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

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.

Déclaration de conflit d'intérêts

Resources for the data collection were provided by the Health Results Innovation Trust Fund administered by the World Bank. The Center for Global Development (CGD) is grateful for contributions from the Australian Department of Foreign Affairs and Trade (grant agreement 72518) and Johnson & Johnson (C2017001214) in support of this work. This does not alter our adherence to PLOS ONE policies on sharing data and materials. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.