1 / 15

C, Thompson 1 , L, Dalgleish 2 , T, Bucknall 3 , C, Estabrookes 4 ,

The effects of time and experience on nurses’ risk assessment decisions: a signal detection analysis. C, Thompson 1 , L, Dalgleish 2 , T, Bucknall 3 , C, Estabrookes 4 , R, De Vos 5 , A, Hutchinson 4 , K, Fraser 4 , J, Binnekade 5 , G, Barrett 6 , J, Saunders 6.

wauna
Download Presentation

C, Thompson 1 , L, Dalgleish 2 , T, Bucknall 3 , C, Estabrookes 4 ,

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. The effects of time and experience on nurses’ risk assessment decisions: a signal detection analysis C, Thompson1, L, Dalgleish2, T, Bucknall3, C, Estabrookes4, R, De Vos5, A, Hutchinson4, K, Fraser4, J, Binnekade5, G, Barrett6, J, Saunders6 1University of York, UK;2University of Stirling, UK; 3 Deakin University, Australia; 4University of Alberta, Canada; 5University of Amsterdam, Netherlands; 6Bradford Hospitals NHS Trust, UK

  2. Background • 60% of cardiac arrests preventable1 • 50% of arrests have documented but not-acted-on changes in “basic” data: heart rate, BP, urine output, conscious level etc. 2 • Nurses key link in preventing “failure to rescue” • 98% of calls to METs nurse-initiated3 • Transforming changes in status to MET call in only 2.8% of cases4 1Hodgetts et al 2002; 2Goldhill 2001; 3Cioffi 2000; 4Daffurn et al 1994

  3. Background • Expertise and experience often “confused”1 • “epidemiological” benefits of experience not easily seen in individual judgements and decisions2 • Intuitive judgement is modus operandi for nurses3 • Time pressure4 and irreducible uncertainty5 important clinical contexts 1Anders Ericsson 2007; 2Aiken et al. 2003; 3Thompson et al. 2005; 4Thompson 2001, 2004, Bucknall 2000; 5Eddy 1994

  4. questions • Does “generic” clinical experience improve the ability to detect the need to take action? • Does “specialist” clinical experience improve the ability to detect the need to take action? • How does time pressure impact on nurses’ decision making performance?

  5. methods Signal detection analysis1 1Stanislaw & Todorov 1999 Calculation of signal detection theory Measures, Behaviour research measures, instruments and computers 31(1), 137-149

  6. methods Thompson C, Dalgleish L et al. The effects of time pressure and experience on nurses' risk assessment decisions: a signal detection analysis. Nursing Research, 2008; 57(12): 302-311

  7. methods • 50 clinical scenarios via power point in wards/units

  8. Methods • “Signal” • MEWs (Modified Early Warning Score) clinical prediction rule1 • MEWS ≥5 = “at risk” • Thus 18 “signals” and 32 “no signals” from 50 scenarios • Scenario values randomly selected from 1 years MEWs assessments in 1 UK acute Trust (n=1350) • Time pressure = 10 seconds and a visual cue (clock symbol). • Time pressure = 26 scenarios; no time pressure = 24. • Cases mixed randomly to prevent primacy and recency effects • Judgement = “would you intervene by contacting a senior nurse or doctor?” • nb: as per protocol in each site 1Subbe et al. 2001

  9. analysis • N and proportions of hits and false alarms calculated • SDT indices d’ and ln(β) calculated1 • Experience made ordinal • 2 x mixed model ANOVA with d’ and ln(β) as dependents and clinical experience (between subjects 4 levels) and time pressure (within subjects 2 levels) • Country as a factor in all analysis • Separate analysis looked at critical care experience and time pressure 1Stanislaw & Todorov 1999

  10. participants • 245 acute or critical care nurses • UK 95; Netherlands 50; Australia 50; Canada 50 • Sampled randomly in UK; convenience elsewhere • Mean years registered 11.6 (SD 8.8) • Mean years in current specialty 8.8 (SD 6.7) • Mean age 34 years (SD 8.1) • 64% had more than a year’s critical care experience • Graduates: • UK 6%; Canada 77%; Netherlands 40%; Australia 100% • nb: assessing critical event risk was a common judgement for all the nurses

  11. Results: time pressure

  12. results: experience under pressure • All nurses performed better with no time pressure • No significant interaction between experience and time pressure on the d’ (signal detection ability) measure.

  13. discussion • More time = greater accuracy and less unwarranted (costly) intervention • Less time = more “failure to rescue” (14% to 32%) • Dangers of spreading expertise too thinly (critical care, METs, rapid response) • Variation in performance ?due to variations in organisational context • “Good enough” fast-and-frugal heuristics used by nurses may (in the absence of feedback) may not be quite as good when analysed systematically.

  14. conclusion • Time pressure masks nursing expertise • Quantity of clinical experience ≠ expertise • Quality of clinical experience = expertise • Nurses need to be taught the value of clinical information, combating cognitive caution: clinical epidemiological ways of thinking • We need to know more about the “signals” and “noise” that surrounds nursing judgement calls and decisions

  15. Reference and contact Thompson C et al. The effects of time pressure and experience on nurses' risk assessment decisions: a signal detection analysis. Nursing Research, 2008; 57(12): 302-311 Dr Carl Thompson Centre for Evidence Based Nursing Department of Health Sciences Area 2, Seebohm Rowntree Building University of York York YO10 5DD United Kingdom e: cat4@york.ac.uk t: +44 1904 321350

More Related