Information

Introduction

The demo grouper is based on the Latvia 2025 NordDRG workbook structure (definition tables) of the Nordic NordDRG grouper©, and can use the original ICD-10 and NCSP (NOMESCO Classification of Surgical Procedures) codes or SNOMED CT preferred terms or synonyms from the SNOMED CT International Release as input (SNOMED CT international release 01/12/2025). The demo grouper tries to find a Major Diagnostic Categories (MDC) and Diagnosis-Related Group (DRG), based on the input.

This version can use English SNOMED CT preferred terms and synonyms. The preferred term (PT) is the preferred synonym within a language reference set (English only in this version). It can use about 78,952 SNOMED CT concepts for diagnoses, but can only use about 489 SNOMED CT concepts for procedures, due to the limitations of this version of the demo grouper. This version of the demo grouper cannot use SNOMED CT Concept IDs (SCTID), WHO ICD-11 or WHO ICHI codes.

You have to chose one of these three modes of operation for the demo grouper:

  1. ICD-NCSP: for ICD-10 diagnosis code with NCSP procedure code
  2. SCT-NCSP: for SNOMED CT diagnosis term with NCSP procedure code
  3. SCT-SCT: for SNOMED CT diagnosis term with SNOMED CT procedure term

Important limitations

This application is only a simple grouper demo based on the Latvia 2025 workbook structure. When no errors occur, the application will probably show a grouper result more or less according to the NordDRG Grouper algorithm. The application does not involve any exchange of personal data. It may be useful for learning, testing, and tracing rules, but it is NOT presented as a certified production grouper. This application comes with absolutely no warranty.

WHO ICD-10, NCSP and SNOMED CT

WHO ICD-10 (International Statistical Classification of Diseases and Related Health Problems, 10th Revision) is the global standard diagnostic tool for epidemiology, health management, and clinical purposes, published by the World Health Organization (WHO). It translates diseases, injuries, and symptoms into alphanumeric codes, enabling tracking of morbidity and mortality.
Note: The World Health Organization's 11th Revision of the International Classification of Diseases (WHO ICD-11) took effect on January 1, 2022. It is a fully digital, comprehensive, and updated system used by clinicians and researchers to track mortality, morbidity, and health trends, enhancing interoperability with electronic health records.

NCSP (NOMESCO Classification of Surgical Procedures) is a standardized, alphanumeric classification system for surgical procedures used across Nordic countries (Denmark, Finland, Iceland, Norway, and Sweden), and Estonia and Latvia. It organizes procedures into 15 main chapters based on anatomy, plus four supplementary chapters, with codes consisting of three letters and two numbers.
Note: The World Health Organization's (WHO) International Classification of Health Interventions (WHO ICHI) is a standardized, multilingual tool designed to report and analyze health interventions across clinical, primary care, and public health settings. It acts as a companion to the WHO ICD-11 (diagnoses) and WHO ICF (functioning) to enable global comparison, coding over 7,000 interventions.

SNOMED CT (Systematized Nomenclature of Medicine - Clinical Terms) is the world's most comprehensive, multilingual, and structured clinical healthcare terminology. It provides a standardized language for electronic health records (EHRs), allowing consistent recording, sharing, and analysis of clinical data across different systems, providers, and countries.

More (external) information about the NordDRG grouper, WHO ICD-10, SNOMED CT, and R is also available here.

How the demo grouper works

This application is a simple demonstration tool for grouping a case with the Latvia 2025 NordDRG workbook structure and SNOMED CT (international release 01/12/2025). It can use ICD-10 codes or SNOMED CT for diagnosis and NCSP codes or SNOMED CT for procedures. For the WHO ICD-10 codes and NCSP codes you can take a look at the available NordDRG documentation. For the SNOMED CT term you can use a SNOMED CT browser to find appropriate preferred terms or synonyms.

This application is only a demonstration tool for grouping a case with the Latvia 2025 NordDRG workbook structure. It uses the Latvia 2025 NordDRG workbook to evaluate:

The output tables are shown after evaluation of the input.

