Introduction

This tutorial page is intended for i2b2 administrators who are new to i2b2 and clinical informatics. Maybe you've been asked to deploy i2b2 for your institution. Maybe your institution already has an i2b2 instance, and you've just inherited it. You are curious about how things work and how to set up or modify the ontology database. This page will bring you some insight.

Part 1: Ontology Basics

What is an ontology?

"Ontology" represents the branch of Philosophy that discusses "existence." But in Information Science, an "ontology" is a way of listing the attributes of a subject area and showing how these attributes are interrelated. An ontology for a subject area or "domain" consists of a set of terms that represent the entities in that domain, along with a set of expressions that define how the entities in that domain are related to each other.

In i2b2's domain of Clinical Research Informatics, an ontology is a collection of healthcare-related "concepts" or terms. These terms represent the various categories of information in clinical and translational science. Categories such as Patient Demographics, Diagnoses, Procedures, Laboratory Tests, and Medications are described using hierarchical lists of "concepts" in an i2b2 ontology.

Unlike some other ontologies in Information Science, an i2b2 ontology does not typically define relationship expressions among the concepts or entities. Therefore, strictly speaking, an i2b2 ontology is generally more akin to a "taxonomy," which is a hierarchical group of terms or concepts, without any expressions defining the relationships among them.

For instance, if you recall (from your high-school biology class) Linnaeus's "binomial nomenclature" model of how to name living organisms, then you already know what a taxonomy is. As another example, a Camry is a Toyota, which is an automobile, which is a type of motorized, wheeled conveyance.

Why does i2b2 have an ontology?

An i2b2 instance requires an ontology so that researchers can locate concepts of interest and locate patients whose medical records contain these concepts.

The healthcare concepts contained in the ontology are the building blocks of an i2b2 query. The i2b2 ontology is represented in the upper-left corner of the i2b2 webclient (user interface). See the screenshot below.

These concepts are rendered as a collection of nested folders and generally represent child-parent relationships. Researchers locate these concepts by using a hierarchical tree. When you use the demo i2b2 site, you will see the i2b2 ontology tree on the upper-left side of the user interface. 

In this screenshot, we would say that i2b2 is displaying an ontology tree with 13 top-level ontology trees or categories, namely: Clinical Trials, Custom Metadata, Demographics, Diagnoses, etc.

Here are some examples of child-parent (or is-a) relationships found in i2b2's ontologies of healthcare concepts:

• diabetes mellitus is an endocrine disorder, which is a type of diagnosis; it will be found in both the "Diagnoses" tree and in the "Diagnoses (ICD10)" tree;
• aspirin is a non-steroidal anti-inflammatory drug, which is an analgesic, which is a type of medication; it will be found in the "Medications" tree;
• tonsillectomy is a type of surgery, which is a procedure; it will be found in the "Procedures" tree;
• hemoglobin A1c is a blood test, which is a type of laboratory test; it will be found in the "Laboratory Tests" tree; and
• race is a patient-demographic descriptor; it will be found in the "Demographics" tree.

Here is another screenshot, where you can see the expansion for "Race" in the "Demographics" tree:

Where do i2b2 ontology "concepts" come from?

Typically, the concepts that make up the various categories or "domains" are based on medical terminology standards published by various institutions. Concepts can also be created ad hoc for a specific i2b2 instance by the i2b2 administrator at that site. (For more information on custom metadata, please see Ontologies 201.)

The commonly used standards representing basic structured clinical patient data are represented by a number of different "coding schemes" — ICD-9, ICD-10, CPT-4, HCPCS, SNOMED CT, LOINC, RxNorm, UMLS, and VA Classes. See Appendix B for further information.

There are many terms related to i2b2 ontologies that are new to me. Is there a Glossary?

Yes! Here is a Glossary that provides explainers for many of the key terms related to i2b2 ontologies and the i2b2 architecture.

Part 2: Choosing Your Ontology

Do all i2b2 instances share the same i2b2 ontology?

