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BMC Health Services Research
Published in: BMC Health Services Research 1/2024

Open Access 01-12-2024 | Atrial Fibrillation | Research

Optimizing the management of electrophysiology labs in Chinese hospitals using a discrete event simulation tool

Authors: Wenjuan Lin, Lin Zhang, Shuqing Wu, Fang Yang, Yueqing Zhang, Xiaoying Xu, Fei Zhu, Zhen Fei, Lihua Shentu, Yi Han

Published in: BMC Health Services Research | Issue 1/2024

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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.
Literature
1.
2.
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.PubMed
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.CrossRefPubMed
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.CrossRef
6.
Wang TJ, Larson MG, Levy D, Vasan RS, Leip EP, Wolf PA, et al. Temporal relations of atrial fibrillation and congestive heart failure and their joint influence on mortality: the Framingham Heart Study. Circulation. 2003;107(23):2920–5.CrossRefPubMed
7.
Bunch TJ, Weiss JP, Crandall BG, May HT, Bair TL, Osborn JS, et al. Atrial fibrillation is independently associated with senile, vascular, and Alzheimer’s dementia. Heart Rhythm. 2010;7(4):433–7.CrossRefPubMed
8.
Miyasaka Y, Barnes ME, Bailey KR, Cha SS, Gersh BJ, Seward JB, et al. Mortality trends in patients diagnosed with first atrial fibrillation: a 21-year community-based study. J Am Coll Cardiol. 2007;49(9):986–92.CrossRefPubMed
9.
Benjamin EJ, Wolf PA, D’Agostino RB, Silbershatz H, Kannel WB, Levy D. Impact of atrial fibrillation on the risk of death: the Framingham Heart Study. Circulation. 1998;98(10):946–52.CrossRefPubMed
10.
Rienstra M, Lubitz SA, Mahida S, Magnani JW, Fontes JD, Sinner MF, et al. Symptoms and functional status of patients with Atrial Fibrillation: state-of-the-art and Future Research opportunities. Circulation. 2012;125(23):2933–43.CrossRefPubMedPubMedCentral
11.
Shi S, Tang Y, Zhao Q, Yan H, Yu B, Zheng Q, et al. Prevalence and risk of atrial fibrillation in China: a national cross-sectional epidemiological study. Lancet Reg Health West Pac. 2022;23:100439.CrossRefPubMedPubMedCentral
12.
Fang EF, Scheibye-Knudsen M, Jahn HJ, Li J, Ling L, Guo H, et al. A research agenda for aging in China in the 21st century. Ageing Res Rev. 2015;24(Pt B):197–205.CrossRefPubMedPubMedCentral
13.
Kneeland PP, Fang MC. Trends in catheter ablation for atrial fibrillation in the United States. J Hosp Med. 2009;4(7):E1–5.CrossRefPubMed
14.
Liu Y, Zhong L, Yuan S, van de Klundert J. Why patients prefer high-level healthcare facilities: a qualitative study using focus groups in rural and urban China. BMJ Glob Health. 2018;3(5):e000854.CrossRefPubMedPubMedCentral
15.
Chen Z, Chen X, Gan X, Bai K, Baležentis T, Cui L. Technical efficiency of Regional Public hospitals in China based on the three-stage DEA. Int J Environ Res Public Health. 2020;17(24):E9383.CrossRef
16.
17.
Jun JB, Jacobson SH, Swisher JR. Application of discrete-event simulation in health care clinics: a survey. J Oper Res Soc. 1999;50(2):109–23.CrossRef
18.
19.
20.
Karnon J, Stahl J, Brennan A, Caro JJ, Mar J, Möller J, et al. Modeling using discrete event simulation: a report of the ISPOR-SMDM modeling Good Research practices Task Force–4. Value Health J Int Soc Pharmacoeconomics Outcomes Res. 2012;15(6):821–7.CrossRef
21.
Wang Y, Zhao Y, Zhou K, Zei PC, Wang Y, Cheng H, et al. Intracardiac echocardiography is a safe and effective alternative to transesophageal echocardiography for left atrial appendage thrombus evaluation at the time of atrial fibrillation ablation: the ICE-TEE study. Pacing Clin Electrophysiol PACE. 2023;46(1):3–10.CrossRefPubMed
22.
