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首页> 外文期刊>BMC Medical Informatics and Decision Making >Evaluating performance of health care facilities at meeting HIV-indicator reporting requirements in Kenya: an application of K-means clustering algorithm
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Evaluating performance of health care facilities at meeting HIV-indicator reporting requirements in Kenya: an application of K-means clustering algorithm

机译：评估肯尼亚艾滋病病毒指标报告要求的医疗设施的表现：K-Means聚类算法的应用

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The ability to report complete, accurate and timely data by HIV care providers and other entities is a key aspect in monitoring trends in HIV prevention, treatment and care, hence contributing to its eradication. In many low-middle-income-countries (LMICs), aggregate HIV data reporting is done through the District Health Information Software 2 (DHIS2). Nevertheless, despite a long-standing requirement to report HIV-indicator data to DHIS2 in LMICs, few rigorous evaluations exist to evaluate adequacy of health facility reporting at meeting completeness and timeliness requirements over time. The aim of this study is to conduct a comprehensive assessment of the reporting status for HIV-indicators, from the time of DHIS2 implementation, using Kenya as a case study. A retrospective observational study was conducted to assess reporting performance of health facilities providing any of the HIV services in all 47 counties in Kenya between 2011 and 2018. Using data extracted from DHIS2, K-means clustering algorithm was used to identify homogeneous groups of health facilities based on their performance in meeting timeliness and completeness facility reporting requirements for each of the six programmatic areas. Average silhouette coefficient was used in measuring the quality of the selected clusters. Based on percentage average facility reporting completeness and timeliness, four homogeneous groups of facilities were identified namely: best performers, average performers, poor performers and outlier performers. Apart from blood safety reports, a distinct pattern was observed in five of the remaining reports, with the proportion of best performing facilities increasing and the proportion of poor performing facilities decreasing over time. However, between 2016 and 2018, the proportion of best performers declined in some of the programmatic areas. Over the study period, no distinct pattern or trend in proportion changes was observed among facilities in the average and outlier groups. The identified clusters revealed general improvements in reporting performance in the various reporting areas over time, but with noticeable decrease in some areas between 2016 and 2018. This signifies the need for continuous performance monitoring with possible integration of machine learning and visualization approaches into national HIV reporting systems.
机译：通过艾滋病毒护理提供者和其他实体报告完整，准确和及时数据的能力是监测艾滋病毒预防，治疗和关怀趋势的关键方面，从而促进其根除。在许多低中中期国家（LMIC）中，聚合HIV数据报告是通过地区健康信息软件2（DHIS2）完成的。尽管如此，尽管在LMIC中向DHIS2报告了艾滋病毒指标数据的长期要求，但存在很少有严格的评估，以评估卫生设施报告的充分性，随着时间的推移会满足完整性和及时性要求。本研究的目的是从DHIS2实施时，通过肯尼亚作为案例研究，对艾滋病毒指标的报告状况进行全面评估。进行了回顾性观察研究，以评估2011年和2018年肯尼亚的所有47个县的卫生设施的报告表现。使用从DHIS2中提取的数据，K-Means聚类算法用于识别均匀的卫生设施组根据举办六个方案区域中的每一个的及时性和完整性设施报告要求的基础表现。平均剪影系数用于测量所选集群的质量。基于百分比的平均设施报告完整性和及时性，确定了四个同质的设施集团：最佳表演者，平均表演者，较差的表演者和异常人员表演者。除了血液安全报告之外，在五个剩余的报告中观察到一个明显的模式，最佳表演设施的比例增加，随着时间的推移，差的表演设施的比例降低。但是，在2016年和2018年期间，一些方案领域的最佳表演者的比例下降。在研究期间，平均和异常值组的设施中没有观察到比例变化的不同模式或趋势。所确定的集群揭示了一般性报告领域报告表现的一般性改进，但2016年和2018年之间的某些领域有明显的减少。这意味着需要在国家艾滋病毒报告中融入机器学习和可视化方法的持续绩效监测。系统。

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