Back

Clinician Discourse on Ambient AI Scribes: A Reddit-based Topic Modelling and Sentiment Analysis

Shankar, R.; Xu, Q.

2026-04-30 health informatics
10.64898/2026.04.26.26351798 medRxiv
Show abstract

BackgroundAmbient AI scribes are rapidly entering clinical workflows, yet end-user perspectives remain underrepresented in the peer-reviewed literature. Online clinician communities offer an unfiltered window into adoption barriers, perceived benefits, and product-level concerns. ObjectiveTo characterise themes and sentiment in clinician discourse on ambient AI scribes across professional Reddit communities. MethodsWe scraped posts from ten clinically oriented subreddits using twelve AI scribe related queries via the public Reddit JSON API. A two-tier keyword filter retained posts mentioning at least one AI scribe term and one clinical or workflow term. Texts were embedded with all-MiniLM-L6-v2, reduced via UMAP, clustered with HDBSCAN, and labelled using BERTopic with c-TF-IDF keyword extraction. Noise topics matching predefined off-topic patterns (for example, residency match, finance) were removed. Themes were assigned concise labels via Claude Sonnet 4. Sentiment was classified per post using cardiffnlp/twitter-roberta-base-sentiment-latest. ResultsAfter filtering, 176 unique relevant posts from seven active subreddits were retained, with r/FamilyMedicine (n = 64) and r/healthIT (n = 34) dominating. BERTopic produced 12 coherent themes spanning workflow integration, vendor comparison (DAX, Heidi, Freed, Abridge), HIPAA and privacy, mobile and device use, templates and formatting, and research versus clinical use. Overall sentiment was 61.4% neutral, 21.6% positive, and 17.0% negative. The most net-positive theme was DAX/Nuance/AI tools (about 55% positive); the most net-negative were charting fatigue and the freed-AI-scribes discussion thread (about 37 to 40% negative). Engagement (median upvotes and comments) was highest for tool-comparison and pricing themes, indicating salience of practical adoption questions. ConclusionsClinician sentiment toward ambient AI scribes is cautiously favourable but dominated by neutral, problem-solving discourse. Vendor selection, cost, HIPAA compliance, and EHR integration are the most actively debated issues. These insights can inform implementation strategy, vendor benchmarking, and policy guidance for ambient documentation tools.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

1
Journal of Medical Internet Research
85 papers in training set
Top 0.1%
28.4%
2
npj Digital Medicine
97 papers in training set
Top 0.5%
10.3%
3
Frontiers in Digital Health
20 papers in training set
Top 0.1%
7.4%
4
Journal of the American Medical Informatics Association
61 papers in training set
Top 0.4%
7.0%
50% of probability mass above
5
BMJ Health & Care Informatics
13 papers in training set
Top 0.1%
5.0%
6
DIGITAL HEALTH
12 papers in training set
Top 0.1%
5.0%
7
JAMIA Open
37 papers in training set
Top 0.2%
5.0%
8
PLOS Digital Health
91 papers in training set
Top 0.7%
3.7%
9
JMIR Public Health and Surveillance
45 papers in training set
Top 1%
2.1%
10
PLOS ONE
4510 papers in training set
Top 51%
1.8%
11
JMIR Medical Informatics
17 papers in training set
Top 0.7%
1.7%
12
Journal of Biomedical Informatics
45 papers in training set
Top 0.8%
1.7%
13
BMC Medical Informatics and Decision Making
39 papers in training set
Top 1%
1.7%
14
JMIR mHealth and uHealth
10 papers in training set
Top 0.2%
1.5%
15
JMIR Formative Research
32 papers in training set
Top 1.0%
1.4%
16
Journal of General Internal Medicine
20 papers in training set
Top 0.7%
1.0%
17
International Journal of Medical Informatics
25 papers in training set
Top 1%
1.0%
18
BMJ Open Quality
15 papers in training set
Top 0.7%
0.8%
19
Healthcare
16 papers in training set
Top 2%
0.8%
20
BMJ Open
554 papers in training set
Top 12%
0.8%
21
JMIRx Med
31 papers in training set
Top 2%
0.7%
22
Scientific Reports
3102 papers in training set
Top 77%
0.7%
23
Cancer Medicine
24 papers in training set
Top 2%
0.5%
24
JAMA Network Open
127 papers in training set
Top 5%
0.5%