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Utilization and Monetization of Healthcare Data in Developing Countries.
Joshua T Bram1, Boyd Warwick-Clark1, Eric Obeysekare1
1Humanitarian Engineering and Social Entrepreneurship (HESE) Program, The Pennsylvania State University , University Park, Pennsylvania.
Big Data
|October 22, 2015
Summary
Leveraging community health data and machine learning can optimize resource allocation in developing countries. This study presents CHW-centric models to incentivize data collection, overcoming key challenges in low-resource settings.
Area of Science:
- Public Health
- Health Informatics
- Machine Learning
Background:
- Developing countries face challenges in healthcare resource allocation due to limited data.
- Community health workers (CHWs) are vital for last-mile data collection but lack incentives.
- Reliable data collection is hindered by contextual, business, communication, and technological factors.
Purpose of the Study:
- To explore applications of health data and machine learning in resource-poor healthcare systems.
- To identify challenges in collecting reliable community health data.
- To propose CHW-centric business models for incentivizing data collection.
Main Methods:
- Review of health data applications in developing countries.
- Analysis of challenges in low-resource data collection.
- Development of four CHW-centric business models with incentive and accountability structures.
Main Results:
- Identified key barriers to reliable health data collection in developing nations.
- Proposed practical, CHW-focused business models to improve data acquisition.
- Highlighted the importance of robust data infrastructure for advanced analytics.
Conclusions:
- Incentivized data collection by CHWs is crucial for optimizing healthcare resources.
- Addressing data collection challenges can transform healthcare in resource-poor settings.
- Strengthening data infrastructure is essential for leveraging big data and machine learning in global health.
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