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Identifying the right surface for the right patient at the right time: generation and content validation of an
Laurie McNichol1, Carolyn Watts, Dianne Mackey
1Laurie McNichol, MSN, RN, GNP, CWOCN, CWON-AP, Clinical Nurse Specialist and WOC Nurse, Cone Health, Wesley Long Hospital, Greensboro, North Carolina Carolyn Watts, MSN, RN, CWON, CBPN-IC, Senior Associate in Surgery, Clinical Nurse Specialist, WOC Nurse, Vanderbilt University Medical Center, Nashville, Tennessee Dianne Mackey, MSN, RN, CWOCN, Staff Educator, Chair, National Wound Management Sourcing and Standards Team, Home Health/Hospice/Palliative Care, Kaiser Permanente, San Diego, California Janice M. Beitz, PhD, RN, CS, CNOR, CWOCN, CRNP, APN-C, FAAN, Professor of Nursing, School of Nursing - Camden, Rutgers University, Camden, New Jersey Mikel Gray, PhD, PNP, FNP, CUNP, CCCN, FAANP, FAAN, Professor and Nurse Practitioner, Department of Urology and School of Nursing, University of Virginia Health Sciences Center, Charlottesville, Virginia.
Abstract:
Support surfaces are an integral component of pressure ulcer prevention and treatment, but there is insufficient evidence to guide clinical decision making in this area. In an effort to provide clinical guidance for selecting support surfaces based on individual patient needs, the Wound, Ostomy and Continence Nurses Society (WOCN®) set out to develop an evidence- and consensus-based algorithm. A Task Force of clinical experts was identified who: 1) reviewed the literature and identified evidence for support surface use in the prevention and treatment of pressure ulcers; 2) developed supporting statements for essential components for the algorithm, 3) developed a draft algorithm for support surface selection; and 4) determined its face validity. A consensus panel of 20 key opinion leaders was then convened that: 1.) reviewed the draft algorithm and supporting statements, 2.) reached consensus on statements lacking robust supporting evidence, 3.) modified the draft algorithm and evaluated its content validity. The Content Validity Index (CVI) for the algorithm was strong (0.95 out of 1.0) with an overall mean score of 3.72 (out of 1 to 4), suggesting that the steps were appropriate to the purpose of the algorithm. To our knowledge, this is the first evidence and consensus based algorithm for support surface selection that has undergone content validation.
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