Browsing by Subject "Medical Devices"

Now showing 1 - 3 of 3
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    Computational Modeling of Cardiac Devices Implanted in Specimens Preserved within the Visible Heart® Laboratories’ Human Heart Library
    (2022-01) Cham, Nyimatoulie
    The Visible Heart® Laboratories’ (VHL) Atlas of Cardiac Anatomy is an open public resource to students, medical professionals, and individuals in medical device companies that contains expansive cardiac anatomy and physiology information. Within this website is the human heart library, which contains 835 specimens overall, including perfusion-fixed and pre-fixed specimens collected from the Advanced Cardiac Anatomy and Physiology Course, as well as donated hearts received from LifeSource. The physical specimens are housed within the VHL (Minneapolis, Minnesota), and individuals that utilize the library can take pictures, make measurements, and perform device draping studies as long as these procedures are non-destructive. In this project, the main aim was to identify heart specimens within the VHL human heart library and build a database of the hearts that had associated implanted cardiac devices within. With the successful imaging of a large number of hearts with a variety of cardiac devices, these images will be made available on the free-access Atlas of Human Cardiac Device website: this will include photos and unity flythrough movies, as well as the resultant generated computational models.
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    The Influence of Human Factors and Ergonomics on Data-Driven Design Criteria for a Handheld Skin Screening Camera System
    (2020-08) Bornstein, Alexandra
    This study is an extension of ongoing research conducted at the Minneapolis VA Health Care. The original study included the development of a long-handled camera system to support skin screening. This project aimed to develop an effective handheld skin screening camera system using human factors and ergonomics (HFE) centered design criteria. This skin screening camera system resulted in the form of a look- and feels-like prototype model to support preventative skin care for persons with spinal cord injuries (SCI) and persons with diabetes mellitus (DM). Each of these populations are at risk of developing wounds such as pressure injuries (PIs) or diabetic foot ulcers (DFUs). The proactive ability to identify wounds in the early stages (a reddened area) and thus prevent further development of these injuries not only reduces financial burden but can also increase the sense of independence and psychosocial wellbeing. This study was conducted with four study participants from SCI and four from DM populations through the Minneapolis Veterans Affairs Health Care System. Using HFE-focused data collection with study participants provided a blueprint for the design and development of the Phase Two prototype. This enhanced user experience through ease of use, dimensions, adjustability, safety and security, comfort, and overall effectiveness of the device. The Phase Two prototype design received an overall satisfaction rating of 4.69 out of 5 (between quite satisfied and very satisfied) from study participants. The results of the study indicated that other design features could be implemented to improve the usability of the device. This includes consideration of how the product would be manufactured, development of the mobile device application, and accommodating user needs.
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    Organizational, Operational, and Behavioral Causes of Product Recalls
    (2015-06) Ball, George
    Research germane to product recalls and their causes is limited. With recall rates rising in many industries, it is timely and pertinent to comprehensively investigate recalls. The focus of my dissertation is on product recalls and their causes, with the objective of recall understanding and prevention. I study three important phases in the product recall process at multiple organizational levels in the high-risk medical device industry: plant-level causes, recall decision-making, and causes and effects of firm and regulator responsiveness within the recall event. First, I study the relationship between Food and Drug Administration (FDA) plant inspections and future recalls. Using a 7-year panel dataset and recurrent event Cox proportional hazard and propensity score matching models, I find that adverse plant inspection outcomes serve as warning signs for future recalls. I incorporate FDA investigator experience to identify reasons for, and effects of, investigator complacency in repeated plant inspections. Repeated visits to the same site by an inspector increases the recall risk and also reduces the predictability of inspection outcomes as a leading indicator of future recalls. FDA investigator rotation is shown to be an effective solution to compensate for investigator complacency. Second, I explore behavioral factors thainfluence managers' decision to recall. Recall guidance provided by the FDA allows for broad managerial interpretation so it is crucial to study which factors influence managers to choose to recall. Using actual industry managers with recall experience in a controlled experiment, I find that product defects which are undetectable to physician customers pre-use are more likely to lead to a recall than detectable ones. When managers have a deeper understanding about the root cause of a defect, they are also more likely to recall. I also study individual dispositional factors unique to each manager, and surprisingly find that the level of cognitive reflection, as measured by the Cognitive Reflection Test (CRT), is the most important predictor of a recall decision in the experiment. Finally, I study firm and regulator recall responsiveness. Responsiveness is critical in this domain: the longer a faulty medical device remains on the marketplace, the more consumers are at risk. Using an 11-year panel dataset with time-stamps for over 4,000 recalls, and multiple hazard and fixed effects panel models, I find that higher recall severity leads to slower firm and faster FDA responsiveness. However, taking longer to close a recall reduces a firm's future recalls, and this may be attributed to learning mechanisms. FDA response times also reduce future recalls.