Electronic data capture in a rural African setting: Evaluating experiences with different systems in Malawi

  • Global Health Action, Volume 7, No. 1, Year 2014

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Study Justification:
– The study aims to evaluate the experiences of using different electronic data capture (EDC) systems in a rural African setting, specifically in Malawi.
– The justification for the study is the increasing availability and affordability of hardware for EDC, such as smartphones and tablets, and the potential for using these tools in routine healthcare and research.
– By highlighting the advantages and disadvantages of different EDC systems, the study aims to inform decision-making on which hardware and software to use for data collection in similar settings.
Study Highlights:
– Fieldworkers preferred using EDC systems over paper-based systems, although some initially struggled with the technology.
– All EDC systems had in-built skip patterns, with CommCare offering the additional ‘case’ function that allowed for reliable linking of multiple interviews.
– CommCare and ODK Collect had user-friendly web interfaces for form development and good technical support.
– Pendragon required more complex programming and had less technical support.
– MIVA, a custom-built application, required more time and expertise to create but facilitated standardized data collection.
– Start-up costs varied between systems, but running costs were generally low, making EDC systems cost-effective over the course of projects.
Study Recommendations:
– The decision on which EDC system to use should be informed by the aim of data collection, budget, and local circumstances.
– Consideration should be given to the level of technical support available for the chosen EDC system.
– Capacity building opportunities should be provided for fieldworkers who may not have prior experience with EDC technology.
– Organizations with limited budgets can consider using existing hardware, such as personal digital assistants, for EDC.
Key Role Players:
– Fieldworkers: Responsible for data collection using EDC systems.
– Project Managers: Oversee the implementation of EDC systems and provide feedback on ease of development and cost.
– EDC System Developers: Provide technical support and expertise in developing and customizing EDC systems.
Cost Items for Planning Recommendations:
– Hardware: Cost of smartphones, tablets, or personal digital assistants for data collection.
– Software: Cost of EDC system licenses or development.
– Technical Support: Cost of accessing technical support for troubleshooting and assistance.
– Training: Cost of providing training to fieldworkers on using EDC systems.
– Internet Connectivity: Cost of internet access for building and downloading applications (if required by the chosen EDC system).

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The abstract provides a detailed description of the different EDC systems used in rural Malawi and highlights the advantages and disadvantages of each system based on the experiences of fieldworkers, project managers, and EDC system developers. The abstract also mentions the specific research projects in which each system was used. However, the abstract does not provide any quantitative data or statistical analysis to support the claims made. To improve the strength of the evidence, the abstract could include specific examples or case studies that demonstrate the effectiveness of the EDC systems in improving data collection efficiency and accuracy. Additionally, including data on the cost-effectiveness of the EDC systems compared to paper-based systems would further strengthen the evidence.

Background: As hardware for electronic data capture (EDC), such as smartphones or tablets, becomes cheaper and more widely available, the potential for using such hardware as data capture tools in routine healthcare and research is increasing. Objective: We aim to highlight the advantages and disadvantages of four EDC systems being used simultaneously in rural Malawi: two for Android devices (CommCare and ODK Collect), one for PALM and Windows OS (Pendragon), and a custom-built application for Android (Mobile InterVA – MIVA). Design: We report on the personal field and development experience of fieldworkers, project managers, and EDC system developers. Results: Fieldworkers preferred using EDC to paper-based systems, although some struggled with the technology at first. Highlighted features include in-built skip patterns for all systems, and specifically the ‘case’ function that CommCare offers. MIVA as a standalone app required considerably more time and expertise than the other systems to create and could not be customised for our specific research needs; however, it facilitates standardised routine data collection. CommCare and ODK Collect both have userfriendly web-interfaces for form development and good technical support. CommCare requires Internet to build an application and download it to a device, whereas all steps can be done offline with ODK Collect, a desirable feature in low connectivity settings. Pendragon required more complex programming of logic, using a Microsoft Access application, and generally had less technical support. Start-up costs varied between systems, and all were considered more expensive than setting up a paper-based system; however running costs were generally low and therefore thought to be cost-effective over the course of our projects. Conclusions: EDC offers many opportunities for efficient data collection, but brings some issues requiring consideration when designing a study; the decision of which hardware and software to use should be informed by the aim of data collection, budget, and local circumstances.

