Epic's electronic patient record systems use SNOMED CT as a reference terminology through the following mechanisms:

• Mappings between a subset of Epic's standard data elements and SNOMED CT concepts;

• Mappings between diagnoses imported from other code systems (e.g. those used in Intelligent Medical Object's and Health Language's products) and SNOMED CT concepts;

• Mappings from additional data elements to SNOMED CT concepts that can be created by clients using an External Concept Mapping activity.

These mechanisms provide linking behind the scenes, so when clinicians add a diagnosis to the problem list by selecting a familiar term, for example, they automatically select the corresponding SNOMED CT concept. This SNOMED CT encoding creates powerful possibilities within Epic's decision support and reporting facilities.

The Epic system calls its decision support alerts 'Best Practice Advisories'. These customized alerts are programmed to fire according to predetermined triggers, such as specific chief complaints, vital signs, diagnoses or medications, either individually or in combination using inclusionary or exclusionary logic. Best Practice Advisories can thus be used to notify clinicians to tend to important tasks, such as reviewing a patient's allergies, writing orders, and completing charting. They can also present order sets and links to third party information sources refined using the clinical context of the patient record being reviewed.

Epic customers can use SNOMED CT's hierarchical structure to group related records, making the setup for clinical decision support much simpler than would be possible if users had to select records or clinical concepts individually. For example, an administrator creating Best Practice Advisories for diabetic patients could use 73211009 |diabetes mellitus| within the SNOMED CT hierarchy as one of the criteria instead of identifying every subtype of diabetes individually.

Within Epic's integrated analytics and reporting suite (i.e. Cogito) customers have achieved benefits by using SNOMED CT's clinical finding hierarchy to aggregate local diagnosis concepts.

The capability has for example been used by oncologists working with cancer-related ICD codes which are unsuited to grouping diagnoses by stage. Using the mapped SNOMED CT codes they are able to facilitate the reporting of staging data by utilizing the SNOMED CT hierarchy.

The 3M Healthcare Data Dictionary (HDD) is a controlled medical vocabulary server. The HDD has been continuously expanded and maintained for over 15 years, both as a standalone product and embedded within several of 3M's core products and services. The 3M HDD enables mapping and management of medical terminologies, integration of content and standardization of healthcare data. The 3M Healthcare Data Dictionary incorporates a selection of standard healthcare terminologies, including (but not limited to) SNOMED CT, LOINC, RxNorm, ICD-9-CM and ICD-10-CM.

Concepts in the HDD are grouped and organized using both hierarchical and non-hierarchical relationships. One of the hierarchical relationships in the HDD is SNOMED CT's 'is a' relationship which allows users to programmatically use and analyze SNOMED CT concepts captured at various levels of granularity. The analytics capabilities of the HDD are also extended through the use of other relationship types.

Data Warehousing

The content within the HDD makes a key contribution to analytics in several settings. For example one large academic research institution uses the HDD to integrate over 100,000 medication concepts from disparate systems for comprehensive data assimilation. Many of the medication concepts are mapped to RxNorm codes and linked through a 'has ingredient' relationship to SNOMED CT codes.

The 3M HDD has a knowledge base and poly-hierarchical structure that defines the relationships between each clinical drug.The following figure shows the relationships that exist for Ramipril including the links to SNOMED CT content, which can be used to query the data warehouse. The knowledge base allows the hospital's researchers to customize their searches by various levels of granularity and organize their clinical content into meaningful relationships.

The HDD supports researchers in performing data mining by:

• Extracting and mapping clinical metadata using a streamlined, systematic approach;

• Translating diverse clinical terminologies using a coded medical vocabulary;

Egton Medical Information Systems (EMIS) began in the 1980s in a rural practice in Egton in North Yorkshire, United Kingdom. The founders, Dr Peter Sowerby and Dr David Stables, wrote the software and adopted the NHS Read Code system during the 1980s. A series of systems have since been deployed in over half of England's primary care practices. The latest product (EMIS Web) moved to a data center based architecture, thin client front end and built in secure web based patient access facilities.

EMIS software looks after the patient records of nearly 40 million people in the UK (30 million using EMIS Web). More than 2 in 3 of those patients can book appointments and order repeat medications online, and more than 1 in 3 can view their own medical record.

EMIS Web features significant advances in terminology use with EMIS adopting a phased approach to SNOMED CT. EMIS Web displays a familiar coding structure based on the construction of a Read Version 2 navigational hierarchy within SNOMED CT. The principle design objective has been to enable SNOMED CT within the clinical system to meet specific requirements, including:

• Supporting advanced decision support capabilities;

Snow Owl is a clinical terminology platform developed by B2i Healthcare. The Snow Owl technology family is deployed in over 2,500 locations in 83+ countries worldwide. The Snow Owl® terminology server has been licensed by SNOMED International to form the basis of SNOMED International Terminology Server.

