Carob captures clinicians' reasoning notes from EHRs, automatically de-identifies them, and maps the content to structured ontologies such as SNOMED‑CT and ICD‑10. The curated, HIPAA‑compliant datasets are stored in a secure cloud and accessed via versioned APIs for AI developers to train and validate diagnostic models, with a revenue‑share model that compensates contributing health institutions.
Funding
Funding not disclosed
Founders
Product
Problem
Healthcare AI developers lack access to large, diverse, and ethically sourced clinical reasoning datasets, which hampers model robustness and limits representation of varied patient populations. Existing data sources are often fragmented, insufficiently de‑identified, or derived from narrow clinical settings, leading to bias and reduced diagnostic accuracy.
Solution
Carob captures the reasoning documented by clinicians at the point of care, applies automated de‑identification, and converts the narratives into structured, standards‑based datasets suitable for machine‑learning pipelines. The platform integrates with electronic health record (EHR) systems to extract decision‑making notes in real time, then normalizes the content using clinical ontologies such as SNOMED‑CT and ICD‑10. Curated datasets are hosted in a secure, HIPAA‑compliant cloud environment and made available through RESTful APIs for downstream AI training. Carob partners with hospitals, health networks, and community health organizations, offering a revenue‑share model that returns a portion of licensing fees to the data‑providing entities. In addition, the company supplies patient‑centric benchmark suites and educational toolkits that help developers evaluate model performance across demographic sub‑groups. This end‑to‑end workflow ensures that diagnostic AI models are trained on high‑quality, representative clinical reasoning while creating a sustainable financial incentive for data contributors.
Target Audience
Primary customers are health systems, academic medical centers, and community health networks that generate clinical reasoning data, as well as AI firms and research laboratories building diagnostic models that require diverse, high‑quality training data.
Features
- Real‑time capture module that ingests clinician reasoning directly from EHR workflow interfaces
- Scalable NLP pipeline that performs automated PHI redaction and maps free‑text notes to structured ontology fields (SNOMED‑CT, LOINC, ICD‑10)
- Encrypted, role‑based cloud storage with immutable audit trails and FHIR‑compatible data export
- Versioned API delivering batch and streaming access to curated reasoning datasets for model training and validation
- Benchmark library containing stratified performance metrics across age, ethnicity, and comorbidity cohorts
- Interactive educational portal that provides annotated case studies and model‑interpretability visualizations for clinicians and developers
- Revenue‑share analytics dashboard that tracks dataset usage, licensing revenue, and payout distribution to partner institutions