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Table 1. A summary of applications, potential, and the most prominent challenges/limitations of NLP in plastic surgery patient
consultations
Application Potential Challenges and limitations
Documentation Information NLP can find relevant information from a Models need to be integrated into EHRs
applications extraction and patient chart and succinctly summarize it
summarization
Ambient transcription NLP can automate clinical documentation to Integration into EHRs. Additionally, inaccuracies or
and coding help providers focus on the patient. They can hallucinations are legal risks
even apply the correct codes for billing
Communication Patient goals, PRO NLP can enable surgeons to better Models need to be integrated into EHRs
applications understand goals to personalize treatment
Translation and NLP can translate patient conversations and Bias may lead to inaccuracies
health literacy documents across languages and literacy
levels
Chatbot NLP chatbots can act as personal health Platforms must provide accurate information and
assistants to engage patients in real time deference to providers. Potential legal risks.
Integration challenges and the need to use protected
health information

NLP: Natural language processing; EHRs: electronic health records; PRO: patient-reported outcomes.

analytics and predictive modeling.

Ambient transcription and coding
Provider burnout is a well-described phenomenon in plastic surgery and healthcare. While the problem is
multifactorial, several studies have described administrative burden as a root cause, including
documentation . The ability of NLP systems to generate clinical documentation could ease this burden
[4-6]
through ambient transcription. This function could be integrated into plastic surgery consultations, which
involve documentation of patient history, treatment plans, and consent forms. Traditionally, this process is
time-consuming as surgeons must manually record patient information during or after consultations.
Additionally, the current medico-legal milieu holds our discipline to the highest standard of accurate
documentation. NLP can automate this process by transcribing spoken consultations into written records.
Voice recognition systems can capture and transcribe the conversation between the patient and surgeon,
reducing the administrative burden on the surgeon and allowing for greater attention toward the patient.
Indeed, several startups are building and field-testing models to perform these tasks, including Ambience ©,
Nabla ©, and others .
[7,8]
Once a patient’s consultation is transcribed, clinicians should aspire to use NLP to automatically assign
diagnosis and procedural codes. An editorial from Venkatesh et al. describes the current state of innovation
in automated clinical coding (ACC), and the challenges facing companies . While the need is apparent, it is
[9]
difficult to integrate clinical data and documents into a few codes. Startups like AKASA © have made strides
in building an ACC tool that performs as well as human coders . In the future, an ACC tool could radically
[10]
streamline the workflow for plastic surgeons.

In summary, the promise of NLP in plastic surgery documentation is widespread: improved accuracy and
consistency of patient records, minimizing the risk of errors from manual entry, and decreased
administrative burden on plastic surgeons.

COMMUNICATION
Patient goals, PRO
Plastic surgery is uniquely focused on patient goals and understanding. This focus arises from the nature of
our services, which primarily aim to augment quality of life. It is no surprise our discipline has pioneered