Title: Data driven modelling of emergency surgical services
Funder: The Academy of Medical Sciences

Abstract: The aim of the project is to understand and optimise emergency surgical patient services by combining patient characteristics and healthcare system metrics to develop improved predictive modelling of their stays and resource needs. Initially I will extend on my previous work and use network analysis to develop timeseries of networks metrics that represent healthcare system stress. These system metrics will be correlated to less comprehensive symptoms of strain, such as ED breach percentage. Secondly, I will analyse patient trajectories, consisting of clinical, physiological and operational information, by using retrospective data from electronic health records (EHR) available at Cambridge University hospitals. Machine learning clustering algorithms will be applied to identify latent groups with common trajectory characteristics, which I will combine with the system metrics representing non-clinical factors for improved predictive modelling and insight into resource needs. The network representation and cluster characteristics will then be used to populate an in-silico simulation of emergency surgical patient perioperative care. This will allow me to accurately model key health service outcomes, particularly effects of perturbations such as arrival of multi-trauma or infective outbreaks. It will also allow the development of testable interventions to improve care, optimise resource utilisation and allow planning for events that stress the provision of services. The project uses newly available resources in form of longitudinal EHR, machine learning and network analysis to bring a data-driven approach to health services research. The combination of my computational expertise and clinical insight will allow me to develop novel methods that will improve care.

Lay abstract: The aim of this research is to better understand and plan for the needs of patients admitted to hospital for emergency surgery. Hospital services are increasingly stressed and to deliver good care we must understand the resources patients need. Hospitals are complex systems with many pieces that dependent on each other. For improvement projects to be successful and resources to be allocated efficiently it is vital that we understand this system and its performance. I previously used a mathematical method called network analysis to demonstrate how complex the system of emergency surgery is, and which departments are integral to the patients’ care. Current measures of hospital performance (e.g. bed occupancy) do not reflect the complexity of the system. In this project I have three aims. Firstly, I will build on my previous work to develop improved measures of hospital system performance based on network analysis. Next, I will use anonymous data from the electronic records in our hospital to find groups of patients that share similar features in terms of their background, admission experience and system performance during their stay. These groups will then allow me to predict the resources needed to care for them. Finally, I will use this to build a computer simulation showing the effects of specific events, e.g. an infectious outbreak in a ward or multiple trauma patients arriving unexpectedly. Additionally, it will be a tool to test the positive and negative repercussions of improvement projects, e.g. increased beds in intensive care, in a safe setting.

Research activity codes: 5.4 SURGERY;8.1 ORGANISATION AND DELIVERY OF SERVICES
Health categories: GENERIC HEALTH RELEVANCE