Healthcare has lagged behind other industries in automating the storage and retrieval of information. While billing, blood testing, and other services are widely computerized, the bulk of patient clinical information is not. The central barrier to coding clinically useful data - patient historical, test, and procedural results - is the absence of effective knowledge frameworks for entry and review of clinical data. For this reason, sofiware for storage and retrieval of patient data remains suboptimal. This project focuses on methods for recording a specific subset of patient data: results of cardiovascular tests, Although only a subset of clinical knowledge, cardiovascular procedure reporting is typical of the larger problem of how knowledge is handled. Moreover, there is an unmet market demand for cardiology reportrng tools. In Phase I we will develop a methodology for creating and maintaining the knowledge bases needed for structured entry of cardiovascular data. We will apply and hone this methodology by creating two important knowledge bases: echocardiography and cardiac catheterization. Together with the methodology we use, these two developments will dovetail into a larger Phase II project of systematically addressing knowledge-representation and structured data entry for cardiology. Beyond Phase II we will apply our methodology to other branches of medicine. PROPOSED COMMERCIAL APPLICATION: This research will provide a means to optimize structured recording of patient test results in a computer-searchable format improving the process of procedural reporting, and facilitating implementation of an electronic medical record.
Thesaurus Terms: artificial intelligence, cardiovascular disorder diagnosis, computer system design /evaluation, data collection methodology /evaluation, data management echocardiography, heart catheterization, informatics human data
