The RICORDO approach to semantic interoperability for biomedical data and models: strategy, standards and solutions.

BACKGROUND: The practice and research of medicine generates considerable quantities of data and model resources (DMRs). Although in principle biomedical resources are re-usable, in practice few can currently be shared. In particular, the clinical communities in physiology and pharmacology research, as well as medical education, (i.e. PPME communities) are facing considerable operational and technical obstacles in sharing data and models. FINDINGS: We outline the efforts of the PPME communities to achieve automated semantic interoperability for clinical resource documentation in collaboration with the RICORDO project. Current community practices in resource documentation and knowledge management are overviewed. Furthermore, requirements and improvements sought by the PPME communities to current documentation practices are discussed. The RICORDO plan and effort in creating a representational framework and associated open software toolkit for the automated management of PPME metadata resources is also described. CONCLUSIONS: RICORDO is providing the PPME community with tools to effect, share and reason over clinical resource annotations. This work is contributing to the semantic interoperability of DMRs through ontology-based annotation by (i) supporting more effective navigation and re-use of clinical DMRs, as well as (ii) sustaining interoperability operations based on the criterion of biological similarity. Operations facilitated by RICORDO will range from automated dataset matching to model merging and managing complex simulation workflows. In effect, RICORDO is contributing to community standards for resource sharing and interoperability.RIGHTS : This article is licensed under the BioMed Central licence at http://www.biomedcentral.com/about/license which is similar to the 'Creative Commons Attribution Licence'. In brief you may : copy, distribute, and display the work; make derivative works; or make commercial use of the work - under the following conditions: the original author must be given credit; for any reuse or distribution, it must be made clear to others what the license terms of this work are

META-GLARE\u2019s Supports to Agent Coordination

Clinical Guidelines (GLs) provide evidence-based recommendations to suggest to physicians the \u201cbest\u201d medical treatments, and are widely used to enhance the quality of patient care, and to optimize it. In many cases, the treatment of patients cannot be provided by a unique healthcare agent, operating in a unique context. For instance, the treatment of chronic patients is usually performed not only in the hospital, but also at home andor in the general practitioner\u2019s ambulatory, and many healthcare agents (e.g., different specialist, nurses, family doctor) may be involved. To grant the quality of the treatments, all such agents must cooperate and interact. A computer-based support to GL execution is important to provide facilities for coordinating such different agents, and for granting that, at any time, the actions to be executed have a \u201cproper\u201d person in charge and executor, and are executed in the correct context. Additionally, also facilities to support the delegation of responsibility should also be considered. In this paper we extend META-GLARE, a computerized GL management system, to support such needs providing facilities for (1) treatment continuity (2) action contextualization, (3) responsibility assignment and delegation (4) check of agent \u201cappropriateness\u201d. Specific attention is also devoted to the temporal dimension, to grant that each action is executed according to the temporal constraints possibly present in the GL. We illustrate our approach by means of a practical case study

An ontological knowledge and multiple abstraction level decision support system in healthcare

The rationalization of the healthcare processes and organizations is a task of fundamental importance to grant both the quality and the standardization of healthcare services, and the minimization of costs. Clinical Practice Guidelines (CPGs) are one of the major tools that have been introduced to achieve such a challenging task. CPGs are widely used to provide decision support to physicians, supplying them with evidence-based predictive and prescriptive information about patients' status and treatments, but usually on individual pathologies. This sets up the urgent need for developing decision support methodologies to assist physicians and healthcare managers in the detection of interactions between guidelines, to help them to devise appropriate patterns of treatment for comorbid patients (i.e., patients affected by multiple diseases). We identify different levels of abstractions in the analysis of interactions, based on both the hierarchical organization of clinical guidelines (in which composite actions are refined into their components) and the hierarchy of drug categories. We then propose a general methodology (data/knowledge structures and reasoning algorithms operating on them) supporting user-driven and flexible interaction detection over the multiple levels of abstraction. Finally, we discuss the impact of the adoption of computerized clinical guidelines in general, and of our methodology in particular, for patients (quality of the received healthcare services), physicians (decision support and quality of provided services), and healthcare managers and organizations (quality and optimization of provided services)