Health system measurement: Harnessing machine learning to advance global health.

ABSTRACT:

Background

Further improvements in population health in low- and middle-income countries demand high-quality care to address an increasingly complex burden of disease. Health facility surveys provide an important but costly source of information on readiness to provide care. To improve the efficiency of health system measurement, we applied unsupervised machine learning methods to assess the performance of the service readiness index (SRI) defined by the World Health Organization and compared it to empirically derived indices.

Methods

We drew data from nationally representative Service Provision Assessment surveys conducted in 10 countries between 2007 and 2015. We extracted 649 items in domains such as infrastructure, medication, and management to calculate an index using a

SUBMITTER: Leslie HH 

PROVIDER: S-EPMC6173424 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature