ChatGPT（Open AI，美國加利福尼亞州舊金山）風靡全球。ChatGPT 於 2022 年 11 月向公眾發布，它基於人工智能 (AI) 技術，並根據人類編寫的互聯網數據進行訓練， 包括對話。 這個由 AI 驅動的聊天機器人具有廣泛的能力，包括詩歌創作、論文寫作、解決編碼問題以及解釋複雜概念，包括“我們如何才能修復英國國民健康服務體系？” 有些人認為 ChatGPT 將在線搜索提升到了一個新的水平；該程序提供了對複雜問題的快速和深入的理解，並以對話的方式針對所提出的確切問題生成自定義響應，並回憶起之前的響應。 雖然仍只是研究預覽，但 ChatGPT 在多個用例中表現出了令人印象深刻的性能。 ChatGPT 的一個潛在應用領域可能是生成出院診斷。撰寫包含必要信息的高質量出院診斷可能非常耗時，而且負擔通常落在初級醫生身上。 因此，在與臨床承諾進行平衡時，出院診斷往往被低估，導致患者出院延遲或出院診斷不充分，這不僅給本已捉襟見肘的初級醫生隊伍帶來壓力，而且還存在潛在的患者安全問題，護理從二級護理過渡到初級護理。 ChatGPT 允許醫生輸入需要包含的特定信息的簡要說明、要詳細說明的概念和要解釋的指導，並在幾秒鐘內輸出正式的出院診斷。出院診斷是這項技術的一個明顯選擇，因為它們在很大程度上是標準化的格式； 這種標準化之前已經通過模板的創建得到了認可——例如，來自皇家內科醫師學院。 因此，這一過程的自動化可以減輕初級醫生的工作負擔，讓他們有更多時間提供病人護理和尋求培訓機會 通常以提供服務為中心。 ChatGPT 還可以提高出院診斷本身的質量，之前的文獻顯示通過傳統方式編寫的出院診斷往往缺少細節。 儘管 ChatGPT 提供了巨大的希望，但部署到臨床實踐中需要克服多個障礙。 首先，ChatGPT 依賴於它接收到的數據，因此仍然需要手動輸入重要信息。 未來的迭代可能允許從患者的電子健康記錄中自動抓取數據，而無需手動輸入診斷。然而，這種做法在數據治理方面存在重大問題。 以前在醫療保健中使用 AI 會導致嚴重損害患者信任的失敗, 因此必須注意數據存儲和訪問。必須考慮這項技術的可接受性，尤其是來自患者的可接受性。 對自動化可能導致護理非個性化的擔憂可能會導致對這項技術的抵制。 因此，試點試驗數據對於收集利益相關者的意見並最終顯示流程效率和質量的改進是必要的。 最後，必須考慮技術失敗的後果。 儘管在家中向聊天機器人詢問一些微不足道的問題可能不會產生什麼後果，但有關藥物變更或跟進的信息不正確或不充分會極大地影響患者護理。 因此，雖然 ChatGPT 可以被視為編寫過程的第一步，但在完成之前需要醫生手動檢查其輸出。ChatGPT 展示了由大型語言模型提供支持的下一代聊天機器人的強大功能。 儘管在醫療保健中使用聊天機器人絕不是什麼新鮮事，之前曾應用於分流等領域，7但下一代機器人旨在通過其生成能力顯著推動該領域的發展。 醫療保健是一個具有很大自動化範圍的行業，尤其是在文檔領域。 我們應該對這些技術進步做出反應，並探索在醫療保健的不同領域使用 ChatGPT 等聊天機器人的潛在用例和可行性。 在中央層面主動儘早採用這項技術也可能會降低未來出現問題的風險； 由於地方層面採用不同的電子健康記錄，互操作性問題可以得出相似之處。8 未來的問題將是我們如何而不是是否採用這項技術。我們聲明沒有競爭利益。 英國國家健康與護理研究所 (NIHR) 帝國生物醫學研究中心提供資金和基礎設施支持。 KL 得到 NIHR 學術臨床獎學金的支持。

