.

Bug fixes

Some more minor annoyances have now been fixed:

• You can now use spaces in file names.
• Copying a dataset now copies the weekly profiling data as well.
• Subtotalling a dataset also subtotals the weekly profiling data too.
• The back button works again throughout the Reports wizard.
• You get the correct Dataset Setting if you set a user-editable value and reselect the default.
• The option to display as a percentage is now copied correctly when copying a Report Style.

We hope you enjoy these upgrades. If you aren’t yet using Gooroo software and would like to learn more, then please contact us.

Gooroo Planner has always been good at bulk analysis: load up dozens or even hundreds of service lines, and it will rip through them in a matter of seconds and do all your planning for you. That means you can generate scenarios rapidly, across your whole hospital or health economy, covering all the different activity or performance scenarios you want to investigate.

But sometimes you want to dive into one particular service – usually a big one like medical emergencies or orthopaedic electives – and tinker. What if this was our waiting time target? What if our length of stay was that? If we did some extra outpatients, what would be the knock-on effect for inpatients? In a situation like this you want to be able to fiddle around with the numbers, try this change and that change, and just see what happens. Not so easy when the calculations are all run in batches, but dead easy with the new report Editing screen.

When you’re in the main Report view, there is now a new link at the top called “Editing”. Click it, and now you can tinker and mess with the data, a service at a time, and see instantly “what would happen if…”. Like the week-by-week profiling screen, it’s designed to be easy to use by operational managers and anybody else whose job isn’t necessarily heavy on spreadsheets and databases. Information and planning analysts can load up the main data, and perhaps run the main report too, and then let operational managers seek out that sweet spot between waiting times targets and available resources.

So how do you use Editing? From the main reports view, click the Editing link above the table. The first step is to select the service you want to edit, using the control that looks like this:

 

In this example, the services in your model have been described using three headers: hospital site, specialty and admission type; the models you use in real life may have fewer or more headers than this. In this example the service you want to pick is orthopaedic elective inpatients on the main hospital site. So use the left hand drop-down to select the main site, then click the Specialty button above it to change the drop-down to show specialties, and use the drop-down again to select orthopaedics, and finally click the Admission type button and then use the dropdown to select elective inpatients. When you’ve selected a unique service, the “Run Editing” button appears, so click that.

If this is your first time using Editing, you’ll need to choose the dataset fields (i.e. the data going in) and results fields (i.e. the results coming out) that you want to look at, using these two drop-downs:

You can select as many fields as you like from each list by ticking them. For instance your dataset drop-down might look something like this when you’re selecting the data you want to change:

 

When you’ve finished selecting dataset and results fields, click the “refresh” button (with the green arrows on it) to update the display. Then, depending on which items you’ve selected, you might see something that looks a bit like this:

Now you’re ready to start tinkering with the numbers. You can enter new values in the “New” boxes in the left-hand (Data) section and, when you click the “Calculate” button above the Results section, the new results values will appear under Results. You can also click any line in the Results section to see a waterfall chart showing the effect of successive changes on the result you chose.

It’s worth remembering that everything is calculated using this report’s existing Calculation Settings, including the activity scenario that determines whether future activity is going to carry on at the past rate, or match demand, or achieve targets, or achieve some other objective. So for instance, here we have changed the target waiting time from 9 weeks to 8 weeks, in a report where the activity scenario was set to achieve the waiting list targets; we’ve chosen the list size as the value to display on the waterfall chart:

You can see that changing the waiting time target has reduced the list size required to achieve that target (the red column shows that the list size has reduced). But what (you may be wondering) is the blank space on the chart labelled “Meeting of 20 Nov”? That is there to show what happens when you want to save a bundle of changes that you’ve made. If you click the “Save” button then the changes you have made are written back to the Report, and you can create an audit log describing the change. This audit log helps you keep track of successive changes if, for instance, you are tracking the negotiation between a commissioner and provider, and whatever you enter as the title of the audit log appears on the axis of the waterfall chart. You can revisit previous changes using the “Select a previous change” drop-down (see the top of the previous image); it is clear from this waterfall chart that, whatever was decided on 20 Nov, it did not affect the list size.

