Doctors want to use health data from smart watches and fitness trackers.

醫生希望使用來自智慧手錶和健身追蹤器的健康數據。

Many people wear smart watches. These watches collect a lot of health data. But doctors have a problem. The data is in different apps. The apps do not talk to the doctor's computer.

許多人都佩戴智慧手錶。這些手錶會收集大量健康數據。但醫生遇到了一個問題，數據分散在不同的應用程式中，而這些應用程式無法與醫生的電腦接軌。

Doctors do not always trust the watches. They do not know how the watch finds the numbers. Some doctors worry about mistakes. Some companies are now making new tools to help doctors see the data easily.

醫生並不總是信任這些手錶。他們不知道手錶是如何得出這些數值的。有些醫生擔心會出現錯誤。目前一些公司正在開發新工具，以幫助醫生輕鬆查看數據。

Some doctors like these watches. The watches can find heart problems. This can save lives. Now, people want to use AI to help doctors read all the data quickly.

有些醫生很喜歡這些手錶。手錶可以發現心臟問題，這能挽救生命。現在，人們希望利用 AI 幫助醫生快速閱讀所有數據。

There are still problems, but companies and schools are working to help doctors use this data.

雖然仍存在問題，但公司和學校正致力於幫助醫生使用這些數據。

The healthcare industry is currently working on a way to move health data from consumer wearable devices into official medical practice.

醫療產業目前正致力於研究如何將消費者穿戴式裝置的健康數據整合到正式的醫療實踐中。

The rise of wearable technology has led to a huge increase in health data created by patients. However, there is a gap between the constant stream of data from these devices and the occasional nature of traditional doctor visits. This problem is made worse by technical difficulties, as the private data clouds used by companies often cannot communicate with Electronic Health Records (EHRs). Furthermore, the lack of standard data formats and the need for doctors to use many different login systems make it difficult to organize patient information efficiently.

穿戴式科技的興起，導致患者產生的健康數據大幅增加。然而，這些裝置提供的持續數據流與傳統就醫的偶發性之間存在落差。由於公司使用的私有數據雲端通常無法與電子健康紀錄 (EHR) 通訊，技術困難使問題更加嚴重。此外，缺乏標準化的數據格式，加上醫生需要使用多套不同的登入系統，使得高效整理患者資訊變得困難。

From a medical point of view, this data is often less useful because the formulas used to calculate metrics like 'strain' or 'recovery' are not transparent. Consequently, doctors face a difficult choice: they may upset patients by ignoring their data, or they may make mistakes by relying on unproven metrics. While the FDA has approved some specific features for medical use, there is still not enough evidence for most functions. To solve these problems, companies are buying other firms—such as Samsung's purchase of Xealth—to improve EHR integration, while academic projects like JupyterHealth are creating open-source systems to handle this data.

從醫療角度來看，這些數據通常較不有用，因為用於計算「壓力」或「恢復」等指標的公式並不透明。因此，醫生面臨一個困難的選擇：他們可能會因為忽略數據而讓患者不滿，或者可能因為依賴未經證實的指標而犯錯。雖然 FDA 已批准部分特定功能可用於醫療用途，但大多數功能仍缺乏足夠證據。為了 l解決這些問題，企業正透過收購其他公司（例如三星收購 Xealth）來改善 EHR 整合，而像 JupyterHealth 這樣的學術項目則在開發開源系統以處理這些數據。

Healthcare professionals are generally optimistic but cautious. Some doctors admit that wearables can save lives by detecting irregular heartbeats, yet they emphasize that the 'flood' of information needs better filtering. Experts suggest that Large Language Models (LLMs) could be used to summarize this data, provided that privacy laws and regulations are updated to cover consumer devices.

醫療專業人士普遍持樂觀但謹慎的態度。部分醫生承認穿戴式裝置能透過偵測心律不整來挽救生命，但他們強調，資訊的「氾濫」需要更好的過濾機制。專家建議，只要更新隱私法律與法規以涵蓋消費者裝置，即可利用大語言模型 (LLM) 來總結這些數據。

Although technical and legal barriers still exist, the effort to include wearable data in medical workflows continues through corporate partnerships and academic development.

