Challenges in Delivering Decision Support Systems: The MATE Experience [chapter]

Dionisio Acosta, Vivek Patkar, Mo Keshtgar, John Fox
<span title="">2010</span> <i title="Springer Berlin Heidelberg"> <a target="_blank" rel="noopener" href="https://fatcat.wiki/container/2w3awgokqne6te4nvlofavy5a4" style="color: black;">Lecture Notes in Computer Science</a> </i> &nbsp;
Cancer Multidisciplinary Meeting (MDM) is a widely endorsed mechanism for ensuring high quality evidence-based health care. However, there are shortcomings that could ultimately result in unintended patient harm. On the other hand, clinical guidelines and clinical decision support systems (DSS) have been shown to improve decision-making in various measures. Nevertheless, their clinical use requires seamlessly interoperation with the existing electronic health record (EHR) platform to avoid the
more &raquo; ... etrimental effects that duplication of data and work has in the quality of care. The aim of this work is to propose a computational framework to provide a clinical guideline-based DSS for breast cancer MDM. We discuss a range of design and implementation issues related to knowledge representation and clinical service delivery of the system, and propose a service oriented architecture based on the HL7 EHR functional model. The main result is the DSS named MATE (Multidisciplinary Assistant and Treatment sElector), which demonstrates that decision support can be effectively deployed in a real clinical setting and suggest that the technology could be generalised to other cancer MDMs.
<span class="external-identifiers"> <a target="_blank" rel="external noopener noreferrer" href="https://doi.org/10.1007/978-3-642-11808-1_11">doi:10.1007/978-3-642-11808-1_11</a> <a target="_blank" rel="external noopener" href="https://fatcat.wiki/release/re5knuofo5epvgu5b4qcjxpzmu">fatcat:re5knuofo5epvgu5b4qcjxpzmu</a> </span>
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