PredictX on predictive insight in Leicester

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    Tuesday20Feb 2018
    Case studies

    Leicester, Leicestershire and Rutland (LLR) Health and Social Care’s Better Care Together programme has been developed to transform health and care delivery across the...

Leicester, Leicestershire and Rutland use predictive insight to optimise Intensive Community Support service

Key learning/Lessons learnt

Leicester, Leicestershire and Rutland use predictive insight to optimise Intensive Community Support (ICS) service

Background

Leicester, Leicestershire and Rutland (LLR) Health and Social Care’s Better Care Together (BCT) programme has been developed to transform health and care delivery across the region. BCT brings together a number of organisations, including University Hospitals of Leicester (UHL), all three Clinical Commissioning Groups, Leicestershire Partnership NHS Trust (LTP), Rutland County Council, Leicester City Council and Leicestershire County Council. Partners across LLR also work closely with patient representatives to ensure that services delivered meet the needs of its 1,000,000 residents and result in increased independence and improved outcomes.

What challenge did we seek to solve?

Acute hospital activity is higher than ever, yet bed capacity is shrinking. The number of over 85s living in LLR is expected to increase by around 150% between 2014 and 2039. Inevitably, increased life expectancy goes hand in hand with loss of functionality, increased levels of disability, frailty and long term conditions (LTCs) that require regular medical intervention. The intensive community support (ICS) service provided by LPT provides an intensive rehabilitation service with an aim to prevent or reduce the need for permanent or long term care. In order to gauge the impact and effectiveness of the ICS service across the local health and care community, an evaluation was required.

What did we do?

An impartial and unbiased assessment of the ICS service could only be achieved if the multiple strands of data generated by all care providers - from UHL to social services - were integrated and analysed.

A specialist public health intelligence team, led by Gabriele Price, Public Health Business Partner at Leicestershire County Council, was therefore tasked to develop a methodology for capturing data on this specific group of patients.

The team worked closely with data analytics experts PredictX, who integrate and aggregate data from East Midlands Ambulance Service, UHL, LPT, Adult Social Care services and NHS 111, creating dynamic dashboards that enables them to monitor the impact of the extended ICS service on patient outcomes and readmissions on an ongoing basis. This data integration also allows them to view these holistic pathways and outcomes across the whole system.

Outcomes achieved

The dashboards are currently being used to evaluate the impact of ICS on:

  • Length of stay at UHL
  • Readmission rates
  • A&E attendance rates
  • Before and after activity costs

For patients referred to ICS following a hospital stay the above outcomes are compared to patients who did not receive ICS and who were matched on age, number of LTCs, and reason and period for the hospital admission to the ICS cohort. PredictX for Care and Health allows for matching and a parallel monitoring of both groups to be carried out in an easy, accessible and robust way.

Using PredictX for Care and Health however, the partnership can assess the impact and benefits of ICS on a month-by-month basis, while also monitoring any changes to service costs and/or benefits.

Future plans

The dashboards will continue to be used even more extensively, for an in-depth analysis of the impact of ICS on care delivery across the region. This will include the development of a whole system cost evaluation, as well as an analysis of staff costs, to assess the overall value of the service on this set of patients.

The ICS evaluation team are also working with PredictX to develop a predictive modelling dashboard, which will help shape future developments and assess which services are likely to be the most beneficial.

Contact details

E info@predictx.com

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