Input fields

The app contains textboxes for the following fields:

Principal diagnosis:
Enter one ICD-10 code or SNOMED CT term, depending on the grouper mode.
Example: 'K358'

Secondary diagnoses:
Enter zero, one, or several ICD-10 codes or SNOMED CT terms, depending on the grouper mode.
Use commas to separate multiple codes.
Example: 'N390, E119'

Procedures:
Enter zero, one, or several NCSP procedure codes or SNOMED CT terms, depending on the grouper mode.
Use commas to separate multiple codes.
Example: 'JEA01'

Age in days:
Age in days, not years.

Length of stay (days):
Use the slider up to a maximum of 28 days.

Discharge code:
The letters N, D, T, W, and L, relate to variables within the DRG definition logic.

Sex value used in DRG logic (optional):
For diagnoses collected in MDC 98 with diagnosis categories beginning 98M.

Grouper mode options:

  1. ICD-NCSP: for ICD-10 diagnosis with NCSP procedure
  2. SCT-NCSP: for SNOMED CT diagnosis with NCSP procedure
  3. SCT-SCT: for SNOMED CT diagnosis with SNOMED CT procedure)

What the application does

After you enter the codes or the SNOMED CT terms, the application:

  1. derives diagnosis and procedure properties
  2. checks whether any secondary diagnosis creates CC (Complication or Comorbidity) or MCC (Major Complication or Comorbidity)
  3. applies exclusion logic
  4. evaluates DRG logic in rule order
  5. returns (if possible) the first matching DRG

Output tables

The result tables show:

Input rules

Default example

The built-in example is:

This represents an uncomplicated appendicitis with laparoscopic appendectomy.

Example test cases in ICD-10/NCSP mode

1. Uncomplicated appendectomy

Principal diagnosis: 'K358'
Secondary diagnoses: blank
Procedures: 'JEA01'

Expected result:
MDC 06
DRG 167

2. CC (Complication or Comorbidity) example

Principal diagnosis: 'K358'
Secondary diagnoses: 'N390' (Urinary tract infection (UTI), site not specified)
Procedures: 'JEA01'

This may move the case to a CC-related appendectomy branch, depending on the workbook logic.

3. MCC (Major Complication or Comorbidity) example

Principal diagnosis: 'K358'
Secondary diagnoses: 'A419' (Sepsis, unspecified organism)
Procedures: 'JEA01'

This may create an MCC result if the secondary diagnosis qualifies and is not excluded.

Example test cases in ICD-10-NCSP, SCT-NCSP and SCT-SCT mode

Principal diagnosis only

Principal diagnosis and procedure

Principal diagnosis, secondary diagnosis and procedure

Principal diagnosis, secondary diagnosis and procedure in French and Dutch

Troubleshooting

Usage summary

Provide the principal diagnosis and when relevant secondary diagnoses, procedures, etc.
Select the appropriate grouper mode:

  1. ICD-NCSP: for ICD-10 diagnosis with NCSP procedure
  2. SCT-NCSP: for SNOMED CT diagnosis with NCSP procedure
  3. SCT-SCT: for SNOMED CT diagnosis with SNOMED CT procedure
Press "Run grouper".

The NordDRG Grouper

The NordDRG Grouper is the software engine that takes a hospital case and assigns it to a NordDRG (Nordic Diagnosis Related Group). In practice, it is the executable part of the broader NordDRG casemix system: the system defines the classification rules, while the grouper applies those rules to a patient record. The Nordic Casemix Centre (NCC) says NordDRG is a DRG system based on WHO ICD-10 and NCSP (NOMESCO Classification of Surgical Procedures) definitions, first completed in 1996 and updated yearly; it coordinates maintenance and development of the system across participating countries.

At a practical level, the grouper is used to classify inpatient stays and outpatient/day-surgery encounters into groups that are expected to have similar resource use. In Finnish THL (Finnish Institute for Health and Welfare) benchmarking material, NordDRG Full is used to assign both outpatient visits and inpatient care periods to patient groups, and each group is linked to a cost weight that reflects relative resource need. That is why the grouper matters for casemix measurement, benchmarking, productivity analysis, and reimbursement models. A useful nuance is that the grouper itself does not set the money amount; rather, it assigns the DRG, and that DRG is then tied to national or payer-specific cost weights and tariffs.

