CMAF FFT: Predicting emergency admissions to hospital specialties using real-time data
Channel: Lancaster CMAF
Date: 2024-03-23

This is the fifth webinar in the Season 4 of "Friday Forecasting Talks", hosted by the Centre for Marketing Analytics and Forecasting of Lancaster University, UK.
Follow us on LinkedIn: https://www.linkedin.com/in/lancastercmaf/
CMAF on Twitter: https://twitter.com/LancasterCMAF
Our webpage: https://lancaster.ac.uk/cmaf
CMAF webinars: https://cmaf-fft.lp151.com/

*Content of this video*
00:00 - Introduction
01:25 - Start of the presentation
06:15 - The "typical" approach to predicting emergency demand
08:40 - Engagement and immersion with stakeholders
18:41 - Predictive analytics
30:50 - Implementation
40:15 - Q&A

The abstract: Hospitals in the UK are currently at capacity for much of the time. Bed planners have the difficult job of ensuring enough emergency beds are available within each clinical speciality, to avoid admission delays and outlying patients. In previous work we reported on an analytical pipeline that applied Machine Learning to real-time health record data to predict aggregate numbers of emergency admissions in the next 8 hours. That application has been in operational use by bed planners at UCLH for over a year to help them manage patient flow into the hospital.
Since then, I have been working as an embedded researcher alongside the bed planners at UCLH. Building on a richer knowledge of their role, I will describe the work of bed planners and their use of data products, and explain how we re-formulated the modelling problem and the technical implementation to make our application better meshed with what they need to know. My talk will cover both the academic elements of the work (the problem framing and  formulation, and evaluation of model performance), and the practical aspects of engagement with bed managers and their wider system to achieve an application supported by UCLH as part of their business-as-usual suite of data products.

Speaker: Zella King

Bio: Zella King is a health data scientist in the Clinical Operational Research Unit at UCL.  She applies data analytics, machine learning, programming and organisational psychology as tools for better decision making in healthcare operations. Working at the interface between universities and the NHS, she collaborates with researchers, clinicians, and NHS leaders to address practical challenges in the delivery of healthcare. With others, she developed a real-time application that is in daily use by the patient flow team at University College London Hospital (UCLH) to predict demand for emergency beds, and is lead author on a paper about this work that has been published in Nature Digital Medicine. She is currently working as an embedded researcher at UCLH, helping tackle some of the hospital’s operational problems using OR and data science, and doing capacity-building for NHS analysts. She gained her PhD in Organizational Psychology from Birkbeck College. Her academic career began with 15 years in business schools. Her work on human resources, careers and networks has been published in top business journals. In 2016, concerned about healthcare in ageing societies, she changed fields to focus on health data research. She gained a distinction in UCL's MSc in Health Data Science and was recognised in the Dean’s List for outstanding students in 2020-21.