If you don’t want to save the changes you’ve made then just change the service selected, or use your web browser to navigate away from this screen. Changes are only written back to the report if you click Save (and the original dataset is never changed at all by the Editing screen). Similarly, your selection of displayed dataset and results fields is only saved when you click Save, so if you change your selection temporarily and don’t want it preserved for your next visit then just don’t click Save.

That’s pretty much it. One more thing… if you are editing a report that has knock-ons switched on (so that, for instance, increases in outpatient activity are reflected in increased demand for elective services) then changes to one service may affect the results for another service. Those knock-on effects won’t be immediately visible on the Editing screen, but a warning that this may happen will appear in the audit log, and you can always see the knock-on effects either back in the main reports view or by editing the affected service.

Previously I outlined why the ‘PTL’ approach to waiting times management is a labour-intensive way of achieving sub-optimal performance, and why it should be replaced with a rules-based approach to patient scheduling that optimises waiting times continuously without needing regular intervention from senior managers.

Here we will illustrate the point, taking a real NHS waiting list and (in a simulator) converting its management to the rules-based approach. We will see the 92nd centile waiting time fall from 14 weeks to 6 weeks,  even though the size of the waiting list has not reduced. We will see how the biggest improvements are seen in the first month and how gains are sustained indefinitely (so long as the waiting is not allowed to grow).

So let us start by introducing our real waiting list. Here it is, and it’s the new outpatient list for one consultant orthopaedic surgeon.

Let’s take a quick tour of this image. The vertical grey stripe shows the date of this analysis, which in this example is the week beginning Monday 20th August 2012. To the left we have the waiting list; each blob is one patient, and they are sorted according to their referral date (so the longest-waiting patient at the far left has been waiting 36 weeks). If a patient does not have an appointment date (like the longest-waiting patient) then they appear hollow. When they are given an appointment they turn solid, and a copy of the patient is placed on the appointments diary; the appointments diary goes off to the right and into the future.

There are 121 patients on this waiting list, and the 92nd centile waiting time (i.e. how long the 112th patient has waited) is 14 weeks. So that is our starting point.

How are this consultant’s outpatients being scheduled?

Looking at the appointments diary (the grid to the lower right), there are plenty of empty clinic slots in the current week. Then for the next two weeks all slots are fully-booked, and after that there are plenty of empty slots again. So it is not clear, looking at the appointments diary, what the rules are for offering slots to patients.

Looking at the waiting list (to the top left), we can see that a majority of short-waiting patients are unbooked, then nearly all the longer-waiting patients are booked, and the few very-long-waiters are mostly unbooked.

So it looks on the face of it as if this is loosely a partially-booked system, booked about 3-4 weeks in advance, with efforts being made to book patients in date order. However slots are not being fully utilised, many short-waiting patients are being booked ahead of longer-waiters, and the very longest-waiting patients are not booked. So there should be scope for improving the management of this consultant’s list if we used all the available clinic slots and booked strictly in date order.

To mimic the possibility that patients are genuinely choosing not to take up offers of early appointments, we will assume that some 10 per cent of patients rearrange their appointments at short notice and put themselves back in the queue. (It is of course possible that the very-longest patients, who are unbooked, are choosing to delay their appointments by several months, but the delays are so long that it is surely legitimate to ask whether they should have been referred in the first place.)

So let’s hand this waiting list over to the computer and let it do the booking. (If you want to do this kind of analysis on your own waiting lists, the actual waiting list was loaded up into Gooroo SimActive, and then after clicking Save you can hand it over to the rules-based booking simulator.)

