The startup offers a HIPAA-compliant mobile and web platform that integrates diverse data sets and oncology care teams to facilitate multidisciplinary treatment planning for cancer patients. This platform enables physicians to create tumor boards and collaboratively discuss treatment strategies, streamlining clinical decision-making and payer authorization processes.
Funding
$22.9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Cancer care teams often struggle with fragmented data across multiple EMRs, labs, and PACS systems, hindering effective multidisciplinary collaboration. This lack of interoperability leads to inefficiencies in tumor board management, delays in treatment planning, and missed opportunities for clinical trial enrollment.
Solution
OncoLens offers a comprehensive, AI-enabled digital health platform that integrates disparate data sources and facilitates multidisciplinary collaboration for oncology care teams. The platform streamlines tumor board operations, automates data extraction using NLP and machine learning, and provides actionable informatics to improve clinical decision-making. By connecting providers across care settings and offering a holistic view of the patient journey, OncoLens ensures consistent, high-quality care regardless of location. The platform also supports clinical trial matching by aligning trial inclusion/exclusion criteria with real-world EMR data, enabling faster and more accurate patient identification.
Target Audience
OncoLens serves integrated delivery networks (IDNs), community cancer centers, academic institutions, NCI-designated cancer centers, and life science organizations seeking to improve cancer care coordination, clinical trial enrollment, and patient outcomes.
Features
- Data interoperability through FHIR, HL7, and JSON integrations with EMR and lab partners
- AI-powered patient identification using natural language processing (NLP) and machine learning (ML) to extract key insights from structured and unstructured data
- Multidisciplinary team collaboration tools for real-time and asynchronous case review, including secure image sharing
- Automated clinical trial matching based on patient-specific data and trial inclusion/exclusion criteria
- Customizable reporting and analytics dashboards for monitoring key metrics, identifying trends, and improving quality of care
- Integration with cancer registry software like OncoLog and CRStar for streamlined reporting and compliance
- Molecular module for visualizing and assessing genomic data, including integrated NGS test results and links to actionable therapies
- Secure single sign-on (SSO) capabilities for enhanced security and ease of use