Biomedical subjects

Jacques Bouaud

Publications and source records attributed to Jacques Bouaud.

10 recordsLinked to original sources

PubMed2006

Design of a decision support system for chronic diseases coupling generic therapeutic algorithms with guideline-based specific rules.

Clinical Decision Support Systems (DSS) help improve health care quality. They usually incorporate an Execution Engine (EE), defined for each disease. We have designed, and present here, a generic execution engine, coupled with guideline-based disease specific rules stored in knowledge base (KB) as part of the prescription-critiquing mode of the ASTI project. This system was designed using two national guidelines for type 2 diabetes and hypertension. It takes into account the patient's clinical data, the tolerance and effectiveness of previous and current treatments and the physician's prescription made at the time. The functioning of the system has been speeded up and its maintenance made easier by indexing the KB rules according to the type of treatment they are linked to (e.g. monotherapy, etc.) and by classifying them into four categories. The EE's design formalizes generic therapeutic algorithms, leading to treatment options for cases of bad tolerance or insufficient effectiveness of the current treatment. Its applicability to other diseases was shown by applying it to dyslipidemia.

Algorithms↗

PubMed2005

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.

Canada↗

PubMed2004

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.

Canada↗

PubMed2004

Restoring the patient therapeutic history from prescription data to enable computerized guideline-based decision support in primary care.

The application of a guideline-based therapeutic strategy in the context of a chronic disease requires a clear picture, at the appropriate level of abstraction, of a patient's particular therapeutic history. However, like most clinical data, information on past treatments is often incompletely specified. We propose temporal abstraction mechanisms based on a simple and heuristic treatment of temporal indeterminacy of period bounds. Applied to prescription data, continuity and simultaneousness of treatments have to be characterized. Even if limited, our method restores the therapeutic history of a patient, with episodes of mono-, bi-, and tritherapies in order to adequately position her within the guideline therapeutic strategy.

Antihypertensive Agents↗

PubMed2004

Development of computerized guidelines for the management of chronic diseases allowing to position any patient within recommended therapeutic strategies.

Chronic diseases are complex to manage. One reason comes from the difficulty to synchronize a patient's therapeutic history with the guideline-based sequence of treatments. We propose to represent guideline knowledge as a two-level decision tree, a clinical level describing theoretical clinical situations and a therapeutic level formalizing the different steps of corresponding recommended therapeutic strategies. Guideline-based strategies are first represented as bidimensional matrices structured in lines of therapy and levels of therapeutic intention. A revised version introducing levels of therapeutic combination is then developed. The therapeutic level is operationalized for any patient therapeutic history to provide the next best step of treatment. Evaluated on actual patient records, our system proved to impact physicians' decisions in 78% of the cases and led to a significant improvement of their compliance with recommendations.

Chronic Disease↗

PubMed2003

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.

Chronic Disease↗

PubMed2003

Modeling patient-specific therapeutic strategy in the guideline-based management of a chronic disease.

Like any chronic disease, hypertension is complex to manage. Despite the availability of evidence-based clinical practice guidelines in most countries, a lot of hypertensive patients remain inadequately managed. One difficulty lies in the synchronization of a patient's own therapeutic history with the guideline strategy. We propose a formal model to represent guideline-based therapeutic strategies as bi-dimensional matrices. We built the knowledge base as a two-level decision tree to be read during an hypertextual navigation. The first level is used to identify a patient-specific clinical situation on the basis of key elements of clinical examination (complication of hypertension, associated diseases). The second level aims at dynamically refining the theoretical strategy, a priori established in the guideline for the corresponding clinical situation, by the specific therapeutic history of the patient. Finally, depending on the patient's response to the ongoing treatment, the system provides a recommendation consistent with the guideline strategy, whatever the patient's past treatments. A first evaluation of the system on simulated cases has been well accepted by general practitioners.

Chronic Disease↗

PubMed2003

Does GEM-encoding clinical practice guidelines improve the quality of knowledge bases? A study with the rule-based formalism.

The aim of this work was to determine whether the GEM-encoding step could improve the representation of clinical practice guidelines as formalized knowledge bases. We used the 1999 Canadian recommendations for the management of hypertension, chosen as the knowledge source in the ASTI project. We first clarified semantic ambiguities of therapeutic sequences recommended in the guideline by proposing an interpretative framework of therapeutic strategies. Then, after a formalization step to standardize the terms used to characterize clinical situations, we created the GEM-encoded instance of the guideline. We developed a module for the automatic derivation of a rule base, BR-GEM, from the instance. BR-GEM was then compared to the rule base, BR-ASTI, embedded within the critic mode of ASTI, and manually built by two physicians from the same Canadian guideline. As compared to BR-ASTI, BR-GEM is more specific and covers more clinical situations. When evaluated on 10 patient cases, the GEM-based approach led to promising results.

Artificial Intelligence↗

PubMed2002

Impact of site-specific customizations on physician compliance with guidelines.

Developed and implemented in the Service d'Oncologie Médicale Pitié-Salpêtrière (Paris, France) as a computer-based guideline system on breast cancer, OncoDoc has already demonstrated high physician compliance rates. To assess how the system could be reused in another institution which was not involved in the development process, we have conducted a new experimentation at the Institut Gustave Roussy. Minor site-specific customizations of the knowledge base have been performed. After four months, 127 cases were recorded. Results showed that there was no significant difference of physician compliance with OncoDoc (85%) when site-specific recommendations were, or not, available, although local recommendations were chosen preferably (55%), thus legitimating the adaptation.

Breast Neoplasms↗

PubMedDate not supplied

Using OncoDoc as a computer-based eligibility screening system to improve accrual onto breast cancer clinical trials.

While clinical trials offer cancer patients the optimum treatment, historical accrual of such patients has not been very successful. OncoDoc is a decision support system designed to provide best therapeutic recommendations for breast cancer patients. Developed as a browsing tool of a knowledge base structured as a decision tree, OncoDoc allows physicians to control the contextual instantiation of patient characteristics to build the best formal equivalent of an actual patient. Used as a computer-based eligibility screening system, depending on whether instantiated patient parameters are matched against guideline knowledge or available clinical trial protocols, it provides either evidence-based therapeutic options or relevant patient-specific clinical trials. Implemented at the Gustave Roussy Institute and routinely used at the point of care during a 4-month period, it significantly improved physician compliance with guideline recommendations and enhanced physician awareness of open trials while increasing patient enrollment to clinical trials by 50%. But, when analyzing reasons of non-accrual of potentially eligible patients, it appeared that physicians' psychological reluctance to refer patients to clinical trials, measured during the experiment at 25%, may not be resolved by the simple dissemination of clinical trial information at the point of care.

Attitude of Health Personnel↗