Proposal: Investment in Predictive Care Optimization System (PCOS)

Proposal: Investment in Predictive Care Optimization System (PCOS)
Subject: Urgent Investment in AI-Enabled Social Care to Address the £35M Budget Shortfall
Recommendation Summary:
Action: Approve the £1.5 Million maximum capital spend for phased implementation of the Predictive Care Optimization System (PCOS).
Investment Level: Low Capital (\le \text{£1.5M}).
Time to ROI: Months (Recouping full investment within the first year of operation).
1. The Critical Need: A Financial and Human Crisis
The Council faces a £35 Million budget shortfall driven by unsustainable growth in the £280.1 Million social care payroll expenditure. Current workforce deficiencies—320 vacancies and rising sickness (13.78 days/FTE)—mean the Council is spending more money for less capacity. The model of reactive care is a false economy that sacrifices long-term stability.
“This trend, at a time when our sector faces unprecedented challenges, prompts a crucial question: are we inadvertently setting ourselves up for a crisis?”
— Richard Keyse, CEO, 2iC-Care (ADASS Report)

2. The Solution: Digital Resilience vs. Physical Protection
The PCOS is a fast, low-capital investment designed to achieve cashable savings by managing demand and optimizing staff scheduling. It is the digital equivalent of a flood barrier for the Council’s revenue budget.
Investment Comparison:
Dumfries Flood Barrier (Physical Infrastructure):
Capital Scale: High Capital Investment (up to \approx \text{£38 Million}).
Delivery Speed: Years of planning and construction.
Risk Protected Against: Catastrophic economic disaster (protects physical assets).
Predictive Care Optimization System (PCOS):
Capital Scale: Low Capital Investment (\le \text{£1.5 Million}).
Delivery Speed: Months to deploy "out-of-the-box" software components and begin realizing efficiency gains.
Risk Protected Against: Catastrophic financial collapse (protects the £280.1M revenue budget).
3. Emphasizing Cost and Human Benefits (Sustained by Case Study)
The PCOS is the single most effective intervention to address the budget shortfall while simultaneously improving care quality.
A. Direct Cost Savings (Financial VFM)
The system works by predicting service users who are about to enter the high-cost acute system, allowing early, low-cost intervention. This prevents demand from hitting the payroll.
Targeted Savings: Annual savings are projected to exceed £5 Million by reducing reliance on expensive agency staff and cutting the need for unplanned, high-cost residential placements.
Case Study Evidence (High VFM): AI-backed social care models in the UK have demonstrated immediate and verifiable financial returns.
“This report confirms the substantial impact we are making, with Cera already saving the NHS and government £1 million a day... increasing to £1 billion a year in 2026.”

 — Ben Maruthappu, Founder and CEO of Cera (HealthTech Innovator)



 B. Human and Care Benefits (Social VFM)

 The PCOS secures the Council’s ability to fulfil its statutory duty to protect vulnerable citizens.


Improved Outcomes: The AI flags risks like potential falls or UTIs, mobilizing care staff to intervene at home.
Case Study Evidence (Improved Safety): Predictive analytics directly reduces the need for disruptive, high-stress interventions.
“Predictive analytics can assist frontline staff to flag specific cases from amongst large caseloads and identify cases which are not yet in crisis but which could worsen if preventative steps are not taken.”

 — Local Government Association (LGA) Report on Predictive Analytics




Workforce Support: Optimizing scheduling improves workload balance, reducing burnout among frontline staff, and helping reverse the trend of rising sickness absence.
Conclusion

 The Dumfries Flood Barrier is said to be a necessary investment in economic capital protection. The Predictive Care Optimization System is a necessary, low-capital investment that protects the Council's financial stability and its most vulnerable citizens from the daily threat of service collapse.

 Given the overwhelming VFM and the speed of delivery—recouping the investment within months—this proposal should be fast-tracked to mitigate the impending £35 million financial risk.
 

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