What’s changed in this picture? We’ve filled all available routine appointment slots (the blue ones) up to 4 weeks ahead, by booking patients in strict date order. Also, more patients have been referred, including a couple of urgent patients (shown in red); those urgent patients have been booked into urgent slots (the red ones). So we can already see how things are going to get better: over the next few weeks all our longest-waiting patients are going to be seen (unless they cancel).

Fast-forward a few weeks, and things are looking very different:

Most of the long-waiters have been seen already, and there’s just one patient from May still waiting. We did have a couple of cancellations who were put back on the list (they are shown with a double letter, to show that they’ve been cancelled). When we hold this week’s clinic, we’ll clear most of the remaining long-waiters and things should look a lot better.

One week later:

This is the new normal shape for our waiting list, under rules-based partial booking up to 4 weeks in advance. If we chose to run a fully-booked service instead, offering patients a choice of slots in up to 3 different weeks (which is compatible with Choose and Book rules), waits would be a week or two longer but otherwise in a similar position.

This is sustainable, so long as the size of the waiting list does not grow. Here is the same list a year later:

We’ve achieved a dramatic reduction in waiting times by implementing some relatively simple rules for booking patients, continuously as they come in. No weekly PTL meeting, no senior managers fire-fighting, just good clerk-level administration.

If you have access to YouTube (i.e. probably not from an NHS computer) you can see this scenario running as a video here:

 

Also new in Gooroo SimActive: there is now a download button that downloads all the data including the meta-data about additions, removals, etc. So now you can build up a library of files for your subspecialties and consultants, without having to type in the meta-data every time you upload something.

You don’t have to be technically-minded to do week-by-week profiling in Gooroo Planner, because it’s straightforward with a visual click-and-edit interface. So now senior and operational managers can take control of their own winter and seasonal planning, as in this worked example.

Because profiling is built right into Gooroo Planner’s reports, it runs off all the same data and assumptions that you (or your information analysts) have already loaded in and run through the model. So when you’re viewing a report, just click the Profiling button above the report to reach the profiling screen. When you get there you will see something like this:

Use the controls on the left to select the service you want to profile, and the control on the right to select how capacity should be subtotalled.

Select the service first. In this example, the services in your model have been described using three headers: hospital site, specialty and admission type; the models you use in real life may have fewer or more headers than this. In this example the service you want to pick is orthopaedic elective inpatients on the main hospital site. So use the left hand drop-down to select the main site, then click the Specialty button above it to change the drop-down to show specialties, and use the drop-down again to select orthopaedics, and finally click the Admission type button and then use the dropdown to select elective inpatients.

When you’ve selected a unique service, the “Run Profiling” button appears. But don’t click it just yet, because first you should choose how you want subtotals to be calculated for beds, theatres and clinics. In this example you want to see totals across the same hospital site as the service we selected (which, in which example, is the main site). So in this example you would use the right hand drop-down to ensure that only Site is ticked. When you’ve done that, the controls look like this:

Under the left hand drop-down you can see the service you selected, and the right hand drop-down shows how subtotals are calculated. Now you’re ready to click the “Run Profiling” button, at which point Gooroo Planner will run profiling for all the services that are included in the subtotal, and display the full profiling screen which looks like this:

The controls you’ve just been using are at the top. Next there’s a big chart with green points on it, and you are going to click and edit this chart to change the profile. The values plotted in green are the weekly activity profile numbers that were loaded up into your original dataset. (If you just get a horizontal line here, then seasonal activity profile wasn’t loaded, so ask your information analyst to make sure the dataset includes the demand and activity profiles; see footnote for details.)

The lower big chart shows either activity, beds, theatres, clinics or waiting times, and you can switch between them using the View buttons under the thumbnails at the bottom. Whenever you change the profile using the top chart, the bottom chart and all the thumbnails are automatically updated, so the whole thing is interactive and immediate. (If any of the charts are blank, and shouldn’t be, then ask your information analyst to add the performance data needed to work them out; again, details are in the footnote.)

