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Nurses and “irreducible” Uncertainty Prof. Carl Thompson RN, PhD.

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Presentation on theme: "Nurses and “irreducible” Uncertainty Prof. Carl Thompson RN, PhD."— Presentation transcript:

1 Nurses and “irreducible” Uncertainty Prof. Carl Thompson RN, PhD

2 Where? York

3 The plan The problem Some evidence Solutions?

4 The problem: irreducible uncertainty David Eddy (MD) Variations & uncertainty linked Definitions Diagnosis Treatment Observing outcomes “Putting it all together” (i.e. judgement and decision making)

5 The problem: nurses face same uncertainties

6 Lets agree to disagree

7 The problem: context

8 The problem: errors 11% admissions suffer adverse events, 50% due to error 1 million patients suffer iatrogenic harm, 1000 per year die 7 - 8.4 additional bed days per adverse event Mandatory reporting does not work (sensitivity 5%) (NAO 2005, NPSA 2002, Akbari and Sheldon 2006)

9 Problem: “getting” care needs experience

10 One learns the basic patterns

11 Then you can see it

12 The good news. Information behaviour is… 1. Think number between 10 and 20 2. Add the digits together (e.g. 13 = 1+3 = 4) 3. Subtract from the first number you thought of 4. Subtract 5 5. Convert to a letter (e.g. 1=A, 2=B etc…) 6. Listen to me…

13 Entirely predictable Denmark Elephant (*maybe Emu… for Australians)

14 uncertainty reduction via synthesis?

15 The problem: everyone hate numbers

16 One solution: intuition “the seasoned nurse’s well honed sixth sense enables her to make lifesaving decisions” Benner & Tanner 1997

17 In common?

18 Critical Event Risk Assessment 50% of cardiac arrests had deteriation documented (Hodgetts 2002) Nursing knowledge “basics”: heart rate, resps, O 2 98% of calls to emergency teams/outreach nurse initiated (Cioffi 2000) 25% of all calls delayed by 1-3 hours (Crispin and Daffurn 1998) Misinterpretation and mismanging valuable clinical information (McQuillan et al. 1998)

19 methods 50 scenarios in wards/units/ITUs 250 nurses (Oz, UK, Canada, Holland) years registered 11.6 (8.8) years in specialty 9 (6.7) age 34 years (SD 8.1) 64% > critical care experience Graduates: UK 6%; Canada 77%; Netherlands 40%; Aus100%

20 methods Signal detection analysis 1 riskNo risk YesTP+FP- noFN-TN+ 1 Stanislaw & Todorov 1999 Calculation of signal detection theory Measures, Behaviour research measures, instruments and computers 31(1), 137-149

21 Tendency toward intervening, misses and false alarms (N = 237) Experience in Critical Care in Years (n) Decision Tendency: Mean β (SD) Mean Proportion of Misses Mean Proportion of False Alarms 0 (70)-.05 (.54)0.270.30 1 (84)-.18 (.51)0.210.34 2 (33)-.47 (.52)0.160.38 ≥ 3 (50)-.10 (.58)0.230.30 SD = standard deviation.

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