Bed occupancy 4: General adult psychiatry 2018-2023

Method

A freedom of information (FOI) request was submitted to all mainland NHS Boards in February 2023. Only mainland Boards were selected since Island Boards typically admit patients to the mainland, and they will not have their own beds.

The text of the request was:

For each calendar month (starting on 1 January 2018 and ending on 31 January 2023), please provide:

1. The total number of admission beds available for 'General Adult Psychiatry' which are grouped as 'acute' psychiatric wards. Under the ISD Data Dictionary code, please include only code 'G1'. Please *exclude* all beds that are allocated explicitly to: rehabilitation; substance misuse; forensic (G3); psychiatry of old age (G4); learning disability (G5); and child/ child and adolescent psychiatry (G2, G21, and G22). If possible, please group by Ward for each month.

2. The total number of occupied beds for each ward by calendar month. This will typically be the occupied beds at the midnight 'census' using whatever bed management system is in operation. Please group by ward of admission and not the speciality code for that admission (so LD admissions to a general psychiatry bed are still counted).

3. The total number of bed days in each ward that are occupied by delayed discharges, irrespective of reason/ code.

Only GAP (G1) beds were requested as I am a General Adult Psychiatrist and was mainly interested in GAP.

I wanted to be able to calculate the no. of beds per capita for each NHS Board, and I could calculate this if I knew the total number of available bed days in any given month.

Bed occupancy can be calculated easily from the data I requested, and asking for the raw data gave me more flexibility in calculating additional information.

Finally, I asked for information on delayed discharges as I was interested in the total proportion of bed capacity that was lost to delayed discharges.

Results

Description of the data

All NHS Boards provided the data requested, with most providing it within the statutory timeframe. One (NHS Ayrshire & Arran) emailed before the deadline to apologise for the delay and they provided it a few days later.

Not all NHS Boards could provide all the data for the whole period requested. NHS Forth Valley did not have available bed data for the period Jan '18 to Apr '19, although they did provide data on occupied bed days (OBDs) for this period. Reasons were attributed to changes in electronic record systems during the reporting period.

Data on delayed discharges was only provided by eight NHS Boards (NHS Tayside, NHS Dumfries & Galloway, NHS Forth Valley, NHS Borders, NHS Lothian, NHS Grampian, NHS Lanarkshire, and NHS Ayrshire & Arran). In two cases data were missing for early parts of the time period: NHS Tayside (Jan '18 - Feb '19); NHS Lanarkshire (Jan '18 - Apr '19).

Some NHS Boards provided data for wards that were requested to be excluded. For example, NHS Dumfries & Galloway provided data for their POA wards as well as GAP wards. This was identified from the corresponding beds per population being abnormally high. The actual GAP wards were identified using information from the Board's website.

NHS Forth Valley provided bed numbers (and OBDs) for many of their long-stay/ rehabilitation wards, even though this was asked to be excluded. Again, the actual GAP admission wards (including IPCU) were identified from information available on the Board's website.

In the tables below, NHS Boards are listed in the order that the data were received. The data have been aggregated in MS Excel and conditional formatting has been used to create heat maps, with better figures being represented by green values, and worse figures being represented by red.

Beds per capita

There is usually endless discussion within NHS Boards about whether they have more or fewer beds than other NHS Boards. I have yet to see a credible set of figures from the Scottish Government. My experience is that people argue this from positions of relative ignorance since they do not understand the data they are looking at.

Therefore, I calculated the beds per 1,000 population using the following data:

1. No. of beds available in a given month = total available bed days / no. of days in that month.
2. Population size = Mid-Year Population Estimates, Scotland, mid-2021 from National Records Scotland.

GAP beds per 1,000 population. NHS Scotland (aggregated figures) are in dark blue and the Scottish average is the orange horizontal line.

Bed occupancy

The following table shows monthly bed occupancy by NHS Board. The occupancy for NHS Scotland has been calculated by adding up all the occupied bed days and dividing that by the number of available beds.

Bed occupancy rates for each NHS Board over time

Differences between NHS Boards

The first thing to note is that some NHS Boards have had high occupancy for a long time, whilst others have just got busier in the last year or so.

