未來研究方向與建議

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報行為的醫事機構，不過其中有可能會發生將正常機構誤判為異常，造成整體正 確率降低，因此未來可再進一步結合其他工具或方法，來提升判別正確性。

由研究結果歸納得知，本研究之貢獻為結合Benford’s law 數字分析方法並 運用SVM 來做健保費用異常申報的初步檢測，並將檢測結果提供給健保局相關 稽查人員，使之能初步了解哪一些醫事機構可能會有異常申報的情況發生，進而 做深入的審查。如此一來便可減少因傳統隨機抽樣調查所造成的不確定性以及過 多的人力資源浪費。

第二節 未來研究方向與建議

本研究利用健保申報費用建立初步的巨量資料分析方式，但健保申報資料中並非 只有健保申報費用，還包含了許許多多可探索的資料。而未來也可朝著以下幾個 方向來繼續研究:

1、 因本研究所取得異常醫事機構的資料量不夠多，若未來能夠取得更多的 資料，則可使研究結果更符合實際健保資料庫之情況。

2、 本研究是以健保申報費用進行實證，但健保申報資料中並非只有健保申 報費用，從用藥清單、國際疾病分類及治療方法；到病人的就診日期、

診療結束日期等。這些資料皆可利用資料分析的方式找出是否有醫療處 置不當的情況發生，而不是被動的等到病人與醫事機構發生醫療糾紛時，

才介入調查。

3、 從納保人的方面來看的話，也可利用就診日期算出就醫的頻率、就診的 科別及就醫詳細記錄，分析出納保人是否有潛在的疾病或慢性病，如此 一來就可主動通知提早治療，提升全國人民的健康醫療品質。

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4、 健保特類別會將醫事機構大致區分為醫學中心、區域醫院、地區醫院及 基層院所等，未來可針對某一特約類別的醫院進行分析，並找出不同醫 事機構類別間，申報費用之特性。

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