NHS Cornwall and Isles of Scilly DataLab

Improving the management of unplanned and urgent care through advanced statistics and machine learning techniques
Entering medical data onto a laptop

NHS Cornwall and Isles of Scilly DataLab

Improving the management of unplanned and urgent care through advanced statistics and machine learning techniques
Title: NHS Cornwall and Isles of Scilly DataLab Project
Funded by: NHS Kernow Clinical Commissioning Group
Funding amount: £74,400
Location: °µÍø½âÃÜ and Cornwall, UK
Dates: March 2022 – October 2023
°µÍø½âÃÜ PI: Professor Shang-Ming Zhou 
Project team: Dr Hossein Ahmadi, Paul Abram, Chris Elson, Chris Ireland, Chris Read
 

Overview

The Cornwall health and social care system is currently under significant pressures due to unprecedented demand in unplanned and urgent care. The linked population health management dataset, including a significant quantity of the data from across health and social partners, is not being maximised to provide understanding, insights and potential solutions to the system's pressures.
By developing and using advanced statistics and machine learning techniques, this project aimed to estimate the likelihood of emergency department attendances and emergency admissions; to identify the most influential patient factors associated with increased risk and/or using the unplanned care system; and to provide insights into the urgent care cohort across the system.

This project revealed that machine learning techniques demonstrated robust performances in accurately predicting respiratory patient emergency department attendance patterns with routinely collected healthcare and demographic data.

Our findings suggest machine learning and AI techniques offer a promising way of supporting practical healthcare planning applications, including resource allocation, staffing optimisation and proactive intervention for high-risk individuals. This project highlights the importance of integrating clinical and socioeconomic data to better understand and respond to demand across the healthcare system.

Shang-Ming ZhouProfessor Shang-Ming Zhou
Professor of e-Health

Objectives

The DataLab focused on the respiratory cohort in urgent care to:
  • Identify value hidden in the system data
  • Use advance statistical approaches to provide deeper insights
  • Provide data visualisations of the flow and key insights
  • Develop tools to support the system in understanding the urgent care system and its partners
  • Develop risk stratification models to support clinical decision-making
  • Explore how predictive models can help forecast future demand and assist Royal Cornwall Hospitals NHS Trust in planning their emergency response and resource allocation.
Medical staff using a tablet

Context of the issue

The Cornwall health and social care system is facing increasing pressure from rising demand for urgent and emergency care. Although large volumes of data are collected across health and social care services, this data is not always fully utilised to generate actionable insights or inform system-wide decision making.

How the project addresses the issue

This project used advanced statistical analysis and machine learning to unlock the potential of linked population health data. By identifying patterns in emergency care usage and key risk factors, it provides insights to support more proactive, data-driven decision-making across the healthcare system.
 
 
 

Centre for Health Technology

Bringing together digital health and health technology expertise from across the University to drive the development, evaluation and implementation of innovative technologies, products, services and approaches to transform health and social care.
 
Centre for Health Technology