全民健康保險自開辦以來，雖使全體國民在醫療照顧方面上有了明顯的提升，但一直以來對於台灣空間醫療資源分配不均的問題，更是急欲改善的重點問題之一。以往我國衛生福利部在探討台灣各區域醫療資源上的分配問題時，往往只看該區域的每萬人口病床數及每萬人口醫療人員數目，若將此兩項變數作為衡量該區域醫療資源是否充足的指標，將得到台灣東部比台灣西部資療資源還充沛的結論，另外這種指標背後的假設為醫療機構與人口是平均分配在該區域上，但此假設與實際情況卻是大相逕庭。若要更公正客觀的去評估該區域醫療資源是否有分配不均的問題，應將就醫移動距離考慮進去。
本研究從就醫流動人次的觀點出發，加入就醫移動距離來探討就醫可近性對於就醫流動人次的影響以期待找出影響民眾就醫的誘因及阻力。在統計方法上，本研究擬利用貝氏分析的方法並結合Huff 模型(又被稱為空間交互模型)，作為我們解釋影響就醫流動人次的現象。本研究發現就醫流動資料矩陣起點的醫院醫事人員數與終點的診所醫事人員數相比或是起點的診所醫事人員數與終點的醫院醫事人員數相比，終點都對於民眾擁有較大的拉力吸引民眾去該地就診。

Since the launch of the National Health Insurance, although all citizens have significantly improved their medical standard, the problem of allocation of medical resources in Taiwan has always been one of the key issues for improvement. In the past, when discussing the allocation of medical resources in various regions of Taiwan, the Ministry of Health and Welfare often only looked at the number of hospital beds per 10000 people and the number of medical personnel per 10000 people in the region. If these two variables are used as indicators to measure the adequacy of medical resources in the region, it will be concluded that there are more abundant medicals resources in east Taiwan than western Taiwan. In addition the assumption is based on that institutions and population are uniformly allocated in the region, but this assumption is quite different from the actual situation. To more equitably and objectively assess the problem of uniformly allocated of medical resources in the region, the distance traveled and the time spent on medical treatment should be taken into account.
This study explored the effect of medical accessibility on the number of medical mobility by the distance traveled to hospital. It is expected to find the incentives and resistances that affect people's medical mobility. The statistical method of Bayesian analysis combined with spatial interaction model was proposed. The study found that the number of hospital medical staff at the starting point compared with the number of clinic medical staff at the end point, or the number of clinic medical staff at the starting point compared with the number of hospital medical staff at the end point, the end point had a greater pull to attract people to the end point.

1.李妙純、沈茂庭(2008)，「全民健保下不同所得群體醫療利用不均因素分析」，台灣衛誌；27：223-231頁。
2.林志銘、林文苑(2012)，「台灣酒精性疾病死亡率之空間聚集分析」，台灣衛誌；31：195-204頁。
3.林民浩、郭年真、陳威全、溫在弘(2016)，「剖析台灣民眾的就醫流動：利用引力模式評估就醫距離與醫療資源分布的影響」，台灣衛誌；35：136-151頁。
4. 林錦鴻、洪乙禎(2010) ，「探討醫療資源和民眾健康差異的相關性－以台灣二十三縣市粗死亡率和事故傷害死亡率為例」，台灣衛誌；29：347-359頁。
5.陳映融、吳治達、潘文驥、陳穆貞、龍世俊、蘇慧貞、張坤城(2016)，「老年人口自殺與熱島效應之空間關聯分析：以2000年至2008年台北都會區生態調查研究為例」，台灣衛誌；35：406-417頁。
6.黃國平、吳青翰(2007) ，「緊急醫療救護系統資源配置及績效評估之
模擬研究—以台南市為例」，台灣衛誌；26：184-195頁。
7. Aitkint, A. , Flowerew, R. (1982). A method of fitting the gravity model based on the Poisson. Journal of regional science, 22(2)：191-202.
8. Dentinho, T , Silveira, P. (2010). Spatial interaction model of land use – An application to Corvo Island from 16th, 19th and 20th centuries. Computers, Environment and urban systems, 34：91-103.
9. Gelfand, A. E. , Smith, A. F. (1990). Sampling-based approaches to calculating marginal densities. Journal of the American statistical association, 85(410)：398–409.
10. Metropolis, N. , Rosenbluth, A. W. , Rosenbluth, M. N. (1953).Equations of state calculations by fast computing machines. Journal of chemical physics, 21(6)：1087–1092.
11. Wilson, A. G.(1974). Some new forms of spatial interaction model ： A review. Transportation research, 9：167-179.