Thema der Dissertation:
Machine learning for data-driven primary prevention at population scale Thema der Disputation:
Data-driven health management and the era of value-based care
Machine learning for data-driven primary prevention at population scale Thema der Disputation:
Data-driven health management and the era of value-based care
Abstract: In recent years, healthcare providers have been shifting their focus towards value-based care, which prioritizes patient outcomes and the efficient use of resources. However, this shift requires a significant overhaul of traditional fee-for-service reimbursement models, and necessitates the use of data-driven approaches to improve patient outcomes and measure the quality of care. This talk will explore how data-driven health management can promote the shift toward value-based reimbursement. Particular focus will be placed on the key metrics that healthcare providers need to track, such as readmission rates and cost per outcome, and how machine learning can be used to both improve these metrics and incentivize efficient and effective care. Envisioning implementation, the talk will explore the requirements for robust data infrastructure, including data standardization, interoperability, and platforms such as electronic health records. Ultimately, ways to integrate data-driven approaches natively into healthcare systems will be discussed to help facilitate the shift toward value-based care and health maintenance.
Zeit & Ort
31.03.2023 | 08:30
Seminarraum 031
(Fachbereich Mathematik und Informatik, Arnimallee 7, 14195 Berlin)