Understanding drivers of increased length of stay

30 Second Summary

NCL has seen a rise in long Length of Stay (LoS) over the past 5 years, causing system strain. This project aims to develop a causal model to identify factors affecting LoS and estimate the impact of interventions. Key aims include building qualitative and quantitative models of LoS and modelling changes in response to interventions.

Casual Analysis

Machine Learning

Explainable AI

Synthetic Data

Streamlit

System Dynamics

Length of Stay

Inpatients

Understanding Drivers

North Central London (NCL) has observed a consistent increase in long Length of Stay (LoS) over the past 5 years. Increasing length of stay means a higher capacity is needed for the same demand which causes system strain.

Stakeholders are interested in understanding why length of stay has increased within NCL so that that can introduce appropriate measures to reduce it.

This project proposes to develop a causal model that can be used to identify relations between admission features, operations and length of stay. The model will be used to understand site level drivers to estimate the potential impact of proposed interventions.

Key Aims:

To build a qualitative model of the drivers of Length of Stay in acute providers in NCL
Build a quantitative model(s) of LoS
Model changes in LoS in response to interventions (stretching)