Spoken Dialogue System for Medical Prescription Acquisition on
  Smartphone: Development, Corpus and Evaluation
Ali Can Kocabiyikoglu François Portet Jean-Marc Babouchkine Prudence Gibert Hervé Blanchon Gaëtan Gavazzi
Hospital information systems (HIS) have become an essential part of
healthcare institutions and now incorporate prescribing support software.
Prescription support software allows for structured information capture, which
improves the safety, appropriateness and efficiency of prescriptions and
reduces the number of adverse drug events (ADEs). However, such a system
increases the amount of time physicians spend at a computer entering
information instead of providing medical care. In addition, any new visiting
clinician must learn to manage complex interfaces since each HIS has its own
interfaces. In this paper, we present a natural language interface for
e-prescribing software in the form of a spoken dialogue system accessible on a
smartphone. This system allows prescribers to record their prescriptions
verbally, a form of interaction closer to their usual practice. The system
extracts the formal representation of the prescription ready to be checked by
the prescribing software and uses the dialogue to request mandatory
information, correct errors or warn of particular situations. Since, to the
best of our knowledge, there is no existing voice-based prescription dialogue
system, we present the system developed in a low-resource environment, focusing
on dialogue modeling, semantic extraction and data augmentation. The system was
evaluated in the wild with 55 participants. This evaluation showed that our
system has an average prescription time of 66.15 seconds for physicians and
35.64 seconds for other experts, and a task success rate of 76\% for physicians
and 72\% for other experts. All evaluation data were recorded and annotated to
form PxCorpus, the first spoken drug prescription corpus that has been made
fully available to the community
(\url{https://doi.org/10.5281/zenodo.6524162}).