DATA INITIATIVE

Currently, the biggest obstruction in sustainable policy making and directed public health efforts is the lack of evidence-based policy making, accurate quantitative and qualitative feedback on initiatives, and democratized data for intergovernmental organizations and research institutions to build a collective knowledge base. The Health Data Initiative aims to target systemic health problems in developing nations by introducing ML-based data collection and analysis techniques in a multidisciplinary fashion in order to drive more informed policy decisions.

Our initiative aims to combine our unique process methodology with cutting-edge machine learning technology to improve global health data analytics by building a longitudinal knowledge base on important public health indicators and to fill the void in the democratization of data and models for other international health efforts and data science efforts. We bring together world ministries, policy makers, data scientists, and policy researchers to maximize global impact by emphasizing information sharing, collaborative developments, and transparency. Our ultimate goal is to develop a comprehensive, effective system that can provide developing nations with the information and resources to make informed health policy decisions. 

The Health Data Initiative currently is being implemented alongside 12 country governments and multiple United Nations agencies. 

Issues of Focus/Current Regional Initiatives​

  • Ministerial Management Capacities

  • Malaria 

  • HIV/AIDS

  • Localized Virus Outbreaks

  • Malnutrition

  • Sustainability and Biotechnology

  • Digital Health Systems

  • Global Bioethics

  • Socioeconomic Health Disparities

 
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POLICY PATHWAY

 

PHASE 1

Identify regional policy interests or issues of focus. Conduct key, critical primary and secondary research to provide relevant foundation for involvement of third parties (e.g. organizations, governments).

PHASE 2

Work with health ministries to identify main initiative objectives and coordinate resources with health volunteers and employees to collect data that is missing from public datasets.

PHASE 3

Source data from a variety of independent research entities, NGOs, and government organizations.

PHASE 4

Work with data scientists, health professionals, UN bodies, and policy makers from across the world to create effective, understandable models.

PHASE 5

Applying cutting-edge machine learning and neural network training to provide effective and streamlined analysis of data and actionable models. These models, along with the accompanying data, are provided to analytical teams for curated suggestions on healthcare policy deployment based on empirical evidence.

PHASE 6

Continue an active feedback loop to continually update and improve models based off of new data. This sustainable feedback loop paves the way for more informed policy decisions.