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Biomedical subjects

Ronald L Arenson

Publications and source records attributed to Ronald L Arenson.

8 recordsLinked to original sources

The academic radiologist's clinical productivity: an update.

RATIONALE AND OBJECTIVES: The purpose of this project was to further understand the academic radiologist's clinical workload with comparison to the prior studies in the past decade. This updated data is very important in determining faculty staffing requirements. MATERIALS AND METHODS: A survey performed by the Society of Chairmen of Academic Radiology Departments (SCARD) collected data in 2003 for radiologists in 23 departments. This data included Current Procedure Terminology (CPT) codes by radiologist. The CPT codes were converted into relative value units (RVUs) per full-time equivalent (FTE) faculty. By grouping the CPT codes into similar examination categories, adjustment factors were created for the RVU values for each CPT in order to compensate for workload variations. These adjustment factors are identical to the adjustments made in 2001 except for a new factor for nuclear medicine. RESULTS: Overall, the average clinical workload in 2003 was 5,872 RVU/FTE, a 32% increase compared to 4,458 RVU/FTE in 1998 and 55% increase compared to 3,790 RVU/FTE in 1996. The average number of examinations per FTE had a smaller (17%) increase since 1998. The adjustment factors remain very similar to those presented in 2001. The only change was a new adjustment factor of 1.3 for nuclear medicine. CONCLUSIONS: Clinical workload as measured by RVU/FTE and adjusted RVU/FTE are very useful for determining optimal staffing in subspecialty sections and in the department as a whole. The workload continues to increase, but more in examination complexity than in numbers of procedures overall.

Academic Medical Centers↗

Addressing the coming radiology crisis-the Society for Computer Applications in Radiology transforming the radiological interpretation process (TRIP) initiative.

The Society for Computer Applications in Radiology (SCAR) Transforming the Radiological Interpretation Process (TRIP) Initiative aims to spearhead research, education, and discovery of innovative solutions to address the problem of information and image data overload. The initiative will foster interdisciplinary research on technological, environmental and human factors to better manage and exploit the massive amounts of data. TRIP will focus on the following basic objectives: improving the efficiency of interpretation of large data sets, improving the timeliness and effectiveness of communication, and decreasing medical errors. The ultimate goal of the initiative is to improve the quality and safety of patient care. Interdisciplinary research into several broad areas will be necessary to make progress in managing the ever-increasing volume of data. The six concepts involved are human perception, image processing and computer-aided detection (CAD), visualization, navigation and usability, databases and integration, and evaluation and validation of methods and performance. The result of this transformation will affect several key processes in radiology, including image interpretation; communication of imaging results; workflow and efficiency within the health care enterprise; diagnostic accuracy and a reduction in medical errors; and, ultimately, the overall quality of care.

Humans↗

Design and applications of a multimodality image data warehouse framework.

A comprehensive data warehouse framework is needed, which encompasses imaging and non-imaging information in supporting disease management and research. The authors propose such a framework, describe general design principles and system architecture, and illustrate a multimodality neuroimaging data warehouse system implemented for clinical epilepsy research. The data warehouse system is built on top of a picture archiving and communication system (PACS) environment and applies an iterative object-oriented analysis and design (OOAD) approach and recognized data interface and design standards. The implementation is based on a Java CORBA (Common Object Request Broker Architecture) and Web-based architecture that separates the graphical user interface presentation, data warehouse business services, data staging area, and backend source systems into distinct software layers. To illustrate the practicality of the data warehouse system, the authors describe two distinct biomedical applications--namely, clinical diagnostic workup of multimodality neuroimaging cases and research data analysis and decision threshold on seizure foci lateralization. The image data warehouse framework can be modified and generalized for new application domains.

Decision Making, Computer-Assisted↗