Background
Data on the utilisation of health services, health behaviours, and health status of the population is vital for informing decision-making on the optimal amount and allocation of resources. In many low-middle-income countries, these data often come from wide-ranging household surveys or increasingly, digital health data capture applications. The effective use of these data depends heavily on the quality of data.1 Most approaches to monitoring data quality have focused on regulating intrinsic factors, such as the tool itself and the system underlying its use. Extrinsic factors include the location, setting, community environment, and enumerator–respondent dynamics, impacting survey implementation and the resulting data quality. Data quality is shaped at its core by the structure and quality of the survey tool itself including the indicators selected, language used, and the nature of the response options. Beyond the tool, data quality is impacted by the implementation of the tool including the modality (digital tool vs. paper, face-to-face vs. phone survey) and location of implementation; the profile, number, training, and supervision of enumerators; and routine monitoring of incoming data.1
India is home to some of the largest digital data capture applications globally both with regard to routine data capture by frontline health workers and in the context of special surveys. Some applications include Integrated Child Development Services-Common Application Software2 used by Anganwadi workers and Auxiliary nurse midwife online (ANMOL),3 which is used by auxiliary nurse midwives. The Indian Comprehensive National Nutrition Survey uses short messaging service (SMS) notifications to monitor the quality of biomedical samples collected in the field.4
The widespread availability and use of technology including tablets and mobile phones, for collecting health data as well as coordinating logistics among supervisors and enumerators, has widened the possibilities of technology use for improving data quality assurance (QA) measures. QA includes those activities that ensure the data are of high quality such as documentation, checks on the data, and adequate reporting.5 Technology use during data collection has also been shown to reduce costs and increase efficiency.6 Technology use for data QA has the potential to expand beyond data captured to additionally harness the use of mobile phones used for communication by those capturing data (survey enumerators, healthcare providers) and their supervisors.
To date, SMS text messages have been used as a communication modality between healthcare providers and seekers for healthcare reminders,7 and adherence to medications7 8 or immunisation schedules.9 The same technology, linked with efforts to identify and automate data capture quality impediments could be implemented to improve QA as part of routine data capture applications or surveys.
In this analysis, we outline findings from efforts in four districts of Madhya Pradesh, India, to improve the quality of household survey data for the evaluation of a maternal-mobile messaging programme.10 We sought to improve quality during implementation through the use of automating identification of errors and feeding these errors back to supervisors in near-real time using SMS text messages. We start by reviewing routine QA procedures and developing a system to enhance survey QA methods. We then examined the perceptions of this system and timeliness of this system as compared with manual notification of errors.