| 中文摘要 |
目標：呼吸器依賴患者整合性照護制度（簡稱IDS）已執行十八年久，本研究目的是釐清歷年來收治呼吸器依賴病患（簡稱VDP）醫院之集群網絡的結構變化與特性差異。方法：採用社會網絡分析法（SNA），針對以2000—2013年中區業務組之所有申報VDP醫院之健保資料，分析醫院間病患移動的互動脈絡，並以程度中心性指標，結合圖形理論與地理資訊進行集群結構分析，以及運用ANOVA比較集群網絡內各項特性值之差異性。結果：依歷年VDP人數消長分為成長期、穩定期與緩降期。前三大醫院集群是以醫學中心為核心醫院的網絡，其結構無大幅度變動，僅呈現明顯的集中、地域性區隔分佈。網絡特性值較明顯一致性的變化，包括醫療費用、病患醫院網絡間轉院次數、與每人平均轉院次數，均在緩降期間時顯著降低，但如ICU回轉率、每人平均ICU回轉次數，各集群則是上升後到緩降期無明顯下降，集群網絡程度中心性平均值僅A01與A03有顯著性增加。研究意涵指出集群間、集群內醫院呈現既競爭又依賴的關係，為因應VDP人數遞減，降低病患轉院頻率，增加ICU回轉率似乎成為醫院的因應對策。結論：運用SNA可以更加瞭解在IDS下各時期醫院網絡結構的樣態與特性變化，不但適用來解釋醫院網絡關係與經營現象，做為未來IDS政策發展的參考，亦可做為其他衛生政策之實行、醫院群體反應研究的參考範例。（台灣衛誌2020|39（2）：—214）
Objectives: The integrated delivery system (IDS) for ventilator-dependent patients (VDPs) has been in implementation for more than 18 years. This study clarified structural changes and differences in characteristics among hospital networks admitting VDPs. Methods: Social network analysis (SNA) was used to analyze the health insurance data of VDPs during 2000-2013 from all hospitals a part of the national health insurance administration, Central Division. Patient transfer and interaction between hospitals were analyzed| thereafter, degree centrality index was used to combine graph theory and geographical information for cluster structure analysis. Finally, analysis of variance (ANOVA) was used to test the differences in various characteristics between hospital networks. Results: According to the number of VDPs, three phases were classified: growing phase, stable phase, and decline phase. The top three hospital clusters comprised the networks in which medical centers operated as core hospitals, showing a clear concentration and geographical boundary over time. Medical expenditures of each cluster network, the average number of patient transfers between hospitals, and the average number of transfers per patient showed a consistent pattern of changes across the networks. However, intensive care unit (ICU) return rate and average number of ICU returns per patient did not decrease significantly during the three phases in each cluster. The average of degree centrality of networks A01 and A03 showed a significant increase. The findings imply that hospitals within and between the cluster networks exhibit competitive and interdependent relationships. In response to the declining number of VDPs, which reduced the frequency of patient transfers, the increased ICU return rate seemed to be a common strategy among hospitals. Conclusions: Employing SNA can broaden the understanding of structures and characteristic changes of hospital networks, which not only helps interpret hospital network relationships and business operating patterns but also can be used as a reference for future IDS policy development. The use of SNA in the IDS study also obtains a reference example for the implementation of other health policies and research into hospital behavior. (Taiwan J Public Health. 2020|39(2):202-) |
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