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Use of the Modified Early Warning Score (MEWS) application among nurses
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Keywords

Early warning systems
Clinical decision-making
Health technology

How to Cite

Use of the Modified Early Warning Score (MEWS) application among nurses. (2026). Revista Eletrônica Acervo Científico, 26, e24959. https://doi.org/10.25248/reac.e24959.2026

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Abstract

Objective: To validate the knowledge, use and difficulties related to the application of the Modified Early Warning Score (MEWS) among nurses working in a medical clinic of a public reference hospital in the Brazilian Amazon. Method: Cross-sectional, descriptive and quantitative study, with 15 healthcare nurses selected for convenience. Application is a structured questionnaire on professional profile, knowledge, use and difficulties related to MEWS. The data were analyzed by descriptive statistics, using frequencies, media and deviation-patrão. Results: Observed predominance of female sex, professionals with healthcare experience greater than ten years and high percentage of previous training on MEWS. Despite the extensive use of the score, difficulties have been identified related to the insufficiency of continuous training, the resistance of the equipment to the systematic use of the protocol and the operational limitations of the absence of computerized tools. Conclusion: As nurses recognize the relevance of MEWS for early identification of clinical deterioration, organizational and educational barriers persist that compromise its full use. The results reinforce the need for investments in permanent education and digital technologies that support clinical monitoring and evidence-based decision making.

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References

BÁR A, et al. Machine learning models outperform conventional early warning scores in predicting clinical deterioration. NPJ Digital Medicine, 2020; 3: 89.

BRASIL. Ministério da Saúde. Agência Nacional de Vigilância Sanitária. Assistência segura: uma reflexão teórica aplicada à prática. Brasília: ANVISA; 2017.

BRASIL. Ministério da Saúde. Portaria GM/MS nº 529, de 1º de abril de 2013. Institui o Programa Nacional de Segurança do Paciente (PNSP). Diário Oficial da União, Brasília, DF; 2013.

BRIGGS J, et al. Safer and more efficient vital signs monitoring protocols to identify the deteriorating patients in the general hospital ward: an observational study. Health and Social Care Delivery Research, 2024; 12(6).

BULECHEK GM, et al. Classificação das Intervenções de Enfermagem (NIC). 8ª ed. Rio de Janeiro: Elsevier; 2023.

CHURPEK MM, et al. Risk stratification of hospitalized patients on the wards. Chest, 2013; 143(6): 1758-1765.

GROOD C, et al. The impact of the implementation of an electronic early warning system on patient outcomes: a systematic review. Resuscitation, 2018; 122: 42-56.

HERDMAN T, et al. NANDA International Diagnósticos de Enfermagem: definições e classificação 2024-2026. Porto Alegre: Artmed; 2024.

JACINTHO TM, et al. Implantação do Modified Early Warning Score (MEWS): percepção da equipe de enfermagem. Revista da Faculdade de Ciências Médicas de Sorocaba, 2020; 22(4): 173-179.

JEPPESTOL K, et al. Registered nurses' adherence to early warning score escalation protocols: an observational study. Nursing Open, 2023; 10(9): 6007-6017.

LOURENÇO AP, et al. Educational interventions to improve nurses' recognition of patient deterioration: integrative review. Journal of Clinical Nursing, 2023.

MCLENNON BM, et al. Simulation-based education to improve nurses' recognition of patient deterioration using early warning scores. Clinical Simulation in Nursing, 2023; 77: 32-39.

MOORHEAD S, et al. Classificação dos Resultados de Enfermagem (NOC). 7ª ed. Rio de Janeiro: Elsevier; 2023.

ODELL M. Detection and management of the deteriorating ward patient: an evaluation of nursing practice. Journal of Clinical Nursing, 2015; 24(1-2): 173-182.

ODELL M, et al. The implementation of a modified early warning scoring system in acute care hospitals: a cluster randomized controlled trial. Trials, 2019; 20: 725.

PIMENTEL MAF, et al. A comparison of the ability of the National Early Warning Score and machine learning models to predict deterioration. Resuscitation, 2020; 151: 10-16.

ROYAL COLLEGE OF PHYSICIANS. National Early Warning Score (NEWS) 2: standardising the assessment of acute-illness severity in the NHS. London: Royal College of Physicians; 2017.

SMITH GB, et al. A comparison of the ability of the physiological components of medical emergency team criteria and the U.K. National Early Warning Score (NEWS) to discriminate patients at risk of a range of adverse clinical outcomes. Critical Care Medicine, 2016; 44(12): 2171-2181.

SMITH GB, et al. The ability of the National Early Warning Score (NEWS) to discriminate patients at risk of early cardiac arrest, unanticipated intensive care unit admission, and death. Resuscitation, 2013; 84(4): 465-470.

SUBBE CP, et al. Validation of a modified Early Warning Score in medical admissions. QJM: An International Journal of Medicine, 2001; 94(10): 521-526.

THOMAS K, et al. Early warning scores: a review of the literature. Journal of Nursing Care Quality, 2020; 35(1): 76-83.

YAZDANPANAH N, et al. Early prediction of clinical deterioration using machine learning approaches: a systematic review. NPJ Digital Medicine, 2022.