  Clinicians often do not sufficiently adhere to evidence-based clinical
guidelines in a manner sensitive to the context of each patient. It is
important to detect such deviations, typically including redundant or missing
actions, even when the detection is performed retrospectively, so as to inform
both the attending clinician and policy makers. Furthermore, it would be
beneficial to detect such deviations in a manner proportional to the level of
the deviation, and not to simply use arbitrary cut-off values. In this study,
we introduce a new approach for automated guideline-based quality assessment of
the care process, the bidirectional knowledge-based assessment of compliance
(BiKBAC) method. Our BiKBAC methodology assesses the degree of compliance when
applying clinical guidelines, with respect to multiple different aspects of the
guideline (e.g., the guideline's process and outcome objectives). The
assessment is performed through a highly detailed, automated quality-assessment
retrospective analysis, which compares a formal representation of the guideline
and of its process and outcome intentions (we use the Asbru language for that
purpose) with the longitudinal electronic medical record of its continuous
application over a significant time period, using both a top-down and a
bottom-up approach, which we explain in detail. Partial matches of the data to
the process and to the outcome objectives are resolved using fuzzy temporal
logic. We also introduce the DiscovErr system, which implements the BiKBAC
approach, and present its detailed architecture. The DiscovErr system was
evaluated in a separate study in the type 2 diabetes management domain, by
comparing its performance to a panel of three clinicians, with highly
encouraging results with respect to the completeness and correctness of its
comments.
