Case study: Director Builds Model to Predict 70-80% of Emergency Readmissions

There's an abundance of data that impacts people's health. But around 80% of it lives outside the NHS—data from services like community and mental health, police and fire, social care, as well as household data like income and employment status.

Enter David Howell. David manages two analytics teams across two organisations: Surrey Heartlands' Integrated Care Board and Surrey County Council. Together, they use the insights from these combined data sources to help improve patient outcomes.

David saw major potential in applying more advanced analytics in healthcare. And he viewed the Level 7 programme as his opportunity to "lead by example" in acquiring those advanced skills.

Apart from just upskilling, David wanted to build a compelling use case for machine learning that would drive NHS funding for similar projects in the future. He saw emergency readmissions, an issue that has worsened since Covid-19, as one such use case.

Emergency readmissions happen when a patient enters a hospital to receive an operation and then returns for urgent treatment within 30 days of being discharged as a result of that first admission.

Besides needlessly drawing from already limited NHS resources, readmissions have broader social consequences, such as the safety of at-risk patients who are without support at home following major surgery.

Being able to accurately predict emergency readmissions has the potential to save lives and expand the capacity of healthcare services across the NHS.

David says there's still governance work needed before the model could be implemented in hospitals. But he hopes it will one day be deployed to augment clinicians at the point of discharge, flagging at-risk patients and informing interventions to prevent readmission.

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