Rapid reconciliation of errors and bottlenecks in data-driven workflows

摘要

A unified framework for healthcare workflows to introduce multiple integrated approaches to error analysis. A first approach uses machine learning to extend probabilistic record linkage and apply it to the task of reconciliation, classifying changes between datasets as intentional or unintentional. A second approach uses process mining to extract maximum information about process diagrams and process bottlenecks.