Neo4j is being used by the Patient Journey project to analyze large populations of patients' treatments and effectiveness, allowing for quick analysis of patterns and outcomes. Before Neo4j, medical records data was typically stored in relational databases, requiring specialized knowledge and resources to query and present information, which was often time-consuming and expensive. The graph database technology enables dynamic presentation of patient journey information with just a few mouse clicks, providing insights into treatment patterns and outcomes. Ravi Anthapu and Matt Holford praise Neo4j's powerful features, including Cypher query language, custom plugins, and user community support, which have enabled them to analyze large datasets quickly and gain valuable insights. The project is expanding to include enhancements for customer projects, predictive analytics, and exploring the application of graph technology beyond healthcare to other business areas.