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.

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.

The SNOMED CT analytics techniques described in the previous chapter only become useful when performing a specific analytics task intended to meet a business need. In this chapter, we consider a range of analytics tasks, which are either enabled or enhanced by using these SNOMED CT techniques.

The analytics tasks which can benefit from the use of SNOMED CT techniques can be considered in three broad categories:

• Point of care analytics, which benefits individual patients and clinicians. This includes historical summaries, decision support and reporting.

• Population-based analytics, which benefits populations. This includes trend analysis, public health surveillance, pharmacovigilance, care delivery audits and healthcare service planning.

• Clinical research, which is used to improve clinical assessment and treatment guidelines. This includes identification of clinical trial candidates, predictive medicine and semantic searching of clinical knowledge.

Many of these tasks use business intelligence capabilities, similar to those used in other sectors, such as manufacturing, retail and transportation. Business intelligence is the provision of historic, current and predictive views of information. Such services include reporting, online analytical processing (OLAP), data mining, process mining, complex event processing, benchmarking, text mining, predictive analysis and prescriptive analytics. In many cases, a data warehouse is used as the platform on which these services are provided.

The combination of these business intelligence techniques with the capabilities of SNOMED CT creates new opportunities to improve healthcare delivery.

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

Population-based analytics encompasses those analytics services that benefit entire populations, including trend analysis, public health surveillance, pharmacovigilance, care delivery audits and healthcare service planning. Population-based analytics contributes to public health programs by helping to identify health threats, inform public policy and manage healthcare resources.

Efficient healthcare delivery and service planning depends on high quality clinical data. Clinical data is typically scattered between multiple different healthcare providers using different clinical systems. Collating this information for analysis requires both standardized terminologies and common information models. Identifying relevant and useful facts in large volumes of collated data also requires this data to be accurate, meaningful and machine processable.

SNOMED CT supports population-based analytics in a number of ways. Firstly, it enables more accurate capture of clinical data by allowing it to be represented at the appropriate level of clinical detail. Secondly, it supports the integration of disparate clinical data sources by serving as a reference terminology into which free text and other code systems can be mapped. And thirdly, it enables more meaningful and powerful queries to be performed over the data using the descriptions, hierarchies and logic-based definitions of each concept.

Vendor products, which provide population health solutions include Caradigm's Intelligence Platform, Allscript's Clinical Quality Management and Clinical Performance Management tools, Cerner's PowerInsight® Data Warehouse and Epic's analytics and reporting suite.

SNOMED CT can be used to assist the process of identifying clinical trial candidates for recruitment into formal clinical trials. Subsets of findings, procedures or medications can be used to filter trial candidates based on their clinical conditions or treatments. Subsumption techniques can be used to identify suitable candidates, irrespective of the level of granularity in which their clinical data is stored. SNOMED CT defining relationships can be used in a number of ways – for example, identifying patients with diseases of specific anatomical sites, with certain morphologies; patients who are taking medications with specific ingredients or dose forms; and patients who have had procedures on a specific body site.

Commercial tools, which can be used to support clinical research include Cerner's clinical research module (PowerTrials), which offers patient identification functionality.

Clinical research is a branch of healthcare science that determines the safety and effectiveness of medications, devices, diagnostic products, and treatment regimens intended for human use. Clinical research may be used for the prevention, treatment, diagnosis or for relieving symptoms of a disease. In contrast to clinical practice, which applies established treatment regimes, clinical research collects evidence to extend knowledge and establish the value of novel treatments and other patient management practices.

Clinical research typically involves the analysis of data from well-defined and homogenous groups of patients with a specific disease, at a specific stage, receiving similar treatments and often without significant co-morbidities. The data may be captured prospectively or retrieved retrospectively.

SNOMED CT helps clinical research activities by assisting in the identification of clinical trial candidates, enabling the powerful analysis of trial data, supporting predictive medicine, and improving the effectiveness of semantic search over clinical knowledge.

In this section, we discuss three key aspects to clinical research that can benefit from the use of SNOMED CT: identification of clinical trial candidates, predictive medicine, and semantic search.

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.

Pharmacovigilance is the collection, detection, assessment, monitoring and prevention of adverse effects with pharmaceutical products. It is concerned with identifying the hazards associated with pharmaceutical products and minimizing the risk of any harm that may come to patients. An important part of pharmacovigilance is postmarketing surveillance, which monitors the safety of a pharmaceutical drug or medical device after it has been released on the market. Since drugs are approved on the basis of clinical trials, which involve relatively small numbers of people, postmarketing surveillance plays an important part in further refining, confirming or denying the safety of a drug in the general population.

