Analytics Overview

Definition

The term 'analytics ' is used broadly in this document to describe the process of extracting useful information from healthcare data.

Most analytical processes are driven by database queries. A 'query' is a means for retrieving information from a database consisting of a machine readable question presented to the database in a predefined format. Queries are used to inform or contribute to a human-readable report or produce a machine-actionable response. A human-readable report may be a list of patients, a graph, historical or projected resource utilization figures, or a summary dashboard display. Machine-actionable responses may include populating an order for a new laboratory test, based on the results of a previous test, or placing an order to restock medical devices on a hospital ward.

Scope and Purpose

Full benefits of electronic health records only accrue with the implementation of effective retrieval and reuse of clinical information. The scope of analysis of health record data may cover:

• An individual patient, across time and/or care providers;

• An individual healthcare worker;

• Patient groups or cohorts, based on demographics, diagnoses, treatments or interventions;

• Enterprise groups, based on teams, wards, clinics, institutions or providers;

• Geographical groups, based on a local area, town, region or country.

The following figure illustrates the three main purposes of analytics with SNOMED CT. These are:

1. Clinical assessment and treatment;

2. Population monitoring; and

3. Research.

Figure: Purposes of analytics with SNOMED CT

SNOMED CT may be used to support analytics that:

• Improves the care of individual patients by enabling:

  • Retrieval of relevant information that better supports clinicians in assessing the condition and needs of a patient

  • Clinical records to be integrated with decision support tools to guide safe, appropriate and effective patient care – for example, allergy checking and potential drug contraindications identified at the point of prescribing

  • Reduction in the duplication of investigations and interventions through the effective retrieval of shared information about the patient

  • Meaning-based sharing of clinical information that is collected by different members of the health care team at different times and places (and potentially in different languages)

  • Identification of patients requiring follow-up or changes to treatment based on updated guidelines

  • Wellness management, for example, using genetic and behavioral risk profiles.

  • Context-sensitive presentation of guidelines and care pathways within the user interface

  • Labor-saving decision support systems for clinicians

  • Adaptive pick lists in clinical user interfaces

  • Professional logs and performance tracking for clinicians

  • Work list generation, for example, patients requiring follow-up based on specific criteria

  • Workload profiling and monitoring.

• Improves the care of populations by enabling:

  • Epidemiological monitoring and reporting, for example, monitoring of epidemic outbreaks, or hypothesis generation for the causes of diseases

  • Audit of clinical care and service delivery

  • Systems that measure and maximize the delivery of cost-effective treatments and minimize the risk of costly errors

• Supports evidence-based healthcare and clinical knowledge research by enabling:

  • Identification of clinical trial candidates

  • Research into the effectiveness of different approaches to disease management

  • Clinical care delivery planning, for example, determining optimum discharge time

  • Planning for future service delivery provision based on emerging health trends, perceived priorities and changes in clinical understanding.

Substrates for Analytics

Analytics with SNOMED CT may be deployed on a wide range of data sources as summarized in the table below. These data sources are also known as the 'substrate' of the analytics. Please note that data which is not natively coded using SNOMED CT may be transformed using one of the techniques described later in this guide. These techniques may be used to transform heterogeneous data recorded using free text or a variety of code systems into SNOMED CT, which can serve as a common reference terminology for analysis.

Direct and indirect substrates for SNOMED CT based analytics

Analytics Substrate	Examples	Coding	Information Model
Unstructured free text document	Dictated clinical letter Typed discharge summary letter	Natural language	None or informal headings
Structured documents with free text fields	Assessment form Discharge summary form	Natural language	Standardized headings and fields
Structured documents with free text and post-coded classification (i.e. added by clinical coders after the clinical event	Discharge summary form with post-coded classification	Classifications (e.g. ICD)	Formal information model (typically simple)
Structured documents with non-SNOMED CT coding (e.g. proprietary, local or other coding system)	Standalone clinical application using departmental codes Enterprise-wide healthcare system using local dictionaries and pick-lists Electronic patient record using regional coding system (such as UK Primary Care systems)	Local code system, controlled vocabulary or legacy clinical terminology	Formal information model
Structured documents with SNOMED CT content	Cardiology report GP event summary	SNOMED CT	Formal information model
'Big data' data store	Data warehouse	Various coding systems	Mixture of both structured and unstructured data

Example of Approaches

There are a number of ways in which SNOMED CT can be used in systems to support analytics, including:

• Analyzing free text with clinical Natural Language Processing (NLP) techniques, which use SNOMED CT as a resource;

• Mapping coded clinical data from SNOMED CT to a classification, to enable analysis using the features of the classification;

• Querying clinical data using the machine-processable definitions of clinical concepts defined in SNOMED CT;

• Mapping clinical data captured using a variety of code systems into SNOMED CT, to enable analysis over heterogeneous data using a common reference terminology.

These approaches (and others) are described in more detail in the following chapters.