Healthcare runs on data, but most of it is unstructured, messy, and hard to use

How can I ever get this data into my relational database?

This problem spans the entire healthcare ecosystem. Clinical information is scattered across notes, forms, and PDFs. As Nature (2023) highlights, unstructured healthcare data remains one of the biggest obstacles to scalable, safe medical AI. Most modern AI systems built on top of EHRs continue to inherit this flaw. They treat documentation as a transcription or summarization challenge, not a data challenge. The result may read well, but it remains static and unstructured, unusable by other systems.

Corti captures and structures clinical facts in real time, exposing them through interoperable APIs that power documentation, coding, and analytics. Rather than generating prose, Corti supports structured, machine-actionable data that drives real clinical intelligence.

You can now safely populate your relational database.

To understand why this shift matters, it’s worth looking at how healthcare data (across systems, tools, and workflows) becomes fragmented in the first place.

Limited interoperability

Even when data is captured, it often can’t travel. Healthcare systems are notoriously fragmented. Information might move between systems, but without shared data types, it loses utility.

Interoperability in healthcare isn’t just about moving files from one place to another. It requires four distinct levels of compatibility:

1. Technical: Can the data get there? (REST APIs, file transfer)
2. Syntactic: Is it structured correctly? (FHIR, HL7v2)
3. Semantic: Does it mean the same thing everywhere? (SNOMED CT, ICD-10)
4. Organizational: Are workflows and governance aligned?

Healthcare systems, including those generating AI-assisted documentation, must not only send data but also integrate it meaningfully. Without semantic interoperability, information remains fragmented, forcing clinicians and developers to bridge the gaps manually.

AI Scribes/Assistants only exacerbate the problem

Perhaps the most overlooked limitation of current AI documentation solutions is their inability to handle the complexity of real healthcare documentation. Hospital forms rarely consist of simple free-text fields. Instead, they feature complex combinations of checkboxes, dropdown menus, coded entries, and structured data fields that require specific values drawn from controlled vocabularies.

AI solutions need to be able to handle this data entry issue so that a physician does not need to spend significant time transferring information from the AI-generated note into the actual EHR fields, checkboxes, and coded elements that the hospital system requires.

Corti’s API holds the ingredients to bring structure

Corti provides a varied set of tools to retrieve the salient information, categorize it, and add data types.

FactsR™: retrieve the salient information

Whether you are working directly on the live audio signal or a textual representation, Corti’s FactsR™ can extract salient clinical information from large unstructured text documents.

Because these facts are modeled as independent entities, they can be queried and reused through Corti’s APIs. Developers can programmatically build downstream logic - whether assembling clinical notes, filling EHR forms, or triggering decision support without losing traceability or context.

Read more about FactsR™

Agents: experts for the next evolution in clinical intelligence

Because Corti models clinical information as structured, API-accessible facts, it creates a foundation for intelligent agents that can act on data in real time. These agents, built on the same reasoning infrastructure, can pre-fill structured forms, trigger workflows, or surface clinical insights as conversations unfold.

For developers, agents represent the composable next layer of the Corti platform: lightweight services that consume the same facts API, apply domain-specific logic, and integrate seamlessly into existing EHRs or clinical tools.

Every action remains traceable, auditable, and clinically interpretable. And the great thing about it is that developers are not tied to using FactsR™ as a prerequisite; they can build their applications however it works best for them.

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Medical Coding Expert

Medical coding represents one of healthcare's most complex challenges. ICD-10-CM contains 70,000+ concepts, while SNOMED CT includes 350,000+ clinical concepts. Traditional AI coding models struggle with this scale, particularly for rare conditions and specialized terminology.

Corti's solution is a coding agent that behaves like a human coder. Instead of attempting to memorize all possible codes, the agent uses search tools and external knowledge bases, just as human coders do. This approach enables accurate coding even for rare conditions and complex cases that would confuse traditional AI systems.

Read more about CodeLikeHumans.‍

Form-Filling Expert

Healthcare documentation extends far beyond narrative notes to include complex questionnaires, assessment forms, and structured data entry requirements. Corti's form-filling agents address this challenge by understanding the relationship between clinical facts and structured form requirements.

The process works by:

1. The agent receives a FHIR Questionnaire resource in combination with contextual information, e.g., FactsR™ outputs.
2. It maps extracted clinical information to relevant form fields and generates appropriate responses for checkboxes, dropdown selections, and coded entries.
3. It produces a completed FHIR QuestionnaireResponse resource. This capability enables healthcare organizations to automate not just documentation, but the entire spectrum of structured data collection that modern healthcare requires.

FHIR Expert

Above, we outline how Agents allow integration into healthcare systems by creating FHIR Questionnaire resources. While this approach seamlessly fits into the workflow of medical professionals who spend large parts of their days filling out forms, one should think a step further and imagine a world in which data is sent directly into the EHR without filling out a form in the first place.

FHIR server supports can provide a resource called CapabilityStatement. Those resources are a machine-readable documentation of what functionality the server supports, including resources, profiles, and operations.

When providing this CapabilityStatement to a specialized FHIR Agent alongside facts and other relevant information, we allow the Agent to map the data directly into a format the EHR's FHIR server understands.

This FHIR data can then either be returned or directly pushed into the EHR through the endpoints defined in the Capability statement.