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. 2024 Mar 20;7(3):e243201. doi: 10.1001/jamanetworkopen.2024.3201

Table 5. Qualitative Encoding of Free-Text Comments From Postsurveys.

Theme Representative quotations No. of comments
Negative Neutral Positive Total
Draft message voice and/or tone Positive: “I was impressed by the tone that varied based on patient’s concerns and questions, and felt messaging was overall very professional and clear.”
Negative: “I think the drafts are great but can further be improved if it did not sound robotic and had a more personable touch.”
14 2 10 26
Future use Positive: “Please continue to allow us to utilize this tool and spread to other SHC clinics!”
Negative: “I still think it’s a good idea but not ready for real life situations.”
1 0 18 19
Draft message tool utility Positive: “Overall this is a very helpful tool.”
Negative: “Also, it struggled with having draft replies of more nuanced concerns.”
4 2 13 19
Draft message content relevance Positive: “I especially appreciated the one example where a patient mentioned having frequent UTIs on a certain medication, and the response had pulled in the last 3 lab results from urinalysis!”
Negative: “The Reponses often did not accurately reflect the questions. Sometimes way off. Often vague.”
9 1 8 18
Impact on workflow Positive: “It helped with the ‘translation’ cognitive work that I hadn’t ever realized I was doing before process of translating my medical understanding into patient-facing language.”
Negative: “I have to read the actual draft before starting to work on the actual request, as I don’t know if the response is even appropriate.”
9 0 7 16
Impact on time Positive: “It helped save me a lot of time starting from scratch.”
Negative: “Right now, it is just piling on top of the work that we are already doing, and it is faster for me to type a prose response that I have generated myself.”
1 0 12 13
Draft message length and/or brevity Positive: “However, the responses are very thorough. I had a patient that needed a refill and the draft wrote out almost a whole letter when I typically would maybe just write a short sentence saying ‘Yes, I will send!’”
Negative: “Overall the responses seemed unnecessarily wordy in noncontributory ways.”
8 2 1 11
Draft message content accuracy Positive: “I found the AI-generated draft replies pretty accurate and helpful.”
Negative: “Sometimes, the AI response was not completely accurate, but it was not difficult to make minor tweaks to the draft.”
5 0 4 9
Impact on patient engagement Positive: “This may have a positive impact on patient satisfaction with longer messages.”
Negative: “Patients can tell these responses were AI generated, they are formatted like the AI responses we get on airline websites.”
2 2 3 7
Draft message content completeness Positive: “Good things are AI can capture all the elements in the message patient sent and address each element.”
Negative: “The AI responses were a great initial draft, though often required some additional information or editing.”
4 0 1 5
Total NA 57 9 77 143

Abbreviations: AI, artificial intelligence; NA, not applicable; SHC, Stanford Health Care; UTI, urinary tract infection.