Our advanced flow, demand and capacity tool seeks to give providers and commissioners a simple-to-use tool to support sophisticated and collective planning

Features

 

Providers and commissioners have long worked together to address the challenges of providing urgent care across primary, community, social and hospital care settings. However, it is often difficult to get to the root causes of problems and there can often be a lack of collective understanding about what is causing performance issues. This is exacerbated by a lack of robust and timely information on which to make decisions. To resolve problems, providers and commissioners need to understand:

  • where there is demand for services and if/when this is likely to change
  • where there is capacity in the system, on a real-time basis
  • where there is a mismatch between demand and capacity
  • the key drivers of changes in capacity and demand
  • where there is the greatest possibility of improvement

Based on this understanding, providers and commissioners need to agree where to prioritise resources and management attention and develop demand and capacity plans that are jointly agreed.

Flow, Demand and Capacity

We have developed an advanced flow, demand and capacity tool which supports a strategic approach to managing urgent and emergency care (UEC) demand, capacity, flow and performance. By integrating data from across health and social care 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.

How will our flow, demand and capacity tool help you?

  • Easy to use online platform
  • Uses real-time data
  • Covers all five stages of UEC pathway from pre-A&E to discharge
  • Providers a perspective for both providers and commissioners
  • Allows forward planning
  • Generates an understanding of the drivers of performance
  • Assess, plan and track demand, capacity, flow and performance
  • Understand the urgent and emergency care 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

Key features of the flow, demand and capacity tool:

  • 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 the plan
  • Allows benchmarking against national indicators and peers
  • Forecasts demand in 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

  

Advanced Flow, Demand and Capacity examples:

 

Visualisation of admission and discharge patterns

Our tool allows you to visualise the peaks and troughs of occupancy allowing you to forward plan, including putting in place support in community care to discharge stranded and super-stranded patients.
Below is an example of the occupancy pattern over winter per day in a typical trust. Reoccurring over a sustained period 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.

Exhibit 1: Admission/Discharge patterns for the typical hospital as displayed in the DCF tool

Visualisation of Non-Elective bed days

The tool allows you to use historic data to build a picture of what has happened and is then able to use predictive algorithms to provide a forward view.
Below is a model of non-elective bed days for three consecutive years, modelled across the peaks and troughs as well as annual growth.  It is possible to predict the occupancy rate for the next six months to support decision making on how to best utilise the services. 

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

This tool is part of our Analytics function. If you would like to read more about our DCF tool, such as a more detailed product offering please click here