Data Cleaning Process for mHealth Log Data to Inform Health Worker Performance Article Swipe

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Simon Savai , Md Kamrul Hasan , Jemima Kamano , Lawrence Misoi , Peter Wakholi , Martin C. Were ·

YOU? · · 2022 · Open Access · · DOI: https://doi.org/10.3233/shti220664

Log data, captured during use of mobile health (mHealth) applications by health providers, can play an important role in informing nature of user engagement with the application. The log data can also be employed in understanding health provider work patterns and performance. However, given that these logs are raw data, they require robust cleaning and curation if accurate conclusions are to be derived from analyzing them. This paper describes a systematic data cleaning process for mHealth-derived logs based on Broeck’s framework, which involves iterative screening, diagnosis, and treatment of the log data. For this study, log data from the demonstrative mUzima mHealth application are used. The employed data cleaning process uncovered data inconsistencies, duplicate logs, missing data within logs that required imputation, among other issues. After the data cleaning process, only 39,229 log records out of the initial 91,432 usage logs (42.9%) could be included in the final dataset suitable for analyses of health provider work patterns. This work highlights the significance of having a systematic data cleaning approach for log data to derive useful information on health provider work patterns and performance.

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mHealth Computer science Raw data Imputation (statistics) Data science Process (computing) Data mining Missing data Health care Machine learning Programming language Operating system Economic growth Economics

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Type: article
Language: en
Landing Page: https://doi.org/10.3233/shti220664
PDF: https://ebooks.iospress.nl/pdf/doi/10.3233/SHTI220664
OA Status: hybrid
Cited By: 2
References: 5
Related Works: 10
OpenAlex ID: https://openalex.org/W4283725646

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DOI: https://doi.org/10.3233/shti220664

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Title: Data Cleaning Process for mHealth Log Data to Inform Health Worker Performance

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Language: en

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Publication year: 2022

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Publication date: 2022-06-29

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Authors: Simon Savai, Md Kamrul Hasan, Jemima Kamano, Lawrence Misoi, Peter Wakholi, Martin C. Were

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Landing page: https://doi.org/10.3233/shti220664

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Concepts: mHealth, Computer science, Raw data, Imputation (statistics), Data science, Process (computing), Data mining, Missing data, Health care, Machine learning, Programming language, Operating system, Economic growth, Economics

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Cited by: 2

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publication_date	2022-06-29
publication_year	2022
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