One major ambition of healthcare IT is to make effective summaries of a patient's clinical history available to healthcare providers (especially in emergency situations). Typically a patient's clinical data is scattered across a number of healthcare institutions using a variety of information models and coding systems. Even within a single institution patient data may be captured across many episodes of care, many devices, and often many software systems.

SNOMED CT can help to support the integration of this information by serving as a common reference terminology into which other code systems can be mapped. It can also be used to unlock clinical data that was captured by source systems in free text narrative, and to summarize large volumes of data by grouping codes together into more general categories. SNOMED CT can also be used to enable clinicians to filter large volumes of data to select those records that are relevant to the current care episode – for example identifying all previous records of a heart attack.

One significant example of this is the UK NHS Summary Care Record (SCR) service,1 which uses SNOMED CT to represent a number of types of clinical information including medical history, medications, adverse reactions and allergies. This service uses a summary extracted from detailed patient care records held in a variety of disparate systems. Where the source data is not stored natively in SNOMED CT, they are mapped into SNOMED CT prior to transmission. Over 40 million people in England (80% of the population) now have a summary care record. This service now contributes to the safe and efficient assessment and treatment of these people, and has greatly improved the accuracy and timeliness of medicines reconciliation.

Point of care analytics encompasses those analytics services that directly benefit individual patients and clinicians, including historical summaries, decision support and point of care reporting. These analytics tasks typically involve the summarization and mapping of patient data, and the linking of terminology with clinical knowledge artefacts.

Clinical decision support systems (CDSS) are designed to assist clinicians at the point of care on decision making tasks. Examples of applications of clinical decision support include:

• Checking conformance with clinical guidelines and protocols

• Guide clinicians through complex care pathways

• Protect against errors in prescribing (e.g. drug-drug and allergy-drug contraindication checking)

• Highlight critical laboratory results

• Display clinical knowledge resources upon request, that are relevant to the given patient's diagnosis, symptoms, procedures or medications

Most CDSSs consist of three parts:

1. The knowledge base, with rules and guidelines – for example:

   1. IF drug = << 48603004 |warfarin| AND 77386006 |pregnant| THEN alert user

   2. IF drug has active ingredient = << 387494007|codeine| AND past history of 292055008 |codeine adverse reaction| THEN alert user

Using a combination of SNOMED CT techniques, including mapping, subsets, subsumption and defining relationships, SNOMED CT helps to support the inference engine in determining the appropriate rules to execute.

For example, Kaiser Permanente's HealthConnect system uses SNOMED CT to support efficient translation of its business rules into decision support rules. The National Board of E-Health in Denmark is developing a centralized decision support service based on the Danish SNOMED CT drug extension, which utilizes the hierarchical and defining relationships of SNOMED CT.

A number of commercial tools also use the capabilities of SNOMED CT to implement Clinical Decision Support. For example, Cambio's COSMIC tool binds GDL (Guideline Definition Language) rules to SNOMED CT concepts to support the triggering of appropriate rules. Allscript's Sunrise InfoButton™ feature provides relevant medical reference content to clinicians wherever patient care decisions are made, by using SNOMED CT encoded patient problem lists and medication data to query third-party medical content. The Epic system provides decision support alerts (called 'Best Practice Advisories'), which are able to use the SNOMED CT hierarchy to help define their criteria. And First DataBank delivers clinical decision support solutions linked to SNOMED CT, primarily to detect safety issues arising from certain combinations of medications, diagnoses and drug adverse reaction histories.

When it comes to reporting needs, the preference of most clinicians is to 'collect once and use many times'. SNOMED CT enables this goal to be achieved by allowing data to be captured at the appropriate level of detail and then queried at the same or less detailed level. SNOMED CT supports point of care reporting requirements using any (or all) of the SNOMED CT analytics techniques described before, including subsets, subsumption, defining relationships and description logic. Examples of point of care reporting requirements may include:

• Helping clinicians remember preventative services (reminders)

• Identifying patients with care gaps and risk factors

• Monitoring patient compliance with prescribed treatments

• Reporting clinical data to registries, such as cancer, stroke, and infectious disease registries

• Billing and reimbursement

When supporting a reporting requirement in which double counting must be avoided (such as statistical reporting, administrative reporting, billing, or reimbursement), SNOMED CT codes can be mapped to statistical classifications (such as ICD-9 and ICD-10).

When the source data uses a coding system without the same reporting capabilities as SNOMED CT, or when a variety of coding systems are used, coded data can be mapped into SNOMED CT to support the reporting requirements.