No. Several i2b2 ontologies have been developed and are openly available for use. Any organization may also modify an existing ontology for its own use, or develop a new ontology. (See Ontologies 201.)

The i2b2 software includes i2b2 ontologies that you can use right away. There are additional ontology trees that can substitute for, or can be added to, the standard i2b2 ontology trees. (See Appendix E – Advanced Ontologies.)

Which ontologies can I use right away ('out of the box')?

The i2b2 database-loading modules come with at least 3 sets of "metadata" or ontology trees. These are the demo ontology, the ACT ontology, and the ACT-on-OMOP ontology. 

To view and compare details of the domains and coding schemes included in each ontology, see Appendix B.

How should I choose which ontology to use?

This is a key question/concept.

The ontology you choose depends on your institution's goals and patient data. If your goal is simply to understand and show how i2b2 works, then the i2b2 demo ontology is sufficient. If your goal is to set up i2b2 for research use, one of the ACT ontologies will be far more useful. The ACT ontologies are more modern, more robust, and will satisfy the needs of more researchers.

Furthermore, any institution that is planning to join the ENACT Network will need to use an ACT ontology to ensure compatibility with the other institutions in the network.

When setting up the patient data for i2b2, your institution needs to decide how it will conduct the ETL mapping from the EHR to the i2b2 CRC database. If your patient data are going to be set up only for i2b2 and SHRINE use, then many institutions set up the patient data in the default i2b2/tranSMART Common Data Model (CDM). In this case, they typically use the ACT Ontology with all its various coding standards; the patient data from the EHR system need to be mapped to the multiple coding schemes in the ACT Ontology as they are loaded into the CRC database.

If your patient data are going to be set up in a database for queries by other systems besides i2b2, then many institutions use the OMOP Common Data Model (CDM) for their patient data in the CRC database. In this case, they typically use the ACT-on-OMOP Ontology, which relies chiefly on the SNOMED CT coding standard; the patient data from the EHR system need to be mapped to the SNOMED CT coding scheme as they are loaded into the CRC database.

Part 3: How Ontologies and Patient Data Are Tied Together

How does it work? How does the i2b2 ontology make the patient data queryable?

First, a little more terminology. The i2b2 "patient data" are stored in the i2b2 Clinical Research Chart database, or "CRC database." The concepts that describe those patient data are known as the "metadata." The metadata are stored in the i2b2 Metadata database, or "ONT database."

The i2b2 metadata in the ONT database work together with the patient data in the CRC database. Each patient "observation" in the CRC database must have a code associated with it, and that code must match a healthcare concept — diagnosis, procedure, medication, lab test, demographic descriptor, etc. — that exists in the ONT database. So the CRC patient data will be queryable in i2b2 only if the patient facts in the CRC database are recorded utilizing standard codes that are referenced in the i2b2 metadata (ONT database).

For instance, if the patient's electronic health record indicates that the patient had the procedure "tonsillectomy with adenoidectomy," then that fact needs to be recorded in the CRC database using the standard code for that particular procedure. In the case of using the default i2b2 Procedures metadata tree, that code would be "ICD9:28.3". When a researcher makes a query in the user interface for "tonsillectomy with adenoidectomy," that query will be translated by i2b2 into a query for patients in the CRC database who have the code ICD9:28.3 in their data records.

What's the relationship between the metadata and the patient data?

This is another key question/concept.

The earlier question about "How does it work? How does the i2b2 ontology make the patient data queryable?" introduced the concept of how the patient data in the CRC database work in tandem with the metadata in the ONT database to allow i2b2 to conduct queries for the researcher.

As a reminder from the earlier answer: the metadata spell out all the various healthcare concepts or terms that a researcher may wish to query for in the user interface, and the patient data must be coded in such a way that they reflect the codes found in the metadata. Only when a patient's codes match the codes in the query terms from the ontology will the patient be counted as part of a query result.

