Design and Functional Validation of an AI-Enabled Social Accounting and Performance Excellence Decision-Support System for Dental Clinic Networks

Abstract

Multi-unit dental service organizations require integrated monitoring of organizational performance, service quality, patient experience, and social impact. Conventional dashboards usually report historical indicators but provide limited support for linking performance deviations to traceable managerial action. Objective: This study aimed to design, develop, and functionally validate an artificial-intelligence (AI)-enabled decision-support system integrating Social Accounting and Performance Excellence principles for dental clinic networks. Methods: A Design Science Research approach guided problem identification, requirements analysis, artifact design, prototype development, demonstration, and functional evaluation. The prototype integrates organizational KPIs, social-impact indicators, branch comparison, SOP/audit functionality, role-based processes, SQLite persistence, and AI-supported managerial insights. In addition to the documented local functional test, a reproducible synthetic engineering dataset comprising 30 branch-month records (five fictional branches over six months) was generated to verify KPI calculations, social-impact aggregation, prioritization, and anomaly-oriented decision logic when real operational data were unavailable. The synthetic observations contain no real patient or clinic records and are not treated as UAT evidence. Results: The study produced an executable Alpha v0.1 prototype that runs on localhost and supports indicator input, data persistence, KPI summarization, analytical insight generation, branch-level comparison, and audit-oriented workflow. The available technical evidence reports 10 of 10 predefined local functional checks as PASS (100%). In the supplementary synthetic verification, the 30 records yielded a network mean performance index of 85.2, mean social-impact index of 80.5, and mean combined score of 83.3. The deliberately stressed fictional branch B04 was classified as high priority in all six simulated months, whereas B02 produced the highest mean combined score (90.4), demonstrating the expected discrimination of the analytical rules. Conclusion: The artifact demonstrates the technical feasibility of integrating Social Accounting, Performance Excellence, and AI-supported organizational analytics in a unified dental-network decision-support system. The synthetic exercise strengthens engineering verification of the analytical logic but does not substitute for relevant-environment user validation, real-data validation, or clinical evaluation.