This project involve implementing a system-wide early warning tool for acute pathways that will allow better response planning, using advanced data analysis techniques.
This project aims to improve the way in which data visualisation is used by clinicians so that it can lead to improvements in quality of care, and will explore clinicians’ requirements of data tools and adapt existing data visualisation outputs.
This project will involve using a best-in-class approach to improving patient outcomes and service efficiency across the joint pain and elective care pathway, with analysts, managers and practitioners working to diagnose whole-system problems.
This project will involve developing a multidisciplinary team approach to data science, including analysts, clinicians and operational staff, which will help analytics to answer more clinically relevant questions.
This project will involve increasing mental health analytic capacity and capability to allow better understanding of the problems faced by people accessing services, and to improve planning and delivery of services.
Mind the gap: developing effective communications between decision-makers and analysts in health and care
This project aims to close the gap between decision-makers and analysts in health care organisations by developing a framework and a set of recommendations to improve communication and the transfer of information.
This project will involve using an existing linked dataset to explore two domains – social isolation and troubled families – and show how connected data can enhance insight.
This project will involve developing a versatile pathway tool that will allow NHS managers to understand and model patient flow, and so improve understanding of how care pathways are performing.
Use of novel modelling techniques and routinely collected data to explore responses to winter pressures
This project will involve refining a novel modelling technique to simulate patient flow using readily available routine data in order to provide new flow models to support decision making and change management.
This project will involve developing a probabilistic early warning system based on machine learning/Artificial Intelligence technologies to promptly identify high-risk patients needing urgent care.