Biomedical subjects

Charles E Kahn

Publications and source records attributed to Charles E Kahn.

6 recordsLinked to original sources

PubMed2007

A presentation system for just-in-time learning in radiology.

There is growing interest in bringing medical educational materials to the point of care. We sought to develop a system for just-in-time learning in radiology. A database of 34 learning modules was derived from previously published journal articles. Learning objectives were specified for each module, and multiple-choice test items were created. A web-based system-called TEMPO-was developed to allow radiologists to select and view the learning modules. Web services were used to exchange clinical context information between TEMPO and the simulated radiology work station. Preliminary evaluation was conducted using the System Usability Scale (SUS) questionnaire. TEMPO identified learning modules that were relevant to the age, sex, imaging modality, and body part or organ system of the patient being viewed by the radiologist on the simulated clinical work station. Users expressed a high degree of satisfaction with the system's design and user interface. TEMPO enables just-in-time learning in radiology, and can be extended to create a fully functional learning management system for point-of-care learning in radiology.

Computer Simulation↗

PubMed2006

Radiologists' preferences for just-in-time learning.

BACKGROUND: Effective learning can occur at the point of care, when opportunities arise to acquire information and apply it to a clinical problem. To assess interest in point-of-care learning, we conducted a survey to explore radiologists' attitudes and preferences regarding the use of just-in-time learning (JITL) in radiology. MATERIALS AND METHODS: Following Institutional Review Board approval, we invited 104 current radiology residents and 86 radiologists in practice to participate in a 12-item Internet-based survey to assess their attitudes toward just-in-time learning. Voluntary participation in the survey was solicited by e-mail; respondents completed the survey on a web-based form. RESULTS: Seventy-nine physicians completed the questionnaire, including 47 radiology residents and 32 radiologists in practice; the overall response rate was 42%. Respondents generally expressed a strong interest for JITL: 96% indicated a willingness to try such a system, and 38% indicated that they definitely would use a JITL system. They expressed a preference for learning interventions of 5-10 min in length. CONCLUSIONS: Current and recent radiology trainees have expressed a strong interest in just-in-time learning. The information from this survey should be useful in pursuing the design of learning interventions and systems for delivering just-in-time learning to radiologists.

Attitude of Health Personnel↗

PubMed2006

An ontology for PACS integration.

An ontology describes a set of classes and the relationships among them. We explored the use of an ontology to integrate picture archiving and communication systems (PACS) with other information systems in the clinical enterprise. We created an ontological model of thoracic radiology that contained knowledge of anatomy, imaging procedures, and performed procedure steps. We explored the use of the model in two use cases: (1) to determine examination completeness and (2) to identify reference (comparison) images obtained in the same imaging projection. The model incorporated a total of 138 classes, including radiology orderables, procedures, procedure steps, imaging modalities, patient positions, and imaging planes. Radiological knowledge was encoded as relationships among these classes. The ontology successfully met the information requirements of the two use-case scenarios. Ontologies can represent radiological and clinical knowledge to integrate PACS with the clinical enterprise and to support the radiology interpretation process.

Humans↗

PubMed2005

Collaborative filtering to improve navigation of large radiology knowledge resources.

OBJECTIVE: Collaborative filtering is a knowledge-discovery technique that can help guide readers to items of potential interest based on the experience of prior users. This study sought to determine the impact of collaborative filtering on navigation of a large, Web-based radiology knowledge resource. MATERIALS AND METHODS: Collaborative filtering was applied to a collection of 1,168 radiology hypertext documents available via the Internet. An item-based collaborative filtering algorithm identified each document's six most closely related documents based on 248,304 page views in an 18-day period. Documents were amended to include links to their related documents, and use was analyzed over the next 5 days. RESULTS: The mean number of documents viewed per visit increased from 1.57 to 1.74 (P < 0.0001). CONCLUSIONS: Collaborative filtering can increase a radiology information resource's utilization and can improve its usefulness and ease of navigation. The technique holds promise for improving navigation of large Internet-based radiology knowledge resources.

Algorithms↗

PubMed2005

Improving outcomes in radiology: bringing computer-based decision support and education to the point of care.

Many computer applications have been developed in radiology and other medical disciplines to help physicians make decisions. Artificial intelligence (AI)--an approach to computer-based manipulation of symbols to simulate human reasoning--forms the basis of many of these systems. This article's goals are to: acquaint the reader with the motivations and opportunities for computer-based medical decision support systems; identify AI techniques and applications in radiology decision making; assess the impact of these technologies; and consider new directions and opportunities for AI in radiology. Among the exciting new directions is the use of AI to integrate radiology reporting, online decision support, and just-in-time learning to provide useful information and continuing education that is embedded within a radiologist's daily workflow.

Decision Making, Computer-Assisted↗

PubMed2005

Knowledge discovery from structured mammography reports using inductive logic programming.

The development of large mammography databases provides an opportunity for knowledge discovery and data mining techniques to recognize patterns not previously appreciated. Using a database from a breast imaging practice containing patient risk factors, imaging findings, and biopsy results, we tested whether inductive logic programming (ILP) could discover interesting hypotheses that could subsequently be tested and validated. The ILP algorithm discovered two hypotheses from the data that were 1) judged as interesting by a subspecialty trained mammographer and 2) validated by analysis of the data itself.

Algorithms↗