Pediatric Informatics: Computer Applications in Child Health (Health ...

11 Clinical Practice Guidelines: Supporting Decisions, Optimizing Care 153
and educated consumers have met with variable success 11,12 . Effective modalities
include reminders 13 (when used sparingly), individual outreach (“academic
detailing”), 14 interactive educational programs, interventions that focus directly on
recognized barriers and combined strategies.
11.6.3 Lack of Standard Implementation Systems
Although computerized systems can be effective in implementing guidelines in clinical
practice, creating computer-mediated implementation systems has proven to be
onerous and not uniformly successful. Challenges include: lack of explicit definitions,
focus on errors of omission rather than errors of commission, not accounting for other
factors (such as comorbid conditions, concurrent treatments, timing of interventions and
follow-up). One suggestion has been for all guideline recommendations to be written in
a simple if-then-else format with all parameters strictly defined using routinely collected
clinical data. Nonetheless, this recommendation has not gained acceptance in the US. 15
Informaticians have long struggled with transforming knowledge contained in
clinical practice guidelines into systems that reliably influence clinician behavior.
Implementers currently create computer-based decision support systems from published
guidelines by applying poorly specified, largely tacit knowledge acquisition
processes to a wide variety of knowledge representations. This approach often
results in inconsistent encoding of guideline knowledge and potential inaccuracy
of the advice that is provided. In addition guideline recommendations regularly fail
to address a topic comprehensively, leaving users to design their own solutions for
situations that are not covered. 16
11.7 Approaches to Guideline Implementation
11.7.1 Knowledge Representation
Recent publications comparing features of guideline implementation systems have
focused on representing guideline knowledge. Projects that represent executable
guideline knowledge (Asbru, EON, GLIF, GUIDE, PRODIGY, and PROforma) 17
vary in their intended scope, the way that decisions are applied and the ways that
clinical goals are represented and utilized. In projects where authoring has been
included (PRODIGY, ZYNX), it has been performed by dedicated multidisciplinary
teams that fit their analyses of the medical literature into templates that
facilitate implementation.
In guideline implementation projects, knowledge acquisition can be identified as
model-centric or document-centered. 18 In a model-centric approach, 19 a knowledge
engineer reads and assimilates the guideline narrative, formulates an internalized
conceptual model and converts the model to a fully operational (i.e., computable)
representation (Table 11.1). Translation is implicit and mediated by the engineer