A cross-sectional study to validate wound care algorithms for use by registered nurses.
Research has shown that providing quality wound care affects outcomes and cost. Heuristic devices that facilitate this process, such as guidelines and algorithms, are widely available, but most have not been validated for use by the practitioners for whom they were designed. Although the validity of one set of wound care algorithms (Solutions, ConvaTec, Inc., Skillman, NJ) has been established by expert wound clinicians and excellent clinical usage outcomes reported, its validity when used by nonexpert clinicians (eg, staff registered nurses) to expedite the clinical decision-making process has not been established. Using a cross-sectional, mixed methods, quantitative survey design, a study was conducted to validate use of these algorithms by 204 nonwound expert registered nurses associated with an urban university. Following a very brief explanation, participants (88% women, mean age 34.8 [range 23 to 60] years, 4 to 9 years nursing experience, currently practicing in an acute care setting) were asked to rate 11 components of the algorithms, apply them to a variety of acute and chronic wounds (N = 15), and comment on the research process and algorithm use. The content validity of all components was strong (CVI >0.96); 71% to 98% of nurses selected the correct (primary) or appropriate but not entirely correct (secondary) algorithm; the correct dressing was selected for 75% to 91% of wounds shown. Correct algorithm and dressing selection percents were lower for wounds with necrotic tissue. Intra-rater reliability (two sets of two wounds) was modest but higher for necrotic wounds. A common qualitative theme was insecurity regarding wound assessment. The study results support that the algorithms tested have face and content validity and facilitate the provision of optimal care with a variety of wound types (construct validity) and confirm that wound education improvements for registered nurses are needed.
Beitz
Janice M
JM
La Salle University, Philadelphia, Pennsylvania 19141, USA. beitz@lasalle.edu
van Rijswijk
Lia
L
D000328:Adult; D000465:Algorithms; D003430:Cross-Sectional Studies; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D014947:Wounds and Injuries / Q000451:nursing*