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.

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.

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.

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.