What the NordDRG Grouper is

Think of it as a deterministic, rule-based classifier for hospital episodes. It reads coded administrative and clinical data from a patient encounter and produces a DRG result that places the case into a resource-homogeneous group. NordDRG is maintained centrally by the Nordic Casemix Centre (NCC), with governance through a board, an expert group, and a formal annual update process. Current NordCase pages list Nordic ownership/governance and participation by Estonia and Latvia in the collaboration.

How the NordDRG Grouper works

At a high level, the grouper follows a rule chain like this:

  1. It receives coded case data such as the principal diagnosis, secondary diagnoses, procedures, patient age, sex, discharge status, and in some situations length of stay. The NordDRG logic documentation also shows return codes for technical or coding problems, such as missing principal diagnosis or incoherent age/sex combinations.
  2. It maps national diagnosis and procedure codes into NordDRG’s common backbone classifications. The Nordic Casemix Centre (NCC)maintains ICD+ and NCSP+ so national ICD-10 and NCSP variants can be linked to a shared platform. This is important because NordDRG is maintained through those backbone classifications, not directly through every national code list.
  3. It evaluates the rule tables in a defined order. According to the NordDRG logic documentation, the grouper checks:
  4. It applies top-down "first match wins" logic. The rule tables are ordered, and the first rule whose conditions are satisfied determines the final DRG. That means NordDRG grouping is not just about finding a matching label somewhere in a table; rule priority matters.
  5. It uses properties and categories, not just raw codes. The logic page explains that diagnoses and procedures are evaluated through things like:
  6. It checks complication/comorbidity logic. NordDRG can split some groups depending on whether secondary diagnoses qualify as complications/comorbidities (CC) under the rule set. The logic documentation notes that a case can become "complicated" if a qualifying secondary diagnosis is present, subject to exclusions and pairing rules. It also distinguish between CC (complications/comorbidities) and MCC (major complications/comorbidities) effects.
  7. It returns a structured result. The output commonly includes the DRG, MDC, a return code, and sometimes a grouping rule ID so the user can trace which logic row produced the assignment.

So, if a hospital submits a case with a principal diagnosis, some secondary diagnoses, one or more procedure codes, and the patient's age/sex/discharge data, the grouper first determines whether the case belongs to a special pre-MDC category. If not, it finds the relevant MDC from the principal diagnosis, tests the ordered rules inside that branch, checks whether there was surgery or another qualifying procedure, checks complication status and any age/sex/discharge restrictions, and then stops at the first rule that matches.

Key features of the NordDRG Grouper

  1. Rule-based and deterministic:
    It is not statistical or probabilistic. Given the same valid coded input and the same NordDRG version, it should produce the same DRG every time. That is why NordDRG compliance requires certified groupers to match the Nordic reference grouper exactly.
  2. Built on a shared Nordic classification backbone:
    NordDRG uses WHO ICD-10 and NCSP-derived backbone classifications, maintained as ICD+ and NCSP+, so multiple national code systems can feed into a common grouping logic.
  3. Supports national versions and a combined version:
    The Nordic Casemix Centre (NCC) publishes or manages national NordDRG definitions and also offers a combined version that brings together ICD+ and NCSP+ content from national basic classifications.
  4. Annual maintenance and versioning:
    NordDRG is updated yearly through a formal maintenance process. The Forum, Expert Group, and board handle proposals, review, and approval of changes.
  5. Certified compliance:
    A vendor's grouper can be marketed as NordDRG compliant only if its results are identical to the Nordic reference grouper on the official test data.
  6. Works for both inpatient and outpatient grouping:
    THL's benchmarking description explicitly notes NordDRG Full grouping for outpatient visits and inpatient care periods, which is one reason the system is useful beyond classic overnight admissions.
  7. Tied to cost weights and casemix analytics:
    Each DRG can be linked to a cost weight representing relative resource need, making the grouper central to reimbursement formulas, hospital benchmarking, productivity analysis, and comparisons across providers.
  8. Traceability and transparency:
    The NordDRG Forum exposes version, task, and case tracking for updates, and some grouper tools expose the exact rule ID used for classification.

Concluding remark

The NordDRG Grouper is a rule-driven software classifier that converts coded hospital encounter data into a single NordDRG group, which can then be used for casemix analysis, benchmarking, and payment based on relative expected resource use.