When you’re looking at beds, theatres or clinics in the bottom big chart (example below), the service you selected is in blue and plotted against the left hand axis, and the totals are in orange and plotted against the right hand axis. Having two y-axes can be a bit confusing at first, but you’ll get used to it; we had to do it this way, otherwise when the subtotals are big the blue line is really hard to see at the bottom.

When you’re looking at waiting times (example below), again the blue line is the service you selected (normally just one stage of treatment) and plotted against the left hand axis. But now the orange total line (plotted against the right hand axis) is not subtotalled like beds, theatres and clinics. Instead it is the end-to-end waiting time for all stages of treatment that include this service, so is normally directly comparable with your referral-to-treatment waiting times target. In this example, for instance, the total waiting time for each week is the sum of the outpatient waiting time, plus the larger of the inpatient and daycase waiting times, for orthopaedics on the main site.

The activity chart (see the whole profile screen shown earlier) doesn’t have any totals on it. Instead, the orange line shows the priority demand for this service (plotted on the same axis as activity) and it’s there as a warning that you shouldn’t reduce activity so low that priority patients would be delayed.

Now that you know what you’re looking at, get stuck in and start changing your activity profile. It’s easy to do: just click one of the green points on the top chart, and a control will pop up that looks like this:

You can drag the slider or type a number into the box: whatever you find easiest. Then click Recalculate. (If you change your mind, just close the control by clicking the X at the top right.) All the charts will be recalculated to take in the change you just made to the profile.

Remember that the green chart is just showing the relative weights used for activity in each week, and the total amount of activity being done in the period does not change. So if you reduce activity in one week, activity will go up slightly in all the other weeks to preserve the total; similarly the waiting times trajectory through the period will change, but the final waiting time achieved at the end of the period remains the same. So the scenario you ran to create your original report stays the same.

If you want to save the changes you have made to the activity profile, then click the Save Changes button which appears after you start moving the green points around. Only the weekly activity profile in this report is changed, all other data (including the demand profile) remains the same, and no changes are made to the dataset that was originally used to create this report.

It’s really easy once you’ve had a play with it. So open up a report, click the Profiling icon, and have a go. You’ll be surprised at how readily profiling brings the numbers to life, and helps you plan for the future with greater confidence.

Footnote for information analysts

To get the most out of profiling, you’ll want your datasets to include enough data to work out beds, theatres and clinics, as well as week-by-week profiles for both demand and activity, for all services. Here is a typical profiling dataset template that includes all that.

When you start using profiling, if you want to get started quickly, you could just get your weekly activity profiles (FutActivWkProfile01 to 52) by counting the activity that was done in each calendar week last year. Gooroo Planner uses the ISO8601 definitions for week numbers but, to save you looking it up, week 1 began on Monday 3 January in 2011, and Monday 2 January in 2012.

Even better, smooth the activity over three years: just add up the activity in each calendar week over the last 3 years. Week 1 started on Mon 5 Jan in 2009 and Mon 4 Jan in 2010.

You can get demand profiles (DemandWkProfile01 to 52) in a similar way. For emergency services the demand is equal to the activity, so you can just copy the activity profile across. For elective services the demand is best measured using additions to the list (i.e. referrals for outpatients, and decisions to admit for elective inpatients and daycases).

Let’s take a look at how week-by-week profiling can help acute providers with winter pressures. We want to maximise capacity utilisation, and minimise the risk of bed crises, cancellations, and 18-week breaches.

We’ll take it in two stages:

1) Preparing for winter: We will look at how emergency and urgent elective demand are likely to vary, week by week, through the winter; then plan routine elective work around the peaks.

2) During winter: As each winter week goes by, we’ll update this profile with outturn demand and activity, so that our plans for the rest of winter can adapt rapidly and continuously to unfolding events.

Preparing for winter

Nobody knows exactly how winter is going to turn out, so we need to make some reasonable assumptions about how much demand is likely to come in, and how it will vary week by week. A good place to start is by looking at what happened last year or, even better, the last three years, and then adjust it for anything else we know is going to happen.

