Clinicians write a billion free text notes per year. These notes are typically replete with errors of all types. No established automated method can extract data from this treasure trove. The practice of medicine therefore remains haphazard and chaotic, resulting in vast economic waste. The lexeme hypotheses are based on our analysis of how records are created. They enable a computer system to predict what issue a clinician will need to address next, based on the environment in which the clinician is working, and what responses the clinician has selected to date. The system uses a lexicon storing the issues (queries) and a range of responses to the issues. When the clinician selects a response, a text fragment is added to the output file. In the first phase of this work, the notes of 69 returning hemophilia patients were scrutinized, and the lexicon was expanded to 847 lexeme queries and 7995 responses to enable the construction of completed notes. The quality of lexeme-generated notes from 20 consecutive subjects was then compared to the clinicians' conventional clinic notes. The system generated grammatically correct notes. In comparison to the traditional clinic note, the lexeme-generated notes were more complete (88 % compared with 62 %), and had less typographical and grammatical errors (0.8 versus 3.5 errors per note). The system notes and traditional notes averaged about 800 words, but the traditional notes had a much wider distribution of lengths. The note-creation rate from marshalling the data to completion using the system averaged 80 wpm, twice as fast as the typical clinician can type. The lexeme method generates more complete, grammatical and organized notes faster than traditional methods. The notes are completely computerized at inception, and they incorporate prompts for clinicians to address otherwise overlooked items. This pilot justifies further exploration of this methodology.

中文翻译：

---

使用lexeme假设进行半自动医学笔记写作的试验性试验。

临床医生每年写十亿篇免费的文字笔记。这些注释通常充满所有类型的错误。没有成熟的自动化方法可以从此宝库中提取数据。因此，医学实践仍然是随意的和混乱的，从而造成了巨大的经济浪费。词素假设基于我们对记录创建方式的分析。它们使计算机系统能够根据临床医生正在工作的环境以及临床医生迄今选择的响应来预测临床医生接下来需要解决的问题。系统使用一个词典来存储问题（查询）和对问题的一系列响应。当临床医生选择响应时，会将文本片段添加到输出文件中。在这项工作的第一阶段，对69名返回的血友病患者的笔记进行了仔细检查，词典又扩展到847个词素查询和7995个响应，以构建完整的注释。然后将来自连续20位受试者的由莱克美产生的音符的质量与临床医生的常规临床音符进行比较。系统生成语法正确的注释。与传统的临床笔记相比，由莱克美生成的笔记更完整（88％比62％），并且印刷和语法错误更少（每个笔记的错误为0.8对3.5）。系统笔记和传统笔记的平均长度约为800个单词，但传统笔记的长度分布更为广泛。使用该系统从整理数据到完成数据的笔记创建速度平均为80 wpm，是典型临床医生键入速度的两倍。lexeme方法生成的更完整，语法和有组织的注释比传统方法更快。注释从一开始就完全计算机化，并且包含了提示临床医生处理否则被忽略的项目的提示。该飞行员证明了对该方法的进一步探索。