JCARM Journal of Contemporary Academic Research and Methodologies JCARM 3139-7247 Ivory and Finch Publishers 10.5281/zenodo.21351095 JCARM_JUN_26_144 Original Research Article PREDICTIVE ANALYTICS IN NURSING: EARLY DETECTION OF PATIENT DETERIORATION IN TEACHING HOSPITALS IN NORTH-CENTRAL NIGERIA JOY ANYO ( Ph.D) EGBUNU, University of Abuja Teaching Hospital, Gwagwalada, FCT, Abuja, FCT, Gwagwalada, joyegbunu20@gmail.com CAROLINE IFEOMA ( Ph.D), UCHENDU, University of Abuja Teaching Hospital, Gwagwalada, FCT, Abuja, FCT, Gwagwalada, NGOZI LINDA ( Ph.D), NWAONONIWU, University of Abuja Teaching Hospital, Gwagwalada, FCT, Abuja, FCT, Gwagwalada, 14 07 2026 1 5

The increasing complexity of healthcare delivery and the persistent occurrence of preventable adverse events have heightened the need for intelligent approaches to patient surveillance, particularly in resource-constrained healthcare settings. This study investigated the determinants of predictive analytics utilisation and its influence on the early detection of patient deterioration among nurses in teaching hospitals in North-Central Nigeria. Anchored on the Technology–Organisation–Environment framework, the study adopted a quantitative cross-sectional survey design. Data were collected from registered nurses in selected teaching hospitals using a structured questionnaire. A total of 596 valid responses were analysed using confirmatory factor analysis and structural equation modelling with AMOS version 29. The measurement model demonstrated satisfactory reliability and validity, while the structural model exhibited acceptable goodness-of-fit indices. The findings revealed that data quality, nursing informatics competence, technological infrastructure and organisational support significantly and positively influenced predictive analytics utilisation. Furthermore, predictive analytics utilisation exerted a strong positive effect on the early detection of patient deterioration. Among the predictor variables, nursing informatics competence emerged as the most influential determinant, highlighting the strategic role of digital capabilities in intelligent patient surveillance. The study demonstrates that the effectiveness of predictive analytics extends beyond technological sophistication to encompass organisational readiness and professional competencies. By validating a context-sensitive structural model within a resource-constrained healthcare environment, the study contributes to the growing body of knowledge on nursing informatics and patient safety in sub-Saharan Africa. The findings underscore the need for sustained investments in digital infrastructure, health information management systems and nursing informatics education to facilitate the integration of predictive technologies into routine clinical practice and enhance proactive patient care in tertiary healthcare institutions.

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