Context-Based Electronic Health Record: Toward Patient Specific Healthcare

• Published in: IEEE journal of biomedical and health informatics 2012-03, Vol.16 (2), p.228-234
• Main Authors: Hsu, W., Taira, R. K., El-Saden, S., Kangarloo, Hooshang, Bui, A. A. T.
• Format: Article
• Language: English
• Subjects: Biological system modeling; Data visualization; Database Management Systems; Diseases; Electronic Health Records; health information management; Humans; knowledge representation; Medical diagnostic imaging; Models, Theoretical; Natural Language Processing; natural language processing (NLP); Ontologies; Precision Medicine - methods; Studies; User-Computer Interface
• ISSN: 1089-7771; 2168-2194; 1558-0032; 2168-2208
• DOI: 10.1109/TITB.2012.2186149

Due to the increasingly data-intensive clinical environment, physicians now have unprecedented access to detailed clinical information from a multitude of sources. However, applying this information to guide medical decisions for a specific patient case remains challenging. One issue is related to presenting information to the practitioner: displaying a large (irrelevant) amount of information often leads to information overload. Next-generation interfaces for the electronic health record (EHR) should not only make patient data easily searchable and accessible, but also synthesize fragments of evidence documented in the entire record to understand the etiology of a disease and its clinical manifestation in individual patients. In this paper, we describe our efforts toward creating a context-based EHR, which employs biomedical ontologies and (graphical) disease models as sources of domain knowledge to identify relevant parts of the record to display. We hypothesize that knowledge (e.g., variables, relationships) from these sources can be used to standardize, annotate, and contextualize information from the patient record, improving access to relevant parts of the record and informing medical decision making. To achieve this goal, we describe a framework that aggregates and extracts findings and attributes from free-text clinical reports, maps findings to concepts in available knowledge sources, and generates a tailored presentation of the record based on the information needs of the user. We have implemented this framework in a system called Adaptive EHR, demonstrating its capabilities to present and synthesize information from neurooncology patients. This paper highlights the challenges and potential applications of leveraging disease models to improve the access, integration, and interpretation of clinical patient data.