大多数临床信息系统基于“增大化现实”技术进行组织ound data types (e.g. lab results, medications, problems, vital signs and notes); however, this organization does not usually match the cognitive patterns of clinicians, which tend to be problem-oriented and cut across data types. Project 3 is dedicated to developing methodologies for modeling and summarizing complex chronically-ill patients’ electronic health records, which will be enhanced with context-appropriate, evidence-based recommendations that improve clinician decision-making under information overload and time pressure. Creating such summaries is challenging, and depends on both a deep understanding of clinician cognitive processes and accurate models of clinical knowledge and practice. We will use the Rapid Assessment Process (RAP), a modification of traditional ethnography, to understand clinician summarization needs and to develop clinical requirements . After this portion of the project is well-underway, we will design automated methods of creating accurate, succinct, condition-dependent and independent computer-generated summaries of complex, chronically-ill patients with the ultimate goal of improving patient safety, clinician efficiency and satisfaction, and reduce the cost of care.
Products
MAPLE Knowledge Base: A validated knowledge base that can be used to infer problems from medications, laboratory results, billing data, procedures and vital signs. The knowledge base is available athttp://jamia.bmj.com/content/18/6/859/suppl/DC1and is described in a paper cited below.
Problem-Medication Linkage Knowledge Base: An ontology-based knowledge base containing nearly 34 million distinct problem-medication pairs by:
1) using the “may_treat” relationship within NDF-RT, mapping the medications and problems to RxNorm and SNOMED-CT, and 2) inferring additional relationships using the “ingredient_of” and “isa” relationships between similar medications in RxNorm and derivative problems in SNOMED-CT.
MedEx:Medication information is one of the most important types of clinical data in electronic medical records. It is critical for healthcare safety and quality, as well as for clinical research that uses electronic medical record data. However, medication data are often recorded in clinical notes as free-text. As such, they are not accessible to other computerized applications that rely on coded data. We describe a new natural language processing system (MedEx), which extracts medication information from clinical notes.
Demonstration of Patient Summarizer within the SMART App Platform
Publications
D’Amore J. The Promise of the Continuity of Care Document. Master’s Thesis School of Biomedical Informatics, University of Texas Health Science Center at Houston. 2011.