Journal of biomedical informatics
-
In this study, we show how medical devices used for patient care can be made safer if various cognitive factors involved in patient management are taken into consideration during the design phase. The objective of this paper is to describe a methodology for obtaining insights into patient safety features--derived from investigations of institutional decision making--that could be incorporated into medical devices by their designers. The design cycle of a product, be it a medical device, software, or any kind of equipment, is similar in concept, and course. ⋯ We recorded and transcribed the responses and conducted a cognitive task analysis of each scenario to identify different entities as "constant," "partially modifiable," or "modifiable." We compared our subjects' responses to the results of the task analysis and then mapped them to the modifiable entities. Lastly, we coded the relationships of these entities to the errors in medical devices. We propose that the incorporation of these modifiable entities into the device design cycle could improve the device end product for better patient safety management.
-
The adoption of electronic medical records (EMRs) and user satisfaction are closely associated with the system's usability. To improve the usability of a results management module of a widely deployed web-based EMR, we conducted two qualitative studies that included multiple focus group and field study sessions. ⋯ Findings from both studies raised issues with the amount and organization of information in the display, interference with workflow patterns of primary care physicians, and the availability of visual cues and feedback. We used the findings of these studies to recommend design changes to the user interface of the results management module.
-
In recent years, multivariate imaging techniques are developed and applied in biomedical research in an increasing degree. In research projects and in clinical studies as well m-dimensional multivariate images (MVI) are recorded and stored to databases for a subsequent analysis. The complexity of the m-dimensional data and the growing number of high throughput applications call for new strategies for the application of image processing and data mining to support the direct interactive analysis by human experts. ⋯ Following this framework, the state-of-the-art solutions from the fields of image processing and data mining are reviewed and discussed. Motivations for MVI data mining in biology and medicine are characterized, followed by an overview of graphical and auditory approaches for interactive data exploration. The paper concludes with summarizing open problems in MVI analysis and remarks upon the future development of biomedical MVI analysis.
-
Data originating from biomedical experiments has provided machine learning researchers with an important source of motivation for developing and evaluating new algorithms. A new wave of algorithmic development has been initiated with the publication of gene expression data derived from microarrays. Microarray data analysis is particularly challenging given the large number of measurements (typically in the order of thousands) that are reported for relatively few samples (typically in the order of dozens). ⋯ In this article, we briefly review the biology underlying microarrays, the process of obtaining gene expression measurements, and the rationale behind the common types of analyses involved in a microarray experiment. We outline the main challenges and reiterate critical considerations regarding the construction of supervised learning models that use this type of data. The goal of this article is to familiarize machine learning researchers with data originated from gene expression microarrays.
-
Propose a cognitive taxonomy of medical errors at the level of individuals and their interactions with technology. ⋯ Theoretically, the proposed cognitive taxonomy provides a method to systematically categorize medical errors at the individual level along cognitive dimensions, leads to a better understanding of the underlying cognitive mechanisms of medical errors, and provides a framework that can guide future studies on medical errors. Practically, it provides guidelines for the development of cognitive interventions to decrease medical errors and foundation for the development of medical error reporting system that not only categorizes errors but also identifies problems and helps to generate solutions. To validate this model empirically, we will next be performing systematic experimental studies.