NHS Tayside, for example, has had high occupancy (the highest average of any NHS Board) since 2018; regularly getting into the high 90's and often exceeding 100%. Only NHS Lothian, NHS Greater Glasgow & Clyde, and NHS Ayrshire & Arran have broadly similar patterns. As noted below, NHS Fife has got busier towards the end of 2022 but was previously running at occupancies in the 80's to 90's.

Some NHS Boards (NHS Forth Valley, NHS Borders, and NHS Lanarkshire) have regularly operated within the 85% upper limit.

These figures have been presented in a line chart below.

Bed occupancy rates for NHS Boards over time. NHS Scotland (aggregated) is shown as thick black line.

Effects of COVID-19

The impact of COVID-19 (discussed in previous posts) can be seen clearly. There was a plateau following the first COVID lockdown (Mar '20) from mid-2020 until the end of 2020. The other observation is that bed occupancy has been clearly rising for Scotland as a whole since early 2021 and in the last six months or so it has been close to 100%.

There are differences between NHS Boards, however. NHS Tayside had a reduction in bed occupancy after the first lockdown in March 2020, due to the segregation of one ward for COVID cases only. Since there never was a massive peak in COVID cases needing psychiatric admission, the ward ran at low occupancy.

All NHS Boards appear to have had a reduction in occupancy after lockdown, although the duration of reduced occupancy varies between Boards. In NHS Tayside, occupancy was back above 85% within six months of lockdown. It lasted a little longer in NHS Fife, and reduced occupancy lasted at least a year in NHS Forth Valley and NHS Lanarkshire. Some of the largest NHS Boards (NHS Lothian and NHS Greater Glasgow & Clyde) saw reductions below 85% lasting only three months or so.

Possible artefacts

There are some anomalies that have not been explained but may well represent data 'artefacts':

1. Why NHS Fife had occupancy rates of sub-90% until Jul '22 when they went above 100% quickly.
2. Why NHS Ayrshire & Arran had a very high occupancy in Aug '21. Their submission reports that for four months around this time they were using a 'Decant Ward (Fireworks)' and whilst some of the other wards had lower figures during this time (suggesting patients being treated in a different, temporary ward), in Aug '21 there was a high total for all the wards. This may be because admissions to the Decant Ward were incorrectly included in more than one ward.

Delayed discharges (DDs)

The proportion of total bed capacity lost to delayed discharges, by NHS Board, is shown below. Again, figures have been aggregated to provide a figure for NHS Scotland MH services as a whole.

Two aggregate figures have been provided: 1) the reported DDs as a proportion of all NHS Scotland beds; 2) the reported DDs as a proportion of only those Boards where DD data was provided with bed data.

Proportion of bed capacity lost to delayed discharges

The total bed capacity of NHS Scotland used by delayed discharges that is recorded is 6.6%. If we assume that the missing figures are similar, and we look across only those NHS Boards that provided both DD information and bed information, we can see that 10.7% of all beds (about 1-in-10) are unavailable due to delayed discharges.

There is, however, wide variation between NHS Boards with some NHS Boards (NHS Dumfries & Galloway, NHS Ayrshire & Arran) having very high figures and others (such as NHS Borders and NHS Grampian) having no delayed discharges for much of the time. Both sets of figures seem improbable.

I think that this variation is best explained by unreliable data: possibly because DDs are either not being recorded consistently or because information about DDs have not been included in this FOI request.

There are anecdotes about delayed discharges being obscured within routinely-reported data. One way to do this is to remove the flag as a delayed discharge from the online system. Since staff will flag an admission as a delayed discharge, others can remove the flag, so these data may be unreliable. Those who want to flag delayed discharges (such as inpatient staff) may have a conflicting set of interests to others who may be responsible for finding accommodation, nursing homes, or other resources needed to allow a patient to be discharged.

It is important to remember that although Integration Joint Boards (IJBs) are responsible for commissioning services, the Local Authority and NHS Board are responsible for providing relevant services and they almost always have separate budgets. It is therefore highly probable that there is tension between the financial needs of different services. Although some (such as the current Scottish Government) may be proposing a National Care Service as a solution, there is no evidence to suggest that this will work and most published evidence indicates that there hasn't been obvious benefit from integration of health and social care (Alderwick, 2021; Kadu, 2019; National Audit Office 2017; Reed, 2021; Rocks, 2020).

