Mapping the patient journey: utilizing clinical informatics for a conceptual approach to identify aspects of emergency department access block

Abstract Background The study aims to map the patient journey in a regional Emergency Department (ED), identify factors causing extended Length of Stay (LOS) in ED during pre-COVID (2016–2019), COVID (2020–2022) and post-COVID (2023), and analyse the patient journey using clinical informatics. Through systematic analysis and root cause identification, the study seeks to establish a learning health system. This system will enhance patient experiences and improve timely and effective care delivery. Methods Summarized data on ED presentations data were sourced from January 2016 to August 2023 for a 294-bed regional hospital in New South Wales (NSW). The patient’s journey from ED to admission inpatient wards was analysed using clinical informatics. The time spent in each segment of the ED journey was analysed separately and subdivided by specialty for further analysis. Results Patient characteristics remained similar year on year. The average ED LOS increased from 5.59 h during the pre-COVID period (2016–2019) to 8.48 h during the COVID period (2020–2022) and 8.52 h during the post-COVID period (2023). The hospital’s bed occupancy rate remained consistent before and during the COVID-19 pandemic, with a slight improvement afterwards, except for a decrease in 2020. The occupancy rate for ED-accessible beds during the pre-COVID period is higher than during the COVID and post-COVID periods. The admission from ED to Inpatient wards decreased from 35% pre-COVID to 28% during COVID. Of the total LOS, the ‘Bed Request’ to ‘Bed Ready’ time segment increased from 11.41% in 2016 to 41.19% in 2022. The admission of medical specialty consistently made up most admissions (32%) which increased during the COVID. The medical specialty also spent the most time in the ED, peaking at 880 min in 2022. Conclusion Clinical Informatics is an effective tool to visualise the bottlenecks in clinical workflows. The ‘Bed Request’ to ‘Bed Ready’ time segment was the largest contributor to ED LOS and may have been attributed to a lack of bed availability. Patients took an average of 50 min to be transferred to Inpatient wards upon admission. Future studies could utilize insights gained to develop specific key performance indicators aimed at improving ED LOS. Moreover, we are currently working on improving real-time access to inpatient beds but are currently hampered by the ‘manual update’ of bed status that is controlled by the individual wards. We aim to build an automated system that can monitor and update bed availability (e.g., patient discharge) with minimal human intervention.