Challenges of developing a digital scribe to reduce clinical documentation burden
Challenges of developing a digital scribe to reduce clinical documentation burden
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DOI：
10.1038/s41746-019-0190-1
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发表时间：
2019-11-22
影响因子：
15.2
通讯作者：
Coiera, Enrico
中科院分区：
文献类型：
--
作者：
Quiroz, Juan C.;Laranjo, Liliana;Coiera, Enrico
临床医生花费大量的时间在患者遭遇的临床文档上，通常影响护理质量和临床医生满意度，并导致医生倦怠。人工智能（AI）和机器学习（ML）的进步为使用数字抄写员自动化临床文档提供了可能性，使用语音识别来消除临床医生或医学抄写员的手动文档。然而，由于临床环境和临床对话的复杂性，开发数字抄写员充满了问题。本文确定并讨论了与在临床环境中开发基于语音的自动化文档相关的主要挑战：记录高质量音频，使用语音识别将音频转换为转录本，从对话数据中诱导主题结构，提取医学概念，生成有临床意义的对话摘要，以及为AI和ML算法获取临床数据。
Clinicians spend a large amount of time on clinical documentation of patient encounters, often impacting quality of care and clinician satisfaction, and causing physician burnout. Advances in artificial intelligence (AI) and machine learning (ML) open the possibility of automating clinical documentation with digital scribes, using speech recognition to eliminate manual documentation by clinicians or medical scribes. However, developing a digital scribe is fraught with problems due to the complex nature of clinical environments and clinical conversations. This paper identifies and discusses major challenges associated with developing automated speech-based documentation in clinical settings: recording high-quality audio, converting audio to transcripts using speech recognition, inducing topic structure from conversation data, extracting medical concepts, generating clinically meaningful summaries of conversations, and obtaining clinical data for AI and ML algorithms.