Skip to content

The Strongest Motivators for Using Routine Health Information in Family Planning: A Prospective Study in Lagos, Nigeria

The Strongest Motivators for Using Routine Health Information in Family Planning: A Prospective Study in Lagos, Nigeria Health system performance depends on the collection, collation, and use of quality health data and information. One of the primary roles of a routine health information system is generating data within the health system for decision making, policy formulation, and implementation. When data are generated and analyzed, they can provide relevant information to support planning and management of high-quality healthcare services at the facility, ward, local government, state, and federal levels.

MEASURE Evaluation, funded by the United States Agency for International Development, supported this study to bridge the knowledge gap concerning the motivators behind using routine health information in family planning (FP) to improve the use of FP services.

The study design was a prospective, cross-sectional study conducted over a period of 12 months in three local government areas of Lagos State, in southwest Nigeria. Twelve key informant interviews were conducted and 425 questionnaires were administered to 105 men and 320 women working in the health sector.

This working paper outlines the study and results and provides a series of recommendations based on these findings.

Implementing Swaziland’s Client Management Information System: Stakeholders’ Views of the Process and Recommendations to Improve It

Implementing Swaziland's Client Management Information System: Stakeholders' Views of the Process and Recommendations to Improve ItSwaziland policymakers and health administrators decided to change from the current paper-based health records system to an electronic health records (EHR) system. This change is being undertaken to harmonize healthcare data and improve patient care. The country’s client management information system (CMIS) is an EHR system that improves patient care by improving data quality and access, reducing duplicated cases within the system, and improving patient flow and wait times within the clinic.

MEASURE Evaluation is conducting an evaluation of the CMIS implementation process through health facility assessments and key stakeholder interviews (conducted from July 2017 through August 2017). The interviewers collected the opinions and experiences of key stakeholders of the CMIS to discover the challenges of implementation and recommend ways to improve the process, especially for the primary users of the system. The results of those interviews and our recommendations based on them are presented in this report.

The CMIS is being implemented in Swaziland by the Ministry of Health’s Health Management Information Systems, with support from the Institute for Health Measurement. It is being financed by the Swaziland Ministry of Health, with help from the Global Fund to Fight AIDS, Tuberculosis and Malaria and United States Agency for International Development.

Access the resource.

Innovations in Geographic Information Systems Mapping Technology: GIS Working Group Meeting, October 2017

Innovations in Geographic Information Systems Mapping Technology: GIS Working Group Meeting, October 2017To promote and improve the use of geospatial data by the implementing partners of the United States President’s Emergency Plan for AIDS Relief (PEFPAR), MEASURE Evaluation—funded by the United States Agency for International Development (USAID) and PEPFAR—convened a meeting of the Geographic Information Systems (GIS) Working Group in Washington, DC, on October 23, 2017. The group has been meeting at least annually since 2000, giving GIS specialists and users a regular opportunity to share their experiences with spatial data and platforms, and to keep up to date on recent developments in GIS technology and its uses for global public health. Over the years, several springboard discussions from these meetings have resulted in publications and have led to further collaborative work within the project.

This report shares the insights, innovations, and research that engaged the working group at this meeting. Presentations covered a wide array of topics but can be distilled to two overarching ones: “innovations” and “research and discoveries.” The Innovations section of this report describes presentations related to new tools, technologies, and other offerings of some of our guest experts. The Research and Discoveries section showcases some of the work that our presenters have done on upcoming tools, techniques, and data analysis.

Access the report and meeting presentations.

Data Use in the Democratic Republic of the Congo’s Malaria Program: Results from Seven Provinces

Data Use in the Democratic Republic of the Congo's Malaria Program: Results from Seven Provinces   Evidence-informed decision making is essential for the success of health systems, programs, and services. Global commitments to improving health systems and outcomes have led to improved monitoring and evaluation (M&E) and health information systems, thus providing an opportunity to use data for decision making and not simply for reporting.

Overall, the relationships among improved information, demand for data, and continued data use constitute a cycle that leads to improved health programs and policies. Improving data demand and use (DDU) is necessary to improve the effectiveness and sustainability of a health system.

MEASURE Evaluation, which is funded by the United States Agency for International Development and the United States President’s Malaria Initiative, undertook an assessment to understand the data use context for those working in the Democratic Republic of the Congo (DRC) in the National Malaria Control Program (NMCP) at the provincial and health zone levels in seven provinces (Bukavu, Haut Lomami, Kasai Central, Kasai Oriental, Lomami, Sankuru, and Tanganyika), as well as implementing partners working with the NMCP at the provincial level. The purpose of this assessment was to identify how data are currently being used for decision making and how future interventions can be designed to promote the demand for and use of data in decision making.

