Studies regarding adverse events with technical devices in the medical context showed, that in most of the cases non-usable interfaces are the cause for use deficiencies and therefore a potential harm for the patient and third parties. This is partially due to the lack of suitable methods for interlinking usability engineering and human-centered risk management. Especially regarding the early identification of human-induced errors and the systematic control of these failures, medical device manufacturers and in particular the developers have to be supported in order to guarantee reliable design and error-tolerant human-machine interfaces (HMI). In this context, we developed the HiFEM methodology and a corresponding software tool (mAIXuse) for model-based human risk analysis. Based on a two-fold approach, HiFEM provides a task-type-sensitive modeling structure with integrated temporal relations in order to represent and analyze the use process in a detailed way. The approach can be used from early developmental stages up to the validation process. Results of a comparative study with the HiFEM method and a classical process-failure mode and effect analysis (FMEA) depict, that the new modeling and analysis technique clearly outperforms the FMEA. Besides, we implemented a new method for systematic human risk control (mAIXcontrol). Accessing information from the method's knowledge base enables the operator to detect the most suitable countermeasures for a respective risk. Forty-one approved generic countermeasure principles have been indexed as a resulting combination of root causes and failures in a matrix. The methodology has been tested in comparison to a conventional approach as well. Evaluation of the matrix and the reassessment of the risk priority numbers by a blind expert demonstrate a substantial benefit of the new mAIXcontrol method.
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http://dx.doi.org/10.1515/bmt-2014-0124 | DOI Listing |
JMIR Diabetes
December 2024
Benten Technologies, Manassas, VA, United States.
Background: Gestational diabetes mellitus (GDM) is an increasingly common high-risk pregnancy condition requiring intensive daily self-management, placing the burden of care directly on the patient. Understanding personal and cultural differences among patients is critical for delivering optimal support for GDM self-management, particularly in high-risk populations. Although mobile apps for GDM self-management are being used, limited research has been done on the personalized and culturally tailored features of these apps and their impact on patient self-management.
View Article and Find Full Text PDFComput Struct Biotechnol J
December 2024
Pontificia Universidad Javeriana - Cali, Calle 18, 118-250 Cali, Valle, Colombia.
J Appl Gerontol
December 2024
School of Nursing, Johns Hopkins University, Baltimore, MD, USA.
Arch Bronconeumol
November 2024
Department of Experimental and Clinical Biomedical Sciences "Mario Serio", University of Florence, 50139 Florence, Italy. Electronic address:
In this narrative review, we address the ongoing challenges of lung cancer (LC) screening using chest low-dose computerized tomography (LDCT) and explore the contributions of artificial intelligence (AI), in overcoming them. We focus on evaluating the initial (baseline) LDCT examination, which provides a wealth of information relevant to the screening participant's health. This includes the detection of large-size prevalent LC and small-size malignant nodules that are typically diagnosed as LCs upon growth in subsequent annual LDCT scans.
View Article and Find Full Text PDFLancet Diabetes Endocrinol
January 2025
Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden; Steno Diabetes Center Copenhagen, Herlev, Denmark; Department of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark.
Background: Advances in paediatric type 1 diabetes management and increased use of diabetes technology have led to improvements in glycaemia, reduced risk of severe hypoglycaemia, and improved quality of life. Since 1993, progressively lower HbA targets have been set. The aim of this study was to perform a longitudinal analysis of HbA, treatment regimens, and acute complications between 2013 and 2022 using data from eight national and one international paediatric diabetes registries.
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