Characteristics of a diagnosis classification used for grouping into Diagnosis-Related Groups

A Diagnosis Related Group (DRG) abstract is a summarized, coded record of a patient's (inpatient) stay, used to classify them into clinically similar groups based on diagnosis, procedures, age, sex, and discharge status. It acts as a standardized data set (often called a "DRG abstract" or "discharge abstract") that assigns a specific weight (e.g., CMS MS-DRGs, NordDRGs, APR-DRGs, ...) to determine fixed hospital reimbursement, replacing cost-based payments. Abstracts are highly structured to ensure uniformity, often containing specific fields that are populated for every patient separation (discharge, death, transfer).

A diagnosis classification can be grouped by a DRG system (Diagnosis-Related Groups) only if it does more than name diseases. It has to provide a machine-readable code structure, a controlled clinical lexicon for consistent coding, and semantics that distinguish diagnoses in ways that matter for treatment pattern and resource use. In NordDRG, WHO ICD-10-based diagnosis codes are not used as raw labels; they are linked to grouping features and then combined with principal diagnosis, secondary diagnoses, procedures, age, and sometimes other episode variables to assign a DRG.

Structural aspects

Lexical aspects

Semantic aspects

To be DRG-groupable, a diagnosis classification needs rigid structure, controlled terminology, and semantics aligned with episode-of-care logic and resource use. ICD-10 provides much of that substrate, while NordDRG adds an extra grouping layer - MDCs, diagnosis categories, diagnosis properties, and complication logic - that turns diagnosis codes into reimbursement and benchmarking classes. Diagnosis classification alone is not sufficient, because DRG grouping also needs procedures and other case variables, but without those structural, lexical, and semantic properties in the diagnosis classification, the grouper cannot work reliably.

SNOMED CT

SNOMED CT is a clinical terminology: a standardized, computer-processable vocabulary used to represent clinical meaning in electronic health records. SNOMED International describes it as the world’s most comprehensive, multilingual clinical healthcare terminology. Its purpose is to let clinicians record care at the right level of detail, while allowing that data to be shared, searched, analyzed, and reused across systems and languages. Unlike a statistical classification such as ICD, SNOMED CT is designed for clinical recording first; the recorded data can then be mapped to classifications for reporting, billing, or public-health use.

What SNOMED CT is

At its core, SNOMED CT is a structured collection of clinical concepts such as disorders, findings, procedures, body structures, organisms, substances, and other healthcare-relevant meanings. Each concept has a unique identifier that stands for the meaning itself, not just a word. Human-readable terms are attached to that concept so different phrases can point to the same underlying meaning. This is why SNOMED CT supports consistent data capture even when different clinicians or countries use different terms.

SNOMED CT is also an ontology-like terminology with a formal logical foundation. Concepts are arranged in hierarchies and linked by defining relationships, which makes the terminology machine-readable in a much deeper way than a flat code list. That structure is what enables advanced search, inference, interoperability, and analytics.

How SNOMED CT works

SNOMED CT works through three main component types: concepts, descriptions, and relationships. A concept is the clinical idea. A description is a term attached to that concept. A relationship links one concept to another, such as a disorder having a finding site or an associated morphology. The official logical model defines these as the core building blocks of the terminology.

Each concept can have multiple descriptions. The most important are:

SNOMED CT also uses hierarchies and defining relationships. Every active concept, except the root, sits under one or more broader concepts via the "Is a" relationship. Because concepts can belong under more than one parent, SNOMED CT supports polyhierarchy, which is more flexible than many classification systems. On top of that, attribute relationships define the meaning of a concept in a formal way.

Its logical basis is description logic. That means the semantics of concepts can be represented formally and classification software can infer additional knowledge from the definitions. In practice, this allows systems to compute subsumption, check consistency, and support meaning-based retrieval rather than relying only on text matching.

SNOMED CT can be used in two ways when recording data:

For implementation and distribution, SNOMED CT is published in Release Format 2 (RF2). It also uses reference sets ("refsets") to package subsets, language preferences, maps, and other implementation artifacts. For example, language reference sets indicate which terms are preferred in a particular language or dialect, and map refsets support links to other code systems such as WHO ICD-10, WHO ICD-O, Orphanet, ... .

