Informing patient management through real-world clinical data analytics

The rise of artificial intelligence (AI) has given us unprecedented opportunities to analyse big data sets and to derive insights from them. One such area is healthcare; the UK NHS have internationally unique quantity and quality of patient data by way of digital medical records. The retrospective analysis of those records in specific clinical settings gives us the potential to develop clinical decision support tools that significantly change the way in which patients will be diagnosed and managed in the future.

However, there are significant challenges to realising this vision: Data are mostly stored in NHS-based systems that are not accessible to industry. Businesses may have the skills and resources to develop new products but are not connected to real-world patient care. Meaningful innovation will therefore require close partnership between NHS, academia and industry. This innovation scholar secondment will enable a joint programme by GE Healthcare, the University of Sheffield and the Sheffield Teaching Hospitals NHS Foundation Trust by allowing Dr Jan Wolber from GE Healthcare to be seconded to the University of Sheffield part-time for a duration of three years to be strategically involved in joint research and development in several disease areas.

The expected outcome of this secondment is the delivery of several proof-of-concept analytics applications that can assist clinical decision making in the areas of cardiology, respiratory medicine and neurology. These applications would then be formally developed into products by GE Healthcare for deployment into clinical practice in the UK, hence benefitting patients across the nation and beyond. The applications may deliver benefits in the quality and consistency of care delivery across the NHS as well as enabling early diagnosis and stratification of patients into appropriate care pathways, which in turn can improve outcomes and quality of life, as well as possibly contributing to the reduction of healthcare costs over the life of a patient by making the right treatment decisions at the right time.