Tempus
Integrations
- Epic
- Cerner
- HL7 FHIR
- Illumina NGS Platforms
- LIMS
Pricing Details
- Service-based pricing model for genomic sequencing and institutional licensing for data platform access.
- API throughput costs are undisclosed.
Features
- Next-Generation Sequencing (NGS) Pipeline
- Tempus ONE (AI Assistant Interface)
- Proprietary Clinical Trial Matching (TIME)
- Digital Pathology Vision Models
- De-identified Multi-modal Repository
- HL7 FHIR & NLP Data Extraction
Description
Tempus Clinical-Genomic Synthesis Architecture
The Tempus platform is engineered as a high-throughput multi-modal ingestion pipeline, designed to integrate phenotypic and molecular datasets into a structured analytical environment. The architecture focuses on the De-identified Multi-modal Repository 🧠, which acts as the core persistence layer for both structured clinical records and raw sequencing outputs.
Multi-Modal Ingestion Pipeline
The platform processes disparate health data through specialized extraction and normalization layers to ensure cross-institutional compatibility.
- EHR Ingestion: Input: FHIR Resources and unstructured clinical notes. Process: NLP-based abstraction and OCR processing 📑. Output: Structured phenotypic data mapped to unified schemas 🧠.
- Genomic Profiling: Input: Tumor/Normal tissue samples. Process: NGS sequencing and bioinformatics variant calling (xT, xF panels) 📑. Output: Molecular profiles and actionable variant reports.
- Tempus ONE (AI Assistant): Voice and text-based interface providing real-time retrieval of patient insights and clinical-genomic reports 📑.
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Clinical Decision Support & Trial Matching
Data synthesized within the repository is utilized by the Proprietary Matching Engine (TIME Trial) 📑 to identify clinical trial eligibility based on real-time molecular and clinical status.
- Digital Pathology Pipeline: Automated analysis of H&E slides to predict molecular biomarkers using vision-based deep learning 📑.
- Predictive Multi-Omics: RNA-seq and DNA data models designed to forecast treatment response and potential toxicity 📑. Technical Constraint: Model hyperparameter transparency remains restricted 🌑.
- Privacy-Aware Mediation: Implementation of layered access controls to ensure data security during multi-institutional research 🧠.
Evaluation Guidance
Engineering teams should prioritize the following validation steps before integration:
- Cross-Institutional Latency Verification: Assessment of data propagation speeds from EHR ingestion to structured output 🌑.
- Schema Reconciliation Validation: Analysis of proprietary normalization mapping for non-standard FHIR extensions 🌑.
- Clinical Trial Engine Accuracy: Performance benchmarking of the TIME Trial engine against manual curation workflows ⌛.
Release History
Year-end update: Launch of the Agentic Precision Hub. Autonomous AI agents now coordinate between labs and clinics to suggest real-time treatment pivots.
General availability of multi-omic predictive models. Trained on the world's largest dataset to forecast drug response and toxicity using RNA-seq and DNA data.
Integration of Acyuta AI. Automated clinical trial matching system that scans real-time patient data to identify eligible candidates for frontier therapies.
Expansion into liquid biopsy. Launched xF panel and Minimal Residual Disease (MRD) monitoring for non-invasive cancer tracking.
Introduction of Tempus One, a voice-enabled portable device that allows clinicians to access patient data and clinical insights instantly.
Launched digital pathology algorithms. AI now analyzes H&E stained slides to predict molecular biomarkers directly from images.
General availability of Tempus xT, a broad panel genomic test. Introduced the integration of molecular data with clinical EHR data.
Tempus founded by Eric Lefkofsky. Initial launch of the Next-Generation Sequencing (NGS) platform for oncologists.
Tool Pros and Cons
Pros
- Advanced AI analysis
- Personalized treatment
- Improved outcomes
- Comprehensive integration
- Actionable insights
- Precision oncology
- Real-time updates
- Genomic analysis
Cons
- Data quality crucial
- Potential bias
- High implementation cost
