Patient Journey Ontology: Representing Medical Encounters for Enhanced Patient-Centric Applications

TL;DR

This paper introduces a Patient Journey Ontology (PJO) that captures comprehensive healthcare encounters to improve patient data management, clinical reasoning, and predictive analytics for better personalized care.

Contribution

The paper presents a novel ontology framework that integrates diverse patient data sources and models temporal, causal, and sequential relationships for healthcare applications.

Findings

Strong capabilities in patient history retrieval and symptom tracking

Effective representation of provider interactions and care pathways

Demonstrated reliability and practical applicability through expert evaluations

Abstract

The healthcare industry is moving towards a patient-centric paradigm that requires advanced methods for managing and representing patient data. This paper presents a Patient Journey Ontology (PJO), a framework that aims to capture the entirety of a patient's healthcare encounters. Utilizing ontologies, the PJO integrates different patient data sources like medical histories, diagnoses, treatment pathways, and outcomes; it enables semantic interoperability and enhances clinical reasoning. By capturing temporal, sequential, and causal relationships between medical encounters, the PJO supports predictive analytics, enabling earlier interventions and optimized treatment plans. The ontology's structure, including its main classes, subclasses, properties, and relationships, as detailed in the paper, demonstrates its ability to provide a holistic view of patient care. Quantitative and qualitative evaluations by Subject Matter Experts (SMEs) demonstrate strong capabilities in patient history retrieval, symptom tracking, and provider interaction representation, while identifying opportunities for enhanced diagnosis-symptom linking. These evaluations reveal the PJO's reliability and practical applicability, demonstrating its potential to enhance patient outcomes and healthcare efficiency. This work contributes to the ongoing efforts of knowledge representation in healthcare, offering a reliable tool for personalized medicine, patient journey analysis and advancing the capabilities of Generative AI in healthcare applications.