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February 19, 20260 citationsOpen Access

The Digital Consolidation of Modern Dentistry: A Comprehensive Meta-Analysis of Enterprise Resource Planning Systems, Revenue Cycle Management, and Operational Efficiency in Dental Support Organizations

OTOwen R. Thornton

Key Points

  • This meta-analysis aims to assess how ERP systems affect the performance and efficiency of Dental Support Organizations (DSOs).
  • Conducted a meta-analysis of peer-reviewed literature and operational data related to DSOs.
  • Aggregated data from thousands of dental clinics to evaluate ERP impacts.
  • Analyzed outcomes in revenue cycle management and operational efficiency.
  • DSOs using ERP systems reported supply costs reduced to 5-6% of overhead, compared to 7-8% in solo practices.
  • Centralized RCM workflows led to a three-day reduction in Days Sales Outstanding (DSO).
  • AI integration showed significant improvements in diagnostic accuracy for radiographic caries detection, with an AUC increase of 0.050 (p < 0.001).

Abstract

The landscape of dental medicine is undergoing a profound structural and economic transformation. The traditional solo practitioner model is rapidly yielding to consolidated, data-driven group practices and Dental Support Organizations (DSOs). This meta-analysis provides an exhaustive synthesis of current peer-reviewed literature, industry reports, and quantitative operational data to evaluate the impact of Enterprise Resource Planning (ERP) systems on DSO performance. By aggregating data across multiple studies encompassing thousands of dental clinics, this report quantifies the operational, financial, and clinical outcomes associated with centralized practice management. Key findings indicate that DSOs leveraging integrated ERP architectures achieve significant improvements in Revenue Cycle Management (RCM), reducing supply costs to 5 to 6 percent of overhead compared to the 7 to 8 percent average in solo practices. Furthermore, centralized RCM workflows reduce Days Sales Outstanding (DSO) metrics by an average of three days and mitigate gross collection losses. The integration of Artificial Intelligence (AI) into these systems demonstrates statistically significant improvements in diagnostic accuracy, with Area Under the Curve (AUC) increases of 0.050 (p

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Cite This Study

Owen R. Thornton (2026) studied this question.

synapsesocial.com/papers/6996a7a5ecb39a600b3ed94dhttps://doi.org/10.17615/fy50-gd89
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