Synthesised geriatric assessment in the Emergency Department setting: is it NEAT?

Aust Health Rev. 2014 Sep;38(4):370-6. doi: 10.1071/AH13217.

Abstract

Objective: To assess the time taken to complete a Synthesised Geriatric Assessment (SGA) in an Emergency Department (ED) and to determine what secondary patient characteristics affect results.

Methods: A convenience sample of 25 patients aged over 65 from an Australian single-centre ED was used for this pilot study. Primary outcome measures included the overall time taken as well as the times for individual screening instruments. Data regarding patient characteristics were taken as secondary outcome measures to assess impact on times. For each of the screening instruments, the mean, median, interquartile range and the 90th percentile for the test duration was calculated. Linear regression was used to evaluate univariate associations between times and patient characteristics. P-values<0.05 were considered as statistically significant.

Results: Time required for completion of the SGA by 90% of the study population was 20 min and 40s. This represents approximately 8.6% of new 4-h ED targets. Secondary characteristics that affected the time taken for screening included patients from non-English-speaking backgrounds (P<0.05).

Conclusions: Use of the SGA for intra-ED geriatric risk stratification is feasible and practical in the time-critical National Emergency Access Target (NEAT) environment. The relatively short amount of time used for screening this vulnerable demographic has implications for interdisciplinary management and potentially represents an efficient intervention to reduce future re-presentations and overcrowding in Australian EDs. Future high-quality trials are required to assess the clinical benefit of the SGA.

Publication types

  • Evaluation Study

MeSH terms

  • Aged
  • Aged, 80 and over
  • Australia
  • Emergency Service, Hospital*
  • Female
  • Geriatric Assessment / methods*
  • Humans
  • Male
  • Pilot Projects
  • Prospective Studies
  • Regression Analysis