<![CDATA[The study employed a descriptive survey research design to examine the impact of AI-based scheduling on hospital performance in Onitsha, focusing on Federal Medical Center Onitsha and Guinness Eye Clinic Onitsha. A stratified random sampling technique was used to select 60 respondents from doctors, nurses, administrative staff, patients, and security personnel. Data were collected using a four-cluster structured questionnaire rated on a four-point Likert scale. The instrument's validity was ensured through expert review, while reliability yielded Cronbach’s alpha values ranging from 0.79 to 0.86. Data collection lasted two weeks, and analysis involved descriptive statistics and ANOVA at a 0.05 significance level. This research found that there was a poor average rate of adoption of AI-based scheduling systems in tertiary hospitals in Onitsha (domain mean: 2.05-2.82 on a scale of 5). Mean of staff training was the highest (2.82) and replacement of manual systems with full replacement of manual systems showed the lowest (2.05). With respect to effect on service delivery, mean values ranged between 2.17 to 2.57 indicating little perceived effects. Some of the top challenges experienced by the participants included, data privacy (3.58), resistance to change (3.20), technical expertise (3.10). Nevertheless, solutions such as government funding (3.68) and staff training (3.50) had high levels of support, the means to increase AI adoption practically in a wide variety of ways. Finally, although the modern implementation and perceived efficiency of the AI-based scheduling of tertiary hospitals in Onitsha are minor, widespread awareness of its positive aspect has been realized. The ability to address highlighted barriers using specific policy interventions and institutional assistance may notably improve performance and efficiency in hospitals.]]>