Indeed, the figures shown above regarding delayed discharges are very likely to be higher (rather than lower) than can be evidenced above.

Has Scotland really run out of beds?

This story started with a colleague flagging a complete lack of general adult psychiatry beds in Scotland on one particular day in February 2023. I therefore wanted to try and determine how likely it was that Scotland regularly ran out of beds.

In order to work out on how many days there were no beds you would need daily data for each NHS Board. However, it was possible to calculate how many 'spare' bed days were available for each month for each NHS Board. This was simply the total number of bed days - the total number of occupied bed days. You could calculate the average number of 'spare' beds each day by dividing the monthly figure by the no. of days in that month.

These figures are shown below. It is assumed that you can't have negative beds so if an NHS Board had more OBDs than beds, it was allocated 0 'spare' beds that month. The figures were aggregated for the whole of NHS Scotland and this value was divided by the days in the month to get an average no. of 'spare' beds per day for that month. There may be days when there are no beds available, but the average will give you an indication of how 'tight' the bed availability is.

Spare beds per month for each NHS Board

The NHS Scotland total is on the far right. It can be seen that in all months, there is a greater-than-zero figure for the average no. of beds available on each day.

As stated above, it is possible that on some days there are no beds. It is also possible that the figures provided by each NHS Board carry enough uncertainty to hide a complete lack of beds. Some admissions may be misattributed to a different specialty, or there are beds unavailable due to repair or other reasons.

What is more possible, however, is that there are many days when an NHS Board has very few beds and they are unable to provide a bed for another NHS Board. Other NHS Boards are frequently out of beds and they may be told that are no beds available to them.

But... General Psychiatry (G1) includes IPCU wards (Intensive Psychiatric Care Unit) and these should, ideally, run at about 60% occupancy. Most large NHS Boards will have at least one IPCU ward and assuming that these are running at optimum occupancy, there will always be beds in the IPCU. However, these are not separated in the figures above so some of the apparent capacity may actually be in ICPUs in Scotland, and these beds would/ should not be used for general psychiatric admissions; especially not for admissions from other NHS Boards.[1]

Conclusions and reflections

I think it makes sense to add some more detailed discussion about what these figures suggest in a later blog so I will keep the conclusions brief for now.

1. There is wide variation between NHS Boards with regards to bed occupancy.
2. There is still recognisable unreliability to the data which may mean that no-one will ever know what the true state of affairs is.
3. National figures are, by nature, unreliable and I would not advise anyone to believe what they are told without understanding exactly how the data are obtained. As Lenin and/ or Reagan said: "Trust, but verify."
4. Whilst it cannot be concluded that there are situations when there are no beds at all in Scotland, the data would suggest that there are probably some beds somewhere. However, NHS Boards who have beds may be unwilling to give these up to other NHS Boards who may seem unable to return them quickly and consistently.

Notes

[1] Most NHS Boards will probably use up their ICPU beds for local admissions in emergencies - such as when they have no other beds anywhere else. However, when you never have beds there is a risk that IPCU is used routinely as 'overspill' for general psychiatric admissions.

References

Alderwick, H., Hutchings, A., Briggs, A., et al (2021) The impacts of collaboration between local health care and non-health care organizations and factors shaping how they work: a systematic review of reviews. BMC Public Health, 21, 753. https://doi.org/10.1186/s12889-021-10630-1

Kadu, M., Ehrenberg, N., Stein, V., et al (2019) Methodological Quality of Economic Evaluations in Integrated Care: Evidence from a Systematic Review. International Journal of Integrated Care, 19, 17. https://doi.org/10.5334/ijic.4675

National Audit Office (2017) Health and social care integration. London: National Audit Office. https://www.nao.org.uk/report/health-and-social-care-integration/

Reed, S., Oung, C., Davies, J., et al (2021) Integrating health and social care: A comparison of policy and progress across the four countries of the UK. London: Nuffield Trust. https://www.nuffieldtrust.org.uk/files/2021-12/integrated-care-web.pdf

Rocks, S., Berntson, D., Gil-Salmerón, A., et al (2020) Cost and effects of integrated care: a systematic literature review and meta-analysis. European Journal of Health Economics, 21, 1211-1221. https://doi.org/10.1007/s10198-020-01217-5

[Last updated: Thu 6 Apr 10:41]

Bed occupancy 3: How is it calculated?

