Jessica Wong, Canadian Memorial Chiropractic College, Toronto, Canada
Objective: The objective was to develop a model to evaluate the impact of policy changes in compensation levels and experience rating programs on the number of workers’ compensation lost-time back claims in Ontario, Canada over a 30-year timeframe. This model tested the hypothesis that a theory- and-policy-driven model would be sufficient in reproducing historical claims data in a robust manner, and that policy changes related to potential financial incentives would have a major impact on modeled data.
Methods: The model was developed using system dynamics methods in the Vensim® simulation program. Sensitivity analysis was used to evaluate the modeled data at extreme endpoints of variable input and timeframes. The degree of predictive value of the modeled data was measured by the coefficient of determination, root mean square error and Theil’s inequality coefficients.
Results: Correlation between modeled data and actual data was found to be meaningful (R2=0.934), and the modeled data was stable at extreme endpoints. Among the relationships explored, policy changes in compensation levels and experience rating programs were found to be relatively minor drivers of back claims data, accounting for a 13% improvement of error. Simulation results suggested unemployment, number of no-lost-time claims, number of injuries per worker and recovery rate from back injuries outside of claims management to be significant drivers of back claims data.
Conclusion: The study findings suggest that certain areas within and outside of the workers’ compensation system need to be considered when evaluating and changing policies around potential financial incentives and back claims.