Exploration of Early Warning Score (EWS) Implementation in Adult Inpatient Wards at Kebumen Hospital: A Qualitative Study
DOI:
https://doi.org/10.48079/jika.v9i1.163Keywords:
Nurses, Implementation, Early Warning Score (EWS)Abstract
Introduction: One of the systems used in inpatient wards to reduce patient deterioration is the Early Warning Score (EWS) (Passa, 2021). The measurement is carried out using six clinical physiological parameters (Royal College of Physicians, 2017). Inappropriate implementation of EWS may negatively affect both patients and nurses in providing nursing care. Inaccuracy in applying EWS can influence the rate of unexpected mortality (Cerly, 2020). A preliminary study conducted through interviews with two nurses in the adult inpatient ward revealed inconsistencies between monitoring frequency and the algorithm. In addition, delays in documenting EWS parameters in the Integrated Patient Progress Notes (CPPT) were also identified. The purpose of this study was to explore the implementation of EWS among nurses in the adult inpatient ward of Kebumen Hospital.
Methods: This study employed a qualitative design with purposive sampling until saturation, involving five respondents. Primary data were collected through semi-structured in-depth interviews, with the researcher as the main instrument. Data validity was ensured through credibility, transferability, dependability, and confirmability. Data were analyzed using Content Analysis.
Results: The study identified two main themes: factors contributing to successful implementation and barriers to applying EWS in high-score conditions. Theme 1 includes three categories: internal human resource factors, continuous coordination, and managerial involvement. Theme 2 includes resource limitations, early identification of high scores, and algorithm mismatch.
Conclusion: The implementation of Early Warning Score (EWS) in adult inpatient wards is influenced by internal human resources, interprofessional coordination, and management support. Barriers include limited resources, documentation noncompliance, and algorithm incompatibility in certain cases.
Recommendation: Optimization of EWS requires strengthened training, adequate facility provision, and algorithm adjustment to support early detection of patient deterioration.
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