Demand and capacity modelling in mental health is difficult
Accurate demand and capacity modelling in mental health, where the vast majority of provision is community based, has always been challenging. Demand for mental health services is hard to predict across a population with widely varying conditions, treatment packages and care settings and non-linear recovery processes. Mental health provider capacity consists predominantly of staff, however the wide range of roles and services and significant variation in patient facing appointment length and frequency make it hard to understand productivity and therefore capacity. Given all of this, it is difficult to find a modelling currency that accurately reflects both the demand and capacity sides of the equation across services.
Existing demand and capacity models usually assume that the mental health world is like an acute trust, where granular data on conditions using defined metrics like HRGs are readily available. These models tend to require the input of large amounts of historical information and expect users to be able to describe simple relationships between demand and capacity.
COVID adds complexity
COVID makes this situation worse. Historical data cannot be relied on as recent events are so extraordinary; future scenarios, even over the next few months, are hard to see; and whilst we know the impact of previous national crises (such as the 2008 recession) the impact of the pandemic will be far more complex. However, we must start somewhere to ensure that capacity is created in the right places and that IAPT and specialist services are not overwhelmed.
We can safely assume that COVID will increase the incidence of mental health problems. Lockdown, isolation and bereavement will increase the number of people experiencing anxiety and depression; the incidence of PTSD resulting from intensive care stays is known to be high; and the longer-term impact of unemployment and financial difficulties will be significant. It is imperative that we can predict demand and capacity for mental health support to guide us through these challenging times, but in order to take the next step we need an approach that accounts for the lack of hard data whilst still allowing some measure of a reliable future view. If we are to usefully model demand and capacity over the coming months, we need a model specifically designed for mental health services which recognises the complexity of the system and uses the wealth of clinical experience within trusts.
A flexible model that works with what you know
Building on these insights, Attain’s mental health demand and capacity model uses a simulation approach that:
- Is less reliant on lots of data: Our model allows the use of assumptions and sketched data – that is, the ability to draw in charts using your mouse – where quantifiable historic inputs are not available or applicable.
- Is a demand model cut by different sub-populations: We find that creating whole population forecasts can be hard, but assumptions about smaller groups are often easier to agree on. The demand element of our model takes a Population Health Management (PHM) approach and segments patients by age and need, which can be changed to reflect the population relevant to a local situation and data.
- Models the currency of patient facing time: Both sides of the demand and capacity model meet in the middle with demand for hours versus the supply of hours from a given capacity. The model allows scenarios to be run to flex demand and capacity to close the gap.
- Includes the volume of demand that is waiting: If demand is greater than capacity then patient hours start building up in a queue. This type of queueing theory is hard to build into Excel based models.
- Flexible: To account for different services and workforce groups and for different COVID scenarios, for example a reduction in workforce over time due to physical or stress related illness.
- Scalable: From a single service, through a multi-service organisation, to a multi-organisation system view.
- Online: Our model is delivered through a web browser, meaning no downloads.
Our model aims to get you started with the next steps in mental health demand and capacity planning. Clinicians, managers and leaders in the mental health sector know their services and service users and are well placed to make strong assumptions based on their collective experience. Trusting their knowledge and judgement, we can use these alongside the data you do have to develop a consensus view of your position, to support robust system level planning.
For more information, contact Matt Jones at email@example.com