Researchers in Ethiopia tested whether a computer system called DHIS2 could measure how well pregnant women and children are receiving healthcare services. They looked at data from health clinics across Ethiopia from July 2022 to June 2023, checking records for prenatal visits, birth attendance, and child care. They found the system works better for tracking pregnancy care than child health, but discovered important gaps in the data. Doctors and health leaders they interviewed said the system has promise but needs improvements to be trusted by decision-makers.

The Quick Take

  • What they studied: Whether a health information computer system (DHIS2) can accurately measure how many pregnant women and children are actually receiving quality healthcare services in Ethiopia
  • Who participated: All public health clinics across 11 regions and 2 major cities in Ethiopia, plus 15 health leaders and officials who were interviewed about their experiences with the system
  • Key finding: The system can track some pregnancy care (showing 16% of women get 4+ prenatal visits and 19% receive skilled birth attendance), but struggles to track child health services because important information is missing from the records
  • What it means for you: If you live in Ethiopia or work in healthcare, this suggests the current tracking system needs improvements before leaders can make confident decisions about where to improve services. The system shows promise but isn’t ready to rely on completely yet.

The Research Details

Researchers used a mixed-methods approach, combining two types of investigation. First, they analyzed actual data from the DHIS2 system—a computer program that collects health information from clinics—looking at records from July 2022 through June 2023. They examined five types of health services: pregnant women getting 4 or more prenatal visits, births attended by trained professionals, care after birth, sick child treatment, and child nutrition support. They checked whether the necessary information was available in the system and how accurate it was.

Second, they conducted interviews with 15 key health leaders and officials to understand their opinions about the data and system. They asked about concerns, usefulness, and what improvements were needed. This combination of numbers and personal perspectives helped them understand both what the data showed and whether people trusted it.

This research approach is important because health systems need reliable data to make good decisions about where to spend money and effort. A computer system is only useful if it captures complete, accurate information. By combining data analysis with interviews, researchers could see both the technical problems and the practical concerns of people who actually use the system.

The study examined real data from actual health clinics across a large area of Ethiopia, which is a strength. However, the researchers noted that data quality varied by region, meaning some areas had better records than others. The health leaders interviewed expressed concerns about whether the data was complete and appropriate for decision-making, which suggests the system has limitations. The study was conducted over one year, providing a reasonable timeframe for assessment.

What the Results Show

The DHIS2 system worked better for tracking pregnancy and birth services than for child health services. For pregnancy care, researchers could measure that only 16% of women nationally received 4 or more prenatal visits—the recommended number. For births, they found that only 19% were attended by trained healthcare workers, which is concerning because trained attendance reduces risks.

However, the system had significant gaps. For postnatal care (care after birth), sick child care, and child nutrition tracking, the system was missing important pieces of information across multiple steps. This meant researchers couldn’t get accurate numbers for these services. The quality of available data also differed significantly depending on which region of Ethiopia was being examined—some areas had much better records than others.

When researchers interviewed health leaders, they found mixed reactions. Everyone agreed the system had potential for helping make decisions, but many expressed concerns. They worried that the data wasn’t complete enough or appropriate for the kinds of decisions they needed to make. Some were concerned that policymakers wouldn’t trust or accept findings based on this data.

An interesting finding was that health leaders appreciated the local-level detail the DHIS2 system provided. Unlike some systems that only show national numbers, DHIS2 could show data for specific regions and districts. This local detail was seen as valuable for identifying problems in specific areas. However, this benefit was overshadowed by concerns about data quality and the lack of standardized methods for calculating coverage across different regions.

This study builds on previous research showing that many health information systems in developing countries struggle with data quality and completeness. The findings align with known challenges: maternal health data is typically easier to track than child health data, and data quality varies significantly by region. The study adds new insight by specifically testing whether DHIS2—a widely used system—can measure ’effective coverage,’ which means not just counting services provided, but measuring whether those services are actually helping people.

The study has several important limitations. The sample size of interviewed stakeholders was relatively small (15 people), so their views may not represent all health workers’ perspectives. The study only looked at data from public health facilities, not private clinics, which means the true coverage numbers might be different. The researchers noted that some health leaders were concerned the findings might not be accepted by policymakers, suggesting there may be political or practical barriers to using this data. Additionally, the study was conducted over one year, which may not capture seasonal variations in healthcare delivery.

The Bottom Line

Based on this research, health officials in Ethiopia should: (1) Improve data collection systems to ensure all required information is captured consistently, especially for child health services; (2) Develop standardized methods for calculating coverage so results are comparable across regions; (3) Invest in training health workers on proper data entry and quality; (4) Use DHIS2 for local-level decision-making while recognizing current limitations. Confidence level: Moderate—the system shows promise but needs improvements before major policy decisions should rely solely on it.

This research is most relevant to: health officials and policymakers in Ethiopia, healthcare workers managing health information systems, international health organizations supporting Ethiopia, and researchers studying health information systems. It’s less directly relevant to individual patients, though better health tracking systems ultimately benefit everyone by improving healthcare quality.

Improvements to data systems typically take 6-12 months to implement and show measurable results. Health leaders should expect a gradual improvement in data quality rather than immediate changes. Seeing the full benefits of a better system might take 1-2 years as new practices become routine.

Want to Apply This Research?

  • If you’re a healthcare worker using a health tracking app, record specific details about each patient visit: date of visit, type of service provided (prenatal, birth, postnatal, child sick care, or nutrition), whether the service met quality standards, and any missing information. This granular tracking helps identify data gaps.
  • Health facilities could use app reminders to ensure complete data entry at the point of care. For example, when a pregnant woman visits, the app could prompt workers to record all required information before closing the visit record, reducing missing data.
  • Track data completeness monthly by region. Create a dashboard showing what percentage of visits have complete information for each service type. Set improvement targets (e.g., 90% complete data entry) and monitor progress quarterly. Use this data to identify which facilities need additional training or support.

This research describes a health information system study in Ethiopia and does not provide medical advice. The findings about low coverage rates (16% for prenatal care, 19% for skilled birth attendance) reflect data collection challenges and do not constitute medical recommendations. If you are pregnant or have a sick child, please consult with a qualified healthcare provider regardless of these statistics. This study is intended for healthcare administrators, policymakers, and health system researchers. Individual health decisions should always be made in consultation with qualified medical professionals.