-
Int J Evid Based Healthc · Mar 2013
Implementing the best available evidence in early delirium identification in elderly hip surgery patients.
- Kathleen Ann Russell-Babin and Helen Miley.
- Meridian Health, Institute for Evidence-Based Care, Neptune, NJ 07753, USA. krussellbabin@meridianhealth.com
- Int J Evid Based Healthc. 2013 Mar 1; 11 (1): 39-45.
AimsDelirium is a frequent complication in the surgical experience of elderly hip surgery patients. Its impact can be severe and may even include death. Implementation of a delirium predictor tool might focus attention on early recognition of delirium, thereby potentially decreasing its impact. A related aim is to evaluate best practices in implementation strategies in this project.MethodsAfter an exhaustive search of the literature, no consensus was found regarding delirium predictors for the elderly hip surgery patient. A local research study was implemented to determine factors that may predict delirium in this population. With evidence secured, a multidisciplinary implementation project augmented by ongoing audit was instituted. A variety of social diffusion and education tools were used. Implementation was guided by the use of the Promoting Action on Research Implementation in Health Services framework assessment tool and the Alberta Context Tool, as well as traditional performance improvement tools, such as fishbone charting. Audit identified the rate of use of the predictor tool and pre- and post-rates of delirium. This project was part of the Joanna Briggs Institute Signature Project, an implementation project consisting of six teams, each representing a different organisation. This overall project was supported by experts in the field of translation and implementation science internationally.ResultsInitial compliance to the use of the predictor tool was assessed at 54% within 3 months of implementation and increased to 56% in the ensuing months. Before the study use of the predictor tool, the delirium rate was 10.4% (12 of 115 patients). An interim analysis 4 months after implementation identified a 20% delirium rate (18 of 70 patients) and an updated analysis 8 months into the project showed a 16.3% delirium rate. Delirium predictor tool use was associated with a lower delirium rate (9/76, 11.84%) than no delirium predictor tool (13/60, 21.67%), but the difference was not statistically significant with a sample size of 133 (P = 0.122).ConclusionsThe delirium predictor tool shows promise as a prompt for best practices in prevention of delirium. This study showed a change in delirium rates as a result of its use. Although the results were not statistically significant, they may be clinically meaningful. Comprehensive assessment and implementation planning by a multidisciplinary team contributed to only 56% compliance in use. Despite this low rate, delirium identification rates were higher.© 2013 The Authors. International Journal of Evidence-Based Healthcare © 2013 The Joanna Briggs Institute.
Notes
Knowledge, pearl, summary or comment to share?You can also include formatting, links, images and footnotes in your notes
- Simple formatting can be added to notes, such as
*italics*
,_underline_
or**bold**
. - Superscript can be denoted by
<sup>text</sup>
and subscript<sub>text</sub>
. - Numbered or bulleted lists can be created using either numbered lines
1. 2. 3.
, hyphens-
or asterisks*
. - Links can be included with:
[my link to pubmed](http://pubmed.com)
- Images can be included with:
![alt text](https://bestmedicaljournal.com/study_graph.jpg "Image Title Text")
- For footnotes use
[^1](This is a footnote.)
inline. - Or use an inline reference
[^1]
to refer to a longer footnote elseweher in the document[^1]: This is a long footnote.
.