All four EDC systems were used in Mchinji district, central Malawi, for research projects (March 2013 onwards), with a total of 64 devices being used in four different projects (Table 1). Mchinji has an estimated population of 500,000, 80% of whom live in rural communities where mobile phone ownership is approximately 35% (6). CommCare was used in two prospective cohort research studies. The first investigated the relationships between pregnancy intentions and maternal and neonatal health. The second was investigating risks of treatment failure in community treatment of pneumonia in children. CommCare was chosen specifically for these two projects because of the ‘case’ function which allowed multiple interviews to be reliably linked, as well as the child’s interviews to be linked to the mother’s in the first project. Pendragon was used in an evaluation of a health education radio programme on health knowledge and behaviours; our organisation already owned the personal digital assistants (PDAs) and given the benefits of EDC, we chose to use these over purchasing new hardware because of a limited budget. This may be a common situation in resource-poor settings, where organisations already own this out-of-date technology, and it is important to know how these fare against newer (more costly) hardware. Fieldworkers using Pendragon and CommCare were recruited from the local communities where they would be working for the duration of the projects. Most did not have experience of fieldwork or EDC technology and were required to have completed at least 4 years of secondary school, providing significant opportunities for capacity building. ODK Collect and Mobile InterVA (MIVA) (7) were used together in a large-scale evaluation of vaccine introduction on post-neonatal infant mortality, to collect information on cause of death from verbal autopsies (VA). MIVA (which we have included to demonstrate a custom-built application) is a bespoke ‘app’ designed in collaboration with the World Health Organisation (WHO) to meet the pressing need for simpler VA data collection and processing, as a means to increasing the coverage of operational and representative cause of death registration systems (8). The app is built for android devices and is comprised of more than 200 questions, with skip patterns corresponding to the WHO 2012 standard VA tool. We used ODK Collect in conjunction with MIVA, as we wanted to collect additional information on socio-economic and vaccine status. MIVA could not be customised to collect additional information as it is a stand-alone phone application. Fieldworkers for this project were our most senior level of fieldworker, with all having more than 5 years’ experience with the organisation, and had been awarded or were studying for diplomas, mostly in ‘Community and Development’. We asked all developers and project managers (between one and two) and at least five fieldworkers from each project to comment on their experiences using an open semi-structured questionnaire with regard to: technical support, and cost and ease of development (project managers and developers); and ease of use, data processing, and available features (all). Themes were synthesised from these responses, and added to from extensive personal field and development experience.

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Based on the provided information, here are some innovations that can be used to improve access to maternal health:

1. Electronic Data Capture (EDC) Systems: The use of EDC systems, such as CommCare, ODK Collect, Pendragon, and Mobile InterVA (MIVA), allows for efficient and accurate data collection in rural settings. These systems can be used on smartphones or tablets, making data collection more accessible and reducing the reliance on paper-based systems.

2. In-built Skip Patterns: All EDC systems mentioned have in-built skip patterns, which allow for streamlined data collection by skipping irrelevant questions based on previous responses. This feature can save time and improve the accuracy of data collected.

3. Case Function: CommCare offers a ‘case’ function that allows for reliable linking of multiple interviews, which is particularly useful for studies involving maternal and child health. This feature enables better tracking and analysis of data related to pregnancy and neonatal health.

4. User-friendly Interfaces and Technical Support: CommCare and ODK Collect have user-friendly web interfaces for form development and provide good technical support. This makes it easier for fieldworkers and project managers to create and manage data collection forms, even with limited technical expertise.

5. Offline Functionality: ODK Collect allows for all steps of form development and data collection to be done offline, which is beneficial in low connectivity settings. This feature ensures that data collection can continue even in areas with limited internet access.

6. Cost-effectiveness: While the initial setup costs for EDC systems may be higher compared to paper-based systems, the running costs are generally low. This makes EDC systems a cost-effective solution in the long run, especially for organizations with limited budgets.

7. Capacity Building Opportunities: The use of EDC systems provides opportunities for capacity building among fieldworkers. By recruiting local community members and providing training on EDC technology, organizations can empower individuals and enhance their skills in data collection and technology usage.

8. Custom-built Applications: Custom-built applications like MIVA can be designed to meet specific research needs, such as collecting cause of death information from verbal autopsies. These applications can be tailored to the requirements of the study, ensuring accurate and standardized data collection.

Overall, the use of EDC systems and related innovations can improve access to maternal health by enabling efficient data collection, reducing errors, and providing opportunities for capacity building in resource-poor settings.
AI Innovations Description
The recommendation to improve access to maternal health is to implement electronic data capture (EDC) systems in rural areas. This recommendation is based on a study conducted in Malawi, where four different EDC systems were evaluated for their advantages and disadvantages.

