>> Jon Walter McKeeby: My name is Jon Walter McKeeby.
I am the chief information officer at the Clinical Center.
The Clinical Center is the hospital portion
of the NIH Intramural Program.
I oversee the EHR and all the clinical information systems
that support clinical research at the Clinical Center.
Today we're going to talk about common data elements.
We're presenting in four parts.
Part one is an overview to provide background information
about common data elements.
We want to be able to use common language
across different sites, studies, and clinical trials.
That way, we can compare and utilize existing standards
to conduct clinical trials and share information.
We want to ensure consistent data collection,
data analysis, and data reporting.
And so we're going to review a background --
what is data, information, knowledge, and wisdom,
review of quantitative versus qualitative data,
the need for data standards, form, and content, problems
with data, terminologies, interoperability standards,
FAIR guiding principles, common data elements,
data management and data sharing plans.
We'll review CDE usage at the NIH.
We'll review National Library of Medicine and data standards
usage at the institute and center level,
and the NIH CDE Task Force.
Data, information, knowledge, and wisdom model --
so data is that specific data discrete element.
It's a collection or organized, named,
but it's not having context yet. It's not information.
You add organization, interpretation,
and then data becomes information.
It becomes knowledge when it's integrated
into more information and understanding.
So within the EHR world,
the specific data element of a lab value is the data.
It becomes information when you identify the medications,
the individual, the patient,
and what's going on with the patient.
It becomes information.
Understanding what to provide in the sense
of what orders to place, what to do with the information,
how to treat the symptoms of the patient,
that is knowledge. It's wisdom when you now apply it
and take it outside the organization
and it's published in a journal. Then it becomes wisdom.
So this is data, information, knowledge, and wisdom model.
Quantitative studies -- measurable, numeric,
provides answers to the how many,
the how much, the how often. Data is fixed and factual.
We can utilize statistical analysis.
Discrete quantitative data is fixed numerical values.
Options are surveys and questionnaires,
analytical tools,
electronic health records that have discrete data,
vital signs, clinical information systems,
laboratory data, blood bank data.
They all provide quantitative data
that we can measure as part of our studies.
Qualitative -- descriptive language,
unstructured, subjective, dynamic.
We use that to analyze and identify themes.
Nominal data is used to label, categorize.
We're looking for clustering of information.
Examples of qualitative data we use for research,
again, electronic health records,
but the textual data, the report data,
interpretations, unstructured data,
clinical information systems such as lab,
radiology, blood bank, it's, again, the textual data,
the unstructured data, the interpretations.
Interviews, surveys, and questionnaires,
and observations are all examples of qualitative data.
It's important to know knowledge, information, wisdom,
and quantitative, qualitative data,
because they provide the foundation
that clinical data elements are standed
[phonetic sp] upon.
The problems with data --
inconsistent results, inconsistent formats,
inconsistent questions, sampling bias,
under-representation, misrepresentation,
interview quality, poorly validated questions,
respondents not answering truthfully,
changes in meaning, changes in coding strategies.
It makes it difficult to have consistent data
that you can utilize with your clinical research studies
to make valid decisions, valid results,
and have valid studies.
And so common data elements help resolve many of these issues.
So here's an example.
This is actually a true letter to my father
from somebody on the railroad.
My father's name was Thom, no A-S, T-H-O-M, McKeeby.
And the person wrote, "Please deliver this to Thom McKeeby.
He drives a train. He lives near the glass factory.
His brother-in-law is Doug."
And it was able to be delivered, right.
The post office knew who Thom was --
small town -- was able to deliver it.
But it's because that human was part of the process,
and they had cognitive understanding
of all the information that was here.
They relied on human knowledge and cognition,
standardization not required for the address.
But it's hard to share and process.
It's hard to share the knowledge.
And so then we moved --
the post office moved to standard formats,
right, where the street, you put the apartment,
the city, the state, the zip code, standard values,
format of the zip code, values for states,
two-letter abbreviations.
And that standardized it so we could share and process
letters quickly, efficiently, effectively.
And so that's example of where common data elements
and standards play into improve
clinical studies and clinical research.
And so some healthcare examples -- patient name format,
prefix, first name, middle name, last name, suffix,
comparing results across organization,
lab results, using coding methods such as LOINC,
the name of the task, the reference range,
the result values, the unit of measure.
An example of questionnaires with scales --
here's an example of a pain measurement scale, smiley face,
light blue going to red, from no pain to worse pain.
These are scales that are used and consistent
and allow the sharing of data
to be easy and efficient and effective.
NLM and data standards.
NLM is the central coordinating body
for clinical terminology standards
within the Department of Health and Human Services.
NLM works closely with the Office
of the National Coordinator for Health Information Technology
to ensure NLM efforts are aligned
with the goal of the president and the HHS secretary.
It's to improve and ensure interoperability
of health information technology infrastructure
to improve the quality and efficiency of health care,
allow the interoperability between systems,
between organizations, for the sharing of clinical data.
NLM HealthIT summarizes health information technology
and health data standards at NLM is also available.
So that's a link that you can follow
to see everything that's done for data standards at NLM.
Medical terminologies.
Medical terminologies facilitate precise and clear communication
by minimizing and eliminating ambiguity.
By having a code, you can identify that value,
that item, across different domains,
different research studies.
Some terminologies serve as a crosswalk
or a mapping between multiple other terminologies.
Key features of a medical terminology
is a unique identifier,
usually a code, an official name, and synonyms.
