Aplicaciones de la Inteligencia Artificial en Enfermería y su impacto en los Resultados del Cuidado: Una Revisión de Alcance.

Avixely Vargas Blanco
Departamento de Enfermería - Investigación, Clínica Bíblica, San José, Costa Rica.

Resumen

La inteligencia artificial (IA) surge como una herramienta prometedora para mejorar la toma de decisiones en enfermería; sin embargo, persiste resistencia a su uso, especialmente en la práctica clínica, y falta evidencia sobre su impacto medido con taxonomías estandarizadas como la Nursing Outcomes Classification (NOC) o las Patient Reported Outcome Measures (PROMs). Esta revisión tuvo como objetivo sintetizar la evidencia sobre las aplicaciones de la IA en enfermería y su impacto en los resultados medidos mediante NOC o PROMs. Se realizó una revisión siguiendo la guía PRISMA-ScR, buscando estudios primarios publicados en los últimos cinco años en PubMed, ScienceDirect, Wiley Online Library, Dialnet y MDPI, tanto en inglés como en español. Se incluyeron estudios que emplearan herramientas de IA en práctica enfermera y reportaran resultados mediante NOC o PROMs. De 20 estudios seleccionados, 4 evaluaron directamente el uso de IA con PROMs y ninguno utilizó NOC de forma explícita, aunque varios estudios reportaron resultados implícitos relacionados con NOC o PROMs. La mayoría de los estudios se realizaron en entornos hospitalarios, principalmente en población adulta, con predominio de IA basada en aprendizaje automático. Las aplicaciones de IA identificadas incluyeron predicción de caídas, delirio, dolor crónico, riesgo de linfedema y optimización de intervenciones en cuidados paliativos. Aunque se identifican aplicaciones prometedoras, persiste heterogeneidad metodológica y escasez de estudios en otros contextos culturales. La IA tiene potencial para impactar positivamente la práctica de enfermería y la medición de resultados del cuidado, aunque su implementación requiere estandarización, validación y el empleo de taxonomías estandarizadas.

Palabras claves

Inteligencia artificial, enfermería, cuidado de enfermería, clasificación de resultados en enfermería, medición de resultados informados por el paciente.

Abstract

Artificial Intelligence (AI) emerges as a promising tool to enhance decision-making in nursing; however, resistance to its use persists, especially in clinical practice, and there is a lack of evidence regarding its impact when measured using standardized taxonomies such as the Nursing Outcomes Classification (NOC) or Patient Reported Outcome Measures (PROMs). This review aimed to synthesize the evidence on AI applications in nursing and their impact on outcomes measured through NOC or PROMs. A review was conducted following the PRISMA-ScR guidelines, searching for primary studies published in the last five years in PubMed, ScienceDirect, Wiley Online Library, Dialnet, and MDPI, in both English and Spanish. Studies were included if they used AI tools in nursing practice and reported outcomes using NOC or PROMs. Of the 20 selected studies, 4 directly evaluated the use of AI with PROMs, and none explicitly used NOC, although several studies reported implicit outcomes related to NOC or PROMs. Most studies were conducted in hospital settings, mainly in adult populations, with a predominance of machine learning-based AI. Identified AI applications included the prediction of falls, delirium, chronic pain, lymphedema risk, and optimization of interventions in palliative care. Although promising applications were identified, methodological heterogeneity and a lack of studies in other cultural contexts persist. AI has the potential to positively impact nursing practice and the measurement of care outcomes, although its implementation requires standardization, validation, and the use of standardized taxonomies.

Keywords

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