Author:
Lin Wenjuan,Zhang Lin,Wu Shuqing,Yang Fang,Zhang Yueqing,Xu Xiaoying,Zhu Fei,Fei Zhen,Shentu Lihua,Han Yi
Abstract
Abstract
Background
The growing demand for electrophysiology (EP) treatment in China presents a challenge for current EP care delivery systems. This study constructed a discrete event simulation (DES) model of an inpatient EP care delivery process, simulating a generalized inpatient journey of EP patients from admission to discharge in the cardiology department of a tertiary hospital in China. The model shows how many more patients the system can serve under different resource constraints by optimizing various phases of the care delivery process.
Methods
Model inputs were based on and validated using real-world data, simulating the scheduling of limited resources among competing demands from different patient types. The patient stay consists of three stages, namely: the pre-operative stay, the EP procedure, and the post-operative stay. The model outcome was the total number of discharges during the simulation period. The scenario analysis presented in this paper covers two capacity-limiting scenarios (CLS): (1) fully occupied ward beds and (2) fully occupied electrophysiology laboratories (EP labs). Within each CLS, we investigated potential throughput when the length of stay or operative time was reduced by 10%, 20%, and 30%. The reductions were applied to patients with atrial fibrillation, the most common indication accounting for almost 30% of patients.
Results
Model validation showed simulation results approximated actual data (137.2 discharges calculated vs. 137 observed). With fully occupied wards, reducing pre- and/or post-operative stay time resulted in a 1–7% increased throughput. With fully occupied EP labs, reduced operative time increased throughput by 3–12%.
Conclusions
Model validation and scenario analyses demonstrated that the DES model reliably reflects the EP care delivery process. Simulations identified which phases of the process should be optimized under different resource constraints, and the expected increases in patients served.
Funder
Johnson & Johnson Medical (Shanghai) Ltd.
Publisher
Springer Science and Business Media LLC
Reference39 articles.
1. Desai DS, Hajouli S. Arrhythmias. In: StatPearls [Internet]. Treasure Island (FL): StatPearls Publishing; 2022 [cited 2022 Oct 17]. Available from: http://www.ncbi.nlm.nih.gov/books/NBK558923/.
2. Kastor JA, Saba MM. Cardiac Arrhythmias. In: eLS [Internet]. John Wiley & Sons, Ltd; 2009 [cited 2022 Sep 26]. Available from: https://onlinelibrary.wiley.com/doi/abs/10.1002/9780470015902.a0002112.pub2.
3. Fuster V, Rydén LE, Cannom DS, Crijns HJ, Curtis AB, Ellenbogen KA, et al. ACC/AHA/ESC 2006 guidelines for the management of patients with Atrial Fibrillation: a report of the American College of Cardiology/American Heart Association Task Force on Practice guidelines and the European Society of Cardiology Committee for Practice Guidelines (Writing Committee to revise the 2001 guidelines for the management of patients with Atrial Fibrillation): developed in collaboration with the European Heart Rhythm Association and the Heart Rhythm Society. Circulation. 2006;114(7):e257–354.
4. Wolf PA, Abbott RD, Kannel WB. Atrial fibrillation as an independent risk factor for stroke: the Framingham Study. Stroke. 1991;22(8):983–8.
5. Hannon N, Sheehan O, Kelly L, Marnane M, Merwick A, Moore A, et al. Stroke Associated with Atrial Fibrillation– Incidence and early outcomes in the North Dublin Population Stroke Study. Cerebrovasc Dis Basel Switz. 2009;29(1):43–9.