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翻譯自: The Lancet Digital Health by Sajan B Patel & Kyle Lam February 06, 2023 

圖片: 歐洲商業評鑑ChatGPT: the future of discharge summaries?--by Sajan B Patel & Kyle Lam February 06, 2023

ChatGPT (Open AI, San Francisco, CA, USA) has taken the world by storm.1 Released to the public in November, 2022, ChatGPT is based on artificial intelligence (AI) technology and trained on data from the internet written by humans, including conversations. This AI-powered chatbot has vast capabilities ranging from poem composition, essay writing, solving coding issues, and explanation of complex concepts including “how can we fix the UK National Health Service?” Some consider that ChatGPT has advanced the online search to the next level; the program offers rapid and in-depth understanding of complex matters and generates custom responses in a conversational manner to the exact question asked, recalling its own previous responses. Although still only a research preview, ChatGPT has shown impressive performance across multiple use cases. One potential area of ChatGPT's application could be to generate discharge summaries. Composition of high-quality discharge summaries containing the requisite information can be time consuming, with the burden usually falling on junior doctors. Therefore, when balanced against clinical commitments, discharge summaries are often left under-prioritised resulting in delays in patients' discharges or insufficient discharge summaries, which not only places pressure on an already stretched junior doctor workforce, but also presents potential patient safety issues in the transition of care from secondary to primary care.ChatGPT allows doctors to input a brief of the specific information to include, concepts to elaborate on, and guidance to explain, outputting a formal discharge summary in a matter of seconds (panel). Discharge summaries are an obvious choice for this technology because of their largely standardised format; this standardisation has previously been recognised through the creation of templates—eg, from the Royal College of Physicians. Automation of this process could therefore ease the work burden upon junior doctors allowing more time for the delivery of patient care and the seeking of training opportunities in often roles centered on service provision. ChatGPT could also improve the quality of the discharge summaries themselves, with previous literature showing discharge summaries that are written through traditional means often missing details.Although ChatGPT offers significant promise, deployment into clinical practice will require multiple barriers to be overcome. First, ChatGPT depends on the data it receives and therefore still necessitates salient information to be manually inputted. Future iterations might allow automatic scraping of data from the patient's electronic health record without the need for manual inputting of a brief. This practice, however, presents significant issues with respect to data governance. Previous use of AI within health care has been associated with failings that significantly damage patients' trust6 and therefore care must be taken around data storage and access.Acceptability of this technology, from patients in particular, must be considered. The concern that automation might lead to depersonalisation of care could lead to resistance to this technology. Pilot trial data is therefore necessary to gather stakeholder views and ultimately show improvements in efficiency and quality of processes. Finally, the consequences of technology failure must be considered. Although asking trivial questions to a chatbot at home might have few consequences, incorrect or inadequate information concerning medication changes or follow-up can greatly affect patient care. Therefore, although ChatGPT can be considered as a first step in the writing process, manual checking of its output by a doctor will be required before completion.ChatGPT demonstrates the power of this next generation of chatbots powered by large language models. Although the use of chatbots within health care is by no means novel, having previously been applied to areas such as triage,7 this next generation aims to advance the field significantly through its generative capacities. Health care is an industry that has significant scope for automation, especially in areas of documentation. We should be reactive to these advances in technology and explore potential use cases and feasibility of using chatbots such as ChatGPT across diverse areas in health care. Proactive early adoption of this technology at a central level might also reduce the risk of issues in the future; parallels can be drawn with interoperability issues due to differing electronic health records adopted at a local level.8 The question for the future will be how, not if, we adopt this technology.We declare no competing interests. Funding and infrastructural support was provided by the UK National Institute for Health and Care Research (NIHR) Imperial Biomedical Research Centre. KL is supported by a NIHR Academic Clinical Fellowship.