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By providing patient-specific advice, clinical decision support systems (CDSSs) are expected to promote the implementation of clinical practice guidelines (CPGs) to improve the quality of care. However, produced as texts, often incomplete and ambiguous, CPGs are difficult to translate into the formal knowledge bases (KBs) of CDSSs. The French National Authority for Health (HAS) decided to update CPGs on the management of type 2 diabetes. This work illustrates the simultaneous development of the text and its formal counterpart in a CDSS named RecosDiab. CPGs were elaborated by a working group according to the guideline development methodology. Textual recommendations were graded, either as evidence-based when evidence existed or as consensus-based when acknowledge by the working group. Knowledge modeling was performed following the steps of de-abstraction, disambiguation, and verification of completeness. This last step generated clinical situations not explicitly mentioned in the text and were graded as expert-based. The resulting KB provides therapeutic advice for 805 clinical situations, among which 2 are graded as evidence-based, 37 are consensus-based, and 766 are expert-based. However, because of the amount of expert-based propositions, the HAS did not endorse the system.

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International audience; Clinical decision support systems have been developed to help physicians to take clinical guidelines into account during consultations. The ASTI critiquing module is one such systems; it provides the physician with automatic criticisms when a drug prescription does not follow the guidelines. It was initially developed for hypertension and type 2 diabetes, but is designed to be generic enough for application to all chronic diseases. We present here the results of usability and satisfaction evaluations for the ASTI critiquing module, obtained with GPs for a newly implemented guideline concerning dyslipaemia, and we discuss the lessons learnt and the difficulties encountered when building a generic DSS for critiquing physicians' prescriptions.

Clinical decision support systems (CDSSs) have the potential to increase guideline adherence, but factors of success are not yet understood. ASTI guiding mode (ASTI-GM) is an on-demand guideline-based CDSS where the user navigates in a knowledge base to get the best treatment for a given patient. We conducted a web-based evaluation of ASTI-GM, carried out as a before-after study, where general practitioners (GPs) were asked to solve 5 clinical cases, first without ASTI-GM, then using the system. Of the 136 GPs that resolved the case on the management of hypertension, compliance with best practices increased from 69.1% to 80.9% with ASTI-GM. When the navigation matched the set of patient parameters described in the clinical case, the increase was even higher and reached 92.9%. E-iatrogenesis has been measured at 19.1%, with 5.1% of commission errors, 8.1% of negative reactance, and 5.9% of neutral reactance. Role of physicians' reactance in noncompliance with guideline-based CDSSs should be further investigated.

Clinical decision support systems (CDSSs) have the potential to increase guideline adherence, but factors of success are not well understood. ASTI-GM is an on demand guideline-based CDSS where the user interactively characterizes her patient by browsing the system knowledge base to obtain the recommended treatment. We conducted a web-based evaluation of ASTI-GM as a before-after study to assess whether the system improves general practitioners' (GPs) performance and how they would use it. Five clinical cases had to be solved, as usual in the before phase, and using ASTI-GM in the after phase. On a 2-month period, 266 GPs participated and 1,981 prescription orders were collected. The overall guideline adherence rate increased from 27.2% to 64.3%. Only 56.4% of ASTI-GM uses corresponded to a "good use" of the system. Adherence increased from 28.5% to 86.1% in the sub-group of "good uses", whereas it only increased from 28.1% to 36.6% in the complementary sub-group. Reasons for non "good uses" of CDSSs should be investigated since they impede their potential impact.

Building clinical decision support systems requires a formalization of clinical practice guidelines (CPGs) including the verification of completeness to ensure all medically relevant situations are addressed. Recommendations that rely on completed knowledge cannot be but expert-based. Using French hypertension management guidelines, we characterized the status of a patient profile as evidence-based (EB), consensus-based (CB), or expert-based (XB). The distribution of these status on the formal patient profiles of ASTIGM knowledge base showed that 12.6% (0.5% EB and 12.1% CB) lead to explicit CPG recommendations. The same analysis on a sample of 435 actual patients medical records showed that 55% were covered by CPGs. The characterization of guideline-based CDSSs should be based on empirical data estimated from the target population of CPGs.