Pharmacovigilance uses a number of data sources to assess and monitor the safety of licensed drugs, including clinical trial data, medical literature, spontaneous reporting databases, prescription events, electronic health records, and patient registries. Data mining of large volumes of clinical data can be used to highlight potential safety concerns. However, current mechanisms to analyze this data is often both costly and insensitive.

The availability of large datasets of richly encoded SNOMED CT data within longitudinal healthcare records can greatly assist pharmacovigilance. Where SNOMED CT is not used natively to capture clinical data, free text narrative and other code systems may be mapped to SNOMED CT to support a homogeneous approach to querying across diseases, signs and symptoms, lab results, medications, devices, procedures, allergies, adverse reactions, body sites and substances. SNOMED CT's polyhierarchy and defining relationships, which provide links between these domains provide a rich source of meaning-based information across which queries can be performed.

Many drug regulatory authorities and pharmaceutical companies currently use the Medical Dictionary for Regulatory Activities (MedDRA) to classify adverse drug events. MedDRA is an international standard adverse event classification used from pre-marketing through to post-marketing activities. However, as MedDRA was not designed to support routine clinical data collection, its penetration into clinical systems is limited. Therefore mapping from SNOMED CT to MedDRA would enable both styles of analysis and reporting to be performed from the same clinical data. The UK Medicines and Healthcare products Regulatory Agency (MHRA) is working (with input from the MedDRA Maintenance and Support Services Organization) to develop a mapping from a subset of SNOMED CT to MedDRA for this purpose.

Predictive medicine involves predicting the probability of disease and implementing measures to either prevent the disease altogether or significantly decrease its impact upon the patient. The outcomes of predictive medicine are often applied to the care of individual patients, but may also inform the deployment of resources to entire populations at high risk.

The goal of predictive medicine is to predict the probability of future disease so that healthcare professionals and the patient themselves can be proactive in implementing lifestyle modifications and increased physician surveillance, such as regular skin exams, mammograms, or colonoscopies. Predictive medicine changes the paradigm of medicine from being reactive to being proactive, and has the potential to significantly extend the duration of health and to decrease the incidence, prevalence and cost of diseases.

Much attention has been focused on the availability of genetic makers of vulnerability to specific illnesses. However the accurate capture of phenotypic (e.g. height and weight, blood pressure), environmental factors (e.g. smoking, alcohol consumption) and other lifestyle factors (e.g. exercise, nutrition, quality of life) is not to be overlooked. For example:

1. Patient is a smoker and has ischemic heart disease ? predict excess risk of myocardial infarction

2. Patient has BRCA1 gene and is a 40 year old woman ? predict (excess) risk of breast cancer

SNOMED CT can help to support predictive medicine by:

• Helping to identify clinical trial candidates

• Helping to analyze clinical data, such as family history, lifestyle and environmental findings, to improve predictive capabilities

• Providing a link between patient data and risk assessment rules, so that rules can be triggered based on subsumption of codes recorded in clinical data. For example, matching against patient records could be improved by defined the above rules as:

Risk: 22298006 |myocardial infarction| 2. Criteria: 412734009 |BRCA1 gene mutation positive| Risk: 254837009 |breast cancer|

Trend analysis is the practice of collecting information and attempting to spot a pattern, or trend, in the information. Trend analysis often refers to techniques for extracting an underlying pattern of behavior in a time series, which would otherwise be partly or nearly completely hidden by noise.

Detecting changes of either incidence or prevalence of a particular disease, treatment, procedure or intervention over time has major utility for population health monitoring, prediction of demand and effective resource allocation at enterprise and national levels. One challenge that is encountered when analyzing routinely collected patient data for trends, is distinguishing minor changes in coding style from real changes in disease incidence. Simply counting the use of individual concept identifiers may be highly misleading. For example, a fall in the use of the code 22298006 |myocardial infarction| might reflect a shift to using more specific codes (such as 314207007 |non-Q wave myocardial infarction| or 304914007 |acute Q wave myocardial infarction|), rather than a reduction in the incidence of myocardial infarctions. Use of subsumption testing on SNOMED CT encoded data can enable higher level trend analysis to be performed over more specific coded data.

SNOMED CT's polyhierarchy allows trends to be analyzed from multiple perspectives. However, deciding which level of aggregation to use for trend analysis can be arbitrary. Novel approaches to this task are emerging as the demand for trend analysis over SNOMED CT enabled data increases.

