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Strengthening the quality of routine MNCH data through learning workshops

by Antoinette Bhattacharya

published 6 March 2019

This report presents an update from the fourth and final Data Quality Learning Workshop held in December 2018 in Gombe State, Nigeria.

Quality routine health data provide useful program monitoring information to identify gaps and take action to improve performance. Under the leadership of the Gombe State Primary Health Care Development Agency (the Agency) and in collaboration with their implementing partners, the LSHTM IDEAS project delivered learning workshops with local government area (LGA) actors to improve the quality of facility-based routine data.

Participants at the fourth Data Quality Workshop in Gombe. Photo Credit: IDEAS 2018

Four workshops, one workshop every six months, took place from March 2017 through December 2018. The overall aim of the workshops is two-fold: (i) to improve the quality of routine maternal and newborn health (MNH) data at the facility- and LGA-levels; and (ii) to strengthen the subsequent use of routine MNH data to inform decision making at the facility- and LGA-levels.

The fourth and final data quality learning workshop took place on 10th and 11th December 2018. This workshop built on the first three workshops’ key concepts in routine data quality review; interpreting, visualizing, and presenting findings for constructive peer-review; and planning for improved performance.

To prepare for the fourth workshop, state- and LGA-level actors conducted a data quality review for May-October 2018 and explored the potential reasons for higher and lower performing facilities. By the end of the workshop, the Agency leadership facilitated four main sessions taking place over two days: a learning session on the elements of a good presentation; a learning session on providing positive and constructive feedback; a presentation session for state and LGA officers to present their bi-annual quality review; and a forward-looking session on actions to improve the quality of data.