Value Proposition
Unmet Need
With the increasing volume of patient data, there is significant interest in utilizing data-driven methods in clinical decision-making and research. These methods have the potential to improve diagnostic accuracy by tailoring the timing and frequency of screenings for various diseases to individual patient profiles. This focus on precision medicine to enhance disease diagnosis has created a substantial demand for data tools capable of visualizing biomarkers and demographic information to predict future disease onset. While current cohort visualization tools primarily group patients based on observed outcomes, they often lack the capability to dynamically model disease progression or incorporate predictive analytics that account for personalized risk factors. As a result, there is a need for advanced clinical tools that can integrate a patient’s individual data to provide clinicians with more accurate insights for intervention.
Technology Description
Researchers at Johns Hopkins have developed a web-based dashboard for enabling physicians to parse patient populations and visualize how different data are correlated with the risk of cancer. The software incorporates clinical data, biomarkers, and demographic data allowing various subpopulations or individuals to be analyzed and visualized. The technology currently incorporates various biomarker and demographic data in myositis patients, allowing clinicians to rapidly identify non-responders to treatment, rapid or slow responders, or outliers that require specialized analysis in order to develop their treatment plan.
Stage of Development
The researchers have developed a working prototype. Currently, the interface allows for visualization of the relationship between myositis patient data and cancer diagnosis.
Data Availability: Data available upon request.
Publication
WO 2023/229980
