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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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  • Dhruv Grover
  • Sebastian Bauhoff
  • Jed Friedman

Abstract

Independent verification is a critical component of performance-based financing (PBF) in health care, in which facilities are offered incentives to increase the volume of specific services but the same incentives may lead them to over-report. We examine alternative strategies for targeted sampling of health clinics for independent verification. Specifically, we empirically compare several methods of random sampling and predictive modeling on data from a Zambian PBF pilot that contains reported and verified performance for quantity indicators of 140 clinics. Our results indicate that machine learning methods, particularly Random Forest, outperform other approaches and can increase the cost-effectiveness of verification activities.

Suggested Citation

  • Dhruv Grover & Sebastian Bauhoff & Jed Friedman, 2019. "Using supervised learning to select audit targets in performance-based financing in health: An example from Zambia," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-13, January.
    • Handle: RePEc:plo:pone00:0211262
    DOI: 10.1371/journal.pone.0211262
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    References listed on IDEAS

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    1. Petra Vergeer & Anna Heard & Erik Josephson & Lisa Fleisher, 2016. "Verification in results-based financing for health," Health, Nutrition and Population (HNP) Discussion Paper Series 112342, The World Bank.
    2. Gerard La Forgia & Somil Nagpal, 2012. "Government-Sponsored Health Insurance in India : Are You Covered?," World Bank Publications - Books, The World Bank Group, number 11957, April.
    3. Adrien Renaud, 2013. "Verification of Performance in Result-Based Financing (RBF): The Case of Burundi," Health, Nutrition and Population (HNP) Discussion Paper Series 86190, The World Bank.
    4. György Bèla Fritsche & Robert Soeters & Bruno Meessen, 2014. "Performance-Based Financing Toolkit [Boîte à outils : Financement basé sur la performance]," World Bank Publications - Books, The World Bank Group, number 17194, April.
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    1. Chiedozie M. Okafor & Owolabi Ogunse & Mercy Nneoma Iheke & Dickson O. Oseghale & Imuetinyan Ogiehor & Ebuka Emmanuel Aniebonam, 2025. "The Role of Advanced Anomaly Detection in Transforming Program Management in Government with Scikit-Learn, A Machine Learning Library in Python," International Journal of Research and Scientific Innovation, International Journal of Research and Scientific Innovation (IJRSI), vol. 12(5), pages 148-165, May.

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    More about this item

    JEL classification:

    • C20 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - General
    • C52 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Evaluation, Validation, and Selection
    • C55 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Large Data Sets: Modeling and Analysis
    • I15 - Health, Education, and Welfare - - Health - - - Health and Economic Development
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health

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