January 2021 – Advanced analytics provider PredictX, with the support of the City of Wolverhampton Council, used data science methodologies to allow local councils an easy way of proactively assessing health and social care outcomes in local populations.
Supported by UK innovation agency, Innovate UK, PredictX sought to help local authorities, starting with the City of Wolverhampton Council, with the consequences and challenges they experienced as a result of the Covid-19 pandemic.
One of these consequences was the rapid discharge process from hospitals to care homes early on in the pandemic. Despite strong international evidence that social care settings were at high risk from serious infection, patients continued to be discharged into care homes, with support measures coming too little and too late.
As such, Covid-19 was a “call to action” for the disparate systems of health and social care to become more integrated in the way they help patients. Known as the “Home First” approach, patients discharged from hospital were not sent to care homes but rather placed on care pathways where they received a wide range of intermediate health and social care services in their own home or a short-term bedded facility. Recent evidence suggests, however, that up to 40% of older people end up in the wrong care pathway. With limited ability to assess if a patient is on the wrong pathway, local authorities can misdirect funds and their populations can experience less favourable health outcomes.
PredictX has introduced a solution for these challenges. Using data science methodologies to combine and analyse pseudonymised health and social care data, PredictX provided the City of Wolverhampton Council with a continuous score against indicator 2B of the Adult Social Care Outcome Framework (ASCOF). Indicator 2B assesses social care success by examining the proportion of older people (65 and over) who were still at home 91 days after discharge from hospital into rehabilitation or reablement services.
Authorities already had an annual requirement to collect data and present an ASCOF score based on many requirements, one of which being the proportion of older people still at home 91 days after discharge. Due to the amount of manual work required to populate this metric, the data is often based on a sample taken out from the fourth quarter of the previous year (Oct-Dec) and is published almost a year later. Authorities can only see a partial view of the population care outcomes long after any opportunity to change them have taken place.
The PredictX analysis allows local authorities and adult social care services to see, on a monthly basis, when the ASCOF score is trending away from agreed targets, so they can implement preventative measures. This is based on a continuous analysis of a year’s worth of data – allowing authorities to accurately predict trends. They can also drill down further – breaking down social care outcomes by demographics and health conditions. Authorities can see if health outcomes for some demographics are worse than others – ensuring all patients have access to quality social care regardless of age, ethnicity, health condition or local area.
“Integrated health and social care data will now be essential to enable the analysis of the patient journey through the whole health and care system and provide the insight, performance and trends based on local activity, support services, health conditions and demographic characteristics to improve decision-making,” said Michael Holden, Project Manager of the Better Care Fund’s Integrated Care Programme for the City of Wolverhampton Council.
The City of Wolverhampton Council, PredictX and the Midlands and Lancashire Commissioning Support Unit (MLCSU) – also known as the PIPP partnership – will now be looking to get more councils involved.
You can learn more about this initiative in the PredictX Care and Health data sheet.