Armed with this information, we’re ready to start working on our plan. Because we’re focusing on the profiles during winter, let’s assume we have already run our strategic plan for the coming months (based on achieving 18 weeks, or filling the available capacity, or whatever scenario we chose). So we have already worked out the overall demand, activity, and capacity for this future period, as well as the waiting list and waiting times we want to end up with. If our dataset already includes demand and activity profiles then we don’t need any more data and can go straight into the week-by-week profiling.

In this worked example the screenshots are taken from Gooroo Planner, where the Profiling screen looks like this:

The large top chart is the interactive activity profile, and we are going to edit this to reprofile elective surgery around the peaks and troughs in emergency and urgent demand. The large bottom chart is interchangeable by clicking for any of the thumbnails at the bottom, so it can show either activity and urgent/emergency demand, beds, theatres, clinics, or waiting times.

Let’s start by zooming in on the bed profile. We start this analysis using data that is based on last year’s demand profile and last year’s outturn activity profile. We’ve picked a major surgical service, and we’re going to see if we can reprofile it to stay out of trouble over winter.

The blue line shows the the number of beds used by our surgical service, plotted against the left axis, and the straight blue line shows the number of beds notionally allocated to this service. The orange line shows the total beds on our whole hospital site, plotted against the right axis, and again the straight orange line shows the physical on-site bed limit. Clearly, we are heading for trouble in January and February, where the number of beds required is far larger than the number available. Looking at the blue line, we can see that we are making things worse by scheduling so much elective surgery during the winter peak; the “red alerts” we experienced last winter are starting to look disturbingly avoidable.

So let’s start by reducing our plans for elective inpatients during the height of the peak. This is a simple matter of clicking and editing the points on the interactive top chart, to reduce the balance of work profiled during January and February until the editable profile looks like this:

After doing that, we get a bed profile that looks like this:

Much better. But what happens to waiting times as a result of this surgical slow-down? A peek at the waiting times chart reveals this:

The blue line shows waiting times just for the elective inpatient stage of treatment, and the orange line shows the RTT wait for this surgical service: that’s the wait for new outpatients, plus the wait for elective inpatients or daycases (whichever is greater). All waits are on a “90 per cent treated within” basis, so the orange line is comparable with the 18 week target. The bad news is that our waiting list is going to spike over winter, rendering the 18 week target unsustainable for 3 or 4 months.

We don’t want that to happen if we can avoid it. So let’s see if we can front-load some surgery to head off the problem. In real life we would have more than one surgical service to reprofile, but for the sake of this example we’ll try to do it all just with this one. So we’ll crack on with as much elective inpatient surgery as possible over the autumn, then slow down for as short a time as possible to keep beds just nicely full over the winter peak (but not too full – we are working to a target occupancy to allow for in-week fluctuations), and then pick things up again in March to deliver the balance of our planned activity towards the end of the year.

When we’ve finished editing the activity profile, it looks like this:

Now our bed profile looks like this:

That’s fine. Waiting times?

That’s fine too: we’ve front-loaded enough surgery to get the list right down before winter, so that even when it spikes we shouldn’t see any breaches. Then the balance of our planned activity is just right to bring us in on target for year end. (In a real hospital you would have several surgical services to play with, rather than just one, so this example is on the extreme side to illustrate the principle.)

That’s our profile done, then, from the comfort of late summer / early autumn. What are we going to do once the snow starts to fall?

Reacting to events during winter

Fast-forward to late January, and it’s cold. Emergency admissions shot up when the GP surgeries reopened after New Year; nothing unusual in that. But last week it shot up again and we had to cancel surgery. How does this affect our plan?

