Return to search

Use of the interRAI Acute Care Assessment Instrument to Predict Adverse Outcomes Among the Hospitalized Elderly

Abstract
Objectives: This research project was undertaken to review two commonly used screening instruments for the elderly who attend at hospital emergency departments in Ontario. These instruments were then contrasted with a new potential screening instrument made up of items drawn from the Minimum Data Set-Acute Care instrument (MDS-AC Version 1_CAN). The hypothesized outcome was better specificity and sensitivity utilizing the newly prepared instrument in predicting at an earlier point if an elderly emergency department patient would become an alternate level of care (ALC) patient. The ability of the screener to predict negative outcomes (delirium, longer length of stay) was also analyzed.
Methods: One dataset from a previous International Resident Assessment Instrument (interRAI) organization study in southern Ontario completed in 2000 was utilized to inform this research. Each of the commonly used screening instruments was crosswalked to the MDS-AC items, then both univariate and bivariate analyses were completed. Three research questions were then posed. By testing various logistic regression models, the research looked to establish whether the newly developed instrument would be able to perform comparably to the other two currently-used instruments, and whether it would be more effective in predicting ALC status and particular adverse patient outcomes.
Results: The newly-developed instrument was found to perform more accurately. While several variables were tested, a core number were found to be more strongly predictive of future need for ALC status.
Conclusions: Future research in this area is recommended.

Identiferoai:union.ndltd.org:LACETR/oai:collectionscanada.gc.ca:OWTU.10012/5463
Date30 August 2010
CreatorsWiens, Heather
Source SetsLibrary and Archives Canada ETDs Repository / Centre d'archives des thèses électroniques de Bibliothèque et Archives Canada
LanguageEnglish
Detected LanguageEnglish
TypeThesis or Dissertation

Page generated in 0.0025 seconds