A ripple effect in prehospital stroke patient care

被引:8
作者
Lee, Brandon W. [1 ]
Yoon, Jiho [2 ]
Lee, Seung Jun [2 ]
机构
[1] Univ Dayton, Sch Business Adm, Dayton, OH 45469 USA
[2] Chung Ang Univ, Sch Business Adm, Seoul, South Korea
关键词
Ripple effect; prehospital stroke care; ripple cancelling effect; centralised prehospital system; healthcare operations; SUPPLY CHAIN; DESIGN; RESILIENCE; DISRUPTION; CASCADE; SCALE; VALIDATION; TIME;
D O I
10.1080/00207543.2020.1825862
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
We examine a ripple effect in prehospital stroke care processes. Stroke patient care in prehospital stages is provided by emergency medical services (EMS). We divide EMS processes into three segments: dispatcher, field provider service without a patient (i.e., en route to the patient scene), and field provider service with a patient (i.e., transporting the patient to a hospital). We use both empirical and analytical models in this study. The results of the empirical analysis suggest that the dispatcher's stroke identification can influence the time performance of the dispatch center itself and of the subsequent downstream processes of the prehospital stroke care, indicating a potential ripple effect in the care system. Our analytical models demonstrate the impact of misidentification (i.e., a disruptive event at the dispatcher stage) and the unavailability of an Advanced Life Support (ALS) ambulance (i.e., a disruptive event at the field provider stage) on the severity of the stroke patient's prehospital condition. The models indicate that there is an optimal diagnostic time on the part of the dispatcher that minimizes the adverse consequences throughout the prehospital stages of care under disruptive events, and that a centralized system can mitigate a ripple effect in prehospital stroke care.
引用
收藏
页码:168 / 187
页数:20
相关论文
共 49 条
[1]  
American Stroke Association, 2020, STROK
[2]  
[Anonymous], 2019, INT J PROD RES, DOI DOI 10.1080/00207543.2019.1627438
[3]   An Empirically Derived Framework of Global Supply Resiliency [J].
Blackhurst, Jennifer ;
Dunn, Kaitlin S. ;
Craighead, Christopher W. .
JOURNAL OF BUSINESS LOGISTICS, 2011, 32 (04) :374-391
[4]   Stock allocation in a two-echelon distribution system controlled by (s, S) policies [J].
Chen, Haoxun ;
Dai, Bo ;
Li, Yuan ;
Zhang, Yidong ;
Wang, Xiaoqing ;
Deng, Yuming .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2022, 60 (03) :894-911
[5]   Cascade effects of medical technology [J].
Deyo, RA .
ANNUAL REVIEW OF PUBLIC HEALTH, 2002, 23 :23-44
[6]   Ripple effect in the supply chain: an analysis and recent literature [J].
Dolgui, Alexandre ;
Ivanov, Dmitry ;
Sokolov, Boris .
INTERNATIONAL JOURNAL OF PRODUCTION RESEARCH, 2018, 56 (1-2) :414-430
[7]   Effect of treatment delay, age, and stroke severity on the effects of intravenous thrombolysis with alteplase for acute ischaemic stroke: a meta-analysis of individual patient data from randomised trials [J].
Emberson, Jonathan ;
Lees, Kennedy R. ;
Lyden, Patrick ;
Blackwell, Lisa ;
Albers, Gregory ;
Bluhmki, Erich ;
Brott, Thomas ;
Cohen, Geoff ;
Davis, Stephen ;
Donnan, Geoffrey ;
Grotta, James ;
Howard, George ;
Kaste, Markku ;
Koga, Masatoshi ;
von Kummer, Ruediger ;
Lansberg, Maarten ;
Lindley, Richard I. ;
Murray, Gordon ;
Olivot, Jean Marc ;
Parsons, Mark ;
Tilley, Barbara ;
Toni, Danilo ;
Toyoda, Kazunori ;
Wahlgren, Nils ;
Wardlaw, Joanna ;
Whiteley, William ;
del Zoppo, Gregory J. ;
Baigent, Colin ;
Sandercock, Peter ;
Hacke, Werner .
LANCET, 2014, 384 (9958) :1929-1935
[8]  
Fitzsimmons J.A., 2010, Service Management
[9]  
Operations, Strategy, Information Technology
[10]  
Gardett I., 2017, EMERGENCY DISPATCH R, V5