應用生態型介面設計方法發展加護病房醫師決策輔助顯示

Abstract

診斷為醫師例行的重要作業之一，然而，在醫療系統中，大量且散落的生理資訊，使醫師在診斷過程須收集與綜合這些資訊，並進行判讀以進行決策，加上生理數據間的關係十分複雜，造成其工作與認知負荷過高，容易產生診斷疏失。因此提供醫師診斷輔助一直是個重要的議題。 回顧過去醫學診斷的研究，可以發現醫師在面對簡單以及複雜的案例時，使用的診斷策略有所不同，大至可分為兩類病症辨識策略(Symptomatic search)與拓樸式推理策略(Topographical search)，前者會利用過去經驗與案例透過模式識別(pattern recognition)快速地進行問題診斷，後者，則需透過一系列較縝密的推論以做決策。 過去學者針對上述拓樸式推理策略提出了抽象分解空間以分析其推論認知過程，更進一步將此架構應用至介面設計，稱作生態型介面設計(EID)，故本研究欲透過此架構進行醫師診斷輔助介面設計，強調透過了解醫師決策過程以及與環境、設備、資訊的互動，找出醫師決策需求以及定義出介面設計特，本研究使用生態介面設計方法進行醫師診斷輔助顯示設計，期望能支援醫師的診斷，提升其診斷品質。

Diagnosis is one of the most important task for physicians’ daily routine. Since the large amount and distributed information in intensive care unit (ICU), physicians struggle to collect and integrate them to make a decision. By this way, physicians usually work under high workload and cognitive load, so they are more likely to make an error and do an inappropriate diagnosis. Consequently, how to provide support for doctor’s decision making is an issue need to be solved. According to the past study of diagnosis, physicians use different strategy when facing the distinct cases. In general, they use two strategies, which are symptomatic search and topographical search. With symptomatic search, they make decision quickly through pattern recognition. This strategy is usually used when physicians handle the familiar or easy case. On the other hand, they do an elaborate reasoning in complicated case when using topographical search strategy. In the past study, researcher provide abstraction decomposition space (ADS) as a structure to analyze reasoning process when using topographical search strategy. Moreover, he apply this structure to interface design, which is called ecological interface design (EID). In EID, the researcher figure out the diagnosis requirement and define the design feature by analyzing physicians’ reasoning process and the interaction with environment, equipment and information. In this study, we apply EID to develop information display for supporting physicians’ decision making in ICU. We expect to enhance the quality of physicians’ diagnosis.