SMS feedback system as a quality assurance mechanism: experience from a household survey in rural India. (26th July 2021)

• Record Type: Journal Article
• Authors: Shah, Neha; Ummer, Osama; Scott, Kerry; Bashingwa, Jean Juste Harrisson; Penugonda, Nehru; Chakraborty, Arpita; Sahore, Agrima; Mohan, Diwakar; LeFevre, Amnesty Elizabeth
• Other Names: author non-byline.; Agarwal Smisha author non-byline.; Arora Salil author non-byline.; Bashingwa Jean JH author non-byline.; Bhatanagar Aarushi author non-byline.; Chamberlain Sara author non-byline.; Chandra Rakesh author non-byline.; Chakraborty Arpita author non-byline.; Dumke Neha author non-byline.; Dutt Priyanka author non-byline.; Godfrey Anna author non-byline.; Gopalakrishnan Suresh author non-byline.; Kumar Nayan author non-byline.; Honikman Simone author non-byline.; Labrique Alain author non-byline.; LeFevre Amnesty author non-byline.; Mendiratta Jai author non-byline.; Miller Molly author non-byline.; Mitra Radharani author non-byline.; Mohan Diwakar author non-byline.; Moodley Deshen author non-byline.; Mulder Nicola author non-byline.; Ng Angela author non-byline.; Parida Dilip author non-byline.; Penugonda Nehru author non-byline.; Rahul Sai author non-byline.; Rajput Shiv author non-byline.; Shah Neha author non-byline.; Scott Kerry author non-byline.; Shinde Aashaka author non-byline.; Singh Aaditya author non-byline.; Tiffin Nicki author non-byline.; Ummer Osama author non-byline.; Ved Rajani author non-byline.; Weiss Falyn author non-byline.; Whitehead Sonia author non-byline.;
• Is Part Of: BMJ global health. Volume 6(2021)Supplement 5
• Publication Date: 2021-07-26
• Subjects: qualitative study -- public health; World health -- Periodicals; 362.105
• DOI: 10.1136/bmjgh-2021-005287
• Languages: English
• ISSNs: 2059-7908

Abstract

Abstract : 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.