Pediatric Pain Assessment in the Intensive Care Unit: An Evidence-Based Algorithm

Abstract

Background: Conducting an adequate pain assessment in the Pediatric Intensive Care Unit (PICU) is multifactorial and complex due to the diversity of the population. It is critical that validated pain assessment methods are used appropriately and consistently to aid in evaluation of pain and pain management interventions.

Purpose: The aim of this evidence-based practice project was to improve pain assessment practices in the PICU through a decision-support algorithm.

Design & methods: The Iowa Model-Revised was used to guide the development and implementation of an evidence-based decision algorithm. Pre- and postdata were collected via surveys (nursing knowledge and confidence) and documentation audits (nursing pain assessments). Various implementation strategies were used to facilitate the integration and sustainability of the algorithm in practice.

Results: The majority of survey items showed an increase in nursing knowledge and confidence. Audits of pain assessment documentation displayed an increase in appropriate pain assessment documentation related to a child's communicative ability. However, there is a need for reinfusion related to the documentation of sedation assessments.

Conclusions: The use of an algorithm supported the ability of PICU nurses to critically consider and choose the pain assessment method most appropriate for the patient's condition. The algorithm promotes nursing clinical judgement, prioritizes pain management, and includes patients receiving sedation. The algorithm supports a comprehensive pain assessment in a difficult pediatric patient population. Future research is needed to strengthen and standardize the usage of terms "assume pain present" and "assume pain managed," and to also improve the overall feasibility and effectiveness of the algorithm.