1. Predicting Social Care Demand
Presented By:
Carl Johnson –
Tom Knight –
Good afternoon, I’m Carl from Middlesbrough Council, my colleague is Tom Knight and we’re here today to share what we’ve been doing in relation to demand modelling in Adult Social Care.

2. Activity Modelling in Middlesbrough
Middlesbrough Council have been using a demand model to inform Commissioning decisions, budget planning and workforce structures since 2012/13
2012/13 - Finance Based Demand Model (V1)
2015/16 – Revised model based on Social Care Finance System (ContrOCC) data (V2)
2018/19 – Collaboration with Affinityworks on new demand model (V3)
For a number of years, Middlesbrough has sought to quantify its service user base, to have a view of current and historic activity levels across a range of service areas and to predict what is likely to occur in future.
The benefits to this approach allow for smarter commissioning, more agility in responding to unanticipated events and supports the Council in managing its budget.
Alongside the more technical work in devising and managing the demand model, we have worked to ensure the culture in the Council is geared up to making use of and reacting to the information we can supply.
Back in 2012/13, we rolled out a version of our demand model which was based exclusively on finance data. This was granular, to an extent, but only showed movement between the start and end of the financial year.
**We’ve had a culture since we started this work to /having the data/tool does nothing on it’s own – we must change the culture to react to this data **
*Next Slide*

3. Demand Model Spreadsheet (V2)
This was refined in 2015/16 based on systems data, it accounted for movement in-year, showing quarterly movement but was not granular, this made it difficult to see what was affecting demand.
On screen we see the Long Term Residential Care model, the black dotted line showing out-turn, the green showing our projection. Within the model, we are unable to break this down further – for example by age group (working age or older people) or by Primary Support Reason (Physical disabilities, mental health, learning disabilities and so on).
The model was time consuming to develop at approximately 2/3 weeks at the start of each year and with quarterly updates in year.

4. Time For a Fresh Approach…
Challenges
Requirements
Time consuming to create and manage (approximately 2-3 weeks to refresh)
Solution automated as far as possible
Could only be updated annually
More regular updates to reflect activity changes
Susceptible to changes in activity trend
Able to handle unexpected changes in activity
High level projections only
Granular forecasts based on age groups and primary support reasons
We felt it was time to look at a new way to model demand, to present and share the information, to review how we make use of it.
Having recognised a number of challenges, we used these to outline a number of requirements. (run through them)
Middlesbrough Council, alongside Redcar and Hartlepool have been working with Affinityworks over recent years on a data hub. We knew they were keen to work with us on anything that would help improve our data intelligence… so we passed these on to them.
*Tom to take over*

5. Affinity Works Design
- Time series forecasting used to replace spreadsheet model
- Huge number of different statistical models and settings available
- An analyst would take months to achieve the optimum result for each forecast as they vary hugely
- With Machine Learning we are able to obtain this optimum forecast in just a few seconds CB

6. Predictive Tool Output
Latest Variance: 1.08%
Social Care Activity Predictions
Long Term Residential Activity (All PSRs and All Ages)
734 Actual
Activity
726 Predicted CB

7. Seasonality Automatically Detected
Latest Variance: 1.67%
Social Care Activity Predictions
Long Term Residential Activity (All PSRs and All Ages)
Activity CB

8. Handling Unexpected Events
Social Care Activity Predictions
Latest Variance: 31.11%
Long Term Nursing Activity (All PSRs and All Ages)
Warning triggered when actual strays outside variance threshold
176
Activity
Difference of 104 * £600p/w = £3.25m 72 CB

9. Unit Costs Used to Forecast Spend
Social Care Commitment Estimates
Long Term Nursing Activity (All PSRs and All Ages)
Latest Variance: 31.11%
Estimated Annual Commitment CB
2018/19
2019/20
2020/21

10.  <1hr to refresh - all predictions  Monthly refreshes supported
Requirements Delivered
Requirement
Delivered
Solution automated as far as possible
 <1hr to refresh - all predictions
More regular updates to reflect activity changes
 Monthly refreshes supported
Able to handle unexpected changes in activity
Multiple concurrent predictions give options for unexpected changes in activity
Granular forecasts based on age groups and primary support reasons
632 sets of predictions being monitored across the Authority at any one time
TK: How long does it take you to refresh your data?

11. The Best Bit!
Just 6 fields of data are required to run the tool (all of which are on statutory returns):
Week Start Date
Service Type/Demand Category
Age Group
Primary Support Reason
Units of Activity
Average Unit Cost CB

12. Benefits Commissioning cycle Financial/budget planning
Market intelligence/oversight
Monitoring process/policy changes
Reliable, consistent activity and cost intelligence
Demand Modelling within Middlesbrough Council is primarily led by the Commissioning Team. It’s purpose was and is to ensure we inform the commissioning cycle with the most accurate intelligence we can.
This, by default, supplies cost projections which are used as part of Council Budget planning and is instrumental in informing the Medium Term Financial Plan – essential at the best of times, let alone during a time of austerity and increasing pressures on Health and Social Care.
We are also able to better understand (visualise and quantify) our local market, essential for any commissioning body – and a legal requirement under the Care Act.
Because of the efficient way data is loaded and processed by the system, we are able to view activity on as near to real-time a basis as we like. This allows us to monitor the impact of policy or process changes to ensure they are having the desired effect.
Combined, this leaves us in a position whereby we have reliable, consistent activity and cost intelligence…
*Change Slide*

13. Key Outcomes
Evidence-based decisions from consistent and accurate forecasts
Finance have reliable data for budget setting
Impact of decisions can be quantified with rapid response to unexpected changes
Corporate/departmental culture moves towards on-demand digital by default
Improvements in service delivery to customers
Efficiencies from automation and smarter commissioning
.and are able to deliver on these key outcomes:
Decisions are evidence-based from consistent and accurate forecasting.
We support the movement to on-demand, digital by default.
Finance are able to make use of the data for budget setting.
The impact of decisions can monitored and we are able to better identify and react to unanticipated changes.
There are efficiencies from automation and smarter commissioning.
Customers experience improvements to service delivery.
We hope you found this session informative, we are around for the rest of today should anyone like to discuss this with us further, are there any questions?

14. Enquiries Contact Carl Johnson – Carl_Johnson@middlesbrough.gov.uk
Tom Knight –