Predictive Modeling for Labor Law Risk Detection in Medicaid Funded Home Care Programs in New York: Forecasting Overtime Exposure, Payroll Errors, and Workforce Compliance Gaps

Abstract

BACKGROUND: In an increasingly complex home care workforce, with a changing employment landscape and limited options for reactive auditing, compliance with labor law regulations has become more difficult for Medicaid-funded home care providers. Predictive modeling can take a proactive stance on compliance risk identification, preventing financial penalties, legal liability, and operational inefficiencies. This study aims to examine the impact of predictive modelling in predicting overtime exposure, payroll records and workforce compliance issues to improve compliance performance in New York's Medicaid funded home care organization.

METHODS: A quantitative, cross-sectional research design was used and the structured questionnaire was sent to 350 professionals who have worked in a Medicaid-funded home care agency, such as compliance officers, payroll managers, human resource managers, home care coordinators, and administrators. The instrument consisted of 25 items across five constructs and on a 5-point Likert scale. The reliability analysis, descriptive statistics, Pearson correlation and multiple linear regression were used for data analysis with IBM SPSS Statistics.

RESULTS: The instrument used in the measurement showed very good internal consistency with the overall Cronbach's Alpha of 0.94. The results of descriptive analysis showed high level of readiness to implement predictive modeling (M = 4.26) and overtime exposure risk (M = 4.21). Correlation analysis revealed that overtime exposure, payroll error risk, and workforce compliance gaps were negatively associated with compliance performance, while predictive modeling readiness ended up with a significant positive correlation (r = 0.67). Multiple regression analysis showed that the model was statistically significant (R² = 0.64, F = 76.42, p < 0.001); predictive modeling readiness was the best predictor (β = 0.41), whereas payroll error risk (β = –0.32), overtime exposure risk (β = –0.29), and workforce compliance gaps (β = –0.24) significantly negatively predicted compliance performance.

CONCLUSION: The results show that predictive modelling significantly boosted compliance with labor laws by allowing the proactive identification of workforce-related risks and evidence-based decision-making. AI, predictive analytics, payroll systems and workforce monitoring technologies can all help to fortify regulatory compliance, lower risk exposure within your organization, increase efficiency of operations, and allow for sustainable workforce governance in Medicaid-funded home care organizations. The study offers an empirical model that can be the basis for moving from reactive compliance auditing to intelligent data-driven labor risk management.