Improvement of inter-services communication through a CDSS dedicated to myocardial perfusion scintigraphy.

This study addresses the question of communication between medical wards and the nuclear medicine department for the realization of myocardial perfusion scintigraphy. It analyses the effects of a reminder for completing the content of an order form. It shows that the CDSS impacted ordering practices. It could be seen as a system enabling to structure the information and improve the quality of orders.

Structuring Clinical Guidelines through the Recognition of Deontic Operators.

In this paper, we present a novel approach to structure Clinical Guidelines through the automatic recognition of syntactic expressions called deontic operators. We defined a grammar and a set of Finite-State Transition Networks (FSTN) to automatically recognize deontic operators in Clinical Guidelines. We then implemented a dedicated FSTN parser that identifies deontic operators and marks up their occurrences in the document, thus producing a structured version of the Guideline. We evaluated our approach on a corpus (not used to define the grammar) of 5 Clinical Guidelines. As a result, 95.5% of the occurrences of deontic expressions are correctly marked up. The automatic detection of deontic operators can be a useful step to support Clinical Guidelines encoding.

Extending the GEM model to support knowledge extraction from textual guidelines.

Clinical Practice Guidelines (CPGs) are being developed as a tool to promote best practice in medicine. However, the diffusion of paper guidelines has been shown to only have a limited impact. This is why computerization of CPGs has recently been suggested as a means to improve their dissemination as well as physicians' compliance. The Guideline Elements Model (GEM) has been proposed to facilitate the encoding of CPGs and support the automatic processing of marked-up documents. In this paper, we explore the automatic generation of a rule base from a textual guideline using GEM. In this study, we propose an extension of the GEM model that introduces additional levels of structuring centered on decision variables. This allows a more efficient representation of the decision processes, which supports the automatic generation of decision rules from textual guidelines. The 1999 Canadian recommendations for the management of hypertension have been marked-up as a GEM-encoded instance of our extended DTD. We derived a rule base using an XML parser to extract the relevant elements to instantiate the IF and THEN clauses of decision rules. The rule base automatically generated compares favourably with the manual generation of decision rules in the ASTI project. This approach is an interesting case study in the computerization of CPGs, as it illustrates processing steps that are relevant to the various aspects of CPGs life-cycle, from production to consultation and use.

An environment for document engineering of clinical guidelines.

In this paper, we present the G-DEE system, a document engineering environment aimed at clinical guidelines. This system represents an extension of current visual interfaces for guidelines encoding, in that it supports automatic text processing functions which identify linguistic markers of document structure, such as recommendations, thereby decreasing the complexity of operations required by the user. Such markers are identified by shallow parsing of free text and are automatically marked up as an early step of document structuring. From this first representation, it is possible to identify elements of guidelines contents, such as decision variables, and produce elements of GEM encoding, using rules defined as XSL style sheets. We tested our automatic structuring system on a set of sentences extracted from French clinical guidelines. As a result, 97% of the occurrences of deontic operators and their scopes were correctly marked up. G-DEE can be used for various purposes, from research into guidelines structure to assisting the encoding of guidelines into a GEM format or into decision rules.

Synthesis of elementary single-disease recommendations to support guideline-based therapeutic decision for complex polypathological patients.

Situations managed by clinical practice guidelines (CPGs) usually correspond to general descriptions of theoretical patients that suffer from only one disease in addition to the specific pathology CPGs focus on. The lack of decision support for complex multiple-disease patients is usually transferred to computer-based systems. Starting from the GEM-encoded instance of CPGs, we developed a module that automatically generated IF-THEN-WITH decision rules. A two-stage unification process has been implemented. All the rules whose IF-part is in partial matching with a patient clinical profile were triggered. A synthesis of triggered rules has then been performed to eliminate redundancies and incoherences. All remaining, eventually contradictory, recommendations were displayed to physicians leaving them the responsibility of handling the controversy and thus the opportunity to control the therapeutic decision.

Interpretative framework of chronic disease management to guide textual guideline GEM-encoding.

The aim of this work is to develop an XML-based application for the automated generation of decision rules from a textual guideline encoded using the Guideline Elements Model (GEM). A formalization of guideline-based chronological steps of treatment has been proposed to resolve the semantic ambiguities of the original document. The GEM DTD has been extended in order to standardize both decision variable and action representations in recommendations. Under these assumptions, the 1999 Canadian Recommendations for the management of hypertension have been marked-up as a GEM-encoded instance of the extended DTD. An XML parser has been used to extract the relevant elements as IF and THEN clauses of decision rules. This GEM application generated 104 rules to be compared to the 98 rules manually developed from the same guideline during the ASTI project.