Introduction

This is the third segment in a total of four posts about bed occupancy. The first asked why it is important. The second post looked at factors that affect bed occupancy, and the third (this one) provides a bit of information on how it is calculated and reported.

Calculating bed occupancy

All NHS Boards use an electronic record for both tracking their admissions, discharges, and also recording all information about someone's care. In some cases, it will be the same system and in other cases there will be two different systems. For example, NHS Lothian uses a system called 'Trakcare' to record almost all aspects of patient care and it is their main Electronic Patient Record (EPR). NHS Tayside uses Trakcare for patient contacts and admissions/ discharges, whilst all patient notes are recorded using a system called EMISWeb.

The principles are the same, however. When a patient is admitted, that admission is recorded on the  system with an admission date of the date that they are first admitted. At midnight their presence is recorded as an occupied bed day (OBD). When a patient is discharged, that date is their discharge date and their length of stay (LOS) is simply the number of days that they were on the ward (discharge date minus admission date).

Reporting bed occupancy

Since bed occupancy is the no. of admitted patients / no. of beds, expressed as a percentage it should be easy to understand how occupancy reflects demand and activity. However, the no. of beds can vary.

Bed unavailability

If a patient damages a room then that room may be out-of-service for a few days whilst it is repaired since no-one can be admitted to a damaged room. Similarly, renovations such as security improvements are often made to rooms on a rolling basis (e.g. two beds at any given time in a ward).

But unless that room unavailability is updated on the system, it may be counted as an available room. So if you have twenty beds but two of which are unavailable due to damage, you will only have eighteen available beds. If you have eighteen patients, then occupancy is 100%. But the system may think that you have eighteen patients and twenty beds and report occupancy as 18/20 = 95%.

Usually, the no. of admitted patients (i.e. the numerator) is likely to be correct but the only way to get accurate bed numbers (i.e. the denominator) is to get the information from a contemporary source such as daily 'safety huddle' records where unavailable beds are updated.

This is relevant when we try and understand how NHS Boards have provided the data. It is possible that differences in how they count beds affects the numbers. However, as we will see, I have tried to get both the numerator (no. of patients) and denominator (no. of beds) separately and to calculate occupancy rates myself.

Effects of COVID measures

When the first COVID-19 lockdown was announced (March 2020) it became clear that there was a high risk of harm to psychiatric inpatients from catching COVID. Indeed, the risk from COVID was likely to be higher for many patients than the risk of not being admitted. Based on available information at the time, the risk of death from COVID was higher than the risk of suicide. Fortunately, we now have more accurate risk data from COVID, but at the time it was true for many patients (for example, older and overweight men with multiple comorbidities such as diabetes and COPD).

As a result, many NHS Boards made sections of their wards (or whole wards) their designated COVID ward so that all inpatients with COVID could be managed in the same ward; thereby reducing the risk of spread to other patients (and staff).

If you designated a ward of twenty beds as your COVID ward, the beds were not available for general admissions of patients who did not have COVID. However, they may still have been recorded on the system as if they were. So there is a possibility that your occupancy figures will be unrealistically low because if you had five admitted patients with COVID in a ward of twenty beds, your reported occupancy would only be 5/20 = 25%.

These beds were not closed per se but they were not used for other admissions due to clinical decisions based on risk. This is likely to explain the observed drops in occupancy at the start of the COVID lockdown.

Very short admissions

In specific situations such as having someone admitted early in the morning and then discharged before midnight the next day, followed by another admission, you may find that there is more actual work than your occupancy figure suggests. This is because your occupancy (100%) is simply the number of people in a bed at midnight but you will have had two admissions and one discharge whilst another full ward will have the same occupancy (100%) but no admissions or discharges.

Locally, approximately 4% of admissions have a zero day LOS. Therefore, when considering actual 'work', admissions and discharges also need to considered alongside occupancy.

Who does the reporting?

In almost all cases the actual numbers reported to committees, Board meetings, or via Freedom of Information (FOI) requests come from a different department within the NHS Board; not the people who may have the detailed knowledge about day-to-day changes in bed numbers.

