Developing Data Science Approaches to Improve Paediatric Critical Care Patient Flows and its Related Health Economics Benefits in Scotland

Paediatric Critical Care (PCC) is an essential service providing health care to critically ill children. In Scotland the PCC service is delivered by two Paediatric Critical Care Units (PCCU); one in Edinburgh, and one in Glasgow. In recent years there has been increasing demand for PCCU beds with a PCC bed crisis being reported. In cases where the critical care units run out of available beds, patients must be diverted to a unit with an available bed or urgent care must be delayed directly impacting patient care. Reasons for the increasing demand for PCCU beds range from seasonal respiratory infections to delays in patient discharges as well as changes in PCC patient characteristics.
Bed usage and staffing data is routinely collected from the PCCUs as well as individual patient level data including admission, discharge and physiological data. This gives a unique opportunity to develop powerful data driven tools which could be used to optimise and improve patient flow and ultimately patient care in the PCC.
The ability to simulate patient flow through a PCCU could provide valuable insights into operational bottlenecks and inefficiencies. Simulation models such as Discrete Event Simulation (DES), System Dynamics (SD) and Agent Based Systems (ABS) within healthcare are well established with proven benefits once developed [1][2][3]. An accurate and representative simulation gives the opportunity to test possible scenarios for improvement before integration into hospital systems unlike other approaches which typically rely heavily on expert opinion as an approach towards improvement.
In recent years Machine Learning (ML) coupled with the availability of large datasets and increase in computational performance has revolutionised many domains. In healthcare this is no different, with the prediction of patient care trajectories using ML becoming an increasingly active area of research [4][5]. With the wealth of data routinely available from the PCCUs at an individual patient level, the development of machine learning models which can predict a probable care trajectory for how a patient may transition through the hospital based on a patient physiological phenotype could be employed to plan future care and improve patient flow.
This project will develop a simulation model which can be used to simulate patient flow in the PCCUs, identify bottlenecks and then test solutions for improvement through simulating the proposed scenarios. Secondarily machine learning methods will be developed which allow for the prediction of patient care trajectories from routinely collected clinical data to aid in the planning of patient care. Finally, it will include a thorough health economic analysis to fully quantify the impact this data driven approach brings.


References
[1] Kusum S Mathews and Elisa F Long. A conceptual framework for improving critical care patient flow and bed use. Annals of the American Thoracic Society, 12(6):886-894, 2015.
[2] Syed Mohiuddin, John Busby, Jelena Savovic, Alison Richards, Kate Northstone, William Hollingworth, Jenny L Donovan, and Christos Vasilakis. Patient flow within UK emergency departments: a systematic review of the use of computer simulation modelling methods. BMJ open, 7(5), 2017.
[3] Eduardo Cabrera, Manel Taboada, Ma Luisa Iglesias, Francisco Epelde, and Emilio Luque.Optimization of healthcare emergency departments by agent-based simulation. Procedia computer science, 4:1880-1889, 2011.
[4] Trang Pham, Truyen Tran, Dinh Phung, and Svetha Venkatesh. Predicting healthcare trajectories from medical records: A deep learning approach. Journal of biomedical informatics, 69:218-229, 2017.
[5] Hongteng Xu, Weichang Wu, Shamim Nemati, and Hongyuan Zha. Patient flow prediction via discriminative learning of mutually-correcting processes. IEEE transactions on Knowledge and Data Engineering, 29(1):157-171, 2016.