In responding to the challenges set out in part one of this series, we have developed an approach that allows organisations and systems to dynamically plan change. At its heart is a whole-system predictive model which builds a baseline position and then applies a range of change assumptions to reach an optimal outcome in terms of system operation.
“We have developed an approach that allows organisations and systems to dynamically plan change.”
By using historical data, demographic projections and disease incidence, we can combine this with wider indicators such as deprivation, public health and socio-economic factors to establish the population needs for health and social care over the short, medium and long-term. This can then be aligned to the future vision for health and social care service provision and be used to create a set of scenarios which describe and quantify the service components required to meet the anticipated need. In turn, it can be used to assess the future workforce, service and environmental/ infrastructure needs across the health and care system. It can also be aligned to cost factors which in turn can inform future spending projections and any potential imbalance between income and expenditure.
The model itself is a highly flexible tool built on a validated baseline, but taking due consideration of population health factors and the impact of change on the wider system. As such it allows a range of scenarios to be developed and reviewed by altering single or multiple assumptions to determine system need and capacity.
“within the acute hospital settings the analysis can look at capacity needs across multiple care settings including A&E, inpatient settings, theatres, imaging and outpatients”
For example, within the acute hospital settings the analysis can look at capacity needs across multiple care settings including A&E, inpatient settings, theatres, imaging and outpatients. It can also assess the impact of enhanced ‘front door’ models focussed on increasing the proportion of unscheduled care patients (including Frailty management) who are cared for in an assessment or ambulatory care setting without the need for specialty admission. Out of hospital the model can be used to quantify the required capacity in community and social care settings to accommodate ‘failure’ demand that can be better managed in non-acute settings including intermediate, residential and home care.
“the model can be used to quantify the required capacity in community and social care settings to accommodate ‘failure’ demand that can be better managed in non-acute settings”
Underpinning any predictive modelling is the question of data accuracy and validation as the foundations for the analysis. This, however, should not be a reason for failing to plan scenarios, but create a focus for how we work around by agreeing a set of validated assumptions.
In this part of our analysis we have described our approach to scenario planning across the health and care system and outlined some of the key elements embedded in our philosophy. In part three, we will provide some practical examples of where we used this approach to help organisations plot their way through complex change challenges and achieve a positive outcome.