The first thing to consider is this: does this spike mean that the total amount of demand has gone up, or might this peak be balanced by troughs later on? Frankly, who knows? Overall the external demand for healthcare rises stepwise every few years, and if demand happens to have gone up just in the last week then that may mean something, or nothing. If you want to add an extra chunk of demand to your forecast then that is easily done but, if the end result is forecasts that are more volatile but no more accurate, then what is the benefit? Ultimately it’s your call, but a compromise position might be to update the demand forecast every month, not every week, to smooth the volatility out a bit.

On the basis of a week’s worth of data, then, let’s assume it’s a wobble in the profile not an uptick in total demand. We also have outturn data on the activity we delivered for electives, as well as emergencies. So let’s update both our demand profile and our activity profile with the latest week’s data and see where we stand now.

The loss of surgery means that we are now heading for a 21 week RTT wait at the peak in mid-March, whereas before we were expecting to peak at 18 weeks. Perhaps we should have allowed a bit more margin for error in our original plan. However if our assumption about demand (that this spike is likely to be offset by less demand at other times) is correct, then we should have capacity to bring in the displaced patients over the coming weeks to restore the position, as the revised bed profile shows.

And so it goes, week by week, month by month, until the days start to lengthen again. Forecasting demand is not an exact science, especially at a week-by-week level of detail, so our plans for winter are always going to have a large amount of guesswork mixed in with the logic.

In this worked example, January’s spike in demand caused problems with cancellations and the risk of waiting times breaches. That kind of thing is a risk unless we can provide a more substantial buffer in capacity (e.g. in the form of lower bed occupancy) to absorb the variation. Nevertheless, in this example we were in a much better position than we had been the year before, when we had been galloping merrily towards a severe, prolonged, and utterly predictable bed crisis before the winter had even begun.

This worked example was illustrated using Gooroo Planner with integrated week-by-week profiling; you can see a slideshow version of it here. If you are already using Gooroo Planner then profiling is available to you now: look for the profiling button at the top of the Reports view page. If you aren’t using Gooroo Planner already, and would like to take a look, then email info@nhsgooroo.co.uk for a free on-site demo.

The first Gooroo user group is being set up for the East Midlands and surrounding areas, where we have a growing cluster of NHS organisations using Gooroo’s planning and scheduling software.

Meetings will be held three times a year, and attendance is free of charge. The first will be on Monday 1st October from 2pm to 4:30pm in Teaching Room 5 of the Education Centre at Derby Hospital. If you’re a current or potential Gooroo user and would like to come along, then you are very welcome, and should email info@nhsgooroo.co.uk to add your name to the mailing list.

The second user group is already being set up in Scotland, and again if you’d like to come then please email us. The first meeting will probably be in late October in Stirling.

If you are a Gooroo user somewhere else in the country, and would like a user group to be established in your area, then please let us know and we’ll see what we can do.

What is the “demand” for a waiting list service?

We could define demand as being the same as additions to the waiting list: then it would match referrals if we were looking at outpatients, or decisions to admit if we were looking at elective admitted patients. But would that be a useful definition? A lot of patients who are added to the waiting list never get treated, because instead they end up being removed from the waiting list for other reasons (for example, they change their mind, or are removed because they DNA). If we were to lay on enough activity to match additions to the list, then our waiting list would actually shrink because of the removals, which would be nice, but not necessarily what we intended if we merely wanted to keep up with demand. So defining demand as being the same as additions isn’t necessarily the most useful approach.

Instead, Gooroo Planner defines “demand” as the number of patients added to the waiting list who will eventually end up as activity. This means that demand and activity can be compared directly: recurring activity is whatever is needed to keep up with demand, and non-recurring activity is anything extra.

The next question, then, is how should we measure demand? Traditionally we have calculated historical demand as being historical activity plus the growth in waiting list, adjusted for removals. This was reasonable in the days when the size of waiting list was scrutinised centrally and agonized over locally, when historical list size data was readily available, and when changes in list size usually reflected reality. In those days, additions data was not closely watched, and was usually less accurate than changes in the list size. However, none of this can be taken for granted now.

