ADVANCING SDG 3 AND 9 IN NIGERIAN HEALTHCARE: COMPUTATIONAL INTEGRATION IN A DUAL-CENTER: STUDY OF TECHNOLOGY ADOPTION CHALLENGES AND POLICY IMPLICATIONS FOR MEDICAL-SURGICAL NURSING

Abstract

This study integrates computational techniques and statistical modeling to analyze the adoption of healthcare technologies in medical-surgical nursing across rural and urban hospitals in Enugu State, Nigeria, advancing SDG 3 (Good Health and Well-being) and SDG 9 (Industry, Innovation, and Infrastructure). Guided by the Technology Acceptance Model (TAM) and Diffusion of Innovation (DOI) theory, data from 276 nurses were collected via structured questionnaires and analyzed using mixed methods: SPSS v26.0 for inferential statistics (t-tests, regression) and NVivo for computational thematic analysis of qualitative responses. Results highlight those technologies like electronic health records (EHRs) and telemedicine enhance procedural accuracy (mean = 3.9) and reduce surgical time (mean = 3.7). However, computational models identified systemic barriers, including high costs (mean = 3.7), technological malfunctions (mean = 3.5), and training gaps (mean = 2.8), with rural settings disproportionately affected (p < 0.05). The study demonstrates how computational integration can uncover nuanced disparities, informing policies for equitable resource distribution and infrastructure investment. Recommendations emphasize public-private partnerships (SDG 17) to fund context-specific solutions, such as AI-driven training platforms and adaptive EHR systems. These findings provide actionable insights for policymakers addressing Nigeria’s rural-urban healthcare divide and offer a computational framework applicable to similar low-resource settings globally.