PubMed Health⌕ Search

PubMed · 10724996

A protocol building software tool for medical device quality control tests.

Abstract

Q-Pro is an application for Quality Control and Inspection of Medical Devices. General system requirements include friendly and comprehensive graphical environment and proper, quick, easy and intuitive user interface. Functions such as, a tool library for protocol design widely used multimedia, as well as, a support of a local database for protocol and inventory data archiving are provided by the system. In order to serve the different categories of users, involved in Quality Control procedures, the system has been split into three modules of different functionality and complexity, each of which can work as a stand-alone application. The implementation of protocols and use of the software functions, as well as, the user interface itself have been proved by the evaluators to be clear and intuitive. The software seems to adapt easily to different kinds of Quality Control procedures and objectives. Q-Pro effectively supports and enhances the processes to attain a highly tuned, professional, responsive and effective quality control and preventive maintenance procedures for biomedical equipment management.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Y Theodorakos, K Gueorguieva, J Bliznakov, Z Kolitsi, N Pallikarakis. 1999. A protocol building software tool for medical device quality control tests.. https://pubmed.ncbi.nlm.nih.gov/10724996/

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related citations

Probability estimation when some observations are grouped.

This paper considers the use of additional questions for decreasing survey non-response rates and an approach for estimating a probability based on the results obtained. In a survey, the respondents are asked to answer an original question and follow-up questions, where the answers for the follow-up questions are grouped answers for the original question. For example, respondents are asked to provide an exact number of incidents, but in cases of 'Do not know' or 'Refuse' responses, they are subsequently asked to pick an answer from a less specific categorical scale. The new estimator obtains smaller variance asymptotically and does not depend on a distribution family. This method is applied to income questions in a survey regarding injury prevention and behaviours. Another application is survey data on intimate partner violence, where some amendments were applied for incorporating post-stratification weights and for using non-random grouping. For additional illustration, an example of parameter estimation on artificially generated data is presented.

Data Collection↗