  Following an interaction with a patient, physicians are responsible for the
submission of clinical documentation, often organized as a SOAP note. A
clinical note is not simply a summary of the conversation but requires the use
of appropriate medical terminology. The relevant information can then be
extracted and organized according to the structure of the SOAP note. In this
paper we analyze two different approaches to generate the different sections of
a SOAP note based on the audio recording of the conversation, and specifically
examine them in terms of note consistency. The first approach generates the
sections independently, while the second method generates them all together. In
this work we make use of PEGASUS-X Transformer models and observe that both
methods lead to similar ROUGE values (less than 1% difference) and have no
difference in terms of the Factuality metric. We perform a human evaluation to
measure aspects of consistency and demonstrate that LLMs like Llama2 can be
used to perform the same tasks with roughly the same agreement as the human
annotators. Between the Llama2 analysis and the human reviewers we observe a
Cohen Kappa inter-rater reliability of 0.79, 1.00, and 0.32 for consistency of
age, gender, and body part injury, respectively. With this we demonstrate the
usefulness of leveraging an LLM to measure quality indicators that can be
identified by humans but are not currently captured by automatic metrics. This
allows scaling evaluation to larger data sets, and we find that clinical note
consistency improves by generating each new section conditioned on the output
of all previously generated sections.
