Healthcare & Personalized Pathways

Healthcare & Personalized Pathways

With the exponential growth of healthcare data fueled by advancements in technology, the traditional approaches to data management, analysis, and visualization are undergoing a profound evolution. Among the emerging methodologies, graph analytics and visualization stand out as powerful tools with the potential to reshape the landscape of digital health.

GraphPolaris offers a novel approach to uncovering hidden patterns, relationships, and insights within complex datasets. By representing data as interconnected nodes and edges, graph analytics transcends the limitations of traditional tabular structures, enabling a more nuanced understanding of interdependencies within healthcare ecosystems.

Our strong visualization capabilities provide intuitive means to interactively explore and comprehend the rich context of interconnected data, empowering stakeholders to derive actionable insights with unprecedented clarity.

Our solutions aim to ‘move the innovation needle’ towards personalized therapy pathways on both population and patient level:

Patient Journey Analysis

Supported by the GraphPolaris platform, you can model the entire patient journey, including interactions with healthcare providers, treatments received, medications prescribed, and outcomes. Analyzing this data can help healthcare organizations optimize patient care pathways, identify inefficiencies, and improve patient outcomes.

Precision Medicine

Providing personalized solutions is a typical multi-variate, spatial-temporal data analytics challenge. GraphPolaris helps you integrate genomic, pathological, imaging data and electronic health records to create comprehensive patient profiles. As a healthcare provider, explore the potential of personalized patient pathways with these profiles, and tailor treatments to patients based on their genotype, medical history, and lifestyle factors.

Disease Surveillance and Outbreak Management

Our platform is suitable for modelling disease transmission networks, healthcare facility networks, and population demographics. This way, public health authorities can track disease outbreaks, monitor the spread of infectious diseases, and allocate resources effectively and with ease.

Healthcare Fraud Detection

GraphPolaris models relationships between patients, healthcare providers, insurance claims, and billing records. Investigating these relationships can help detect patterns suggesting fraudulent activities, such as overbilling, phantom billing, and kickbacks.