title: Implementing the best available evidence in early delirium identification in elderly hip surgery patients.
abstract: OBJECTIVE
  Delirium 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.
  
  
  METHODS
  After 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.
  
  
  RESULTS
  Initial 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).
  
  
  CONCLUSIONS
  The 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.
authors: Meridian Health, Institute for Evidence-Based Care, Neptune, NJ 07753, USA. krussellbabin@meridianhealth.com
authors: Kathleen Ann
authors: KA
authors: Russell-Babin
authors: Helen
authors: H
authors: Miley
mesh_terms: D000368:Aged; D003693:Delirium / Q000175:diagnosis* / Q000453:epidemiology / Q000517:prevention & control; D042241:Early Diagnosis; D019317:Evidence-Based Medicine; D005260:Female; D006615:Hip / Q000601:surgery*; D006801:Humans; D008297:Male; D008485:Medical Audit; D009515:New Jersey / Q000453:epidemiology