PubMed Health⌕ Search

PubMed · 9099035

A defined minimum data set. Will it work for direct patient care?

Abstract

As health care becomes geographically and institutionally distributed, sharing clinical information becomes necessary for efficiency but harder to achieve. The computerization of patient data promises to facilitate its sharing and reuse. The kind and specificity of data needed, however, vary with the intended use, so defining a data set that is limited in size but broad in application has been problematic. Collecting information that is relevant to direct patient care, and useful for ancillary purposes, requires an understanding of how clinical data are recorded and used. There must be agreement on the vocabulary and the definitions of words. To support clinical decision-making, it must be possible to represent information at various cognitive levels and to different degrees of specificity. Accommodation of ambiguity and uncertainty should be possible. The contextual, temporal, and relational properties of clinical facts must be capable of representation in the data that are shared.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

D A Nelson. A defined minimum data set. Will it work for direct patient care?. https://pubmed.ncbi.nlm.nih.gov/9099035/

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↗