Data Quality And Bias in Health Informatics for Regulated Utilities: Post-Pandemic Lessons

Dissertation 4.3

Data Quality And Bias in Health Informatics for Regulated Utilities: Post-Pandemic Lessons

Pages

Harvard

Style

~ 9–13 mins

Reading Time

DevSecOps AI Supply Chain

Abstract

This dissertation investigates “Data Quality And Bias in Health Informatics for Regulated Utilities: Post-Pandemic Lessons” using a Delphi study. Through a economic lens, the analysis integrates multi-source data to derive an actionable roadmap for researchers and practitioners.

Download PDF Order Similar Paper Save

Data Quality And Bias in Health Informatics for Regulated Utilities: Post-Pandemic Lessons

ABSTRACT

Data Quality And Bias in Health Informatics for Regulated Utilities: Post-Pandemic Lessons is unpacked across themes: change enablement, costs, interoperability, and scalability. Limitations and future research paths are noted.

Open full preview

View Full Paper