Most NHS Boards will have a 'business intelligence' department who can extract the data but it is quite common for those reporting the figures to have little knowledge about how beds are used in practice or who is actually in them.

For example, there are different specialty codes in use across NHS Scotland and if a CAMHS patient (G2) is admitted to a General Adult Psychiatry (G1) ward, the bed will be filled but not by a GAP patient. This can create slight variations in perceived demand: if your GAP ward is 50% of full of CAMHS patients, you may think that there is increased demand for GAP beds but the demand is actually coming from CAMHS.

An occupied bed in a GAP ward does not, therefore, automatically mean that it has a GAP patient in it.

Psychiatry specialty codes

The following is a list of the most commonly-used psychiatry specialty codes.

G1 General Psychiatry (Mental Illness) (GAP)
G2 Child & Adolescent Psychiatry (CAMHS)
G3 Forensic Psychiatry
G4 Psychiatry of Old Age (POA)
G5 Learning Disability (LD)

Reporting the wrong thing

Issues with coding are quite common. Historically, when different NHS Boards compare the number of psychiatric beds they have, they usually group together beds for all specialties even though the specialties very rarely share beds. These figures are usually reported to the Scottish Government (ISD but now Public Health Scotland) so assumptions are carried through the system and errors become lost.

Not all NHS Boards have forensic beds since these facilities are typically commissioned on a regional (or national) basis. This means that those NHS Boards with large numbers of forensic beds (e.g. NHS Tayside) will appear to have more psychiatric beds than other NHS Boards that don't have forensic or LD beds. However, they are only 'hosting' those beds for other NHS Boards - but they will appear on their bed numbers.

NHS Tayside has an inpatient unit for CAMHS but this is commissioned (and funded) regionally. On paper, NHS Tayside has 12 CAMHS beds but only about 5 are paid for by (and belong to) NHS Tayside. The rest belong to other Boards in the North of Scotland catchment area.

Until this is understood, there is a risk of comparing apples with oranges and coming to the wrong conclusions. Unfortunately, it is often misunderstood and NHS Boards will spend lots of time trying to figure out how to reduce their bed numbers because they've been told by those that count beds (wrongly, in many cases) that they have more than other NHS Boards.

Bed occupancy 2: What factors affect it?

Introduction

In the first instalment of this series we looked at bed occupancy in general, and how it works in psychiatry. This blog will look in more detail at the factors which affect bed occupancy as they may be helpful in understanding the figures later on.

An overview of patient flow

Put simply, there are four key drivers that affect patient flow:

1. Demand;
2. Capacity;
3. Activity;
4. Queue.

These factors are commonly known as DCAQ and we will look at each in turn. Some of the definitions have been taken from the Scottish Government resources to healthcare providers.

1. Demand

Demand is the total sum of all requests into the service from all sources. In inpatient psychiatry this could include: Community Mental Health Teams (CMHTs); primary care (GPs); Crisis Teams; the Police; Crisis Hubs; and pretty much anywhere that might make a request for, or identify a need for, inpatient admission.

Gatekeeping and CRHTTs

It is not just those needing inpatient admission and it includes all those who may be perceived to need inpatient admission. This is important because much of the demand for inpatient care may not be the same population that inpatient psychiatrists think the service might be for. Most inpatient services do not have very explicit criteria for service provision, and there is fuzziness at the boundaries.

Let's assume that you have a limited number of inpatient beds and these are being 'gatekept' by a Crisis Response and Home Treatment Team (CRHTT). This is the model for CRHTTs that was developed many years ago (Department of Health, 2006). Since CRHTTs were funded from closing inpatient beds, they were considered to be essential for 'gatekeeping' to inpatient beds:

"It is important that mental health inpatient services and crisis services are joined up locally. It is necessary for a crisis team to act as gatekeeper for all people requiring access to inpatient services or other emergency care. Gatekeeping is an essential component of CRHT." (Department of Health, 2006)

Since they could offer home treatment as an alternative to admission, they could assess whether a patient was suitable for home treatment instead of an inpatient bed, thereby reducing demand for inpatient services.

The research on whether CRHTTs have achieved their core aim (reducing inpatient admission) is broadly supportive, but sometimes contradictory (for example: Barker, 2011; Jacobs, 2011; Stulz, 2020).