雖然技術與法律障礙依然存在，但透過企業合作與學術開發，將穿戴式數據納入醫療工作流程的努力仍在持續。

The healthcare sector is currently navigating the transition of biometric data from consumer-grade wearable devices into formal clinical practice.

醫療健康產業目前正處於將消費級穿戴裝置的生物識別數據轉化為正式臨床實踐的過渡階段。

The proliferation of wearable technology has resulted in a substantial increase in patient-generated health metrics. However, a systemic misalignment exists between the continuous data streams produced by these devices and the episodic nature of traditional clinical care. This discrepancy is compounded by significant technical impediments regarding the interoperability of proprietary data clouds and Electronic Health Records (EHRs). Furthermore, the lack of standardized data formats and the necessity for clinicians to manage multiple disparate logins hinder the efficient synthesis of patient information.

穿戴式科技的普及，導致患者產生的健康指標大幅增加。然而，這些裝置產生的持續數據流與傳統臨床護理的片段性特質之間，存在系統性的不匹配。由於專有數據雲與電子健康紀錄（EHR）之間的互操作性存在重大技術障礙，使得這種差異更加複雜。此外，缺乏標準化的數據格式，以及臨床醫生必須管理多組不同的登入帳號，阻礙了患者資訊的高效綜合分析。

From a clinical perspective, the utility of this data is frequently undermined by a lack of transparency regarding the algorithms used to derive metrics such as 'strain' or 'recovery.' Consequently, practitioners face a professional dichotomy: the potential alienation of patients by dismissing their data, or the risk of clinical error by relying on non-validated metrics. While the Food and Drug Administration (FDA) has authorized specific features for clinical use, the broader evidence base remains nascent. To mitigate these frictions, industry actors are pursuing strategic acquisitions—such as Samsung's acquisition of Xealth—to facilitate EHR integration, while academic initiatives like JupyterHealth seek to establish open-source public infrastructure for data ingestion.

從臨床角度來看，由於推導出如「壓力」或「恢復」等指標的演算法缺乏透明度，這些數據的實用價值經常被削弱。因此，執業醫師面臨一種專業上的兩難：若否定患者的數據，可能會使患者感到被疏離；若依賴未經驗證的指標，則面臨臨床錯誤的風險。雖然美國食品藥品監督管理局（FDA）已授權特定功能用於臨床，但更廣泛的證據基礎仍處於起步階段。為了緩解這些摩擦，產業參與者正採取策略性收購——例如三星收購 Xealth——以促進 EHR 整合，而如 JupyterHealth 等學術計劃則尋求建立開源的公共基礎設施以進行數據導入。

Stakeholder positioning reflects a cautious optimism. Some clinicians acknowledge the life-saving potential of wearables in detecting irregular heart rhythms, yet emphasize that the 'digital avalanche' of information requires sophisticated filtration. The prospective implementation of Large Language Models (LLMs) is hypothesized as a potential mechanism for synthesizing this data, provided that regulatory frameworks regarding HIPAA and medical privacy are sufficiently evolved to encompass consumer devices.

利益相關者的定位反映出審慎的樂觀。部分臨床醫生承認穿戴裝置在偵測心律不整方面具有救命潛力，但強調資訊的「數位雪崩」需要精密的過濾機制。目前假設導入大型語言模型（LLM）將成為綜合分析這些數據的潛在機制，前提是關於 HIPAA 和醫療隱私的監管框架已足夠演進以涵蓋消費級裝置。

While technological and regulatory barriers persist, the movement toward integrating wearable data into clinical workflows continues through a combination of corporate integration and academic infrastructure development.

雖然技術與監管障礙依然存在，但透過企業整合與學術基礎設施發展，將穿戴數據整合至臨床工作流的趨勢仍持續推進。