The Snow Owl® terminology server scales from a small kernel embedded in single-user products to n-tier clusters supporting hundreds of concurrent users. Clients can easily access and query SNOMED CT, LOINC, ATC, ICD-10, and dozens of additional terminologies via REST or Java APIs. Collaborative distributed authoring is also supported, including creating and maintaining local code systems, mapping between terminologies, and creating terminology subsets.

Terminology server features include:

• Extensive support for expression constraints and semantic queries, including Extended SNOMED CT Compositional Grammar and Groovy scripts.

This section includes brief reviews of a variety of commercial tools which support analytics over SNOMED CT enabled data. These reviews focus predominantly on those tooling features that are relevant to supporting analytics services.

We welcome additional input to this section and anticipate updates to this report as new information becomes available.

Provide Feedback

Building on the capabilities of its CLiX Clinical Natural Language Processing (CNLP) platform, Clinithink has created a solution to enable the analysis of healthcare data sourced directly from clinical narrative called CLiX ENRICH. This technology can be integrated into existing healthcare solutions to process any relevant narrative and encode the key clinical elements – medications, diagnoses, procedures, symptoms and findings – using SNOMED CT. These features can then be queried using powerful, user-definable SNOMED CT queries to present structured data in a form that can be easily consumed by existing BI platforms.

For example, when the narrative text below is typed into diagnosis and observation fields (respectively), Clinithink is able to provide a list of possible coding options for manual confirmation (as shown below).

SNOMED CT plays a central role in many Apelon products and projects. Apelon tools feature navigation and visualization tools to support SNOMED CT in a variety of ways. Apelon also undertakes bespoke content development and consultancy work in healthcare and biomedicine using SNOMED CT.

The Apelon Terminology Development Environment (TDE) software was used by the College of American Pathologists to build and maintain the SNOMED CT International Edition prior to the formation of IHTSDO. Apelon software continues to be used by major healthcare organizations and some National Release Centers to maintain SNOMED CT extensions, maps and subsets.

Apelon Distributed Terminology System

The Apelon Distributed Terminology System (DTS) offers a variety of human and computer interfaces to navigate, visualize and query SNOMED CT. DTS allows users to create custom extensions to SNOMED CT and perform incremental description logic classification to ensure that the extensions are consistent with the base version of SNOMED CT. DTS permits navigation, and side-by-side comparison of concepts across multiple SNOMED CT versions. Features of Apelon DTS supporting analytics and data retrieval include:

• Subsetting : DTS allows users to create customized SNOMED CT subsets using advanced logic techniques. The user is able to create extensional and intentional value sets of concepts for queries based on both hierarchical and non-hierarchical relationships.

• Data normalization : DTS supports the matching of text input to standardized terms and concepts via word order analysis, word stemming, spelling correction and term completion

• Code translation : DTS supports the mapping of clinical data to standard coding systems such as SNOMED CT, ICD-9, 10 and CPT

Apelon frequently choose to rely upon SNOMED CT in their consulting work as the overarching reference terminology. Recent projects using SNOMED CT include:

• Work with a major performance measure developer to create a large number of SNOMED CT value sets representing the inclusion and exclusion criteria for quality measures. SNOMED CT supplies the expressivity for the detailed distinctions amongst disorders and patient characteristics that is essential for this work.

• Use of SNOMED CT as the "backbone" terminology in a number of mapping projects for Health Information Exchanges. A small value set of SNOMED CT concepts serves as the "source of truth" for determining appropriate maps, and then codes from other terminologies are assigned based on whether they are a good fit with the SNOMED CT concept. This strategy provides a way to capture the precise intent behind the often-fuzzy language found in clinical documents.

• Use of SNOMED CT to index patient education materials for a major content provider. Documents are retrieved via an 'Infobutton' request in the EMR based on codes found in the patient record.

Allscripts released their first version of Vocabulary Management utilizing SNOMED CT in 2005. Since then Allscripts systems have been able to utilize SNOMED CT for clinical decision support and reporting. Allscripts uses a common terminology platform for all three electronic health record systems: Sunrise Clinical Manager™, Sunrise Acute Care™ and Sunrise Ambulatory Care™.

When a query is performed over a health record that requires clinical terminology, the terminology service always returns a SNOMED CT code. If the primary code stored in the health record is not SNOMED CT (e.g. ICD-9 or ICD-10), then the terminology service performs the mapping to SNOMED CT, saves the SNOMED CT code in the health record next to the original code (to make future queries more efficient), and returns the SNOMED CT code.

