Design and implementation of a mobile health electronic data capture platform that functions in fully-disconnected settings: a pilot study in rural Liberia.

BACKGROUND: Mobile phones and personal digital assistants have been used for data collection in developing world settings for over three decades, and have become increasingly common. However, the use of electronic data capture (EDC) through mobile phones is limited in many areas by inconsistent network connectivity and poor access to electricity, which thwart data transmission and device usage. This is the case in rural Liberia, where many health workers live and work in areas without any access to cellular connectivity or reliable power. Many existing EDC mobile software tools are built for occasionally-disconnected settings, allowing a user to collect data while out of range of a cell tower and transmit data to a central server when he/she regains a network connection. However, few tools exist that can be used indefinitely in fully-disconnected settings, where a user will never have access to the internet or a cell network. This led us to create and implement an EDC software tool that allows for completely offline data transfer and application updating. RESULTS: We designed, pilot-tested, and scaled an open-source fork of Open Data Kit Collect (an Android application that can be used to create EDC systems) that allows for offline Bluetooth-based bidirectional data transfer, enabling a system in which permanently-offline users can collect data and receive application updates. We implemented this platform among a cohort of 317 community health workers and 28 supervisors in a remote area of rural Liberia with incomplete cellular connectivity and low access to power sources. CONCLUSIONS: Running a fully-offline EDC program that completely bypasses the cellular network was found to be feasible; the system is still running, over 4 years after the initial pilot program. The users of this program can theoretically collect data offline for months or years, assuming they receive hardware support when needed. Fully-offline EDC has applications in settings where cellular network coverage is poor, as well as in disaster relief settings in which portions of the communications infrastructure may be temporarily nonfunctional.

A conceptual framework for measuring community health workforce performance within primary health care systems.

BACKGROUND: With the 40th anniversary of the Declaration of Alma-Ata, a global effort is underway to re-focus on strengthening primary health care systems, with emphasis on leveraging community health workers (CHWs) towards the goal of achieving universal health coverage for all. Institutionalizing effective, sustainable community health systems is currently limited by a lack of standard metrics for measuring CHW performance and the systems they work within. Developed through iterative consultations, supported by the Bill & Melinda Gates Foundation and in partnership with USAID and UNICEF, this paper details a framework, list of indicators, and measurement considerations for monitoring CHW performance in low- and middle-income countries. METHODS: A review of peer-reviewed articles, reports, and global data collection tools was conducted to identify key measurement domains in monitoring CHW performance. Three consultations were successively convened with global stakeholders, community health implementers, advocates, measurement experts, and Ministry of Health representatives using a modified Delphi approach to build consensus on priority indicators. During this process, a structured, web-based survey was administered to identify the importance and value of specific measurement domains, sub-domains, and indicators determined through the literature reviews and initial stakeholder consultations. Indicators with more than 75% support from participants were further refined with expert qualitative input. RESULTS: Twenty-one sub-domains for measurement were identified including measurement of incentives for CHWs, supervision and performance appraisal, data use, data reporting, service delivery, quality of services, CHW absenteeism and attrition, community use of services, experience of services, referral/counter-referral, credibility/trust, and programmatic costs. Forty-six indicators were agreed upon to measure the sub-domains. In the absence of complete population enumeration and digitized health information systems, the quality of metrics to monitor CHW programs is limited. CONCLUSIONS: Better data collection approaches at the community level are needed to strengthen management of CHW programs and community health systems. The proposed list of metrics balances exhaustive and pragmatic measurement of CHW performance within primary healthcare systems. Adoption of the proposed framework and associated indicators by CHW program implementors may improve programmatic effectiveness, strengthen their accountability to national community health systems, drive programmatic quality improvement, and plausibly improve the impact of these programs.