PubMed Health⌕ Search
Home/Archive/PubMed

PubMed · 10288343

Actualizing system benefits--Part IV.

T K Zinn·L W DiGiulio
View original source ↗Export metadata ↓

Abstract

Although hospitals continue to gain benefits from computerization, these benefits often fall short of expectations. Part IV of this series examines why department heads and administrators fail to achieve the kind of tangible, quantitative benefits that convinced them to buy information systems in the first place.

Explore related subjects

Data CollectionEfficiencyEvaluation Studies as TopicHealth Facility AdministratorsHospital AdministratorsHospital Information SystemsOrganization and AdministrationOrganizational InnovationUnited StatesJournal Article

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

T K Zinn, L W DiGiulio. 1988. Actualizing system benefits--Part IV.. https://pubmed.ncbi.nlm.nih.gov/10288343/

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↗