RESUMO
Many design considerations must be addressed in order to provide researchers with full text and semantic search of unstructured healthcare data such as clinical notes and reports. Institutions looking at providing this functionality must also address the big data aspects of their unstructured corpora. Because these systems are complex and demand a non-trivial investment, there is an incentive to make the system capable of servicing future needs as well, further complicating the design. We present architectural best practices as lessons learned in the design and implementation NLP-PIER (Patient Information Extraction for Research), a scalable, extensible, and secure system for processing, indexing, and searching clinical notes at the University of Minnesota.
RESUMO
Fairview Health Services is an affiliated integrated health system partnering with the University of Minnesota to establish a secure research-oriented clinical data repository that includes large numbers of clinical documents. Standardization of clinical document names and associated attributes is essential for their exchange and secondary use. The HL7/LOINC Document Ontology (DO) was developed to provide a standard representation of clinical document attributes with a multi-axis structure. In this study, we evaluated the adequacy of DO to represent documents in the clinical data repository from legacy and current EHR systems across community and academic practice sites. The results indicate that a large portion of repository data items can be mapped to the current DO ontology but that document attributes do not always link consistently with DO axes and additional values for certain axes, particularly "Setting" and "Role" are needed for better coverage. To achieve a more comprehensive representation of clinical documents, more effort on algorithms, DO value sets, and data governance over clinical document attributes is needed.
