The Tempus data pipeline: Architecting the future of precision medicine

Ingressing Complex Multimodal Data

 

The lifecycle begins at the source. To establish a complete record of a patient’s journey, Tempus must integrate time series data across multiple modalities: clinical, molecular, imaging, biotelemetry, claims, and biometrics.

 

The primary engine for data ingress and egress is Tempus Edge, a virtual machine that is accessible to software systems within care sites. Edge serves as a secure gateway, establishing bidirectional interfaces (including API/HL7, FHIR, and PACS connectivity) to ingest data directly from any EHR, such as Epic, Cerner, Meditech, among others. This approach reduces IT resource requirements by over 80%, allowing for near real-time transfer of structured and unstructured data. To support this, Tempus maintains rigorous compliance frameworks and subject matter expertise to ensure that confidential health information is accessed, shared or utilized in accordance with applicable laws. The scope of these articles are strictly technical and do not address broader legal matters.

 

Secure Governance: Context-Based Access and Data Product Management

 

Managing a proprietary multimodal dataset that exceeds 250 petabytes requires more than just high-speed pipes; it demands a rigorous governance framework. Tempus ensures that data is treated as a high-fidelity "product," stamped with specific metadata and governed by strict context-based access controls to maintain security and compliance across its vast ecosystem.

 

Clinical Data Normalization: Orchestrating a Unified Schema

 

Transforming disparate telemetry from thousands of healthcare institutions into a research-ready dataset requires a robust orchestration layer and a commitment to rigorous standardization. The Tempus pipeline is designed to turn fragmented clinical data into a unified schema that supports high-fidelity analysis at scale.
Tempus utilizes HL7 and FHIR interfaces for the near real-time transfer of structured data, such as laboratory results and patient demographics. For high-volume documents and historical records, the platform employs Secure Data Exchange (SDX) for efficient batch transfers.
By centralizing these diverse streams into a single orchestration layer, Tempus can manage bidirectional data flows while reducing the IT burden on healthcare sites by over 80%. Our data processing involves:

• Structured data normalization
• Unstructured data pipelines

 

Establishing a common unified language

 

Once processed, data must be modeled to a common, unified framework. The Tempus Data Model leverages subject matter expertise and industry standards (e.g., FHIR, OMOP, other) to provide both pan-cancer and cancer-specific views.
This model harmonizes diverse inputs ranging from whole transcriptome RNA and tumor DNA to germline sequencing and radiology images into a structured format. By curating lines of therapy based on clinical guidelines and supplemental heuristics, Tempus ensures the data is ready for immediate high-fidelity analysis by computational biologists and bioinformatics experts.

 

Normalizing and Harmonizing Molecular Data

 

A cornerstone of the Tempus data platform is its unique ability to normalize and harmonize complex molecular data at scale. Raw genomic information from diverse sources, including in-house next-generation sequencing and third-party molecular PDFs, is often highly variable in format and quality.
The Tempus data platform overcomes this by:

• Standardizing Genomic Features: Normalizing disparate data types like Tumor/Normal DNA and whole transcriptome RNA into unified tables that support both rapid access to key features and deeper analysis of raw molecular files.
• Bridging Data Modalities: Linking deep molecular data with longitudinal clinical outcomes, imaging narrative, and pathology reports to create a truly multimodal record.
• Ensuring Scientific Rigor: Utilizing purpose-built algorithms to predict IHC/ISH positivity from RNA data, allowing for the identification of patients who may benefit from confirmatory biomarker testing even when initial records are incomplete.

 

Processing Clinical Data Using AI Agents and Human Expertise

 

Raw data from healthcare institutions is often fragmented and buried in unstructured notes. Tempus employs a hybrid processing model to unlock this information. Over 1,000 AI-enabled quality checks and AI agents work in tandem with more than 200 abstraction specialists to parse over 740 million clinical docs, raw images, and PDFs. Tempus has designed and implemented a human-in-the-loop workflow that is a core component of our data quality framework. An AI governance committee has established a risk-based fit for purpose review of agent development and implementation based on the intended use case for each agent.
This stage focuses on "patient journey reconstruction," transforming raw telemetry into a chronological narrative. By reviewing unstructured notes combined with structured clinical data, AI agents can surface critical care gaps—such as patients eligible for targeted biomarker testing but not yet tested—ensuring that every data point contributes to a clearer clinical picture.

