Anticipating the economic resilience of dental practices in crises
A machine learning approach
Bibliographic Data
| ID | 22088647 |
|---|---|
| Authors | Delia Radoi (Carol Davila University of Medicine and Pharmacy), Dan Curavale (University of Science and Technology, corresponding author), Silviu-Mirel Pituru (Carol Davila University of Medicine and Pharmacy) |
| Year | 2026 |
| Volume | 14 |
| Publication date | 2026-06-30 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | Frontiers in Public Health (JOURNAL) |
| Journal identifiers | ISSN: 2296-2565 • E-ISSN: 2296-2565 |
| Publisher | Frontiers Media SA (PUBLISHER • CH) |
| DOI | 10.3389/fpubh.2026.1858605 |
| OpenAlex | W7166683604 |
| Language | EN |
| References cited | 18 |
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
Decision tree · Dental practice · Predictive modelling · Psychological resilience · Random forest · Regression · Regression analysis · Dental Research and COVID-19 · Disaster Response and Management · Supply Chain Resilience and Risk Management
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| Citation velocity | historical |
|---|---|
| Highly cited | No |