Key features of SNOMED CT

SNOMED CT and grouping

SNOMED CT is a clinical terminology, not a statistical classification

SNOMED CT is generally not used directly by standard DRG groupers such as NordDRG. NordDRG’s grouping logic is based on primary classifications such as ICD-10 and NCSP, and the Nordic Casemix Centre (NCC) maintains ICD+/NCSP+ backbone classifications specifically to link national ICD-10/NCSP codes into the grouping logic. Likewise, mainstream DRG logic such as MS-DRG or APR-DRG is defined around principal diagnosis, secondary diagnoses, and procedures coded in ICD-based classifications.

The core reason is that SNOMED CT is a clinical terminology, not a statistical classification. SNOMED International states that classifications are used where terms must be grouped into categories and double counting must be avoided, including for billing and reimbursement; by contrast, SNOMED CT is a much more granular terminology, and it uses a polyhierarchy, where one concept may belong to more than one supertype. That is excellent for clinical documentation and analytics, but it is a poor fit for a reimbursement grouper that needs mutually exclusive case-assignment logic.

A second reason is semantic representation. In SNOMED CT, meaning is carried not only by the term itself but also by defining relationships, and sometimes by postcoordination-multiple linked concepts used to express a finer-grained clinical statement. SNOMED's own mapping specification says that ICD-10 and SNOMED CT have different structures and semantics, and that mapping requires identifying the best location of a SNOMED concept within the ICD-10 semantic space. In other words, a grouper built for ICD logic cannot usually consume raw SNOMED CT concepts or expressions without an intermediate classification step.

SNOMED CT can support DRG grouping indirectly. The standard pattern is to capture clinical data in SNOMED CT, then map the relevant findings to ICD-10 or a national ICD-10 derivative, plus the relevant procedure classification, and then run the DRG grouper on those classified codes. SNOMED International explicitly says its SNOMED CT to ICD-10 map supports semi-automated generation of ICD-10 data from SNOMED CT-encoded records and aids use in diagnosis groupers.

In theory, you could build a custom "direct SNOMED CT-to-DRG" grouper, but only by imposing a classification layer on top of SNOMED CT-normalization rules, exclusion logic, severity handling, mutually exclusive buckets, and resolution of postcoordination/polyhierarchy. At that point, you have effectively recreated the missing classification semantics that DRGs require.

Classification Layer Components

Turning SNOMED CT into a grouper-ready discharge abstract

Preparing SNOMED CT for NordDRG by turning it into a grouper-ready discharge abstract, not by feeding raw the SNOMED CT concepts into the grouper. NordDRG is maintained around ICD-10 and NCSP, with ICD+ and NCSP+ as backbone classifications linking national variants into the grouping logic. SNOMED International, by contrast, describes SNOMED CT as a terminology that often needs mapping into statistical classifications for billing, reimbursement, and reporting where double counting must be avoided.

A workable preparation pipeline looks like this:

