IVF cycle cost estimation using Activity Based Costing and Monte Carlo simulation.

Area of Science:

• Health Economics
• Biostatistics
• Healthcare Management

Background:

• Deterministic cost estimation methods are insufficient for healthcare processes with variable activity durations.
• Accurate cost determination for Assisted Reproductive Technology (ART) treatments is crucial for financial planning and benchmarking.
• Existing methods fail to account for the inherent variability in ART treatment components.

Purpose of the Study:

• To develop and apply a new stochastic methodological approach for determining the actual costs of healthcare processes.
• To accurately estimate the cost of Assisted Reproductive Technology (ART) treatments in Italy, considering variable activity durations.
• To provide statistically robust cost data with confidence intervals for ART cycles.

Main Methods:

• Utilized Activity Based Costing (ABC) to identify and cost individual activities within the ART process.
• Employed Monte Carlo simulation to model cost variations and construct tolerance intervals.
• Incorporated control of experimental error to ensure the reliability of the simulation results.

Main Results:

• The stochastic approach yielded statistically robust cost estimates for different ART cycles (IVF, ICSI).
• Calculated tolerance intervals around the mean cost were sufficiently narrow, indicating reliable and usable data.
• The methodology provides a foundation for accurate cost benchmarking across different countries.

Conclusions:

• The proposed stochastic methodology offers a more accurate and reliable way to determine healthcare process costs compared to deterministic approaches.
• The application to ART treatments in Italy demonstrates the practical utility and robustness of the method.
• The findings support the use of this approach for financial analysis, resource allocation, and international cost comparisons in reproductive medicine.