Improving Data Quality in Mobile Community-Based Health Information Systems

Intended for those designing a mobile application or planning an mHealth programme, these guidelines from MEASURE Evaluation focus on strengthening data quality in mobile community-based health information system(s) (HIS) around the world.

"Given the power of community-based data to inform decision making, it is essential that these data are of high quality....The true power of community-based health data can be realized when stakeholders across all levels of the system use this information to make decisions that can improve health services and outcomes."

Intended for those designing a mobile application or planning an mHealth programme, these guidelines from MEASURE Evaluation focus on strengthening data quality in mobile community-based health information system(s) (HIS) around the world. The guidelines were written to help users strengthen the quality of data in mobile community-based HIS (CBHIS), in order to: inform the design and implementation of a mobile CBHIS to collect, manage, and report quality data; assess the system's strengths and weaknesses; implement corrective measures with action plans to strengthen the system and improve data quality; and monitor and track the system's capacity improvements and performance. The guidelines could be used, for example, for routine quality checks and ongoing supervision of frontline health workers.

The context is that national programmes and donor-funded projects increasingly rely on decentralised models of care to expand coverage of health services, ensure linkages to health facilities, and reach the most vulnerable populations. Thus, new emphasis has been placed on community-based models in which frontline health workers are expected to provide services and collect and report data. The United States President’s Emergency Plan for AIDS Relief (PEPFAR) has embarked on a strategy to deliver the right types of interventions, in the right places, at the right time. This will require accurate, reliable, and timely data at district and subdistrict levels to provide an in-depth picture of community health so that programmes can focus on populations most at need (PEPFAR, 2014). Mobile technologies can help programmes improve the completeness and accuracy of data, tap the potential for real-time reporting, and strengthen communication and supervisory feedback practices.

According to MEASURE Evaluation, the quality of data reported in a mobile data collection system depends on many factors, two of which are highlighted in these guidelines: (1) design of the mobile data collection system: Built-in components in the data collection workflow (such as skip logic, automated calculations, data validations, and instructional prompts) are used to increase the accuracy and completeness in data collection; (2) implementation of data accountability protocols: Data quality is affected by behavioural factors of the people collecting data, such as their attitudes, values, and motivation. In addition to well-designed data collection tools and formats, mHealth provides an opportunity to streamline work processes for data collectors and facilitate more real-time communication and feedback between data collectors and their supervisors. Greater communication and motivation among community health workers (CHWs) can lead to greater ownership of data and commitment to high-quality data.

These guidelines have 4 parts:

  1. Part 1. Designing Mobile Data Collection Systems for Improved Data Quality: explains how to design a mobile data collection system and offers a checklist for assessing mobile data collection forms and systems.
  2. Part 2. Implementing Programs to Increase Ownership of Data and Commitment to Data Quality and Data Use: explains how to engender accountability and ownership for data quality and offers a checklist for assessing feedback loops, supervisory structures for motivating CHWs on data quality issues, and systems and processes to promote the use of data for decision making.
  3. Part 3. Verifying Field-Level CBHIS Data: explains how to adapt a community trace and verify (CTV) tool to verify whether people reported to have received services did so.
  4. Part 4. Conducting a Mobile CBHIS Data Quality Assessment: explains how to determine the assessment's purpose, conduct community visits, and develop a system strengthening plan with follow-up actions.

Concrete guidance is provided throughout, such as this instruction from Part 4: "During community visits, the assessment team should observe the implementation and use of the mobile CBHIS and talk to CHWs to observe them interfacing with the mobile device to assess them against the criteria in the guidelines. Additionally, key informants should be interviewed (such as mobile application developers, field engineers, service designers, project deployment managers, mHealth specialists, CHW managers, and monitoring and evaluation officers and staff)." References and resources for further reading are provided throughout.

Republished from The Communication Initiative Network.