Case-based decision support system for breast cancer management

Breast cancer is identified as the most common type of cancer in women worldwide with 1.6 million women around the world diagnosed every year. This prompts many active areas of research in identifying better ways to prevent, detect, and treat breast cancer. DESIREE is a European Union funded project, which aims at developing a web-based software ecosystem for the multidisciplinary management of primary breast cancer. The development of an intelligent clinical decision support system offering various modalities of decision support is one of the key objectives of the project. This paper explores case-based reasoning as a problem solving paradigm and discusses the use of an explicit domain knowledge ontology in the development of a knowledge-intensive case-based decision support system for breast cancer management

Computerized Clinical Decision Support: Contributions from 2014.

To summarize recent research and propose a selection of best papers published in 2014 in the field of computerized clinical decision support for the Decision Support section of the IMIA yearbook. A literature review was performed by searching two bibliographic databases for papers related to clinical decision support systems (CDSSs) and computerized provider order entry systems in order to select a list of candidate best papers to be then peer-reviewed by external reviewers. A consensus meeting between the two section editors and the editorial team was finally organized to conclude on the selection of best papers. Among the 1,254 returned papers published in 2014, the full review process selected four best papers. The first one is an experimental contribution to a better understanding of unintended uses of CDSSs. The second paper describes the effective use of previously collected data to tailor and adapt a CDSS. The third paper presents an innovative application that uses pharmacogenomic information to support personalized medicine. The fourth paper reports on the long-term effect of the routine use of a CDSS for antibiotic therapy. As health information technologies spread more and more meaningfully, CDSSs are improving to answer users' needs more accurately. The exploitation of previously collected data and the use of genomic data for decision support has started to materialize. However, more work is still needed to address issues related to the correct usage of such technologies, and to assess their effective impact in the long term

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

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

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

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

A 2014 medical informatics perspective on clinical decision support systems: do we hit the ceiling of effectiveness?

To summarize recent research and propose a selection of best papers published in 2013 in the field of computer-based decision support in health care. Two literature reviews were performed by the two section editors from bibliographic databases with a focus on clinical decision support systems (CDSSs) and computer provider order entry in order to select a list of candidate best papers to be peer-reviewed by external reviewers. The full review process highlighted three papers, illustrating current trends in the domain of clinical decision support. The first trend is the development of theoretical approaches for CDSSs, and is exemplified by a paper proposing the integration of family histories and pedigrees in a CDSS. The second trend is illustrated by well-designed CDSSs, showing good theoretical performances and acceptance, while failing to show a clinical impact. An example is given with a paper reporting on scorecards aiming to reduce adverse drug events. The third trend is represented by research works that try to understand the limits of CDSS use, for instance by analyzing interactions between general practitioners, patients, and a CDSS. CDSSs can achieve good theoretical results in terms of sensibility and specificity, as well as a good acceptance, but evaluations often fail to demonstrate a clinical impact. Future research is needed to better understand the causes of this observation and imagine new effective solutions for CDSS implementation