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  1. NTU Theses and Dissertations Repository
  2. 公共衛生學院
  3. 健康政策與管理研究所
請用此 Handle URI 來引用此文件: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99890
標題: 探討醫療聯盟對分級醫療轉銜照護推動之影響 - 以北部某醫學中心為例
Exploring the Impact of Provider Alliances on the Hierarchical Referral: A Case Study of a Medical Center in Northern Taiwan
作者: 楊亦平
I-PING YANG
指導教授: 郭年真
Raymond N. Kuo
關鍵字: 分級醫療,轉診,醫療聯盟,資訊科技,
Hierarchical Medical Care,Referral,Medical Alliance,Information Technology,
出版年 : 2025
學位: 碩士
摘要: 研究背景
隨著臺灣邁入高齡化社會,醫療的需求日漸增加,為能善用醫療資源,衛生福利部自2017年起推動「分級醫療,落實雙向轉診」,衛生福利部中央健保署為落實分級醫療政策執行,自2018年起要求醫學中心、區域醫院配合執行門診減量,超額門診量不給付醫院醫療費用。為配合國家政策及維持醫院穩定經營,全臺各醫療機構紛紛成立分級醫療策略聯盟,投入推動分級醫療讓醫療永續的行列。
北市某醫學中心(以下簡稱本研究個案醫院)為配合國家政策推廣分級醫療與雙向轉診,於2018年9月啟動「本研究個案醫院醫療體系星月計畫」,透過與各層級的醫療院所聯盟合作以健全醫療體系垂直整合健康照護網絡,並建有專屬醫療聯盟合作院所的資訊平台,以提升雙向轉診之醫療轉銜照護服務及更有效的資訊傳遞。
本研究欲以本研究個案醫院為例,進一步探討醫療機構採以醫療聯盟方式,並善用資訊科技建置聯盟院所專用之資訊平台,是否有助於醫療機構於分級醫療制度之推行與門診轉出,此外並分析本研究個案醫院在推行分級醫療實際面臨的挑戰與成效。
研究方法
本研究分兩部分包括個案分析及回溯性量性分析,以本研究個案醫院2024年1月1日起至2024年12月31日止經其他院所轉診,並於本研究個案醫院完成門診留有紀錄者,進一步分析門診轉出情形。資料來源為本研究個案醫院轉診報表,研究資料包括門診轉出情形(轉入、轉出)、使用資訊平台、及就診科別、疾病類別及年齡、性別、教育程度等。研究統計採描述性統計及推論性統計,推論性統計包括雙變項分析及二元羅吉斯迴歸分析與各變項之關聯。
研究結果
本研究經二元羅吉斯迴歸分析顯示,醫療聯盟轉診類型與疾病類別對於門診轉出是重要的影響因素。整體迴歸模型預測力達81%,ROC分析AUC(曲線下面積)為70.9%,高於0.7的標準、誤差值僅0.003且統計上達顯著差異(p<.001),數據顯示此模型在預測門診轉出有良好的顯著效果。
結論
醫療聯盟有助於門診轉出,科別及疾病類別對於醫療聯盟、門診轉出有連帶影響,年齡亦是影響門診轉出的因素,未來醫療機構為深化分級醫療與轉診,可針對不同科別或疾病擬訂不同的轉診策略。
Background and Motivation
As Taiwan enters an aging society, the demand for medical care has been steadily increasing. In response, the Ministry of Health and Welfare launched the policy of " The hierarchy medical services and referral system " in 2017, aiming to optimize the use of healthcare resources. To implement this policy effectively, the National Health Insurance Administration, Ministry of Health and Welfare mandated from 2018 that medical centers and regional hospitals should reduce outpatient volumes, with no reimbursement provided for services exceeding set limits. In line with this national initiative and to ensure sustainable hospital operations, healthcare institutions across Taiwan have formed strategic alliances to promote hierarchical medical care and contribute to the sustainability of the healthcare system.
A medical center in Taipei (hereafter referred to as the case hospital) initiated the " Patient-centered Seamless Transition and Referral System (P-STARs)" in September 2018 to support national policies on hierarchical care and referral system. Through strategic alliances with medical institutions of various levels, case hospital sought to establish a vertically integrated health care network. Additionally, a dedicated information platform was developed to facilitate cooperation among alliance hospitals, aiming to improve the continuity of care during patient referrals and to enhance information exchange efficiency.
This study takes the case hospital as an example to explore whether forming a medical alliance and utilizing information technology to build a dedicated platform for alliance hospitals contributes to the implementation of hierarchical medical care and the effective referral of outpatient services. Furthermore, the study analyzes the outcomes and practical challenges faced by the case hospital in implementing these policies.
Methodology
This is a retrospective study. The study subjects were patients who were referred through outpatient services and had documented visits at the case hospital from January 1 to December 31, 2024. The data were derived from the case hospital's referral reports and include variables such as referral status (inbound and outbound), use of the information platform, medical departments visited, disease categories, age, gender, and education level. Both descriptive and inferential statistical analyses were conducted. Inferential analysis included bivariate analysis and binary logistic regression to examine associations between variables.
Results
Binary logistic regression analysis revealed that participation in a medical alliance and disease category were significant factors influencing outpatient referral outflow. The overall predictive accuracy of the regression model was 81%, with an ROC AUC (Area Under the Curve) of 70.9%, exceeding the standard threshold of 0.7. The model's standard error was only 0.003, statistical significance was achieved (p < .001), indicating strong statistical significance and robust predictive performance for outpatient referral behavior.
Conclusion
Alliances within hierarchical healthcare organizations exert a significantly positive influence on outpatient referrals. Both medical specialties and disease classifications are correlated with the frequency of such referrals. Additionally, age serves as a significant factor in determining outpatient referral patterns. To optimize tiered healthcare systems and referral processes in the future, medical institutions should consider developing specialized referral strategies tailored to specific specialties and disease categories.
URI: http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/99890
DOI: 10.6342/NTU202503799
全文授權: 未授權
電子全文公開日期: N/A
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