Grounded in Research, Backed by Evidence
At Recorded Health, we believe in evidence-based innovation. Below are a selection of peer-reviewed articles, white papers, and case studies that support our work and demonstrate our expertise.
From Patient Consultations to Graphs: Leveraging LLMs for Patient Journey Knowledge Graph Construction
Abstract—The transition towards patient-centric healthcare necessitates a comprehensive understanding of patient journeys, which encompass all healthcare experiences and interactions across the care spectrum. Existing healthcare data systems are often fragmented and lack a holistic representation of patient trajectories, creating challenges for coordinated care and personalized interventions...
Patient Journey Ontology: Representing Medical Encounters for Enhanced Patient-Centric Applications
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...
CLINICSUM: Utilizing Language Models for Generating Clinical Summaries from Patient-Doctor Conversations
Abstract—This paper presents CLINICSUM, a novel frame- work designed to automatically generate clinical summaries from patient-doctor conversations. It utilizes a two-module architec- ture: a retrieval-based filtering module that extracts Subjec- tive, Objective, Assessment, and Plan (SOAP) information from Coconversation transcripts, and an inference module powered by fine-tuned Pre-trained Language Models (PLMs)...
Patient-centric Knowledge Graphs: a Survey of Current Methods, Challenges, and Applications.
Patient-Centric Knowledge Graphs (PCKGs) represent an important shift in healthcare that focuses on individualized patient care by mapping the patient’s health information holistically and multi-dimensionally. PCKGs integrate various types of health data to provide healthcare professionals with a comprehensive understanding of a patient’s health, enabling more personalized and effective care...
MedInsight: A Multi-Source Context Augmentation Framework for Generating Patient-Centric Medical Responses Using
Large Language Models
Providing contextual and comprehensive medical information tailored to individual patients is critical for enabling effective care in the healthcare domain. However, existing approaches often struggle to deliver personalized responses due to the distributed nature of medical data across multiple sources such as patient records, medical literature, and online...
Medical GAT: Cancer Document Classification Leveraging Graph-Based Residual Network Scenarios with Limited Data
Abstract—Accurate classification of cancer-related medical ab- stracts is crucial for healthcare management and research. However, obtaining large, labeled datasets in the medical domain is challenging due to privacy concerns and the complexity of detailed clinical data. This scarcity of annotated data impedes the development of effective...