Early identification of potentially low performing community health workers using an ensemble classification model

Girdhari Bora, Rajiv Kumar and Ajil Joseph

International Journal of Productivity and Performance Management, 2024, vol. 74, issue 6, 2053-2070

Abstract: Purpose - Community health workers (CHWs) are vital to addressing public health system limitations in developing countries. However, effective identification and support of underperforming CHWs remains a challenge. This study develops a predictive model to proactively identify underperforming CHWs, facilitating targeted interventions for improved CHW programmes. Design/methodology/approach - We developed a predictive model to identify underperforming CHWs in Uttar Pradesh, India. Data from 140,101 CHWs over a 12-month period was used to build, test and validate the model. Classification techniques, ensemble modeling and a model tuning algorithm were employed for accuracy optimization and early identification. Findings - Logistic regression, decision trees and random forests yielded the best performance. While ensemble models offered no significant performance improvements over the base models, the model tuning algorithm effectively increased prediction accuracy by 19 percentage points. This enabled early identification of poor-performing CHWs and high-risk CHW clusters early in the year. Practical implications - The developed model has significant potential to improve CHW programmes. It enables targeted support, feedback and resource allocation, leading to enhanced CHW performance, motivation and healthcare outcomes in the communities they serve. The model can provide personalised feedback to help CHWs overcome challenges and dynamic clustering facilitates proactive identification and tailored support for those at risk of underperformance. Originality/value - This study is the first attempt to use predictive modelling to identify underperforming CHWs, advancing the nascent field of CHW performance analytics. It underscores the effectiveness of digital technologies and data in improving CHW programmes.

Keywords: Community health workers (CHW); Frontline health workers (FLW); Accredited social health activist (ASHA); Predictive model; CHW performance; Ensemble classification (search for similar items in EconPapers)
Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (text/html)
https://www.emerald.com/insight/content/doi/10.110 ... d&utm_campaign=repec (application/pdf)
Access to full text is restricted to subscribers

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eme:ijppmp:ijppm-02-2024-0116

DOI: 10.1108/IJPPM-02-2024-0116

Access Statistics for this article

International Journal of Productivity and Performance Management is currently edited by Dr Aylin Ates and Dr Berk Kucukaltan

More articles in International Journal of Productivity and Performance Management from Emerald Group Publishing Limited
Bibliographic data for series maintained by Emerald Support ().