  1. Freeze the target grouping environment first.
    Pick the exact NordDRG version, country variant, and the exact ICD-10 / NCSP release that the grouper expects. In NordDRG, the national versions are localized around those code systems, and the combined/basic classification layer exists precisely to align national ICD-10 and NCSP variants with the grouper logic. Your SNOMED processing has to target that exact release set.
  2. Constrain SNOMED CT to the data that can legally become coding input.
    Separate source SNOMED CT content into at least these buckets: Do not treat every SNOMED CT concept as a billable diagnosis or procedure. Concepts in Situation with explicit context can represent things like history, family history, planned procedures, ruled-out findings, or other contextual statements; those often should not become acute coded input for a DRG abstract.
  3. Normalize the SNOMED CT representation before mapping.
    For each source item: This matters because SNOMED CT meaning may be carried by defining relationships and postcoordination, not just by one label. SNOMED CT’s own grammar and postcoordination guidance make clear that expressions may need classifier support before they are interpreted consistently.
  4. Extract the clinical context explicitly.
    Before any ICD/NCSP mapping, derive and store attributes such as: This is the point where you exclude "history of," "family history of," "planned," "cancelled," "ruled out," and similar concepts unless national coding rules say otherwise. If you skip this step, you will over-code and create false DRG inputs.
  5. Map diagnoses from SNOMED CT into the grouper's diagnosis classification.
    For diagnoses, the normal target is the relevant ICD-10 national version, typically via the SNOMED CT to ICD-10 map as a starting point and then localization to the country/NordDRG environment. SNOMED International states that the ICD-10 map is intended to support semi-automated generation of ICD-10 data from SNOMED CT-encoded records and to aid use in diagnosis groupers. The mapping guidance also says the task is to find the best location of a SNOMED CT concept in the ICD-10 semantic space, which is why raw SNOMED CT cannot simply be treated as ICD logic.
  6. Map procedures from SNOMED CT into NCSP or NCSP+.
    NordDRG expects procedure logic based on NCSP, not SNOMED procedure concepts. So you need a dedicated procedure mapping layer from SNOMED CT procedures to the relevant national NCSP or NCSP+ representation. For postcoordinated procedure expressions, decompose things like method, site, device, and approach, then map the whole normalized clinical statement to the correct NCSP target ("Procedure" + "Finding Site" + "Approach" + "Device"). The grouping layer should never infer procedure codes just from SNOMED CT ancestry.
  7. Resolve polyhierarchy by mapping, not by traversing parents.
    SNOMED CT is polyhierarchical, while statistical classifications are monohierarchical so categories can be counted without double counting. That means your classifier should not decide DRG relevance by asking "which SNOMED parent wins?" Instead, each normalized diagnosis or procedure statement should map to the single ICD/NCSP category appropriate for the grouper context. In practice, the target classification resolves the ambiguity, not the SNOMED tree.
  8. Assemble a proper case abstract after mapping.
    Once you have mapped outputs, build the exact record NordDRG expects: The principal diagnosis should be selected from the discharge episode using the national coding rules, not by simply choosing the "most specific" SNOMED CT concept. Secondary diagnoses should be filtered to those that satisfy coding criteria for inclusion.
    NordDRG-related materials describe the grouper as operating on ICD-10 and NCSP codes, and user-facing grouping tools likewise require those diagnoses, procedures, and case attributes.
  9. Implement explicit exclusion and severity rules at case level.
    Some SNOMED-derived items may map to valid ICD-10 codes yet still not belong in the grouped abstract for a given episode. You therefore need case-level rules for: This is where you recreate the administrative discipline that SNOMED CT, as a terminology, does not enforce by itself. SNOMED CT guidance explicitly distinguishes detailed clinical recording from the grouped statistical classifications used for reimbursement.
  10. Send ambiguous mappings to coder review.
    Do not fully automate everything. SNOMED CT-to-ICD maps are explicitly described as semi-automated, and map rules may depend on context such as age, gender, episode details, or documentation specificity.
    The pipeline should surface "needs review" states where one SNOMED CT concept can map to multiple ICD outcomes depending on record context (Human-In-The-Loop (HITL)).
  11. Version and test the whole chain end to end (configuration management).
    Maintain:

Then validate on real discharge records against a gold standard of human-coded ICD/NCSP and final DRG assignment. The right test metric is not just concept-map accuracy, but final DRG concordance and the reasons for discordance.

The practical architecture is therefore: SNOMED CT capture → normalization/context resolution → diagnosis/procedure mapping to national ICD-10 and NCSP (via ICD+/NCSP+ where relevant) → principal/secondary/procedure abstract assembly → NordDRG grouping.

The most important implementation rule is this: prepare SNOMED CT into classified episode-level inputs, not concept-level inputs. A DRG grouper such as NordDRG does not want a terminology graph; it wants a tightly curated, mutually exclusive case abstract in the exact statistical classifications it was built to consume.

Concluding remark on SNOMED CT

SNOMED CT is a logic-based, multilingual clinical terminology that lets healthcare systems record precise clinical meaning in a standardized way and then reuse that data for interoperability, decision support, search, analytics, and mapping to other coding systems.
The closing remark at this moment on SNOMED CT and grouping is: not directly as SNOMED CT alone; yes indirectly via mapping to ICD-based classification inputs for diagnoses and NCSP-based classification inputs for procedures. These principles could also be relevant for other DRG-based systems, such as MS-DRG, etc. .

Additional information

NordDRG Grouper - information

WHO Family of International Classifications (WHO-FIC)

SNOMED CT - information

R - information

Bibliography (selection)

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