Utilization and Monetization of Healthcare Data in Developing Countries

In developing countries with fledgling healthcare systems, the efficient deployment of scarce resources is paramount. Comprehensive community health data and machine learning techniques can optimize the allocation of resources to areas, epidemics, or populations most in need of medical aid or servic...

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Main Authors:	Bram, Joshua T., Warwick-Clark, Boyd, Obeysekare, Eric, Mehta, Khanjan
Format:	Online
Language:	English
Published:	Mary Ann Liebert, Inc. 2015
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4605478/

id	pubmed-4605478
recordtype	oai_dc
spelling	pubmed-46054782015-10-20 Utilization and Monetization of Healthcare Data in Developing Countries Bram, Joshua T. Warwick-Clark, Boyd Obeysekare, Eric Mehta, Khanjan Perspective In developing countries with fledgling healthcare systems, the efficient deployment of scarce resources is paramount. Comprehensive community health data and machine learning techniques can optimize the allocation of resources to areas, epidemics, or populations most in need of medical aid or services. However, reliable data collection in low-resource settings is challenging due to a wide range of contextual, business-related, communication, and technological factors. Community health workers (CHWs) are trusted community members who deliver basic health education and services to their friends and neighbors. While an increasing number of programs leverage CHWs for last mile data collection, a fundamental challenge to such programs is the lack of tangible incentives for the CHWs. This article describes potential applications of health data in developing countries and reviews the challenges to reliable data collection. Four practical CHW-centric business models that provide incentive and accountability structures to facilitate data collection are presented. Creating and strengthening the data collection infrastructure is a prerequisite for big data scientists, machine learning experts, and public health administrators to ultimately elevate and transform healthcare systems in resource-poor settings. Mary Ann Liebert, Inc. 2015-06-01 /pmc/articles/PMC4605478/ /pubmed/26487984 http://dx.doi.org/10.1089/big.2014.0053 Text en © J. Bram et al., 2015; Published by Mary Ann Liebert, Inc. This Open Access article is distributed under the terms of the Creative Commons License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Bram, Joshua T. Warwick-Clark, Boyd Obeysekare, Eric Mehta, Khanjan
spellingShingle	Bram, Joshua T. Warwick-Clark, Boyd Obeysekare, Eric Mehta, Khanjan Utilization and Monetization of Healthcare Data in Developing Countries
author_facet	Bram, Joshua T. Warwick-Clark, Boyd Obeysekare, Eric Mehta, Khanjan
author_sort	Bram, Joshua T.
title	Utilization and Monetization of Healthcare Data in Developing Countries
title_short	Utilization and Monetization of Healthcare Data in Developing Countries
title_full	Utilization and Monetization of Healthcare Data in Developing Countries
title_fullStr	Utilization and Monetization of Healthcare Data in Developing Countries
title_full_unstemmed	Utilization and Monetization of Healthcare Data in Developing Countries
title_sort	utilization and monetization of healthcare data in developing countries
description	In developing countries with fledgling healthcare systems, the efficient deployment of scarce resources is paramount. Comprehensive community health data and machine learning techniques can optimize the allocation of resources to areas, epidemics, or populations most in need of medical aid or services. However, reliable data collection in low-resource settings is challenging due to a wide range of contextual, business-related, communication, and technological factors. Community health workers (CHWs) are trusted community members who deliver basic health education and services to their friends and neighbors. While an increasing number of programs leverage CHWs for last mile data collection, a fundamental challenge to such programs is the lack of tangible incentives for the CHWs. This article describes potential applications of health data in developing countries and reviews the challenges to reliable data collection. Four practical CHW-centric business models that provide incentive and accountability structures to facilitate data collection are presented. Creating and strengthening the data collection infrastructure is a prerequisite for big data scientists, machine learning experts, and public health administrators to ultimately elevate and transform healthcare systems in resource-poor settings.
publisher	Mary Ann Liebert, Inc.
publishDate	2015
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4605478/
_version_	1613488058164838400

Utilization and Monetization of Healthcare Data in Developing Countries

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