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In BMJ global health

The increasing use of digital health solutions to support data capture both as part of routine delivery of health services and through special surveys presents unique opportunities to enhance quality assurance measures. This study aims to demonstrate the feasibility and acceptability of using back-end data analytics and machine learning to identify impediments in data quality and feedback issues requiring follow-up to field teams using automated short messaging service (SMS) text messages. Data were collected as part of a postpartum women's survey (n=5095) in four districts of Madhya Pradesh, India, from October 2019 to February 2020. SMSs on common errors found in the data were sent to supervisors and coordinators. Before/after differences in time to correction of errors were examined, and qualitative interviews conducted with supervisors, coordinators, and enumerators. Study activities resulted in declines in the average number of errors per week after the implementation of automated feedback loops. Supervisors and coordinators found the direct format, complete information, and automated nature of feedback convenient to work with and valued the more rapid notification of errors. However, coordinators and supervisors reported preferring group WhatsApp messages as compared with individual SMSs to each supervisor/coordinator. In contrast, enumerators preferred the SMS system over in-person group meetings where data quality impediments were discussed. This study demonstrates that automated SMS feedback loops can be used to enhance survey data quality at minimal cost. Testing is needed among data capture applications in use by frontline health workers in India and elsewhere globally.

Shah Neha, Ummer Osama, Scott Kerry, Bashingwa Jean Juste Harrisson, Penugonda Nehru, Chakraborty Arpita, Sahore Agrima, Mohan Diwakar, LeFevre Amnesty Elizabeth


public health, qualitative study