本文目的是要介紹海量資料分析在醫瘡領域的應用。海量資料的定義是「超過典型資料庫軟體工具所能擷取、儲存、處理和分析能力的資料」，這定義是非常相對性，會隨著年代、產業與專業領域的不同而有所不同。海量資料分析之所以受到重視，是因為近年來半結構化資料(譬如電郵、臉書、簡訊與網路搜尋紀錄等)與非結構化資料(譬如手機照片與網路影片等)的急速増加，再加上一些資訊技術的突破。本文接著介紹十五種海量資料分析在醫瘡領域的應用模式，其中比較重要的模式包括：相對瘡效研究、臨床決策支援系統、醫瘡資料的透明化、遠端病患監測、進階分析應用於病患侧寫、藥物不良反應與再定位分析、個人化醫瘡、整合相關資料庫、線上平台與社群等。最後再檢視台灣推動醫瘡相關海量資料分析的利基與障礙。

The aim of this paper was to demonstrate the applications of big data analysis in healthcare. The definition of big data is “datasets whose size is beyond the ability of typical database software tools to capture, store, manage, and analyze”，which is very relative and differs in different years, industries and professionals. Why the big data analysis matters? It is due to the burgeoning increase of semi-structured data (such as e-mail, cellular phone message, face book and records of web searches etc.) and unstructured data (such as photo and video posted on web etc.) during the past few years and the advance of information technology in managing big data. We then illustrated 15 models of big data analysis in healthcare and most promising models are comparative effectiveness research, clinical decision support systems, transparency about medical data, remote patient monitoring, advanced analytics applied to patient profiles, drug adverse effects and repositioning analysis, personalized medicine, aggregating and synthesizing patient clinical records and claims datasets, and online platforms and communities. Finally, we examined the niches and barriers in developing big data analysis models in healthcare in Taiwan.