標題: 應用分析層級程式法(AHP)建立 智慧型醫療穿戴式裝置設計評價模式之研究
Applying analytical hierarchy process (AHP) for intelligent medical wearable device Design estimation
作者: 張恩瑋
Chang, En-Wei
林亭汝
Lin, TING-RU
科技管理研究所
關鍵字: 智慧型醫療穿戴式裝置;應用分析層級程式法;intelligent medical wearable device;Applying analytical hierarchy process (AHP)
公開日期: 2015
摘要: 既智慧型手機後,智慧型穿戴式科技興起,國際調查機構 Gartner 預估, 至 2016 年,全球穿戴式科技裝置產值將上看 100 億美元,而時下最熱門的醫療健 康促進產業也結合了智慧型穿戴式科技,利用智慧型穿戴手環將患者的健康數據 (行為記錄、血壓指數、血糖指數等...)透過 Wi-Fi 連結智慧型裝置(手機、平 板)並透過網路上傳至雲端,後台由遠端的營養師、醫師對患者進行健康照護遠 距醫療服務,此外,此系統也可藉雲端數據建立 Big Data,進行產業分析,以上 健康照護的流程我們統稱為行動雲端健康照護平台。本研究基於此科技之興起, 希望透過專業分析方法對此平台所搭配的智慧型穿戴裝置之設計進行研究。 一般決選新產品開發案時,通常需要進行多重準則決策,新產品開發為了 要之後提高消費者對此項科技接受度,並使這項產品從萌芽創新期邁向成長期, 通常是公司內部高階層主管的主觀判斷,沒有一個客觀的依據輔助判斷,缺乏系 統性分析方法,使得企業無法做出正確的決策。本研究之研究目的主要為透過有 效之方法瞭解智慧型醫療穿戴式裝置之設計需求以及評價準則,且透過瞭解消費 者多年使用經驗專家對於本研究設計準則於以評估並得出準則權重。 本研究之設計準則參考智慧型手機興起時設計相關文獻,並透過與任職於 現有的健康促進企業及瞭解多年消費者使用經驗之專家進行深度問卷訪談,針對 智慧型醫療穿戴式科技之設計需求利用 Fuzzy delphi 模糊德爾菲法篩選出初步準 則,並利用 AHP 分析層級程式法進行準則之相對權重探討,以期在未來智慧型醫 療穿戴式科技產品開發過程中提供設計師及決策者有效的參考價值。 本研究歸納出智慧型醫療穿戴式產品在設計上最關鍵的構面是「功能屬 性」,其次則是「造型意象」,而「綠色環保」則是影響性較低的因素。在進一步 的個別構面子要素分析上,「便利功能屬性」乃是功能屬性中最重要的因子,因為 此智慧型醫療穿戴裝置,除了可以量測健康數據上傳至雲端,也可透過震動提醒 使用者量測時間等等,而造型意象的次準則是「整體感」最為重要,最後本研究 之結論希望可針對智慧型醫療穿戴式產品在未來層出不窮的新產品開發中以及健 康促進產業之發展趨勢提供參考。 雖然綠色環保被評估為較不具考量之因素,此類產品偵測數據不同往往只 有內部晶片以及搭載裝置更換,在電池以及外殼的汰換將造成許多無謂的浪費, 因此綠色環保將是未來可多加考量的部分,而在未來研究建議可以針對此平台所 收集到之 Big Data 分類族群,可以依族群與此研究結果再次進行交叉分析,針對 不同族群做設計準則不同權重之探討。
With the rapidly growing trend of wearable technology, the development of these devices is becoming more important as technology is continuously being integrated into our daily life. International investigation firm Gartner predicted that global output of wearable technology devices will be $ 10 billion by 2016. The application potential in many different industries are limitless and of a time sensitive nature as many firms are already developing and bringing products to market. In the healthcare industry alone, the wearable technology device provides many self-management health platforms including front-end physiological parameter gathering, and back-end expert consultation. In one specific example, they are currently developing a smart phone APP, which accumulates users' physiological parameters from either intelligent wearable technology devices or manual input, and feeds it into an online cloud database, where experts such as a nutritionist can effectively offer timely feedback to the user. The main purpose of this study is to obtain critical factors regarding how new products such as intelligent medical wearable devices should be designed, based on experts’ opinions, in order to improve the customers’ acceptance of this technology, and assist in new product development from start to growth. The initial criteria of this study were based on previous smart phone design references and in-depth interviews with experts who have years of experience understanding adapted use of such medical devices by customers. We then screened the initial criteria with the Fuzzy Delphi method and used the analytical hierarchy process (AHP) method to obtain critical factors with relative importance weights that can provide designers as well as business planners suggestions in the new product development stage. The result showed that the most significant dimension of intelligent wearable device in the health-care field is the "functional properties"; the second is the "style image", while the "environmental concern" has less impact. Further analysis of the factors under dimensions also showed that "the functional convenience properties" is the most important. This suggests that in addition to providing on-time measuring functions through cloud computing technology, the intelligent wearable medical device should also provide other fundamental and useful functions such as vibration warning, etc. Although our study showed that the environmental factor is not significant, we suggest that the recycling technology is worth concerned, as it could eliminate unnecessary waste or even lower related costs of the intelligent medical wearable devices. The recommendation for future research is that we can use the big data we have collected from this platform to classify customer groups, and cross analyze the results of this study with other research areas for future and varying industry analysis.
URI: http://140.113.39.130/cdrfb3/record/nctu/#GT070153504
http://hdl.handle.net/11536/125640
Appears in Collections:Thesis