 

Maintaining Integrity and Privacy through De-identification

 

Before any data can be used for research or licensed to 3rd parties for research purposes, it must undergo a rigorous de-identification process. Tempus employs an expert determination method to ensure patient confidentiality in accordance with HIPAA standards.

 

This process is critical for establishing a de-identified national dataset while maintaining the integrity of the longitudinal information. It allows for the integration of claims and mortality data without compromising patient privacy, enabling researchers to track outcomes across vast populations.

 

From Pipeline to Patient: Engineering the Future of AI-Enabled Care

 

The ultimate validation of the Tempus data pipeline is its ability to power a suite of clinical AI applications that move beyond retrospective research into real-time decision support. By leveraging the exabyte-scale foundation of the platform, Tempus engineers have built an application layer that assists physicians in oncology, cardiology, and beyond. Tempus has developed:

• An engineering stack for clinical decision support: The technical approach to developing these tools relies on the seamless integration between the Data Platform and the point of care. Because the pipeline has already normalized, harmonized, and structured the patient journey, AI models can be deployed on top of high-fidelity, multimodal "feature sets" rather than raw, noisy data. This stack supports predictive modeling and deploying LLMs at the point of care.
• Powering clinical pathways and trial matching: The Tempus Apps engineering stack automates trial matching by programmatically screening the entire patient population at a site against trial criteria. When a match is identified, the TIME infrastructure can open a trial site in as little as 10 business days, ensuring that "just-in-time" precision medicine is a reality. Additionally, Tempus Apps scan for “care gaps” in both oncology and cardiology, Tempus AI apps scan for "care gaps." By identifying patients who meet guidelines for specific tests or treatments but haven't yet received them, the platform helps health systems standardize care and improve outcomes.

 

Tempus Lens: Technical Orchestration of a Dataset

 

The Tempus Lens platform represents the data delivery orchestration layer of the pipeline, designed to transform a proprietary multimodal dataset into a searchable, actionable research environment. It serves as the primary technical interface for researchers to interact with the analytics-ready data model at scale. Tempus Lens allows for:

• Natural language cohort stratification: This technical approach allows researchers to apply rich molecular and clinical filters using natural language—such as "Show me EGFR mutations in NSCLC patients with RNA profiles"—to instantly stratify populations across indications, somatic mutations, and treatments. This eliminates the need for manual, time-intensive chart reviews by programmatically scanning both structured data and unstructured notes reconstructed by the pipeline’s AI agents.
• Technical workspaces: Architecture to support AI-enabled analysis, multimodal integration, and rapid data egress. A library of purpose-built tools allows computational biologists to execute complex RWD analyses directly within the platform.

 

Delivering Data Through Scaled Solutions

 

The final stage of the pipeline is delivery. Tempus provides a scalable egress platform through Tempus Lens, a cohort building solution and AI-enabled analysis environment. Lens allows researchers to stratify populations by indication, mutation, or treatment using natural language queries.
Security and compliance are maintained through context-based access controls. Sites contributing data through the Secure Data Exchange (SDX) can access their own curated datasets or the broader national dataset for research.

 

The Impact: Transforming Research at Scale

 

Delivering multimodal data at this scale fundamentally shifts the landscape for life sciences and academic researchers. By providing a database 90x larger than The Cancer Genome Atlas (TCGA), Tempus enables pan-cancer development across 250+ tumor types, far exceeding the 33 cancers typically available in public datasets.

 

For researchers, this massive scale translates into:

• Accelerated Discovery: Target validation can occur up to 2x faster, and researchers can analyze response to the latest approved therapies (like checkpoint inhibitors) that stopped being ingested by public databases years ago.
• Enhanced Trial Design: Access to real-world, late-stage, and metastatic patient records—rather than just primary tumors—allows for more accurate study designs. Life science partners have seen an average 5-10% increase in the probability of technical and regulatory success (PTRS) by leveraging this data.
• Rapid Population Stratification: What used to take months of manual chart review now takes seconds. Researchers can find specific cohorts (e.g., EGFR mutations in NSCLC patients with RNA profiles) using natural language, accelerating the path from hypothesis to evidence.

 

Ultimately, this pipeline moves research from isolated snapshots to a continuous, longitudinal understanding of disease, providing the high-fidelity evidence needed to bring better treatments to patients faster.

 

Today, Tempus is launching a new engineering blog series focused on our Data & Apps businesses. In this blog series, we will have subject matter experts and technical architects sharing more details across Tempus’ data platform and capabilities – and we hope you will continue following along!