Modalités d'interaction avec des systèmes d'aide à la décision médicale par alerte ou à la demande pour délivrer des recommandations : une étude préliminaire dans le cadre de la prise en charge de l'hypertension

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

Reminder-based or on-demand decision support systems: a preliminary study in primary care with the management of hypertension.

ASTI is a guideline-based decision support system for therapeutic prescribing in primary care with two modes of interaction. The "critic mode" operates as a reminder system to detect non guideline-compliant physician drug orders, whereas the "guided mode" operates on demand and provides physician guidance to help her establishing best recommended drug prescriptions for the management of hypertension. A preliminary evaluation study was conducted with 10 GPs to test the complementary nature of both modes of decision support. Results tend to validate our assumption that reminder-based interaction is appropriate for simple cases and that physicians are willing to use on-demand systems as clinical situations become more complex

Characterizing the dimensions of clinical practice guideline evolution.

The ever growing pace at which medical knowledge is produced requires clinical practice guidelines (CPGs) to be regularly updated. Since clinical decision support systems (CDSSs) are effective means to implement guidelines in routine care, they have to be revised as their knowledge sources evolve. From one version to another, some parts are kept unchanged whereas others are more or less modified. We propose to characterize formally the different dimensions of recommendation evolution in two successive guideline versions from the knowledge modelling perspective. Each atomic recommendation is represented as a rule connecting a clinical condition to recommended action plans. Using subsumption-based comparisons, seven evolution patterns were identified: No change, Action plan refinement, New action plan, Condition refinement, Recommendation refinement, New practice, and Unmatched recommendation. The method has been evaluated on French bladder cancer guidelines in the revisions of 2002 and 2004

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

Adoption of a Nationwide Shared Medical Record in France: Lessons Learnt after 5 Years of Deployment.

Information sharing among health practitioners, either for coordinated or unscheduled care, is necessary to guarantee care quality and patient safety. In most countries, nationwide programs have provided tools to support information sharing, from centralized care records to health information exchange between electronic health records (EHRs). The French personal medical record (DMP) is a centralized patient-controlled record, created according to the opt-in consent model. It contains the documents health practitioners voluntarily push into the DMP from their EHRs. Five years after the launching of the program in December 2010, there were nearly 570,000 DMPs covering only 1.5% of the target population in December 2015. Reasons for this poor level of adoption are discussed in the perspective of other countries' initiatives. The new French governmental strategy for the DMP deployment in 2016 is outlined, with the implementation of measures similar to the US Meaningful Use

Automatic generation of a metamodel from an existing knowledge base to assist the development of a new analogous knowledge base.

Knowledge acquisition is a key step in the development of knowledge-based systems and methods have been proposed to help elicitating a domain-specific task model from a generic task model. We explored how an existing validated knowledge base (KB) represented by a decision tree could be automatically processed to infer a higher level domain-specific task model. On-codoc is a guideline-based decision support system applied to breast cancer therapy. Assuming task identity and ontological proximity between breast and lung cancer domains, the generalization of the breast can-cer KB should allow to build a metamodel to serve as a guide for the elaboration of a new specific KB on lung cancer. Two types of parametrized generalization methods based on tree structure simplification and ontological abstraction were used. We defined a similarity distance and a generalization coefficient to select the best metamodel identified as the closest to the original decision tree of the most generalized metamodels

The (Re)-Relaunching of the DMP, the French Shared Medical Record: New Features to Improve Uptake and Use.

Health care delivery for patients with chronic conditions is complex and confusing. Care is often fragmented, less effective, and sometimes harmful. Given the potential for EHRs and other health IT systems to facilitate information flow between providers, patients, and settings, health IT-based measures are of particular interest for care coordination. Health information exchange (HIE) has the potential to improve the quality of healthcare by enabling providers with better access to patient information from multiple sources at the point of care. However, many barriers to HIE use have been reported. Another solution relies on the implementation of a nationwide centralized framework of clinical information sharing with "new" secure online care records stored in specifically created platforms. The French DMP follows this model but the adoption of the tool has been historically poor. In 2016, a renovation of the DMP program was launched in nine pilot French departments and new features have been implemented: the DMP content has been fully specified, patients can create their DMP by themselves, DMPs are automatically filled in by data claims, a mobile app has been developed, and technical issues about DMP and EHRs interoperability have been resolved. In October 2017, over 900,000 people have a DMP with an average new DMP being created every minute. These results have to be confirmed in 2018 when the new DMP will be deployed on the whole French territory