Effect of the Nursing Process with Standardized Taxonomy on Bowel Cleansing Prior to Colonoscopy in Outpatients


Zengin H., TEZEL A.

ENFERMERIA CLINICA, no.1, 2025 (ESCI) identifier

  • Publication Type: Article / Article
  • Publication Date: 2025
  • Doi Number: 10.1016/j.enfcli.2024.07.004
  • Journal Name: ENFERMERIA CLINICA
  • Journal Indexes: Emerging Sources Citation Index (ESCI), Scopus, CINAHL, MEDLINE, DIALNET
  • Hacettepe University Affiliated: Yes

Abstract

Objective: The aim of this study was to examine the effect of the nursing process applied by using standard nursing terminologies on colonoscopy preparation of outpatients on bowel cleansing. Methods: The sample of the prospective, single-blind, randomized controlled study consisted of 116 patients (intervention n=57, control n=59). Both groups were interviewed face to face one week before the procedure day, nursing diagnoses were determined individually, and nursing outcome scales were employed as a baseline assessment. In the intervention group, the nursing process was applied to the patients with standard nursing terminology, training was given with an information booklet, and then the preparation in structions were reminded through telephone calls, text messages and instant messages. The adequacy of bowel cleansing was evaluated by the endoscopist using Boston Bowel Preparation Score (BBPS) who blindly performed the procedure for both groups. Results: The mean BBPS score of the intervention group was 7.00 +/- 1.43, and the control group was 4.16 +/- 2.15. A significant difference found between the rates of adequate preparation in the intervention group (82.5%) and in the control group (16.9%) (p<0.05). A significant difference was found after the intervention in terms of nursing outcome scales (p< 0.05). Conclusions: According to the study adequate preparation for the colonoscopy procedure can be achieved by applying the nursing process based on NANDA-I, NOC and NIC. (c) 2024 Elsevier Espana, S.L.U. All rights are reserved, including those for text and data mining, AI training, and similar technologies.