Journal of Information & Systems Management Koodichimma Ibe-Ariwa 16 3 2026 https://doi.org/10.6025/jism/2026/16/3/93-109 https://www.dline.info/jism/fulltext/v16n3/jismv16n3_1.pdf Background: Healthcare organizations increasingly require information systems that support not only clinical data storage but also efficient retrieval, integration, and advanced analytics. Poorly designed database schemas introduce redundancy, update anomalies, and integrity risks that compromise both operational efficiency and data quality. Objective: This study presents a normalized relational database schema for hospital management and demonstrates its capacity to support diverse operational and patient level analyses. Methods: A fourteen table relational schema was designed applying first, second, and third normal forms (1NF-3NF) with primary and foreign key constraints to ensure referential integrity across patient demographics, staff management, departmental organization, physical resources, appointments, clinical records, surgical events, shift scheduling, and billing. The schema was then evaluated through mediation analysis, moderation analysis, bipartite patient doctor network analysis, four class Gaussian mixture latent profile analysis, group comparisons with effect sizes, and robustness/sensitivity checks. Results: Mediation of payment through appointment completion was not statistically supported (bootstrap 95% CI:- 53.45 to +6.08). Moderation by age and department was non-significant. The patient doctor network exhibited strong centralization, with one physician accounting for 505 weighted links and 426 unique patients among 726 patients and 1,000 appointment edges. Four distinct utilization phenotypes emerged. Appointment modality showed negligible practical effects on payment or completion ( 2 = 0.0012; Cramér's V = 0.056). Findings were robust across alternative specifications. Conclusion: The normalized schema functions as an integrated analytical foundation bridging data management and data services, supporting network analytics, patient phenotyping, and reusable clinical phenotype development for future hospital information infrastructures.