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

25 Pages Posted: 26 Jul 2018

See all articles by Dhruv Grover

Dhruv Grover

University of California, San Diego (UCSD)

Sebastian Bauhoff

Center for Global Development

Jed Friedman

World Bank - Development Research Group (DECRG); World Bank Group

Date Written: April 11, 2018

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.

Keywords: performance-based financing, performance verification, audits, machine learning, health care finance, health care providers

JEL Classification: C20, C52, C55, I15, I18

Suggested Citation

Grover, Dhruv and Bauhoff, Sebastian and Friedman, Jed Arnold, Using Supervised Learning to Select Audit Targets in Performance-Based Financing in Health: An Example from Zambia (April 11, 2018). Center for Global Development Working Paper No. 481. Available at SSRN: https://ssrn.com/abstract=3208855 or http://dx.doi.org/10.2139/ssrn.3208855

Dhruv Grover

University of California, San Diego (UCSD)

9500 Gilman Drive
Mail Code 0502
La Jolla, CA 92093-0112
United States

Sebastian Bauhoff (Contact Author)

Center for Global Development ( email )

2055 L Street NW
Washington, DC DC 20009
United States

HOME PAGE: http://scholar.harvard.edu/bauhoff/

Jed Arnold Friedman

World Bank - Development Research Group (DECRG)

1818 H. Street, N.W.
MSN3-311
Washington, DC 20433
United States

World Bank Group ( email )

1818 H Street, N.W.
Washington, DC 20433
United States

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