手術室是任何醫院的核心，他們創造巨大的收入，但也伴隨著可觀的成本。因此，對手術室效率的關注是必要的。在這樣的情況下，基於精益醫療概念的 ORE 指標被提出來，用來計算手術室表現及識別時間損失。根據 ORE 指標的結果，本研究進一步描述手術流程中的各步驟，目的是減少非增值步驟的時間。並與台灣一家教學醫院合作，透過實際的手術室使用資料，驗證改進的指標。
隨著現代醫療技術的發展，機器人和計算機輔助系統被廣泛應用於手術室，協助外科醫師執行手術。在不干預手術流程的狀況下，這些設備可用於自動地搜集大量的手術視頻，以利後續的分析。手術流程識別是其中一項重要的工作，它提供計算機輔助系統最基本的資訊。然而，目前大多數的手術流程識別方法為了提高性能，使得計算效率較低，這阻礙他們進行實際應用。因此，本研究提出新穎的時空網路，更有效地利用視覺與時間的信息，不但在公開資料集中取得出色的表現，且超過其他流程識別的方法。
關鍵字：手術室、效率指標、手術流程識別、深度學習

Operating rooms are the core of any hospital, they generate huge revenues but are also accompanied by considerable costs. Therefore, it is necessary to pay attention to the efficiency of the operating room. In this context, the ORE indicator, based on the concept of lean healthcare, has been proposed to calculate OR performance and identify time loss. Based on the results of the ORE indicator, this study further describes the steps in the surgical process with the aim of reducing the time spent on non-value-added steps. We also collaborate with a teaching hospital in Taiwan to validate the improved metrics through actual operating room usage data.
With the development of modern medical technology, robotic and computer-assisted systems are widely used in the operating room to assist surgeons in performing surgery. Without interfering with the surgical process, these devices can be used to automatically collect large amounts of surgical video for subsequent analysis. Surgical workflow recognition is one of the important tasks that provide the most essential information for computer-assisted systems. However, most of the current surgical workflow recognition methods are computationally inefficient in order to improve performance, which prevents them from practical applications. Thus, this study proposes a novel spatial-temporal network that makes more efficient use of visual and temporal information, which achieves excellent performance not only in public datasets, but also outperforms other workflow recognition methods.
Keywords: Operating Room, Efficiency Indicator, Surgical Workflow Recognition, Deep Learning

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