Allscripts Sunrise applications are able to link SNOMED CT to all orders, order form pick lists, observations pick lists and results.

Point of Care Decision Support

Sunrise Clinical Manager integrates reference content from medical publishers into clinician workflow. The Sunrise InfoButton™ feature provides clinicians access to relevant medical reference content wherever patient care decisions are made, without requiring them to log into or visit another site. Sunrise InfoButton uses encoded patient problem lists and medication data elements to query third-party medical content selected by the clinician. Several healthcare reference content providers index their content using SNOMED CT. This enables the delivery of on-topic information without manual searching. Resources with SNOMED CT indexed content include Wolters Kluwer Clin-eguide which provides context-specific evidence on particular medical issues and diseases and Lexi-Comp, providing drug and drug-interaction information, diagnosis and disease management, formulary services, patient-education resources and clinical support tools.

Reporting

The Allscripts Clinical Quality Management (CQM) is an automated chart abstraction and analytics system. CQM is able to create population sets with SNOMED CT encoded patient records and use these patient sets for reporting. CQM is a flexible, powerful reporting and analytics system presenting information in a variety of formats ranging from simple list style reports, to Online Analytical Processing (OLAP) Data Cubes with Pivot reports.

Allscripts Clinical Performance Management (CPM) is a business intelligence solution for monitoring clinical performance, improving patient outcomes and reducing costs. With prebuilt or customized reporting and dashboards, healthcare leaders have powerful access to performance information enabling quality improvement across the health enterprise.

Applications include:

• Alert usage analysis: User-customizable reports drill down into clinical decision support usage data revealing the reasons and circumstances for bypassed or overridden alerts. Seeing the impact of decision support enables a sharper focus on patient outcomes and supports the refinement of rule logic.

• Order-set usage analysis: Organizations can evaluate their order set usage patterns of computerized provider order entry. By observing order set configuration, deployment and use, organizations can revise them to enhance their effectiveness and usability.

• Clinician utilization analysis: Clinicians can examine the vast array of health issues and clinical observations for discharged patients to support patient treatment decisions and protocol implementation. In addition, patient cohorts can be tracked over time to determine if the proper treatments are being delivered.

The Cambio COSMIC® Electronic Patient Record system has been under continuous development since 1993. Cambio has applied innovations within healthcare informatics in areas such as information models, clinical terminology and formal languages for expressing clinical decision support rules. The COSMIC® EPR combines openEHR archetypes, SNOMED CT terminology and Guideline Definition Language rules in implementations which benefit patients, clinical staff and healthcare enterprise management. Using these technologies, their system is able to incorporate advanced analytics capabilities.

Cambio uses the Guideline Definition Language (GDL) to combine archetypes, terminologies and clinical decision support rules. GDL provides:

• Bindings between archetype elements and variables in the rules;

• Rule expressions that are easily converted to industry rule engine languages;

Cerner Corporation's Millennium healthcare system manages terminologies, classifications and other code systems within a terminology service - the Cerner Millennium Terminology (CMT) package. CMT accommodates and integrates SNOMED CT International Release data and National extension content - such as concepts, relationships, descriptions, subsets, maps etc.

At the Cerner Millennium user interface, content can be captured at point of care as SNOMED CT codes. Modules used with SNOMED CT include: Problems and Diagnoses, Allergies, Procedures, Pharmacy Orders, Radiology Orders and Cellular Pathology Reports.

Either Cerner or third party clinical encoding software can process SNOMED CT Diagnoses and Procedures captured in Millennium and suggest ICD-10 and other classification codes to Clinical Coders for activity reporting and billing.

SNOMED CT is also used extensively behind the scenes to support more sophisticated analytic facilities within their Natural Language Processing (NLP) tools and reporting tools. These Cerner products and services exploit unique features and content of SNOMED CT to extend the power of these applications.

The Cerner product suite includes two data warehousing applications. These applications share much of their terminology-related technology, including supporting subsumption queries with the CMT Concept Explode/Transitive Closure facility, which utilizes the SNOMED 'is a' relationships.

The PowerInsight® Data Warehouse (PIDW) is an enterprise level data warehouse which updates on a nightly basis from the live electronic patient record. PIDW services standard operational reporting, mandatory reports (e.g. for National or State governments and regulatory bodies), and

IMO produces a medical terminology service for healthcare solutions, allowing over 2,500 hospitals and 350,000 clinicians to focus on patient care. IMO bridges the information gap between clinicians, coders, and patients in the US and internationally. IMO enable and support the accurate capture and preservation of clinical intent for clinical documentation, decision support, reimbursement, reporting, data analysis, research, and health education.'

IMO's clinical interface terminology is designed pragmatically to capture clinical intent at point of care. However it is also intended to enable and simplify the adoption of standard ontologies by vendor partners.