The UK Data Migration Workbench, for example, includes a trend module which analyses the frequency with which individual SNOMED CT codes are used in the Electronic Patient Record (EPR) instance data, looking for those whose recording frequency has changed over the course of the data collection period. It also includes an Induce module, which performs a more sophisticated analysis of case mix and caseload trends within a clinical department. Instead of returning the most frequently used individual codes, the Induce module identifies the most frequently used types of codes. For example, an emergency department may use roughly 500 different SNOMED CT codes for a laceration in a particular anatomical location. While none of the site-specific codes may appear in a list of most common codes, the descendants of 312608009 |laceration| may collectively account for a significant part of the department's workload.

The algorithm used picks aggregation points at defined levels for analysis. The default setting finds roughly 100 sub-trees within the SNOMED CT hierarchy, where each sub-tree accounts for a more or less constant proportion of all coded episodes (around 1% of all coded events per sub-tree). The algorithm completes once the set of all codes within all identified sub-trees collectively accounts for the large majority of the dataset being analyzed. When applied to real emergency department attendance data, relatively low numbers of presentations (about 0.2%) were coded as occurring primarily as a result of endocrine disease. As a result, in order to get a big enough grouping of episodes, the algorithm chooses 362969004 |disorder of endocrine system| as the root of a single sub-tree covering these reasons for the patient's attendance. By contrast, a very high proportion (9.4%) of presentations relate to some subtype of 928000 |disorder of musculoskeletal system|. Therefore this part of the caseload is aggregated under multiple more granular sub-trees, including (separately) burns, abrasions, lacerations, blunt injury, crush injury and foreign body.

These code aggregations can then be tracked across time to reveal trends in demand, disease incidence or resource utilization.

With an ever increasing volume of medical literature and clinical reports, it is becoming increasingly important to be able to meaningfully search this information. A major application for Natural Language Processing technologies is to index collections of free text transcripts or documents such that topic specific searches may be run on them. The challenge is to move beyond the limitations of plain keyword searching strategies towards more advanced search techniques, which return ranked matches with high sensitivity and specificity. Clinical searches may be performed over documents within an electronic library, within medical records, or on the internet. Examples of searches include:

• "Show me articles on this website concerned with inflammatory bowel disease"

• "Does this patient have transcripts in their record suggesting a heart rhythm disturbance?"

SNOMED CT was used in techniques developed by Koopman to improve search performance by addressing vocabulary mismatch (using synonyms, e.g. hypertension vs high blood pressure), granularity mismatch (using hierarchical relationships, e.g. antipsychotic vs haloperidol), conceptual implication (using defining relationships, e.g. from renal cyst infer kidney) and inferences of similarity (e.g. using subset membership, e.g. comorbidities anxiety and depression). Koopman also assigned a measure of similarity to each SNOMED CT relationship type, and use this weighting to determine the relevance of each document.

Some commercial tools also provide semantic search, including Cerner's semantic search tool.

Clinical audit seeks to improve patient care and outcomes through systematic review of care against defined standards and the implementation of change. It informs care providers and patients where their healthcare services are doing well and where there could be improvements. The key component of clinical audit is that performance is reviewed (or audited) to ensure that what should be done is being done , and if not it provides a framework to enable improvements to be made.

Clinical audits can be performed in primary care facilities, individual clinics, hospitals, enterprises or jurisdictions. Audit can have major beneficial impacts in ensuring the consistent delivery of quality healthcare. The questions asked in audit are often chosen pragmatically according to local data collection practices. For example

• What proportion of patients invited to attend cervical screening did so?

• How many patents with ischemic heart disease are receiving appropriate drug treatments?

• Are all patients with diabetes mellitus reviewed within a stated time interval?

Current audit schemes use a combination of reporting against the classifications or questions specifically collected for audit purposes. SNOMED CT will facilitate an increase in such audits being able to collect some of the data by extracting data from the patient record thus reducing the additional burden of collection; it will also enable more a more accurate picture from say tertiary centers where some of their procedures may fall into the NOS or NEC classification and provide an unrepresentative comparison with other centers when the procedures are complex and innovative/new.

SNOMED CT is well suited to service the ad hoc requirements that emerge in clinical audit questions, using the techniques described before. Using the SNOMED CT codes recorded during care delivery can reduce the additional burden of data collection specifically for audit purposes. SNOMED CT may also facilitate more accurate audit results than classifications, by distinguishing between distinct concepts (e.g. clinical findings or procedures) which may fall into the 'Not Otherwise Specified' or 'Not Elsewhere Classified' categories in these classifications.

A number of vendor products, such as Cerner's PowerInsight® Data Warehouse, are able to support clinical audit using SNOMED CT enabled analytics tools.