Utilization and Monetization of Healthcare Data in Developing Countries.
Utilization and Monetization of Healthcare Data in Developing Countries.
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DOI：
10.1089/big.2014.0053
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发表时间：
2015-06-01
期刊：
影响因子：
4.6
通讯作者：
Mehta K
中科院分区：
文献类型：
--
作者：
Bram JT;Warwick-Clark B;Obeysekare E;Mehta K
在发展中国家，医疗保健系统刚刚起步，有效地部署稀缺资源至关重要。综合的社区健康数据和机器学习技术可以优化资源分配给最需要医疗援助或服务的地区、流行病或人群。然而，在低资源环境下可靠的数据收集是具有挑战性的，由于广泛的上下文，业务相关，通信和技术因素。社区卫生工作者（CHW）是值得信赖的社区成员，他们向朋友和邻居提供基本的健康教育和服务。虽然越来越多的计划利用CHW进行最后一英里数据收集，但这些计划面临的一个根本挑战是缺乏对CHW的切实激励。本文介绍了卫生数据在发展中国家的潜在应用，并审查了可靠的数据收集的挑战。四个实用的CHW为中心的商业模式，提供激励和问责结构，以促进数据收集。创建和加强数据收集基础设施是大数据科学家、机器学习专家和公共卫生管理人员在资源匮乏的环境中最终提升和改造医疗保健系统的先决条件。
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.