Financial impact of medical technology

Wang, Binseng; Eliason, Richard W.; Richards, S; Hertzler, Lawrence W.; Moorey, Robert

doi:10.1109/memb.2008.921548

Cited by 11 publications

(7 citation statements)

References 2 publications

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“…( (3) To compare the performance between the eCCA method, the ms-eCCA method, the eTRCA method, the ms-eTRCA method, and the ms-eCCA+ms-eTRCA method, we calculate their accuracies and information transfer rates (ITRs) [40] under different N train (2 N train 5) and T w (T w = 0.2, 0.3, • • • , 1.0). Similarly, the accuracy (or ITR) is evaluated by using an N rep -fold cross validation scheme as stated in step (2), where the ITR is calculated by,…”

Section: Offline Data Analysismentioning

confidence: 99%

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A steady-state visual evoked potential (SSVEP)-based brain-computer interface (BCI) has been recognized as a promising BCI paradigm owing to its relatively high performance, little user training, and low user variation [1][2][3]. Time-locked and phase-locked to the periodic visual stimulus, SSVEP contains significant frequency components at the fundamental and harmonic frequencies of the stimulus [4].

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confidence: 99%

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Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs

et al. 2020

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Objective. Latest target recognition methods that are equipped with learning from the subject’s calibration data, represented by the extended canonical correlation analysis (eCCA) and the ensemble task-related component analysis (eTRCA), can achieve extra high performance in the steady-state visual evoked potential (SSVEP)-based brain–computer interfaces (BCIs), however their performance deteriorate drastically if the calibration trials are insufficient. This paper develops a new scheme to learn from limited calibration data. Approach. A learning across multiple stimuli scheme is proposed for the target recognition methods, which applies to learning the data corresponding to not only the target stimulus but also the other stimuli. The resulting optimization problems can be simplified and solved utilizing the prior knowledge and properties of SSVEPs across different stimuli. With the new learning scheme, the eCCA and the eTRCA can be extended to the multi-stimulus eCCA (ms-eCCA) and the multi-stimulus eTRCA (ms-eTRCA), respectively, as well as a combination of them (i.e. ms-eCCA+ms-eTRCA) that incorporates their merits. Main results. Evaluation and comparison using an SSVEP-BCI benchmark dataset with 35 subjects show that the ms-eCCA (or ms-eTRCA) performs significantly better than the eCCA (or eTRCA) method while the ms-eCCA+ms-eTRCA performs the best. With the learning across stimuli scheme, the existing target recognition methods can be further improved in terms of the target recognition performance and the ability against insufficient calibration. Significance. A new learning scheme is proposed towards the efficient use of the calibration data, providing enhanced performance and saving calibration time in the SSVEP-based BCIs.

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Section: Offline Data Analysismentioning

confidence: 99%

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confidence: 99%

Learning across multi-stimulus enhances target recognition methods in SSVEP-based BCIs

et al. 2020

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“…, Wang et al. ). A substantial part of the health care cost is due to the cost of medical devices and equipment (Wang et al.…”

Section: Introductionmentioning

confidence: 99%

“…The context that we analyze is of lasting significance for several reasons. The rapidly increasing cost of health care in the United States and globally is well recognized (Moses et al 2013, Wang et al 2008. A substantial part of the health care cost is due to the cost of medical devices and equipment (Wang et al 2008).…”

Section: Introductionmentioning

confidence: 99%

“…The rapidly increasing cost of health care in the United States and globally is well recognized (Moses et al 2013, Wang et al 2008. A substantial part of the health care cost is due to the cost of medical devices and equipment (Wang et al 2008). Medical device recalls or the failure to recall in a timely manner contributes significantly toward this increasing health care delivery cost.…”

Section: Introductionmentioning

confidence: 99%

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Product Recall Decisions in Medical Device Supply Chains: A Big Data Analytic Approach to Evaluating Judgment Bias

Mukherjee

Sinha

2018

Production and Operations Management

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This study investigates judgment bias (under‐reaction or over‐reaction) in product recall decisions by firms when they respond to adverse event reports generated by users of their products. We develop an integrative theoretical framework for identifying the sources of judgment bias in product recall decisions. We analyze user‐generated reports (big and unstructured data) on adverse events related to medical devices, using a combination of econometric and predictive analytic methods. We find that (i) noisy signals in user feedback, that is, high noise‐to‐signal ratio, are associated with under‐reaction likelihood; and (ii) user feedback related to adverse events characterized by high severity is associated with high over‐reaction likelihood. We also identify conditions related to the situated context of managers that are associated with under‐reaction or over‐reaction likelihood. The findings of this study are consequential for firms and government regulatory agencies, in that they shed light on the sources of judgment bias in recall decisions, thereby ensuring that such decisions are made correctly and in a timely manner. Our findings also contribute toward improving the post‐launch market surveillance of products (e.g., medical devices) by making it more evidence‐based and predictive.

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