PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 11187628

An object-oriented approach for structuring the electronic medical record.

F Banhart·R Lohmann
View original source ↗Export metadata ↓

Abstract

We implemented a framework for modelling the electronic medical record on top of an object-oriented model. Clinical patient data are structured in a uniform way through the use of a comprehensive data model. The meaning of the information elements is explicitly determined by a medical data dictionary. The data structures of both, medical record and data dictionary are implemented, using a semantically rich, object-oriented data model. We examined several possibilities for the graphical preparation of the inherently recursive data structures. Again, we use object-oriented frameworks for the implementation of flexible user interfaces to the electronic medical record with a consistent look-and-feel.

Explore related subjects

Data CollectionDatabase Management SystemsDictionaries, Medical as TopicHumansMedical Records Systems, ComputerizedSoftwareTerminology as TopicJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

F Banhart, R Lohmann. 2000. An object-oriented approach for structuring the electronic medical record.. https://pubmed.ncbi.nlm.nih.gov/11187628/

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↗