Frontiers in public health · 2026
Anticipating the economic resilience of dental practices in crises: a machine learning approach.
СтатьяНазвание и аннотация: Перевод в очереди
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Radoi Delia, Curavale Dan, Pituru Silviu-Mirel
Аннотация
BACKGROUND/OBJECTIVES: The COVID-19 pandemic exposed the need for economic resilience in dental practices, which faced reduced patient numbers, increased costs, and service restrictions. This study aimed to develop a predictive model for dental practice resilience and to identify the financial characteristics associated with better performance during and after a crisis.
METHODS: We analyzed official financial statements for 2,474 Bucharest dental practices. After applying mode-specific inclusion criteria, the final analytic cohorts comprised 850 firms for crisis-year prediction in 2020 and 801 firms for the three 2021 recovery scenarios. The Dental Practice Resilience Index (DPRI) was developed as a composite measure of profitability, expense efficiency, debt-to-turnover, turnover per employee, and profit per employee. Seven regression model families were evaluated across four prediction scenarios, and continuous predictions were also translated into tertile-based risk classes for decision support.
RESULTS: Crisis-year prediction showed moderate but useful accuracy, with Linear Regression narrowly achieving the highest cross-validated performance (R 2 = 0.537 ± 0.078). Predicting 2021 recovery from pre-crisis data alone was more difficult, and Random Forest narrowly ranked first (R 2 = 0.452 ± 0.084). The strongest scenario-level results were obtained when observed 2020 data were included, with Elastic Net reaching R 2 = 0.643 ± 0.071 and Random Forest reaching balanced accuracy = 0.648 ± 0.044. When observed crisis-year data were unavailable, a two-step synthetic recovery approach recovered part of that information, but remained below the crisis-informed configuration.
CONCLUSIONS: Machine learning methods proved effective not only in predicting dental practice resilience during a crisis, but also in anticipating post-crisis recovery potential. The DPRI provided an interpretable resilience measure, while the combination of continuous prediction and tertile-based classification offered both nuanced forecasts and practical risk stratification. Observed crisis-year information substantially improved recovery forecasting, although pre-crisis indicators alone still retained meaningful predictive value.
METHODS: We analyzed official financial statements for 2,474 Bucharest dental practices. After applying mode-specific inclusion criteria, the final analytic cohorts comprised 850 firms for crisis-year prediction in 2020 and 801 firms for the three 2021 recovery scenarios. The Dental Practice Resilience Index (DPRI) was developed as a composite measure of profitability, expense efficiency, debt-to-turnover, turnover per employee, and profit per employee. Seven regression model families were evaluated across four prediction scenarios, and continuous predictions were also translated into tertile-based risk classes for decision support.
RESULTS: Crisis-year prediction showed moderate but useful accuracy, with Linear Regression narrowly achieving the highest cross-validated performance (R 2 = 0.537 ± 0.078). Predicting 2021 recovery from pre-crisis data alone was more difficult, and Random Forest narrowly ranked first (R 2 = 0.452 ± 0.084). The strongest scenario-level results were obtained when observed 2020 data were included, with Elastic Net reaching R 2 = 0.643 ± 0.071 and Random Forest reaching balanced accuracy = 0.648 ± 0.044. When observed crisis-year data were unavailable, a two-step synthetic recovery approach recovered part of that information, but remained below the crisis-informed configuration.
CONCLUSIONS: Machine learning methods proved effective not only in predicting dental practice resilience during a crisis, but also in anticipating post-crisis recovery potential. The DPRI provided an interpretable resilience measure, while the combination of continuous prediction and tertile-based classification offered both nuanced forecasts and practical risk stratification. Observed crisis-year information substantially improved recovery forecasting, although pre-crisis indicators alone still retained meaningful predictive value.