Delivering excellent health services requires accurate health information systems (HIS) data. Poor-quality data can lead to poor judgments and outcomes. Unlike probability surveys, which are representative of the population and carry accuracy estimates, HIS do not, but in many countries the HIS is the primary source of data used for administrative estimates. However, HIS are not structured to detect gaps in service coverage and leave communities exposed to unnecessary health risks. Here we propose a method to improve informatics by combining HIS and probability survey data to construct a hybrid estimator. This technique provides a more accurate estimator than either data source alone and facilitates informed decision-making. We use data from vitamin A and polio vaccination campaigns in children from Madagascar and Benin to demonstrate the effect. The hybrid estimator is a weighted average of two measurements and produces SEs and 95% confidence intervals (CIs) for the hybrid and HIS estimators. The estimates of coverage proportions using the combined data and the survey estimates differ by no more than 3%, while decreasing the SE by 1–6%; the administrative estimates from the HIS and combined data estimates are very different, with 3–25 times larger CI, questioning the value of administrative estimates. Estimators of unknown accuracy may lead to poorly formulated policies and wasted resources. The hybrid estimator technique can be applied to disease prevention services for which population coverages are measured. This methodology creates more accurate estimators, alongside measured HIS errors, to improve tracking the public’s health.

提供优质的健康服务需要准确的健康信息系统（HIS）数据。数据质量差会导致判断和结果不佳。与代表人口并具有准确度估计值的概率调查不同，HIS不会，但是在许多国家中，HIS是用于行政估计的主要数据来源。但是，HIS的结构无法检测服务覆盖范围的差距，并使社区面临不必要的健康风险。在这里，我们提出了一种通过将HIS和概率调查数据相结合来构建混合估计量的信息学方法。与单独使用任一数据源相比，此技术提供的估算器更为准确，并有助于做出明智的决策。我们使用来自马达加斯加和贝宁儿童的维生素A和小儿麻痹症疫苗接种活动的数据来证明这种效果。混合估计器是两次测量的加权平均值，并为混合和HIS估计器产生SE和95％置信区间（CI）。使用合并数据和调查估计值得出的覆盖率估计值相差不超过3％，而SE则降低了1-6％；HIS的行政估算和合并数据估算之间有很大差异，CI值大3到25倍，这对行政估算的价值提出了质疑。准确性未知的估算器可能会导致制定不良的政策和资源浪费。混合估计器技术可以应用于测量人口覆盖率的疾病预防服务。这种方法可以创建更准确的估算器，以及可测量的HIS错误，以改善对公众健康的跟踪。