ASTI is a guideline-based decision support system to be used in primary care. We analyzed the knowledge modelling carried out in the development of ASTI knowledge base (KB) from French clinical practice guidelines (CPGs) on arterial hypertension management to evaluate the evidence status of therapeutic propositions issued by the system. We defined three status: "evidence-based" (EB) when propositions are graded A, B, or C, "consensus-based" (CB) when propositions are explicitly mentioned in CPGs but supported by professional agreement (grade D), and "non-supported" (NS) when propositions are expert-based and provided by a domain specialist. We compared the distributions of evidence status on the 44,571 theoretical patient profiles extracted from ASTI KB, and on a data set of 435 actual hypertensive patients. Only 8.3% of actual patients, managed by 0.5% of the KB, have an EB profile and 46.9% of patients, managed by 12.6% of the KB, have a CB profile. Thus, there is no CPG recommendation for nearly half of the patients (44.8% have a NS profile).

International audience; Well-designed medical decision support system (DSS) have been shown to improve health care quality. However, before they can be used in real clinical situations, these systems must be extensively tested, to ensure that they conform to the clinical guidelines (CG) on which they are based. Existing methods cannot be used for the systematic testing of all possible test cases. We describe here a new exhaustive dynamic verification method. In this method, the DSS is considered to be a black box, and the Quinlan C4.5 algorithm is used to build a decision tree from an exhaustive set of DSS input vectors and outputs. This method was successfully used for the testing of a medical DSS relating to chronic diseases: the ASTI critiquing module for type 2 diabetes.

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.

Computer-based decision support systems (CDSSs) are currently mostly reminder systems. However, the effectiveness of such systems to modify physician behavior is not always observed. We assume that this approach is appropriate when physicians think they know how to prescribe and consider they don't need to be helped, i.e. for simple clinical cases. On the opposite, on-demand approaches allowing for flexibility in the interpretation of patient conditions are more appropriate for more complex cases, e.g. in chronic disease management. ASTI is a CDSS operating in two modes, a critiquing mode working as a reminder-based system and a user-initiated guiding mode. Using a clinical case complexity score, a pre/post-intervention experiment with 10 GPs and 15 cases of hypertensive patients has been performed. Preliminary results tend to indicate that reminder-based interaction is appropriate for simple cases and that physicians are willing to use on-demand systems as clinical situations become complex, making both modes complementary.

Classiquement développés comme des systèmes d'alertes produisant automatiquement des thérapeutiques centrées patient, les systèmes d'aide à la décision médicale sont appréciés par les médecins utilisateurs de façon variable selon les études. Nous pensons que ce mode d'interaction n'est pertinent que dans les cas simples où le médecin pense a priori qu'il n'a pas besoin d'être aidé. Une approche " à la demande " nous semble, par ailleurs, adaptée dans les cas plus compliqués. Nous avons testé cette hypothèse avec le système ASTI développé de façon à proposer deux modes d'interaction. Dédié aux cas simples, le mode " critique ", entièrement automatique, produit des alertes lorsque la prescription médicamenteuse du médecin n'est pas conforme aux recommandations. Au contraire, le mode " guidé " est utilisé de façon volontaire par le médecin qui, au cours d'une navigation active au sein d'une base de connaissances, accède dans les cas complexes aux recommandations thérapeutiques. Un score de complexité des cas cliniques a été proposé. Une étude préliminaire a été conduite sur 15 cas cliniques et 10 généralistes qui valide notre hypothèse de travail.

Existing computer-based ordering systems for physicians provide effective drug-centered checks but offer little assistance for optimizing the overall patient-centered treatment strategy. Evidence-based clinical practice guidelines have been developed to disseminate state-of-the-art information concerning treatment strategy but these guidelines are poorly used in routine practice. The ASTI project aims to design a guideline-based ordering system to enable general practitioners to avoid prescription errors and to improve compliance with best therapeutic practices. The " critic mode " operates as a background process and corrects the physician's prescription on the basis of automatically triggered elementary rules that account for isolated guideline recommendations. The " guided mode " directs the physician to the best treatment by browsing a comprehensive guideline knowledge base represented as a decision tree. A first prototype, applied to hypertension, is currently under development.

Recently, National agencies in charge of the development of clinical practice guidelines (CPGs) have started to improve the usual narrative CPGs to provide guidance for different clinical pathways. In France, in conjunction with the development of the type 2 diabetes National CPGs, we have developed the system RecosDoc-Diabète which allows to interactively build a patient-centred pathway and get the appropriate recommendations. National narrative CPGs and RecosDoc-Diabète were published and made available online at the same time (February 2013). A questionnaire was provided to collect visitors' judgement about the system. Between February 12th and December 31st, 2013, 55,203 visitors accessed the narrative CPGs whereas 10,565 accessed the system. Among them, 186 (2%) responded to the questionnaire. One third of the comments were criticisms towards the CPG content. The system was globally positively evaluated although assessments were mixed illustrating that users' needs may be contradictory.