The tool supports a strategic approach to managing UEC demand, capacity, flow and performance. By integrating system data into a ‘single version of the truth’, it reflects interdependencies and enables joined-up planning. Using a cloud platform, users can forecast demand, test different assumptions, and see the impact on system performance.
- Secure cloud platform accessible by operational and management teams
- Integrates data from all settings on demand, capacity and flow
- Uses machine learning to generate monthly or weekly forecasts
- Tracks current demand, capacity and flow data against plan
- Allows benchmarking against national indicators and peers
- Forecasts demand for A&E attendance, non-elective beds, post-acute capacity
- Captures capacity plans for bedded and non-bedded care
- Forecasts expected occupancy and performance
- Allows development and sharing of demand and capacity plans
- Supports users to prioritise interventions
- Assess, plan and track demand, capacity, flow and performance
- Understand UEC pathway at site, trust or system level
- Understand demand at practice, cluster, CCG or system level
- Identify pressure points and opportunities to transform practice
- Understand capability and capacity for transformation within the system
- Support planning by commissioners, providers, regulators
- Track current performance vs. plan and performance drivers
- Enable ‘early warning’ with near term forecasts
- Support A&E, MIU, Ambulance, 111, MDTs and community hospitals
- Reduce pressure on A&E departments, breaches and associated costs
Demand, Capacity, and Flow tool extracts
Exhibit 1 below shows a typical pattern over winter: admissions outstrip discharges by one to two patients per day in a typical trust. Reoccurring over a sustained eriod of October to February, the pattern results in a surge of 40-80 beds filled per month, which within 2-3 months fills the hospital. However, it also means the issue is more addressable as only 1-2 more patients per day need to be discharged.
Exhibit 1: Admission/Discharge patterns for the typical hospital as displayed in the DCF tool
Unaddressed, occupied bed days will increase, driven mainly by an upward drift in length of stay, itself driven by longer delays. Occupancy will rise and performance against four-hour waits will continue to fall. Below is the prediction of continued growth in one system if it simply continues on in Exhibit 2.
Exhibit 2: NEL bed days for the typical hospital as displayed in the DCF tool
We also know that it is the elderly who will suffer. Nationally, over 65s represent 16% of the population, 28% of GP visits, 27% of A&E admissions, 40% of admissions and 76% of occupied bed days. They also face disproportionately long times waiting times in A&E - in one system the average waiting time for this population group was as high as 8 hours (Exhibit 3). Once admitted, the elderly population face long delays to assessment and even longer to discharge, with some waiting for 28 days for discharge once medically fit. Not only are they stuck in the system with nothing of value being done; they are also exposed to an avoidable risk of infection and well-evidenced deterioration.
Exhibit 3: Average length of stay in ED by month and age group