Global medical device nomenclature for the purpose of regulatory data exchange

2015 ◽  
Keyword(s):  
Medical Device ◽  
Data Exchange ◽  
Regulatory Data

Medical Device Interoperability A Standards-Based Testing Approach

2011 ◽  
Vol 45 (3) ◽  
pp. 249-255
Author(s):  
John J. Garguilo ◽  
Sandra Martinez ◽  
Maria Cherkaoui
Keyword(s):  
Information System ◽  
Medical Device ◽  
Information Exchange ◽  
Data Exchange ◽  
Infusion Pump ◽  
Test System ◽  
Healthcare Information ◽  
System Validation ◽  
Healthcare Information System ◽  
Device Test

Abstract We present a black-box messaging test approach employed to achieve a level of rigor which improves—if not assures, given no optionality—correct data exchange. In particular, we verify that physiological information derived and communicated via messaging from a source medical device (e.g., an infusion pump) or healthcare information system, to another medical device (e.g., a patient monitor) or healthcare information system, which consumes or makes use of the data, is syntactically and semantically correct. In other words, the structure of information exchanged within the healthcare system is compliant to a defined specification(s) and the information meaning conveyed and interpreted by the consumer is exactly the same and as intended by the source. Our approach for developing a test system to validate messages is based on constraining identified and recognized specifications. The test system validation performed uses codified assertions derived from the specifications and constraints placed upon those specifications. To first show conformance—which subsequently enables interoperability—these assertions, which are atomic requirements traceable by clause to the base specifications, are employed by our medical device test tools to rigorously enforce standards to facilitate safe and effective plug-and-play information exchange.

Machine-Scored Syntax: Comparison of the CLAN Automatic Scoring Program to Manual Scoring

2020 ◽  
Vol 51 (2) ◽  
pp. 479-493
Author(s):  
Jenny A. Roberts ◽  
Evelyn P. Altenberg ◽  
Madison Hunter
Keyword(s):  
Data Exchange ◽  
Child Language ◽  
Exchange System ◽  
Absolute Point ◽  
Language Analysis ◽  
Search Patterns ◽  
Language Data ◽  
Report Accuracy ◽  
Point To Point ◽  
Automatic Scoring

Purpose The results of automatic machine scoring of the Index of Productive Syntax from the Computerized Language ANalysis (CLAN) tools of the Child Language Data Exchange System of TalkBank (MacWhinney, 2000) were compared to manual scoring to determine the accuracy of the machine-scored method. Method Twenty transcripts of 10 children from archival data of the Weismer Corpus from the Child Language Data Exchange System at 30 and 42 months were examined. Measures of absolute point difference and point-to-point accuracy were compared, as well as points erroneously given and missed. Two new measures for evaluating automatic scoring of the Index of Productive Syntax were introduced: Machine Item Accuracy (MIA) and Cascade Failure Rate— these measures further analyze points erroneously given and missed. Differences in total scores, subscale scores, and individual structures were also reported. Results Mean absolute point difference between machine and hand scoring was 3.65, point-to-point agreement was 72.6%, and MIA was 74.9%. There were large differences in subscales, with Noun Phrase and Verb Phrase subscales generally providing greater accuracy and agreement than Question/Negation and Sentence Structures subscales. There were significantly more erroneous than missed items in machine scoring, attributed to problems of mistagging of elements, imprecise search patterns, and other errors. Cascade failure resulted in an average of 4.65 points lost per transcript. Conclusions The CLAN program showed relatively inaccurate outcomes in comparison to manual scoring on both traditional and new measures of accuracy. Recommendations for improvement of the program include accounting for second exemplar violations and applying cascaded credit, among other suggestions. It was proposed that research on machine-scored syntax routinely report accuracy measures detailing erroneous and missed scores, including MIA, so that researchers and clinicians are aware of the limitations of a machine-scoring program. Supplemental Material https://doi.org/10.23641/asha.11984364

An analysis of home and hospital medical device incidents in the Maude database

2004 ◽  
Author(s):  
Ibraheem S. Al-Tarawneh ◽  
Walter J. Stevens ◽  
Steven R. Arndt
Keyword(s):  
Medical Device ◽  
Hospital Medical
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