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Abstract
Grant Number: 1R01LM007595-01 Project Title: Decision Support Systems for MAG3 Renography
PI Information: Name Title TAYLOR, ANDREW T. ataylor@emory.edu Abstract: DESCRIPTION (provided by applicant): The current health care system requires physicians to master an ever-expanding knowledge base while the hours available to master this knowledge base and apply it to specific tasks (e.g., image interpretation, seeing a new patient) are steadily shrinking. The convergence of an expanding knowledge base and escalating time constraints poses a serious problem that will inevitably lead to physician errors. Solutions to this problem require the development of new tools and processes to save physician time and facilitate the application of a broad and sophisticated knowledge base to speci5c clinical problems. Our long-term objective is to improve the care of nephrourology patients and we propose to meet this objective by developing new tools (Decision Support Systems) to (1) assist and educate physicians and trainees to appropriately perform and interpret Tc-99m mercaptoacetyltriglycine (MAG3) renography and (2) to process, check quality control and actually interpret MAG3 renograms. Specijkally, we hypothesize that we can develop Decision Support Systems to interpret adult MAG3 scans as well as a panel of experts. We have chosen to develop Decision Support Systems for MAG3 renography because (1) the vast majority of the estimated 400,000 radionuclide renal scans performed annually in the United States are interpreted by radiologists who lack the time to develop the needed expertise and (2) MAG3 is used in over 70% of studies. We propose to develop two separate Decision Support Systems using totally different approaches, a heuristic model (knowledge based expert system) and a predictive statistical system. These Systems will be developed from a database of 3500-4000 MAG3 studies and will be designed to acquire the study, generate images and curves from a dynamic radionuclide renogram, check for errors, extract the relevant quantitative data and then use these data to interpret the study. Moreover, the user will be able to query the Systems to obtain the supporting data and rules justifying a specific interpretation. The heuristic and predictive statistical approaches will clarify the relative merits of each approach and will provide a template for the development and application of similar diagnostic and educational aids in the future. Finally, the Decision Support Systems will allow us to calculate the incremental contribution each parameter makes to the diagnostic accuracy, delete superfluous measurements, minimize the time and effort required to acquire, process and interpret renal scans, provide an objective analysis of renogram data, facilitate wide dissemination of interpretative criteria, foster standardized interpretation, teach physicians and trainees to perform and interpret renal scans as competently as experts, encourage more appropriate utilization and, most of all, improve the accuracy of MAG3 scan interpretation and thereby enhance the care of nephro-urology patients.
Public Health Relevance:
This Public Health Relevance is not available.Thesaurus Terms:
computer assisted medical decision making, computer system design /evaluation, kidney imaging /visualization
computer assisted diagnosis, informatics, interactive multimedia
bioimaging /biomedical imaging, clinical research, human data, patient oriented research
Institution: EMORY UNIVERSITY 1599 CLIFTON ROAD, 4TH FLOOR ATLANTA, GA 30322 Fiscal Year: 2002 Department: RADIOLOGY Project Start: 01-SEP-2002 Project End: 31-AUG-2006 ICD: NATIONAL LIBRARY OF MEDICINE IRG: DMG