Pilot trial of semi-automated medical note writing using lexeme hypotheses

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.