  Clinical decision support systems are software tools that help clinicians to
make medical decisions. However, their acceptance by clinicians is usually
rather low. A known problem is that they often require clinicians to manually
enter lots of patient data, which is long and tedious. Existing solutions, such
as the automatic data extraction from electronic health record, are not fully
satisfying, because of low data quality and availability. In practice, many
systems still include long questionnaire for data entry.
  In this paper, we propose an original solution to simplify patient data
entry, using an adaptive questionnaire, i.e. a questionnaire that evolves
during user interaction, showing or hiding questions dynamically. Considering a
rule-based decision support systems, we designed methods for translating the
system's clinical rules into display rules that determine the items to show in
the questionnaire, and methods for determining the optimal order of priority
among the items in the questionnaire. We applied this approach to a decision
support system implementing STOPP/START v2, a guideline for managing
polypharmacy. We show that it permits reducing by about two thirds the number
of clinical conditions displayed in the questionnaire. Presented to clinicians
during focus group sessions, the adaptive questionnaire was found "pretty easy
to use". In the future, this approach could be applied to other guidelines, and
adapted for data entry by patients.

Artificial Intelligence;Human-Computer Interaction