Every healthcare organization runs on clinical documentation as physicians write notes quickly, in whatever format works for them, whether it's shorthand, abbreviations, mixed narrative styles across departments and specialties.
These notes are easily understood by clinicians, and they hold the most important medical information in the system, including;
- Diagnoses
- Active medications
- Procedures performed
- Clinical observations
The problem is that most of it stays exactly where it was written, still in its unstructured free text that other people in the healthcare field and digital systems cannot read, query, or code against without manual intervention.
DynaTech's Clinical Notes Enrichment agent addresses that directly and applies AI-powered clinical data extraction to decipher unstructured notes. Our AI clinical documentation solution identifies medical entities that matter and converts them into structured, standardized, interoperable records.
What Makes Clinical Notes Enrichment Solution Different from Built-In Copilot?
A general-purpose Copilot can do the basic tasks expected of an AI tool, including generating text, summarizing notes, answering questions, and more. But these AI tools are not built for clinical notes enrichment, which is a fundamentally different challenge, as this task requires domain-specific medical NLP, extraction logic calibrated to clinical terminology.
Moreover, to decipher and organize coding support, AI systems must be tied to ICD-10 and SNOMED CT reference sets and structured output formatted to HL7/FHIR standards.
DynaTech's agent is purpose-built for this workflow and works within Copilot Studio, where the core function is powered by Azure OpenAI to handle the user-facing interface inside Microsoft Teams.
In addition, Azure AI Foundry manages orchestration and pipeline evaluation, which includes clinical data extraction using NLP, coding logic, and FHIR formatting. The result is an AI system with defined responsibilities at each layer, not a chat tool doing double duty.
Key Capabilities of Clinical Notes Enrichment Solution
1. Medical NLP
The NLP layer processes protected health information through secured, governed pipelines built to operate within HIPAA compliance parameters, not through open consumer endpoints, ensuring clinical data stays where it should and is always secure.
2. Structured Extraction from Free Text
Clinical shorthand, narrative descriptions, and non-standard abbreviations are parsed by the clinical data extraction software to pull out discrete data elements, like;
- Diagnosis
- Medications
- Dosages
- Procedures
- Clinical observations
All unstructured text becomes structured fields that are easy to read, understand, and use.
3. Clinical Data Enrichment
Raw extracted references get enriched with standardized and approved clinical terminology, preserving and sharing accurately what the physician wrote to ensure downstream systems understand. This is where clinical notes analysis moves beyond keyword spotting into meaningful data enrichment.
4. ICD/SNOMED Coding Support
Extracted clinical entities are mapped to ICD-10 diagnosis codes and SNOMED CT clinical terms, which reduces manual burden on coding teams and, with it, improves consistency across clinical documentation.
5. Searchable Records
Once structured and coded, records become queryable, which means you and your team can search for the data they need as and when required. Data that was earlier locked behind raw and unstructured text is now accessible for clinical research, audits, reporting, and population health workflows.
6. HL7/FHIR Compatible
Enriched output is formatted to HL7/FHIR standards, enabling integration with EHR systems, payer platforms, and other health data infrastructure without custom conversion work at every handoff point.
DynaTech Clinical Notes Enrichment and Medical NLP Solution
The Problem Our Clinical Notes Enrichment System Solves
Healthcare organizations generate clinical notes at scale, as every doctor writes notes and all of them jot down information in their own style. Most of that data stays trapped in free-text form, readable by a few humans but completely invisible to systems. Experienced coding teams manually comb through documentation to assign diagnosis and procedure codes.
However, even with the coding and extraction, analysts cannot run queries against narrative fields, and interoperability requirements break down when records don't conform to FHIR output standards.
The downstream effects are real;
- Delayed reimbursements
- Incomplete patient histories
- Compliance exposure
- Stalled data pipelines
The issue isn't the volume of notes but the free and unstructured text that provides any useful information until something converts it into structured insights.
What the Clinical Data Enrichment Solution Actually Does?
Once connected to the clinical documentation environment, the agent processes and understands free-text notes to figure out clinical entities from diagnosis to medication and procedures.
Plus, our clinical notes analysis solution also applies coding logic against ICD-10 and SNOMED CT reference sets. This feature helps enrich extracted data with standardized terminology and produces HL7/FHIR-compatible records for downstream use.
Clinical and administrative staff interact with the agent through a Copilot Studio interface inside Microsoft Teams while the extraction and enrichment pipeline runs in the background. This entire exercise does not require any manual intervention or re-entry, but exceptions and edge cases are filtered out for human review before they are added to the final analyzed data.
Agentic AI in Action | Clinical Notes Enrichment Scenarios
Scenario 1 - Multi-Condition Discharge Summary
A hospitalist completes a discharge note covering heart failure, newly diagnosed type 2 diabetes, and a revised medication regimen. Now this entire set of information must be manually coded in a time-intensive manner, and this introduces variability depending on which coder handles it. The agent parses the note, identifies each clinical entity, maps them to the appropriate ICD-10 codes, and surfaces the output for review, without holding up the documentation queue behind it.
Scenario 2 - Retrospective Record Analysis
A clinical informatics team needs to identify all patients in a given cohort who received a specific medication class over the previous two years. Because those earlier notes were unstructured, it's nearly impossible to query that data now.
