Future-proofing ICB’s operations with an AI-powered SCC

In August 2023, NHS England released a new specification for a System Coordination Centre (SCC) (previously System Control Centres) that called for three expected outcomes: Enhance operational visibility of capacity and flow, enable real-time co-ordination, and improve clinical outcomes.

An AI-powered SCC is a significant step in the right direction to help ICBs, particularly with managing winter pressures. And with the right technology in place, this could just be the starting point.

Integrated care systems (ICSs) face challenges in effectively managing healthcare demand, predicting patient flow, and optimising resource allocation due to siloed data and limited predictive capabilities. Bristol, North Somerset and South Gloucestershire (BNSSG) ICB, seeks to overcome these hurdles by integrating advanced AI-powered predictive analytics to enhance operational visibility, improve patient outcomes, and future-proof its CTCC.

We implemented an AI-powered CTCC designed to provide real-time and predictive insights into healthcare demand, capacity, and flow across the ICB. The solution offers a range of key features designed to enhance operational efficiency and decision-making across healthcare systems. One of the primary features is real-time operational visibility, which consolidates data from GP practices, social care, ambulance services, NHS 111, and mental health providers. This integration provides a comprehensive overview of demand and capacity across primary, secondary, and social care sectors. Another important feature is predictive analytics, which forecasts patient flow, identifies capacity constraints, and highlights system bottlenecks. By detecting trends and patterns, this capability enables proactive interventions to address demand surges effectively. System integration plays a crucial role by connecting existing data systems to ensure seamless information flow throughout the healthcare network. Anonymised primary care data from GP practices serves as a cornerstone of the solution, enabling the early identification of system-wide stressors while safeguarding patient privacy. The user-centric design ensures the system provides actionable insights to frontline staff without being perceived as intrusive or overly controlling.

AWS services played a pivotal role in ensuring a successful and seamless implementation of their cloud infrastructure. Amazon Elastic Compute Cloud (EC2) provided the flexibility to launch and scale virtual servers on demand, enabling us to efficiently handle fluctuating workloads during both development and production phases. For the client’s database needs, Amazon Relational Database Service (RDS) proved invaluable, automating tasks like backups and updates while maintaining high availability and performance, which allowed the team to focus on application development rather than database management. Leveraging Amazon Elastic Container Service (ECS) streamlined the deployment and management of containerized applications, ensuring the client's microservices were easily scalable and resilient. To secure the architecture, Amazon Virtual Private Cloud (VPC) provided a custom network environment, enabling precise control over network traffic and ensuring a high level of security. Finally, Amazon CloudWatch was instrumental in monitoring system performance, generating real-time insights, and proactively identifying potential issues, which minimised downtime and optimised resource utilisation.

The system enhanced visibility by providing real-time dashboards for unified views of capacity, demand, and patient flow across healthcare services. Integration of data from various sectors reduced operational silos. Predictive analytics successfully forecasted patient flow and demand surges, allowing for proactive resource allocation and improving system resilience; which resulted in early interventions to reduce bottlenecks, and optimise patient flow and care coordination. The system was especially crucial to prepare for winter, aligning resources to manage increased demand.

Overall the solution has improved operational performance by reducing disruptions and saving time and costs through fewer hospital admissions and the automation of manual tasks. It has also enhanced patient outcomes with quicker interventions and positive feedback from staff, due to better coordination and reduced stress during busy periods.