We have all, at least once, been assigned the responsibility of taking notes for a meeting. One of the business needs that nobody told us existed until the moment we were assigned to write them and, probably, the most undesired important task of running a business. Meeting notes are the memory of what was discussed and the record of what was decided. Even if only the date of the next meeting. This leaves the person assigned to it torn between taking accurate notes and actively participating in the meeting. Voice recorders were used to increase the effectiveness of note-taking, enabling the person responsible for the notes to interact without the fear of losing something. This, of course, resulted in having to go through the meeting again to transcribe whatever topics were mentioned. Recordings are a great form for documenting a meeting, but they are still not ideal for the minutes of a meeting.
Voice recognition and text to speech technology came to the meeting room as soon as they became reliable enough. The secretary would be able to re-read the meeting instead of listening to it. Copying text from a meeting transcription quickly solved the issue of trying to remember the exact wording of a sentence. The mechanical, boring of work of having to write the minutes of the meeting continues, usually resulting in an e-mail that no one would read unless a problem happens.
Talking and taking notes are part of several professional activities. They are the memory of a human interaction. A sales representative will spend time writing a follow-up e-mail as soon as the call with the customer is over. A medical doctor will spend at least another hour transcribing notes to medical records after an appointment, and an additional hour dealing with health insurance paperwork if they work alone. These mechanical writing tasks are tedious but, at the same time, necessary for business. A wrong decimal point in the incorrect field prevents a doctor from receiving payment for a medical appointment. A poorly written follow-up e-mail becomes a finger-pointing thread with the client. Most of the situations created by faulty note-taking are easily solved but, typically, demand time and effort that hadn’t been planned.
Medical doctors are specially sensitive to these problems. A medical appointment is an intensive doctor – patient interaction where symptoms are described, advice given, and treatments planned. All of this has to be written down for future reference and for reporting. A doctor’s file cabinet is a trove of medical experience to which they resort when that feeling of “I’ve seen this before” happens. The files are also the memory of the patient’s health history, exams, and medications. They are also the source of frustration when a doctor works in a large clinic. With patients coming at every hour, the doctor usually leaves the writing of files for after office time. Often when they should be spending time with their families or resting.
From a patient’s perspective, to see the doctor pouring over a keyboard is frustrating. Waiting as they hardly look at us while they type something on the online health insurance system gives the word “patient” a whole perspective meaning. Part of the feeling that doctors have lost their bedside manners has come from this need to document every interaction to produce reports and billing statements. I miss the days when a medical appointment was more than the handing over of lab results and the printing of prescriptions. Most, if not all, doctors are equally frustrated by the automation of medical care.
Could Generative Large Language Models be the answer to this? The University of Kansas Health System, for example, is betting on it.
By having the AI do the note-taking and freeing the doctor from the task, the pilot the University of Kansas is running with Abridge promises to bring back the human factor to the doctor – patient interaction. Aiming towards the increase in the quality of medical attention and the accuracy of the files. The model acts as a personal assistant, capturing the full audio of the conversation and extracting the relevant information for the structured medical files. Identifying patterns and structuring information in a specific format is a scenario where AI excels. Every interaction becomes a record that can be reviewed by the doctor without sacrificing the human aspect of the profession.
A medical personal-assistant is not a first step in replacing the doctor with artificial intelligence. IBM’s experience with Watson Health has shown the limitations of the technology when dealing with treatment recommendations. Such an assistant is a good example of how the use of artificial intelligence can bring back the human care of our professional interactions. This points to a more human experience through the use of technology, especially with the use of generative language technologies.
An assistant that is not bored by the repetitive nature of their work is the ideal one for an overworked doctor. A similar assistant, working for a lawyer or a sales representative, would make sure that accurate notes be available while remembering the professional of any important detail that they did not notice in the conversation. An assistant that lets us concentrate on the essential parts of the job, taking over the boring ones that we dislike. This seems to be an interesting future where Large Language models have a critical role: write the reports and fill out the forms that we love to hate.
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