The study found that fieldworkers preferred using EDC systems over paper-based systems, although some initially struggled with the technology. The highlighted features of the EDC systems included in-built skip patterns and the ability to link multiple interviews. The study also compared the technical support, cost, and ease of development for each system.

The four EDC systems used in the study were CommCare and ODK Collect for Android devices, Pendragon for PALM and Windows OS, and a custom-built application called Mobile InterVA (MIVA) for Android. CommCare and ODK Collect were found to have user-friendly web interfaces and good technical support. CommCare required internet connectivity for application building and downloading, while ODK Collect could be used offline, which is beneficial in low connectivity settings. Pendragon required more complex programming and had less technical support. MIVA, although a custom-built application, required more time and expertise to create and could not be customized for specific research needs.

The study concluded that EDC offers many opportunities for efficient data collection, but certain considerations need to be taken into account when designing a study, such as the aim of data collection, budget, and local circumstances. The start-up costs for implementing EDC systems varied, but the running costs were generally low and considered cost-effective over the course of the projects.

Overall, implementing EDC systems in rural areas can improve access to maternal health by enabling more efficient data collection and processing. It is important to choose the appropriate hardware and software based on the specific research needs, budget, and local circumstances.
AI Innovations Methodology
Based on the provided information, here are some potential recommendations for improving access to maternal health:

1. Implementing Electronic Data Capture (EDC) Systems: The use of EDC systems, such as CommCare, ODK Collect, Pendragon, and Mobile InterVA (MIVA), can streamline data collection processes and improve efficiency in maternal health programs. These systems allow for real-time data entry, automatic skip patterns, and standardized data collection.

2. Providing Training and Capacity Building: To ensure successful implementation of EDC systems, it is important to provide training and capacity building for fieldworkers and healthcare providers. This will help them become familiar with the technology and overcome any initial challenges they may face.

3. Customizing EDC Systems for Specific Research Needs: Depending on the specific research project or program, it may be necessary to customize the EDC system to collect additional information or meet specific requirements. This customization can be done through collaboration with developers or by using a custom-built application like MIVA.

4. Considering Connectivity and Hardware Requirements: In low-connectivity settings, it may be beneficial to choose EDC systems that can be used offline, such as ODK Collect. Additionally, considering the availability and cost of hardware, such as smartphones or tablets, is important when selecting an EDC system.

To simulate the impact of these recommendations on improving access to maternal health, a methodology could be developed as follows:

1. Define the Objectives: Clearly define the objectives of the simulation, such as measuring the impact of EDC systems on data collection efficiency, healthcare provider satisfaction, or patient outcomes related to maternal health.

2. Identify Key Variables: Identify the key variables that will be measured or simulated, such as the time required for data collection, the accuracy of data entry, or the availability of real-time data for decision-making.

3. Collect Baseline Data: Gather baseline data on the current state of maternal health access and data collection processes. This can include data on paper-based systems, existing technology usage, and any challenges or limitations faced.

4. Develop Simulation Models: Develop simulation models that incorporate the recommended innovations, such as the implementation of EDC systems and the associated training and capacity building efforts. These models should simulate the impact of these innovations on the identified key variables.

5. Run Simulations: Run the simulations using the developed models and input the baseline data. Analyze the results to determine the potential impact of the recommended innovations on improving access to maternal health.

6. Evaluate and Refine: Evaluate the simulation results and compare them to the baseline data. Identify any gaps or areas for improvement and refine the simulation models accordingly.

7. Communicate Findings: Communicate the findings of the simulation to relevant stakeholders, such as healthcare providers, policymakers, and researchers. Highlight the potential benefits and challenges of implementing the recommended innovations and provide recommendations for further action.

By following this methodology, it is possible to simulate the impact of innovations, such as EDC systems, on improving access to maternal health and make informed decisions regarding their implementation.

Statistics:
Citations: 36
Authors: 7
Identifiers:
DOI: 10.3402/gha.v7.25878
Research Areas:
Community Interventions, Health System and Policy, Maternal Access, Maternal and Child Health, Quality of Care, Sexual and Reproductive Health, Social Determinants, Technology and Innovations
Study Countries:
Malawi
Study Design:
Cohort Study, Cross Sectional Study, Grounded Theory
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