Law and Health Technology Assessment Research Center (DATS)
Using machine learning to extract and analyze data from health-related judicial litigation and health technology assessment reports.
Lead: Daniel Wang
In Brazil, disputes over access to medicines, treatments, and health coverage frequently end up in court — a phenomenon known as the "judicialization" of health. Each case generates documents that, in principle, hold valuable information about unmet health needs and gaps in technology assessment and coverage decisions. In practice, that information is locked away in unstructured legal text, scattered across court systems, and effectively invisible to the health policy researchers and technology assessment bodies who could act on it.
This project develops machine learning tools to extract and structure data from judicial health-litigation records and health technology assessment reports, turning an underused legal archive into a usable evidence base. The resulting data supports researchers and government bodies — including the Procuradoria Geral do Estado and the Secretaria Estadual de Saúde — in understanding patterns in health-related litigation and how they relate to technology assessment and coverage policy.
The center is led by Daniel Wei Liang Wang (FGV Direito SP), with LABORA contributing on machine learning and data extraction components of the research.
Funding: FAPESP (Programa Ciência para o Desenvolvimento)