PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 10725018

Document versus data centred approach to the EPR.

R Baud·A M Rassinoux·C Lovis
View original source ↗Export metadata ↓

Abstract

This paper presents the document centred Electronic Patient Record (EPR) as currently in production in a large university hospital and subject to multiple additional developments in the coming years. A number of basic hypothesis have been developed in order to reach the best medical practice and the success of this application. In addition, the alternative approach of data centred EPR is compared, and different benefits and pitfalls are highlighted. It is not easy to evaluate the consequences of such an initial trend, but changing one's mind after having a system in daily production is anyway costly and difficult. Therefore, the selection of the right orientation in a given hospital necessitates a scientific debate.

Explore related subjects

Data CollectionDocumentationHospital Information SystemsHospitals, UniversityHumansMedical Records Systems, ComputerizedSwitzerlandJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

R Baud, A M Rassinoux, C Lovis. 1999. Document versus data centred approach to the EPR.. https://pubmed.ncbi.nlm.nih.gov/10725018/

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

KEEP EXPLORING

Related citations

PubMed2007

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↗