Semantic Web-Based Medical Documentation Processing for Healthcare Collaboration

点击翻译按钮获取中文摘要

The present landscape of healthcare is one of escalating costs coupled with an aging population with increasing complexity in concurrent chronic medical illnesses. Care provisioned to these patients are carried out by large interdisciplinary teams that are challenged with being synchronously aware of each member's view points and generating coherent and safe treatment plans. Part of challenges relating to this environment exists in how documentation occurs. The vast majority of documentation consists of unstructured/semi-structured text that occurs in silos. In order to fully understand the team's point of view, a team member would have to inspect every individual entry before being able to provide an integrated contribution to the treatment plan. Therefore, if one agrees that within medical documentation lies the very heart of clinical impressions and planning, what if a computer can (i) correctly parse and annotate medical documentation; (ii) build high semantic structures out of unstructured documents from various electronic sources, and (iii) predict clinical contexts. What actions could then a computer take that benefit the patient, the healthcare team, and all levels of decision makers? This research program addresses the above challenges by investigating a novel approach to medical documentation processing and analysis, whereby the document is no longer inert to information technology, but a rich source for knowledge acquisition and a road map to drive a wide range of software processes which range from collaborative treatment planning to just-in-time decision support. To accomplish this level of intelligence, we adopt a new approach to combining benefits of Model Driven Software Development (MDSD), ontology engineering, and context processing to solve (i), (ii) and (iii).