Access the resource.

Evaluation Capacity Building: A collective learning experience

By Stephanie Watson-Grant, DrPH, MEASURE Evaluation

Capacity building for rigorous evaluations is not a phrase that was part of my vocabulary ten or even five years ago. I got involved in evaluation capacity building (ECB) through my work in Liberia where I was part of a team from MEASURE Evaluation working with the Ministry of Health and M&E officers from seven counties on outcome monitoring studies. I loved working with my Liberian colleagues. They were enthusiastic about the activities and an assistant minister of health always came to our training and dissemination events.

My project—MEASURE Evaluation, funded by the United States Agency for International Development (USAID)—was ending one of its phases as we developed a three-year process for capacity building to help Liberians eventually conduct future studies. I didn’t get the opportunity to implement the plan, but my experience in Liberia led to my coordinating a group to develop capacity planning guidance for our project’s next phase.

We developed guidelines and a capacity-building plan and then we wondered: what does capacity building for evaluation look like in the real world? Where and how do we apply it?

The opportunity came in Kenya. An evaluation was in the works to monitor outcomes of activities to assist orphans and vulnerable children. As the three studies were being planned, a colleague and I worked with the study lead to design a way to partner with a local research organization. The experience was an excellent start and gave us a lot to think about. So, we did it again in Kenya and in Malawi, South Africa, and Uganda.

I learned so much from this experience. For starters, there are parts of evaluation capacity building I had never considered. I realized it was more than a methods outline, sampling plan, and data analysis to demonstrate change. Those abilities are critical, but others are equally important—for example, ensuring effective leadership and that the appropriate staff are involved; operations and management processes with a known communication structure and a sound work plan; skills, and experience with electronic data capture and analysis; efficient data collection and data management; and—probably most important—the ability to share understandable findings and use them to improve health outcomes.

As we implemented the assessment tool and capacity plan in different settings, we tweaked it as we learned what evaluation capacity building looks like in practical terms. For example, the assessment process was necessarily subjective as we shared a collaborative learning experience. The planning process was unavoidably simple, because it had to be achievable within the timeline of our contract with the partner.

The lesson I take away is we aren’t really building capacity, we are enhancing existing capacity—ours and theirs. Our research partners are already high-functioning research entities when we start working with them. But, through our work together, through our shared experience, we’ve each left the other a little more capable.

For more information, visit:

Stages of Health Information System Improvement: Strengthening the Health Information System for Improved Performance

Stages of Health Information System Improvement: Strengthening the Health Information System for Improved PerformanceThis brief describes a suite of tools under development by MEASURE Evaluation to provide systematic guidance on how to assess the existing status of a health information system (HIS) and identify specific improvements that take an HIS through a defined progression toward optimum functioning. The goal of this suite of tools is to answer the question: “What are the stages of HIS development?”

Access the brief.

Health Information Systems Interoperability Toolkit

HIS Interoperability Maturity Toolkit slider-min

Enabling exchange of data between disparate health information systems—or interoperability of health information systems—holds great promise for overcoming barriers to data quantity, quality, and accessibility.

Many low-resource settings, however, do not have the guidance and tools to assess their capacity to implement interoperable systems. Some factors critical to successful implementation of interoperable information systems have not previously been well-defined. To address this gap, the MEASURE Evaluation project, funded by the United States Agency for International Development (USAID), in collaboration with the Digital Health and Interoperability Technical Working Group of the Health Data Collaborative, have developed an HIS Interoperability Maturity Toolkit.

The kit contains three main pieces: a maturity model, an assessment tool, and a users’ guide. It also offers a complete list of the references consulted in a literature review that was conducted as part of the toolkit’s development.

The HIS interoperability maturity model identifies the major components of HIS interoperability and lays out an organization’s growth pathway through these components. Countries can use the assessment tool to determine their HIS interoperability maturity level systematically. Using the assessment results, countries can create a path toward strengthening their HIS interoperability and building resilient systems.

The toolkit is version 0.5. In the coming months, we will be learning from early adopters and pilot testing the toolkit. In late 2018, we will publish an updated version with material gleaned from lessons learned and knowledge gained from users.

Access the toolkit.