However, let's also assume that your CRHTT only has a locum consultant psychiatrist, many experienced staff have left, and it has seen what many CRHTTs in the UK have seen: their ability to provide home treatment has been compromised by the high demand for crisis assessment. This problem was recognised many years ago (Morgan, 2007) and the crisis response component of CRHTTs was recommended to be kept separate from the home treatment pathway, since the benefits of CRHTTs are only delivered when you can provide home treatment instead of hospital admission.

In these circumstances staff often find themselves simply trying to find a bed rather than delivering the higher-risk and more time-consuming process of home treatment. With regards to risk, it was recognised a long time ago that the suicide rate in CRHTTs was becoming higher than that in inpatient wards (Hunt, 2016). This was due to CRHTTs managing an increasingly risky population in line with their core purpose of being an alternative to hospital admission.

However, this increase in risk needs to be understood and managed by the service and without robust leadership and operational management, there is a likelihood that staff become wary of risk and will lower their threshold for admission. So, rather than preventing admissions to hospital it is possible for CRHTTs to increase admissions, since referrals that would not normally be considered for admission (and would receive home treatment) are now being admitted.

2. Capacity

Capacity is all the resources needed to do the work. In inpatient settings it consists of the buildings (i.e. beds and rooms) and also all the staff. If you close bedrooms (due to renovation, for example) or have staff off sick, then your capacity is reduced.

An inpatient bed is best understood as a combination of a physical bed and also the staff needed to provide care. Someone who needs a bed will also need nursing staff to provide care, and if their needs are more intense, they will need more staff. So, not all patients are equal but it is usually possible to average out admissions, beds, and staff to understand what your staffing requirements are.

Delayed discharges

Key things that can affect capacity include 'delayed discharges'. According to the Scottish Government (2016):

"A delayed discharge is a hospital inpatient who is clinically ready for discharge from inpatient hospital care and who continues to occupy a hospital bed beyond the ready for discharge date."

The higher the number of delayed discharges, the fewer beds will be available for new admissions. Reasons for a delayed discharge can include a lack of social care, or other factors needed that cannot be provided by the inpatient ward (such as renovation to someone's property, or white goods). In some cases, someone may be waiting for a nursing home or other specialist placement to be available and until it is, they cannot be discharged.

Delayed discharges in NHS Scotland are slowly increasing and are reported regularly by NHS Scotland. The most recent set of figures is shown below. The big drop in response to the COVID lockdown was created by large numbers of patients being discharged from hospital

Delayed discharges - NHS Scotland (April 2023)

3. Activity

Activity is the work done. In inpatient environments it consists of all the admissions, all the treatment, and the discharges (and everything in between). A number of things can affect overall activity. For example, a ward that has a higher turnover will have more activity: more people can be admitted and discharged.

Length of stay (LOS)

Often, higher turnover is due to a shorter length-of-stay (LOS). This means that more patients can be admitted and discharged in any given period of time. Whilst there is a concern that an insufficient LOS may increase the risk of readmission, in reality most wards will have a variation in LOS without seeing obvious differences in risk of readmission.

Local data would suggest that there is a range of LOS in which there will be no change in readmission, but as LOS gets very short the risk does appear to go up. Of course, LOS varies according to patient need but there are average values that can be used to understand how different wards are managing broadly similar patient groups.

Perverse incentives

It would be rare if such incentives did not exist within complex pathways and LOS is one example. It is easier to understand if we look at two wards, in the same hospital, and consider how individuals are incentivised to behave.

Ward A is led by a substantive consultant psychiatrist (Consultant A). The average LOS of Ward A is four days shorter than that of Ward B, but the risk of readmission after discharge is the same. The rate of incidents in Ward A is no higher than in Ward B so there is no indication that care is being compromised. Since the LOS is shorter, more patients are admitted and discharged.

Ward B is led by a locum consultant psychiatrist (Consultant B). The LOS is longer and fewer patients are admitted and discharged in any given time period. The Ward B consultant is more cautious about discharging patients because they are only a locum and they don't have as much experience.

Consultant A knows they are admitting and discharging more patients and has no hang-ups about not working as hard. They've seen the data and they know that their turnover indicates that they are working harder.

