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Forecasting future care demand – are there brighter skies beyond the storm?

A new guide has been launched highlighting how to utilise health and social care data to predict demand and provide invaluable insight for local authorities.

Our PAMMS software is taking the sector out of the norm with its latest solution that accurately models both future care demand and capacity; invaluable for forecasting and supporting the budgetary process.

Using historic data, the PAMMS Demand Model tool helps local authorities to better understand customer behaviour, manage unexpected events and predict areas of risk, invaluable in the current challenging social care environment.

To showcase the benefits of the tool, the team behind the award winning technology have launched a guide highlighting how to utilise health and social care data to predict demand and accurately give local authorities an insight into the budget required across different service types and primary support reasons.

Featured in the guide, Middlesbrough Metropolitan Borough Council is one local authority already predicting future care demand with the PAMMS Model. Historic data analysis predicted a requirement of 726 residential social care beds for 2019, with the actual requirement being 734, as little as 1% variance.

Carl Johnson, Information Analyst at Middlesbrough Metropolitan Borough Council commented: “The system is efficient, processes data quickly and doesn’t take a great deal of resource to manage. We can see what is happening on a real time basis and it gives us lots of intelligence about the local care market.

“Other benefits include the ability for us to make evidence-based decisions from consistent, accurate forecasts, allows us to react quickly to unexpected changes, improves the quality of our service and derives efficiencies from automation and smarter commissioning. We can target our attention to where it is needed, rather than where we think it should be!”

The Covid-19 pandemic was a major event that triggered a disruptive change in activity levels for those using the PAMMS model. In scenarios like this, the technology captures forecasts made for a care category immediately prior to the event, acting as a crucial baseline while it is ongoing. It provides the best possible view of what happens during a crisis and helps care planners establish the ‘new normal’.

Ben Chance, Head of PAMMS, commented: “There are vast amounts of health and social care data being collected by local authorities, central government and a plethora of organisations across other sectors. This data sits under-exploited in separate silos. But what if this data was utilised and understood, and gave us the power to radically transform the way care is planned and delivered?

“The PAMMS Demand Model can really empower us to forecast with levels of accuracy that weren’t possible before.”

To download the Demand Model guide, or to find out more about Demand Model forecasting, visit  https://www.hastechnology.com/pamms-demand-model-guide