Network analysis of stroke systems of care in Korea
Background The landscape of stroke care has shifted from stand-alone hospitals to cooperative networks among hospitals. Despite the importance of these networks, limited information exists on their characteristics and functional attributes.Methods We extracted patient-level data on acute stroke care...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2024-04-01
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| Series: | BMJ Neurology Open |
| Online Access: | https://neurologyopen.bmj.com/content/6/1/e000578.full |
| _version_ | 1797216015068692480 |
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| author | Juneyoung Lee Ji Sung Lee Jun Yup Kim Jihoon Kang Hee-Joon Bae Kyung Bok Lee Philip B Gorelick Yong-Jin Cho Hong-Kyun Park Seong Eun Kim Hyunjoo Song Ah Rum Choi Mi Yeon Kang |
| author_facet | Juneyoung Lee Ji Sung Lee Jun Yup Kim Jihoon Kang Hee-Joon Bae Kyung Bok Lee Philip B Gorelick Yong-Jin Cho Hong-Kyun Park Seong Eun Kim Hyunjoo Song Ah Rum Choi Mi Yeon Kang |
| author_sort | Juneyoung Lee |
| collection | DOAJ |
| description | Background The landscape of stroke care has shifted from stand-alone hospitals to cooperative networks among hospitals. Despite the importance of these networks, limited information exists on their characteristics and functional attributes.Methods We extracted patient-level data on acute stroke care and hospital connectivity by integrating national stroke audit data with reimbursement claims data. We then used this information to transform interhospital transfers into a network framework, where hospitals were designated as nodes and transfers as edges. Using the Louvain algorithm, we grouped densely connected hospitals into distinct stroke care communities. The quality and characteristics in given stroke communities were analysed, and their distinct types were derived using network parameters. The clinical implications of this network model were also explored.Results Over 6 months, 19 113 patients with acute ischaemic stroke initially presented to 1009 hospitals, with 3114 (16.3%) transferred to 246 stroke care hospitals. These connected hospitals formed 93 communities, with a median of 9 hospitals treating a median of 201 patients. Derived communities demonstrated a modularity of 0.904, indicating a strong community structure, highly centralised around one or two hubs. Three distinct types of structures were identified: single-hub (n=60), double-hub (n=22) and hubless systems (n=11). The endovascular treatment rate was highest in double-hub systems, followed by single-hub systems, and was almost zero in hubless systems. The hubless communities were characterised by lower patient volumes, fewer hospitals, no hub hospital and no stroke unit.Conclusions This network analysis could quantify the national stroke care system and point out areas where the organisation and functionality of acute stroke care could be improved. |
| first_indexed | 2024-04-24T11:39:14Z |
| format | Article |
| id | doaj.art-378b58a98fc6423bbd964c84e57a7b62 |
| institution | Directory Open Access Journal |
| issn | 2632-6140 |
| language | English |
| last_indexed | 2024-04-24T11:39:14Z |
| publishDate | 2024-04-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Neurology Open |
| spelling | doaj.art-378b58a98fc6423bbd964c84e57a7b622024-04-10T03:05:08ZengBMJ Publishing GroupBMJ Neurology Open2632-61402024-04-016110.1136/bmjno-2023-000578Network analysis of stroke systems of care in KoreaJuneyoung Lee0Ji Sung Lee1Jun Yup Kim2Jihoon Kang3Hee-Joon Bae4Kyung Bok Lee5Philip B Gorelick6Yong-Jin Cho7Hong-Kyun Park8Seong Eun Kim9Hyunjoo Song10Ah Rum Choi11Mi Yeon Kang1218 Department of Biostatistics, Korea University College of Medicine, Seoul, KoreaClinical Research Center, Asan Institute for Life Sciences, Asan Medical Center, University of Ulsan College of Medicine, Seoul, KoreaSeoul National University Bundang Hospital, Seoul National University College of Medicine, Department of Neurology, Seongnam, South KoreaSeoul National University Bundang Hospital, Seoul National University College of Medicine, Department of Neurology, Seongnam, South KoreaSeoul National University Bundang Hospital, Seoul National University College of Medicine, Department of Neurology, Seongnam, South KoreaDepartment of Neurology, Soonchunhyang University Hospital, Seoul, Korea2Michigan State University, Grand Rapids, Michigan, USA11 Department of Neurology, Inje University Ilsan Paik Hospital, Inje University College of Medicine, Goyang, KoreaDepartment of Neurology, Inje University Ilsan Paik Hospital, Inje University College of Medicine, Goyang, Korea8 Department of Opthalmology, CHA Bundang Medical Center, CHA Univerisity, Seongnam, South KoreaSchool of Computer Science and Engineering, Soongsil University, Seoul, Korea (the Republic of)Health Insurance Review & Assessment Service, Wonju, Korea (the Republic of)Health Insurance Review & Assessment Service, Wonju, Korea (the Republic of)Background The landscape of stroke care has shifted from stand-alone hospitals to cooperative networks among hospitals. Despite the importance of these networks, limited information exists on their characteristics and functional attributes.Methods We extracted patient-level data on acute stroke care and hospital connectivity by integrating national stroke audit data with reimbursement claims data. We then used this information to transform interhospital transfers into a network framework, where hospitals were designated as nodes and transfers as edges. Using the Louvain algorithm, we grouped densely connected hospitals into distinct stroke care communities. The quality and characteristics in given stroke communities were analysed, and their distinct types were derived using network parameters. The clinical implications of this network model were also explored.Results Over 6 months, 19 113 patients with acute ischaemic stroke initially presented to 1009 hospitals, with 3114 (16.3%) transferred to 246 stroke care hospitals. These connected hospitals formed 93 communities, with a median of 9 hospitals treating a median of 201 patients. Derived communities demonstrated a modularity of 0.904, indicating a strong community structure, highly centralised around one or two hubs. Three distinct types of structures were identified: single-hub (n=60), double-hub (n=22) and hubless systems (n=11). The endovascular treatment rate was highest in double-hub systems, followed by single-hub systems, and was almost zero in hubless systems. The hubless communities were characterised by lower patient volumes, fewer hospitals, no hub hospital and no stroke unit.Conclusions This network analysis could quantify the national stroke care system and point out areas where the organisation and functionality of acute stroke care could be improved.https://neurologyopen.bmj.com/content/6/1/e000578.full |
| spellingShingle | Juneyoung Lee Ji Sung Lee Jun Yup Kim Jihoon Kang Hee-Joon Bae Kyung Bok Lee Philip B Gorelick Yong-Jin Cho Hong-Kyun Park Seong Eun Kim Hyunjoo Song Ah Rum Choi Mi Yeon Kang Network analysis of stroke systems of care in Korea BMJ Neurology Open |
| title | Network analysis of stroke systems of care in Korea |
| title_full | Network analysis of stroke systems of care in Korea |
| title_fullStr | Network analysis of stroke systems of care in Korea |
| title_full_unstemmed | Network analysis of stroke systems of care in Korea |
| title_short | Network analysis of stroke systems of care in Korea |
| title_sort | network analysis of stroke systems of care in korea |
| url | https://neurologyopen.bmj.com/content/6/1/e000578.full |
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