But they are also exposing themselves to more risk since it is very difficult (if not impossible) to predict which individual patients might have an adverse event (such as suicide) during their admission or shortly after discharge. If they admit and discharge more patients, their individual risk is greater as a result of seeing more patients. They also spend more time managing patients who are most unwell (since the total proportion of any admission when symptoms are greatest is higher with shorter admissions than with longer admissions).

The two wards are in the same hospital and managed by the same NHS Board. The notion of each ward admitting from a particular area (or group of CMHTs) has long gone and if a bed is available in either ward, the patient will be admitted there regardless of their GP, area, or CMHT.

Now let's imagine it's Thursday afternoon and both consultants are asked to discharge some patients to free up beds before the weekend. Consultant A knows that their LOS is already shorter than Consultant B and if they discharge patients from their own ward they will have the same number of new (and more unwell patients) tomorrow, and this will affect how much time they can spend with their existing patients. Meanwhile, Consultant B (by making no changes to their current practice) will have no new patients and will have a more predictable day.

Of course, it is the job of both consultants to admit and discharge patients and deliver optimum care in the safest way, but the incentives are not equal. Consultant A is incentivised to work less efficiently because they have already seen that this is a successful strategy for Consultant B.

Such perverse incentives will be familiar to most people in the NHS. If you work more efficiently than your colleagues, you usually get more work. Most high-performing doctors don't mind this most of the time because doing the work is rewarding and there were opportunities for teaching, research, and other meaningful activities. But the circumstances in which the NHS is currently working post-COVID (relentless demand, higher stress and burnout, real-world pay cuts over time, increases in moral injury, fewer opportunities for research) are different. Working in systems where unfairness is a daily lived experience will affect how people make choices about different priorities. If they didn't, we wouldn't be human.

4. Queue

Queue is also known as 'backlog' and is the activity that has not been dealt with. Typically, it will represent those patients who have been admitted elsewhere and need a bed locally, and also those patients who require a bed but have not had one available. They may have been becoming increasingly unwell in the community.

A queue is inevitable when demand exceeds capacity, and there will be work that has been displaced if inpatient services lack the capacity and activity to manage demand.

Queues can also form when activity is displaced. For example, if a CMHT has a high rate of staff absence or lacks consistent medical staff (For example: The Courier, 2023) then inpatient services may end up being the 'provider of last resort' and have to provide aspects of patient care that have not/ cannot be managed elsewhere in the system.

This brings us back to inpatient demand being a proxy for the functioning of all other parts of the mental health system.

References

Barker, V., Taylor, M., Kader, I., et al (2011) Impact of crisis resolution and home treatment services on user experience and admission to psychiatric hospital. The Psychiatrist, 35, 106-110. http://pb.rcpsych.org/cgi/content/abstract/35/3/106

Department of Health (2006) Guidance Statement on Fidelity and Best Practice for Crisis Services. London: HMSO. https://webarchive.nationalarchives.gov.uk/+/www.dh.gov.uk/assetRoot/04/14/16/73/04141673.pdf

Hunt, I. M., Appleby, L. & Kapur, N. (2016) Suicide under crisis resolution home treatment - a key setting for patient safety. The Psychiatrist, 40, 172-174. http://doi.org/10.1192/pb.bp.115.051227

Healey, D. (2023). EXCLUSIVE: Tayside psychiatry vacancies worst in Scotland as expert warns national services ‘unsafe’. The Courier, 24 January 2023. https://www.thecourier.co.uk/fp/politics/scottish-politics/4082341/tayside-psychiatry-scotland-unsafe/

Jacobs, R. & Barrenho, E. (2011) Impact of crisis resolution and home treatment teams on psychiatric admissions in England. British Journal of Psychiatry, 199, 71-76. http://dx.doi.org/10.1192/bjp.bp.110.079830

Morgan, S. (2007) Are Crisis Resolution & Home Treatment Services Seeing the Patients They Are Supposed To See? London: National Audit Office. http://www.nao.org.uk/idoc.ashx?docId=640def6f-837d-4f1e-a3e8-6a62194a30f1&version=-1

Stulz, N., Wyder, L., Maeck, L., et al (2020) Home treatment for acute mental healthcare: randomised controlled trial. British Journal of Psychiatry, 216, 323-330. https://doi.org/10.1192/bjp.2019.31