The enrichment agent processes the historical records, extracts the relevant medication references, and makes the dataset available for analysis without requiring manual chart review.
Scenario 3 - Cross-Facility Care Handoff
A patient transfers between care facilities, and there must be a seamless connection between the caregiver and the care facilities. The receiving system requires FHIR-formatted records, and without a conversion step, clinical staff would need to manually reformat or re-enter documentation.
The agent converts the existing clinical notes into FHIR-compatible output, so the handoff happens without rework on either end.
Operational Impact of Clinical Data Extraction Software
| Business Challenge | Agentic AI Solution |
| Clinical coders manually review free-text notes to assign ICD and SNOMED codes, creating backlogs and coding inconsistencies across teams. | The agent extracts clinical entities and maps them to standard codes, surfacing results for review and reducing manual read-through time per note. |
| Unstructured records cannot be queried for clinical research, population health reporting, or audit purposes. | Structured extraction converts narrative text into queryable fields, making previously inaccessible data available for analysis without chart review. |
| FHIR interoperability requirements generate rework when source records aren't in the right output format. | Enriched records are produced in HL7/FHIR-compatible format, reducing conversion effort at EHR integration and payer submission points. |
| Reimbursement delays occur when documentation is incomplete or coded incorrectly at the point of submission. | Accurate structured data at the point of extraction reduces downstream correction cycles and resubmission rates. |
| Inconsistent terminology across departments makes longitudinal patient data difficult to aggregate and use. | Standardized extraction logic applies consistent clinical terminology across all processed notes, regardless of which clinician wrote them. |
How the Structured Data Extraction System Works Technically?
The clinical data enrichment system runs on a configured Microsoft architecture within the Copilot Studio;
- Azure OpenAI: Teams-based interface where clinical and administrative staff interact is provided by Azure OpenAI.
- Azure AI Foundry: It handles orchestration, pipeline evaluation, and workflow governance across the enrichment process.
- Azure AI Language: The clinical NLP and extraction logic runs through Azure AI Language and Azure Healthcare APIs.
FHIR output is structured through the configured integration layer, and other aspects like accessibility and data permissions are managed through Entra ID, with service principals and API configurations governing what the agent can access and where enriched data is written.
The DynaTech team will take you through the specifics of the healthcare NLP tool configuration and work on;
- Extraction scope
- Coding reference sets
- FHIR mapping rules
All these aspects are defined during deployment and calibrated to the organization's existing documentation environment.
Who Benefits from Intelligent Data Extraction for Healthcare Solution?
- Clinical Documentation Specialists: These professionals now work with an AI-powered clinical notes analysis and extraction layer that handles structured data work, freeing them to focus on review and exception handling.
- Medical Coding Teams: With the NLP for healthcare solution, these teams will spend less time reading through narrative text to identify billable codes as the agent surfaces extracted entities and suggested codes for review.
- Clinical Informatics and Analytics Teams: Professionals gain access to structured datasets built from records that were previously weren't easy to search and query. This has streamlined population health analysis, outcome research, and reporting.
- Compliance Teams: With standardized, traceable documentation output that aligns with recognized coding standards and FHIR interoperability requirements, it's easier for compliance teams to conduct audits, submissions, and regulatory reviews more defensible.
What Does Deployment Actually Look Like?
DynaTech handles scoping, configuration, and deployment, and the process begins with;
- Identity
- Integration planning
- Entra ID setup
- Service principal configuration
- API permissions
- Connect the AI tool to existing clinical systems and EHR environments.
In the entire process, no core EHR schema changes or customizations are required. The extraction scope, coding logic, FHIR mapping rules, and access controls are all configured during implementation.
The Return is Measurable, Not Theoretical
Once deployed, the FHIR integration healthcare solution brings about a load of advantages for the entire organization. This includes, but is not limited to, faster coding cycles, fewer documentation backlogs, and cleaner data access for downstream analytics.
Moreover, these outcomes further lead to reduced rework, fewer resubmissions, shorter audit cycles, and data pipelines that no longer stall on unstructured input.
Frequently Asked Questions
Does the agent replace clinical coders or documentation specialists?
Our solution is built to extract entities and map suggested codes for human review, and not replace them entirely. Even with the clinical data enrichment AI solution, clinical coders retain final decision authority, especially on complex or ambiguous documentation. The goal is to reduce manual review time and improve consistency, not remove clinical judgment from the process.
What types of clinical notes can it process?
The agent is configured to handle a range of free-text documentation formats, including;
- Discharge summaries
- Progress notes
- Referral letters
The specific scope is defined during deployment based on the organization's documentation environment and EHR setup
Is the solution designed with HIPAA compliance in mind?
The intelligent data extraction for healthcare pipeline is built to operate within HIPAA compliance parameters, using secured, governed infrastructure rather than consumer-grade endpoints. Specific compliance controls and Business Associate Agreement requirements are reviewed during the deployment scoping process.
Does it require changes to our existing EHR system?
No core EHR customization or schema changes are required to deploy the medical data extraction solutions. Integration runs through APIs, configured connectors, and appropriate identity and access controls, leaving existing clinical systems and workflows intact while connecting the enrichment layer cleanly.