Nowadays, all stages of the patient pathway are linked up to track referral-to-treatment waiting times, and this has helpfully improved the accuracy of additions data. At the same time, investments in IT have sometimes meant that changes in list size reflect administrative actions, not an imbalance between activity and demand; new IT systems can lead to short-term errors in counting, and then one-off waiting list validation exercises can cause dramatic apparent cuts in the list size. There is another problem too: if waiting list snapshots are not regularly archived over time, it can be difficult to recreate this data afterwards. Counting the patients on the waiting list today is much easier than working out how many were on the list a year ago.

What is the solution? An good alternative method for calculating demand might ignore past changes in the size of waiting list, and instead calculate demand as additions less removals. We know that additions are always balanced by activity, removals and the change in list size, so the maths must be fairly straighforward. Indeed it is, and if you want the details they are all laid out in the link below.

Starting in a few weeks time, users of Gooroo Planner will have a choice between these two methods for calculating demand: either based on activity plus growth in list size (if list size data is more reliable), or on additions less removals (if additions data is more reliable).

If you want to make a permanent choice of method and use it always, then you will be able to set that up in your profile under Dataset Settings. Alternatively you can choose between the two methods on a case by case basis, and even use different methods for different services within the same model.

Before you ask, yes the change will be “backwards compatible” and your existing datasets will still work fine. That’s the beauty of Gooroo Planner’s flexible design: we can add the new without overturning the old.

Follow this link for the maths: Demand calculation method explained

Step forward Andy Bailey, Information & Contracts Manager at Sheffield Teaching Hospitals NHS Foundation Trust. With characteristic modesty, he comments: “Not sure it’s anything special, but better for users to create their own datasets rather than going through me.”

Well, it is certainly better if Gooroo users don’t have to bother an information analyst every time they want to do some planning. But as for it not being anything special, well I would disagree. So what is it?

As Andy explains to Gooroo users around the Trust: “it will allow you to generate the core dataset and make alterations as you see fit.  The screen shot below provides you with the gist of how it works, you choose some dates, run the report and the table below will appear.  Any row can be updated thereby allowing you to tweak LOS, theatre minutes, etc (if you’re unhappy with the proxies).  When you press the ‘export data’ button, a csv file will be generated and you’ll be prompted to save it somewhere.  It’s this file that you can then upload into the Gooroo system.”

In the screenshot below I have obscured the actual numbers for privacy:

In the old days (i.e. a few years ago) hospitals used to be places where 2,000 people put information into the computer system but only 5 people could get it back out again. Not any more. Increasingly, NHS Trusts and Commissioners have modern data warehouses with user-friendly interfaces, so that managers in all parts of the organisation can pull the data out themselves.

Andy’s interface is a great example of this. This apparently-simple dataset is enough to run multiple activity scenarios through Gooroo Planner, to work out waiting times, waiting lists, activity, beds, and theatres. By automating the information analyst’s role, Andy has saved himself a lot of work, and removed himself as a possible bottleneck when others want to do some planning.

You’ve thrown the data in, picked an activity scenario, and now you want to see the results.

More than that, you’ve loaded up the entire hospital – a couple of hundred service lines in all. So you’re slightly dreading the massive table – over ten thousand numbers – that will make up your detailed plan for the coming year.

You needn’t have worried, because Gooroo Planner’s brand-new Report viewer makes it all digestible.

Want to see the biggest waiting lists? Just click the row you want to sort (or use the drop-down), and select your sort order.

Want to see Orthopaedics? Just type “ortho” into the filter box.

Want to subtotal across hospital sites and specialties to see the big picture? Just un-tick the headers you want to subtotal across.

Want to use all these features at the same time? No problem: just click Apply.

The new Report viewer means that chucking the numbers around is now a lot easier. So you can quickly pick out the detail that matters, without losing sight of the big picture. You get the power of a database, yet the controls are simpler than a spreadsheet.

To see this and everything else about Gooroo, just get in touch: email info@nhsgooroo.co.uk or phone 01743 232149.