Clinicians would bene t from decision support systems incorporating the knowledge of clinical practice guidelines. However, the unstructured form of the guidelines makes them unsuitable for formal representation. To remedy this shortcoming we translated a set of pediatric guidelines into Attempto Controlled English (ACE). An experienced pediatrician and a knowledge engineer assessed that ACE can accurately represent the clinical concepts and the proposed actions of the guidelines. Currently, we are developing a systematic and replicable approach to authoring guideline recommendations in ACE.
Clinical practice guidelines are de ned as \systematically developed statements
to assist practitioner and patient decisions about appropriate healthcare for
speci c clinical circumstances" [
However, there is a mismatch between the unstructured narrative form of the
published guidelines and the formality that is necessary for the operationalization
of guideline knowledge [
In order to address this problem, we investigate writing guidelines in
Attempto Controlled English (ACE) [
guideline application domain experts committee
clinician expertise authoring tool decisions & feedback recommendations formal guidelines decision support system patient data
electronic health record system a precisely de ned subset of English with restrictions on vocabulary and grammar. These restrictions result in increased terminological consistency, reduced ambiguity, consistent vocabulary, potentially templated phrases, and a generally simpli ed sentence and text structure. ACE has the additional bene t of being supported by a parsing engine that translates ACE texts into rst-order logic, thereby providing a computable format and supporting automatic reasoning.
In the initial phases of the ERGO Project (E ective Representation of Guidelines with Ontologies)3 we will demonstrate the feasibility of translating guideline knowledge into rules. We propose to use ACE as an intermediary representation between the implicit knowledge contained in the minds of the domain experts and the representation of that knowledge in an explicit computable form. Our goal is to develop an authoring tool that helps guideline authors to reduce ambiguity, vagueness, incompleteness, and inconsistency, and facilitates the translation of guideline recommendations into logic statements that can be implemented in decision support systems. These systems generally depend on production rules derived from guideline recommendations to create a knowledge base. The decision support system compares an individual patient's characteristics (demographic descriptors and clinical ndings) against these rules to guide a health provider by o ering patient-speci c and situation-speci c advice. A second goal is to demonstrate that ACE is a good candidate controlled natural language for writing recommendations. Figure 1 shows the general architecture of our approach.
If an infant or young child 2 months to 2 If years of age with unexplained fever is assessed the patient is a young child who has an unexplained fever and as being su ciently ill to warrant immedi- the patient is su ciently-ill ate antimicrobial therapy, a urine specimen then should be obtained by SPA or transurethral the clinician should analyze a culture of a urine-specimen bladder catheterization; [...] (strength of evi- that is obtained-by SPA or dence: good). that is obtained-by Transurethral-catheterization. 2
Expressing Clinical Practice Guidelines in ACE
We plan to use ACE to encode the summary recommendation statements that
form the backbone of guideline documents. Often published in boldface, these
recommendation statements embody the critical knowledge about appropriate
practice that is ampli ed by supporting text. A rst critical step is to establish
whether clinical guidelines can be adequately expressed in ACE, and to identify
potential barriers to the e ective translation. To answer this question we decided
to manually \ACE'ify" the set of recommendations contained in the guideline
\Diagnosis, Treatment, and Evaluation of the Initial Urinary Tract Infection
in Febrile Infants and Young Children" (UTI) [
The recommendations were translated by 3 members of the ACE team at the University of Zurich. Once translated into ACE, the recommendations were reviewed by a pediatrician with expertise in clinical guidelines and by a knowledge engineer. Judgements were made regarding the accuracy of translation, the naturalness of the ACE statements, and potential solutions to encountered impediments of the translation. Altogether, the reviewers concluded that ACE is capable of accurately stating the clinical concepts and the actions described in the guideline's recommendations. Nevertheless, the reviewers identi ed some problem areas.
The example guidelines use specialized medical terms that are not part of the basic lexicon of the ACE parser. Though many of these terms can be found in lexicons like UMLS and SNOMED, the problem remains that terms | such as \ability to retain oral intake,", \su ciently ill" and \SPA" | require clear and consistent speci cations by guideline authors. We plan to solve this problem by providing an authoring tool that accepts only terms that are known to the system and that have a clearly de ned meaning.
Considerable uncertainty accompanies most medical decision making. Evidence validity as well as the accuracy of clinical observations and measurements contribute to this uncertainty. Guideline authors express the uncertainty by using deontic modals and by including coded representations of evidence quality and recommendation strength with their recommendations. Strength of recommendation is a judgment based on the anticipated bene ts, risks, harms, and costs of the proposed actions.
The modals \can" and \must" originally o ered by ACE are not su cient to capture the levels of obligation imposed by recommendations. In guideline recommendations \should" is the most frequently used modal with a level of obligation between \can" and \must". To adequately represent the required levels, ACE was extended by the modal \should". This is already re ected in the examples of table 1.
While \ACE'ifying" UTI, we noticed that a systematic approach is needed to consistently author clinical guidelines, and to adequately support clinicians in the use of guidelines. All knowledge should be made explicit, all terms should be used consistently, and guidelines should be rendered operational to be executed under the control of the responsible clinician | who ultimately must decide whether, or not, to follow the recommendations of a guideline.
To make all knowledge explicit and to enforce a consistent use of this knowledge we introduce a domain-speci c lexicon and a background ontology. Here is a sample of the UTI background ontology:
To make the guidelines operational we express them as linked rules that are executed under the control of the clinician. Every rule (see Table 1) consists of preconditions that must be ful lled to trigger the rule, and conclusions that are true after the rule red, and that can be used as preconditions for other rules. To get the rule machinery running, a number of initial facts are asserted that originate from the patient's electronic health record or that are manually asserted by the clinician, for instance:
The ring of a guideline rule can enable other rules, so that potentially every rule can be red at some point. 3
Conclusions and Future Work We showed that ACE can be used to adequately express clinical practice guidelines. Furthermore, we prototypically developed a systematic approach to author and to transparently use clinical practice guidelines stated in ACE.
Our immediate plan is to build a \look-ahead" editor for clinical practice
guidelines expressed in ACE that dynamically displays the knowledge de ned so
far and the speci c options available for extending or revising it, similiar to the
existing ACE Editor4. This approach was described by Scott et al. [