Tripathi B, Arora S, Kumar V, Abdelrahman M, Lahewala S, Dave M, et al. Temporal trends of in-hospital complications associated with catheter ablation of atrial fibrillation in the United States: an update from Nationwide Inpatient Sample database (2011–2014). J Cardiovasc Electrophysiol. 2018;29(5):715–24.CrossRefPubMed
23.
Mathew ST, Po SS. Atrial Fibrillation catheter ablation: overcoming complications and improving success. J Innov Card Rhythm Manag. 2017;8(10):2874–85.CrossRefPubMedPubMedCentral
24.
Wu L, Narasimhan B, Ho KS, Zheng Y, Shah AN, Kantharia BK. Safety and complications of catheter ablation for atrial fibrillation: predictors of complications from an updated analysis the National Inpatient database. J Cardiovasc Electrophysiol. 2021;32(4):1024–34.CrossRefPubMed
25.
Akula N, Mariam D, Luthra W, Edward P, Katz FJ, Levi DA. Safety of same day discharge after Atrial Fibrillation ablation. J Atr Fibrillation. 2020;12(5):2150.CrossRef
26.
Felder S. The variance of length of Stay and the optimal DRG outlier payments. Int J Health Care Finance Econ. 2009;9(3):279–89.CrossRefPubMed
27.
Ma Y, Wang W. The impact of diagnosis related group payment on the performance of public hospitals. Am J Transl Res. 2021;13(6):6796–801.PubMedPubMedCentral
28.
Kirchhof P, Benussi S, Kotecha D, Ahlsson A, Atar D, Casadei B, et al. 2016 ESC guidelines for the management of atrial fibrillation developed in collaboration with EACTS. Eur Heart J. 2016;37(38):2893–962.CrossRefPubMed
29.
Haegeli LM, Calkins H. Catheter ablation of atrial fibrillation: an update. Eur Heart J. 2014;35(36):2454–9.CrossRefPubMed
30.
Reddy VY, Dukkipati SR, Neuzil P, Natale A, Albenque JP, Kautzner J, et al. Randomized, controlled trial of the Safety and Effectiveness of a contact force-sensing irrigated catheter for ablation of Paroxysmal Atrial Fibrillation: results of the TactiCath Contact Force ablation catheter study for Atrial Fibrillation (TOCCASTAR) Study. Circulation. 2015;132(10):907–15.CrossRefPubMed
31.
Elisabeth Noten AM, Kis Z, Akca F, Bhagwandien R, Wijchers S, Yap SC, et al. Robotic navigation shows superior improvement in efficiency for atrial fibrillation ablation. J Atr Fibrillation. 2019;11(5):2108.CrossRefPubMedPubMedCentral
32.
Richard Tilz R, Sano M, Vogler J, Fink T, Saraei R, Sciacca V, et al. Very high-power short-duration temperature-controlled ablation versus conventional power-controlled ablation for pulmonary vein isolation: the fast and furious - AF study. Int J Cardiol Heart Vasc. 2021;35:100847.PubMedPubMedCentral
33.
He AJ. Scaling-up through piloting: dual-track provider payment reforms in China’s health system. Health Policy Plan. 2023;38(2):218–27.CrossRefPubMed
34.
Kowalski M, DeVille JB, Svinarich JT, Dan D, Wickliffe A, Kantipudi C, et al. Using Discrete Event Simulation to Model the Economic Value of Shorter Procedure Times on EP Lab Efficiency in the VALUE PVI study. J Invasive Cardiol. 2016;28(5):176–82.PubMed
35.
36.
Hamrock E, Paige K, Parks J, Scheulen J, Levin S. Discrete event simulation for healthcare organizations: a tool for decision making. J Healthc Manag Am Coll Healthc Exec. 2013;58(2):110–24. discussion 124–125.
37.
38.
Proudlove NC, Black S, Fletcher A. OR and the challenge to improve the NHS: modelling for insight and improvement in in-patient flows. J Oper Res Soc. 2007;58(2):145–58.CrossRef
39.
Cai H, Jia J. Using Discrete Event Simulation (DES) to support performance-driven Healthcare Design. HERD. 2019;12(3):89–106.CrossRefPubMed
Metadata
Title
Optimizing the management of electrophysiology labs in Chinese hospitals using a discrete event simulation tool
Authors
Wenjuan Lin
Lin Zhang
Shuqing Wu
Fang Yang
Yueqing Zhang
Xiaoying Xu
Fei Zhu
Zhen Fei
Lihua Shentu
Yi Han
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Health Services Research / Issue 1/2024
Electronic ISSN: 1472-6963
DOI
https://doi.org/10.1186/s12913-024-10548-5

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