Every day the NHS spends around £6 million on falls; falls occurring in hospitals account for 25% of this expenditure. The human cost of falls ranges from distress and loss of confidence to head injuries, bone fractures, and even death. Our research will use routine data from electronic patient records and incident reporting systems to better understand why patients fall in hospitals, with a particular emphasis on under-represented groups (e.g. patients with English as a second language). We will develop and evaluate an integrated informatics platform that applies Natural Language Processing to falls documentation to provide semi-automated, near-real-time reports to Imperial College Healthcare NHS Trust for coordinated safety monitoring and improvement. 

Ovarian cancer is a devastating disease with most patients presenting at an advanced, incurable stage. There is a wealth of data held across NHS systems that could help improve outcomes for such patients but it is often written in clinical notes across the electronic health record, and not held in a single location. This makes finding all the relevant information quickly challenging, with the potential for some aspects to be missed. 

Our team of healthcare professionals, researchers, and data scientists have built a new system using advanced text analysis called natural language processing bringing important and relevant clinical facts together into a single location such that it can be easily accessed by the multi-disciplinary cancer team. We are now evaluating the impact of the system on delivering benefits for patients and their outcomes, improving data quality, and saving money through improved processes and time savings.