But there is more to the relationship between the ONT and CRC databases. One additional topic is the i2b2 "project." The other additional topic is the "secret sauce" of the relationship.

i2b2 Projects

The pairing of a metadata database with a patient database (ONT plus CRC) defines a "project" in i2b2. For instance, the pairing of the i2b2 demo ontology with the i2b2 demo patient data would define a "demo" project in i2b2. In that same i2b2 instance, an institution could create a second ONT database for the ACT ontology, and a second CRC database for the institution's actual patient data; the pairing of those two new databases would define a genuine research project in i2b2.

It's important to note that each ONT database is designed to be associated with only one CRC database; that is, each ONT database is designed to belong to only one i2b2 project.

The converse is not true. Each CRC database is designed to be paired with any number of ONT databases; that is, each CRC database may belong to multiple i2b2 projects.

The instructions for creating multiple projects are outside the scope of this Ontologies tutorial page.

Metadata "Secret Sauce"

There is a special metadata table that resides in the CRC database. This metadata table is called the CONCEPT_DIMENSION table. It resides in the CRC patient database, and it is part of the i2b2/tranSMART patient CDM, but it is still definitely metadata. You may consider this the "secret sauce" that joins the metadata with the patient data.

So what is the role of this metadata in the CRC database?

The ONT database provides a hierarchy of healthcare concepts, and each concept is identified in the ONT database by a unique "path." The concept's path resembles the path of a file in a computer filesystem. Just as directories in a computer filesystem are hierarchical, so the paths of the concepts reflect the hierarchy of healthcare concepts. In the CRC database, all the healthcare concepts (diagnoses, medications, procedures, and measurements) are coded in the patient records using coding schemes like ICD, RxNorm, CPT, and LOINC; the patient data do not include the hierarchical paths from the ONT database. The role of the CONCEPT_DIMENSION table is to map each hierarchical path from the ONT database to its matching code in the CRC patient data. It's this mapping that allows a path from the ONT database to be used in a query on the CRC database; without this mapping, i2b2 would not be able to match ONT concepts with CRC patients.

The CONCEPT_DIMENSION table provides the mapping between all the healthcare concepts (paths) present in an ONT database and the diagnosis, procedure, medication, and measurement codes present in that ONT database's associated CRC patient data. (If a CRC database is associated with more than one ONT database — more than one project — then the CONCEPT_DIMENSION table would need to include the mappings for all the relevant hierarchical paths from all the associated ONT databases for that CRC database.)

How do I deploy my chosen ontology tree?

<Add links here to link to the appropriate sections of the installation instructions>

What should my metadata database look like when I am done?

When your metadata database is installed, it will have the following tables (per i2b2 v1.8.1):

• TABLE_ACCESS: a configuration table that tells i2b2 which ontology tables to load into the user interface (required)
• SCHEMES: a configuration table that tells i2b2 which coding schemes are being used in the ontology tables (required, but may be empty)
• one or more "ontology" tables (required):
  • each ontology table will typically consist of a hierarchy of concepts for a particular category or domain of healthcare data; for instance:
    • in the i2b2 demo ontology, the I2B2 table contains concepts for patient demographics
    • in the ACT ontology, the ACT_DEM_V41 table contains concepts for patient demographics
• CUSTOM_META: an empty ontology table; may include custom concepts added by you
• totalnum and totalnum_report: tables dedicated to recording the number of patients who have the concepts present in the ontology trees; these tables are NOT displayed to the user as part of the ontology tree in the user interface; these table are empty until you run a "totalnum" report (see Ontologies 102 – Patient Counts ("totalnum"))

After installation, all required tables will be populated, indexed, and ready to use.

After installation, there will also be a number of SQL-based procedures added to the ONT database, to prepare for running the "totalnum" report

This table shows all the tables you should expect to find in your ONT database when you have loaded your i2b2 ontology:

In addition, in your CRC database, there should be a CONCEPT_DIMENSION table.

Suggested Next Steps?

• Visit Appendix C – Ontology Tables to learn more about the structure of the ontology database.
• Visit Appendix D – Test Queries to learn how to run some "sanity check" queries on your new database.
• Visit Ontologies 102 – Patient Counts ("totalnum") to learn how to add patient counts to your ontology concepts in the user interface.