By choice, the editorial process requires all IMO interface terms to have one or many qualified maps to SNOMED CT. Clients can then use SNOMED CT to drive reporting, analytics, clinical decision support, and research.

The following examples demonstrate how IMO uses SNOMED CT for analytical purposes:

1. Helping patients find health professionals who have expertise or interest in specific areas of medicine. These areas include disorders, procedures, devices, medications, patient demographics, and medical specialties. These areas of expertise or interest include those that are self-reported by clinicians and those documented in clinical encounters. The search algorithms use hierarchies in SNOMED CT to retrieve and rank search results.

2. Helping clinicians use patient diagnoses and procedures documented at varying levels of granularity to find appropriate patient education materials using SNOMED CT is-a hierarchies.

3. Grouping together related clinical concepts in patient records for creating focused patient reports and driving clinical workflows.

4. Forming subsumption queries for cohort selection within patient data repositories and document libraries.

5. IMO uses natural language processing (NLP) to extract information coded in SNOMED CT from clinical narratives.

Caradigm is a joint venture between Microsoft and GE Healthcare, which is dedicated to population health management. Caradigm's cornerstone product is the Intelligence Platform. This platform can connect over 295 types of source systems, including Allscripts, Athenahealth, Cerner, Epic, GE, McKesson and Meditech. Data from disparate systems within one or more healthcare organizations is collected, normalized and standardized to enable applications to leverage this data in a unified and consistent way.

Caradigm's solutions provide explorative, comparative, predictive and guided elements aimed at analyzing the disparate data and driving the insight that is gained into action. Caradigm's three main solution areas are:

• Healthcare analytics (including clinical, operational and financial analytics);

• Coordination management; and

• Wellness promotion and patient engagement.

Some of Caradigm's customers use SNOMED CT natively in their clinical systems, while others use natural language and other code systems. In order to aggregate data from disparate sources, it must first be standardized by mapping into a common code system. Code systems used to standardize the disparate data include SNOMED CT, ICD-9 and ICD-10. By standardizing the data, users are able to leverage the analytics tools - for example, to understand trends within different diagnoses, to look at a comprehensive list of everything that has happened to a patient in a longitudinal patient record, and to support care management by displaying the different diagnoses or problems of a patient in a consistent manner.

Caradigm currently implements an approach to SNOMED CT based analytics using clinical value sets. These value sets are developed manually by a team of clinical analysts for topics such as diabetes and heart disease. Clinical users are then able to create queries in a user friendly interface, which allows them to (for example) define cohorts built on criteria such as age, gender, specific diseases, conditions, medication or other treatments. These queries are then converted behind the scenes into SQL statements which are executed against a SQL Server database and return records containing data in the selected clinical value sets.

Caradigm also has Natural Language Processing (NLP) tools, which are able to extract and encode data, such as problems and medications, from natural language notes within documents such as discharge summaries and radiology reports.

As part of their strategic roadmap, Caradigm are exploring ways to enhance the capabilities of their tooling platforms by leveraging the architecture of the terminology sets that they are using for analytics. In particular, they are planning to start utilizing the hierarchical and non-hierarchical relationships of SNOMED CT to enable more powerful query capabilities and to extend NLP processing options.

First DataBank (FDB) were in the first wave of suppliers to recognize the potential of SNOMED CT and begin to integrate support for SNOMED CT into their existing clinical decision support solutions. Their primary use of SNOMED CT in the patient's electronic health record (EHR) is to detect safety issues arising from certain combinations of medications, diagnoses and drug adverse reaction histories. In 2006 FDB introduced support for products and packs encoded using the NHS SNOMED CT UK Drug Extension. In the following year FDB launched new modules within the Multilex drug knowledge base supporting Drug-Condition Checking and Drug Sensitivity (Allergy) checking for the SNOMED CT EHR.

System vendors implementing Multilex decision support within SNOMED CT-enabled medical record applications include CSC (Lorenzo system), EPIC and JAC in secondary care, and CSE Servelec (RiO system) in community/mental health. Currently only pre-coordinated expressions are supported by the live Multilex SNOMED CT based decision support solutions.

The contraindications module alerts the clinician when a medication proposed to treat a disorder is incompatible with another of the patient's disorders or clinical states. For example a beta blocker like propranolol might be prescribed to treat someone with high blood pressure. However if that patient also has asthma, their asthma might significantly worsen or a dangerous acute attack might be produced by the drug.

Thousands of such drug-condition contraindications exist and nearly all medications have at least one. Without point of care decision support, the clinician must rely on memory or search reference sources for each drug prescribed. Also there is a risk that a contraindicating condition may be in the record but unknown to the prescribing clinician.