AI Scribing May Streamline Clinical Notes — But at What Cost to the Illness Narrative?

Sarah B Calvert

Summary: This essay, published in Clinical Infectious Diseases, examines the tension between AI-assisted clinical documentation and the preservation of the illness narrative in infectious diseases outpatient practice. The author argues that NLP-based scribing tools, while reducing administrative burden, risk producing flattened, standardised prose that strips away the interpretive, patient-centred language historically embedded in clinical notes. In the era of open notes — where patients routinely read their own records — this loss carries both clinical and therapeutic consequences. The central argument is that clinician authorship of the illness narrative is not merely stylistic but functionally important to patient care.

Why it matters: Clinical documentation has always served dual purposes: medicolegal record-keeping and a site of diagnostic reasoning. In specialties like infectious diseases, where ambiguity is the norm, the narrative structure of a note can itself reflect and communicate the clinician's interpretive process. If AI-generated notes systematically smooth over uncertainty and individualise less, downstream readers — including patients, consultants, and future treating clinicians — may receive a less accurate picture of the clinical situation. The open-notes policy in the United States (mandated since 2021) adds another layer: patients now read these documents, and the language used may influence their understanding of their illness and their relationship with their clinician.

Expert perspective: This is a thoughtful essay rather than an empirical study, and it should be read as such. The argument is intellectually coherent and raises legitimate concerns, but it is supported by reasoning and professional observation rather than data. The claim that AI scribing "flattens narrative nuance" is plausible and consistent with early qualitative reports from clinicians using ambient documentation tools, but it has not yet been rigorously demonstrated through systematic content analysis of AI-generated versus clinician-authored notes. Whether this putative flattening actually affects patient outcomes, diagnostic accuracy, or the therapeutic value patients derive from reading their records remains an open empirical question.

The open-notes dimension deserves particular attention. Research on patient-facing records suggests that language choice — including how diagnoses are framed, how uncertainty is communicated, and whether stigmatising terminology appears — influences patient experience and engagement. NLP tools trained on existing clinical corpora may reproduce or amplify the least patient-centred conventions in that corpus unless specifically designed and audited to avoid doing so.

Less convincing is any implicit suggestion that the status quo ante was uniformly superior. Pre-AI clinical notes varied enormously in quality, completeness, and narrative richness — many were template-driven, copy-forwarded, and filled with generic language long before large language models arrived. The essay would benefit from acknowledging this baseline problem.

The practical challenge — preserving clinician interpretive authorship while gaining efficiency benefits — is real and unsolved. Hybrid workflows, in which AI generates a structured draft that the clinician substantively edits rather than simply approves, may represent a reasonable path, but implementation discipline is required. Institutions deploying ambient scribing tools should be thinking now about documentation quality standards, audit mechanisms, and training that foregrounds narrative responsibility. Overall, this essay makes a valuable contribution by naming a risk easy to overlook in the enthusiasm surrounding AI-assisted documentation; the underlying empirical questions remain open and are worth investigating rigorously.

Relevance: Clinical Informatics and Health Data Methods

Source: https://pubmed.ncbi.nlm.nih.gov/42617064/

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