Using Supervised Learning to Select Audit Targets in Performance-Based Financing in Health: An Example from Zambia

Citation:

Grover, Dhruv, Sebastian Bauhoff, and Jed Friedman. 2019. “Using Supervised Learning to Select Audit Targets in Performance-Based Financing in Health: An Example from Zambia.” PLoS ONE 14 (1): e0211262. Copy at https://j.mp/2HtT9do

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.

Published paper (open access)

Replication files: https://doi.org/10.7910/DVN/LHUIBO
Last updated on 01/31/2019