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DC Field | Value | Language |
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dc.contributor.author | Gesicho, Milka Bochere | - |
dc.contributor.author | Babic, Ankica | - |
dc.contributor.author | Were, Martin C. | - |
dc.date.accessioned | 2021-08-09T07:53:03Z | - |
dc.date.available | 2021-08-09T07:53:03Z | - |
dc.date.issued | 2020 | - |
dc.identifier.uri | http://ir.mu.ac.ke:8080/jspui/handle/123456789/4984 | - |
dc.description.abstract | Health management information systems (HMISs) in low- and middle-income countries have been used to collect large amounts of data after years of implementation, especially in support of HIV care services. National-level aggregate reporting data derived from HMISs are essential for informed decision-making. However, the optimal statistical approaches and algorithms for deriving key insights from these data are yet to be fully and adequately utilized. This paper demonstrates use of the k-means clustering algorithm as an approach in supporting monitoring of facility reporting and data-informed decision-making, using the case example of Kenya HIV national reporting data. Results reveal four homogeneous cluster categories that can be used in assessing overall facility performance and rating of that performance. | en_US |
dc.language.iso | en | en_US |
dc.publisher | IOS Press | en_US |
dc.subject | Health management information systems | en_US |
dc.subject | Clustering | en_US |
dc.title | K-Means Clustering in Monitoring Facility Reporting of HIV Indicator Data: Case of Kenya | en_US |
dc.type | Article | en_US |
Appears in Collections: | School of Medicine |
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