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;
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.
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.
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.
Clinical shorthand, narrative descriptions, and non-standard abbreviations are parsed by the clinical data extraction software to pull out discrete data elements, like;
All unstructured text becomes structured fields that are easy to read, understand, and use.
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.
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.
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.
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.
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;
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.
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.
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.
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.
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.
| 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. |
The clinical data enrichment system runs on a configured Microsoft architecture within the Copilot Studio;
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;
All these aspects are defined during deployment and calibrated to the organization's existing documentation environment.
DynaTech handles scoping, configuration, and deployment, and the process begins with;
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.
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.