Client feedback system: An enhanced mechanism with results-driven data analytics and generative AI

Aim: This study developed a Client Feedback System (CFS) for Heramil Hospital in Polomolok, South Cotabato, integrating data analytics and generative artificial intelligence, and evaluated its usability, reliability, and user satisfaction.It also examined whether significant differences existed between patients and watchers, and between administrative personnel and healthcare workers, in their system evaluations.Methodology: A descriptive quantitative research design was employed.Data were collected from 100 respondents using structured questionnaires and analyzed through descriptive statistics and independent-samples t-tests.Results: Findings showed excellent ratings across all dimensions: client-side usability (M = 4.62), reliability (M = 4.53), and satisfaction (M = 4.69); administrator-side usability (M = 4.66), reliability (M = 4.61), and satisfaction (M = 4.80).No significant differences were found between patients and watchers or between administrative personnel and healthcare workers (α = 0.05), indicating comparable system acceptance across user groups.Conclusion: The Client Feedback System demonstrated strong usability, dependable performance, and high user satisfaction, supporting its effectiveness as a digital tool for capturing feedback and informing data-driven service improvements.However, the presence of a ceiling effect suggests the need for long-term evaluation and continuous refinement to enhance decision-support insights.