Abstract
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.
| Original language | English (US) |
|---|---|
| Pages (from-to) | 59-66 |
| Number of pages | 8 |
| Journal | Big Data |
| Volume | 3 |
| Issue number | 2 |
| DOIs | |
| State | Published - Jun 1 2015 |
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All Science Journal Classification (ASJC) codes
- Information Systems
- Computer Science Applications
- Information Systems and Management
Cite this
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Utilization and Monetization of Healthcare Data in Developing Countries. / Bram, Joshua T.; Warwick-Clark, Boyd; Obeysekare, Eric; Mehta, Khanjan.
In: Big Data, Vol. 3, No. 2, 01.06.2015, p. 59-66.Research output: Contribution to journal › Review article
TY - JOUR
T1 - Utilization and Monetization of Healthcare Data in Developing Countries
AU - Bram, Joshua T.
AU - Warwick-Clark, Boyd
AU - Obeysekare, Eric
AU - Mehta, Khanjan
PY - 2015/6/1
Y1 - 2015/6/1
N2 - 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.
AB - 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.
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UR - http://www.scopus.com/inward/citedby.url?scp=84991781205&partnerID=8YFLogxK
U2 - 10.1089/big.2014.0053
DO - 10.1089/big.2014.0053
M3 - Review article
VL - 3
SP - 59
EP - 66
JO - Big Data
JF - Big Data
SN - 2167-6461
IS - 2
ER -