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Using supervised learning to select audit targets in performance-based financing in health: An example from Zambia.

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Independent verification in healthcare performance-based financing (PBF) can be improved. Machine learning, specifically Random Forest, offers a more cost-effective approach to targeted sampling for verifying reported service volumes.

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Area of Science:

  • Health economics
  • Health services research
  • Health policy and management

Background:

  • Performance-based financing (PBF) in healthcare incentivizes service volume but risks data over-reporting.
  • Independent verification is crucial to ensure PBF integrity and accurate performance measurement.
  • Targeted sampling strategies are needed to optimize verification efficiency and cost-effectiveness.

Purpose of the Study:

  • To evaluate alternative sampling strategies for independent verification in PBF programs.
  • To compare the effectiveness of random sampling versus predictive modeling for targeted clinic selection.
  • To identify methods that enhance the cost-effectiveness of healthcare verification processes.

Main Methods:

  • Empirical comparison of various random sampling and predictive modeling techniques.
  • Utilized data from a Zambian PBF pilot, including reported and verified quantity indicators for 140 clinics.
  • Applied machine learning algorithms, including Random Forest, for predictive sampling.

Main Results:

  • Machine learning methods, particularly Random Forest, demonstrated superior performance over traditional sampling.
  • Predictive modeling significantly improved the efficiency of identifying clinics for verification.
  • The study identified opportunities to increase the cost-effectiveness of independent verification activities.

Conclusions:

  • Machine learning-based targeted sampling is a highly effective strategy for independent verification in PBF.
  • Random Forest models offer a promising approach to optimize resource allocation in healthcare verification.
  • Implementing advanced sampling methods can strengthen the integrity and efficiency of PBF programs.