So it allows you to identify the item and its values
and what's possible,
ensuring that you can utilize it across systems.
And so here is example of medical terminologies.
Current procedural terminology, CPT,
used to describe procedures performed, used for billing.
International classification of disease, ICD-10,
sponsored by the World Health Organization --
main terminology used worldwide to identify diseases
and classification of diseases across the world.
LOINC -- Logical Observation Identifiers Names and Codes --
laboratory and clinical codes identifying tasks,
so you know that a sodium is a sodium across the world
based on its LOINC code, and any other test.
Medical subject headings -- created and used by NLM,
used to tag medical abstracts with concept-based information,
allows authors and searchers to use different terminology
for the name concept.
National drug codes identify codes for the medications,
so you know what product that you're utilizing.
It's typically used for billing reimbursement,
but it's also used to identify
what medications are being used in a study.
RxNorm -- produced by NLM and is included as part of UMLS.
It's a standardized clinical drug terminology naming system,
a curated collection of drug names, has synonyms,
has a thesaurus to identify ingredients of a medication,
components of a medication,
but you can share that information.
SNOMED-CT, created by SNOMED International --
it is managed by NLM,
and it's used to identify diagnosis,
concepts, different information to codify unstructured data
to make value with it.
And then Unified Medical Language Systems, UMLS,
it's a collection of over 100 controlled vocabularies
in biomedicine and health.
It maps concepts between vocabularies
and enables linking between systems.
And so the United States Core Data Interoperability
provides standards to identify components
of data elements, data classes, to be able to share.
So specific standardized elements
include items such as patient name,
sex assigned at birth, ethnicity, smoking status,
date of birth, preferred language,
race, lab values, procedures,
medication allergies, immunization,
vital signs, medications, and goals.
It's both format as well as values,
so sex being coded to female,
male, and unknown, values for ethnicity,
a format for date of birth.
These are all part of interoperability standards
to share information, again,
put in some common data element component
to make it easy to share information
to use for analysis studies,
but also to convey to other systems,
to interface to other systems.
And so here is the example of vocabulary for ethnicity group,
so Hispanic or Latino, not Hispanic or Latino.
There's a concept code to it,
so then you can share this information.
And then you see the race category,
so the main level of race categories:
American, Indian, or Alaskan Native,
Asian, Black or African American,
Native Hawaiian or Other Pacific Islander, and then White.
And then each one of these can be broken down to another level
if you need to go to a deeper level of American Indian
or Alaskan Native, sub-level of Asian,
sub-level of Black or African American,
a sub-level of Native Hawaiian
or Other Pacific Islander, or sub-level of White.
So each of them have a hierarchical structure.
Health Level Seven, HL seven,
is that main interoperability standard
used to share information between systems.
So if you have a laboratory information system,
an electronic health record, you want to provide the orders
from the electronic health record
to the laboratory information standard
utilizing HL seven.
It identifies the time and date of the message,
the patient, any next of kin information,
and information of orders,
and then it can convey the results back
to the electronic health record.
And so this is a standard format created in 1987.
It has an ANSI-accredited standard,
and again, it's focused on the exchange, integration,
sharing, and retrieval of electronic health record.
The FAIR guideline principle -- so findable,
assessable, interoperable, reusable --
and there's a link for the FAIR principles.
And so these are the guidelines to make data usable, beneficial,
shareable, consistent -- ensures the integrity.
And so findable, finding that there's sufficient metadata --
there's a unique code
that identifies the value of the item.
My example of a sodium is a sodium across systems
based on the LOINC code.
So the instrument that produces the sodium result
will have a code that you can search
and identifies that it's the sodium result.
Assessable -- to be assessable, metadata,
as well as the data itself,
should be readable by humans and machines.
It must reside in a trusted repository
that you can access and utilize between systems,
between humans, as well as for different items
such as research as well as clinical purposes.
Interoperability -- data must share a common structure.
It must be recognized, formal terminologies for description,
so the format as well as the code for the item.
And reusable -- data and collections
must have clear usage licenses, clear provenance,
meet relevant community standards for the domain.
And so there's sub-categories for each.
So findable -- metadata is assigned a globally unique
and persistent identifier.
Data are described with rich metadata.
Metadata is clearly and explicitly include
the identifier the data they describe.
And the metadata is registered or indexed
in a searchable resource.
Accessible -- metadata is retrievable by identifier
using a standardized communication protocol.
The protocol is open, free, and universally implemented.
The protocol allows for an authentication
and authorization procedure, and the metadata is accessible
even when the data are no longer available.
So they have to be always available.
Interoperability -- the metadata uses a formal,
accessible, shared,
and applicable language for knowledge representation.
Utilizes vocabularies that follow FAIR principles.
The metadata data included qualified references
to other metadata.
And then reusability --
the metadata are richly described
with a plurality of accurate and relevant attributes.
The metadata is released with a clear
and accessible data usage license,
so you know how you can use it, when it's available,
what you need to identify, when you use it.
The data is associated with a detailed provenance,
so you know the value of the source
and integrity of the metadata.
And the metadata meets the main relevant community standards --
so for healthcare, HL7.
For financial, there's other standards,
so each domain might have its own standard.
Challenges with the FAIR guideline principles --
not everybody wants to share data,
and that's true with common data elements.
Not everybody wants to share their research questions.
Not everybody wants to make it available
for other people to do the same research.
And if you do not measure something the same way,
you're not measuring the same thing.
So in the common data element world,
if I want my study to be across multiple sites
and my question is different,
I'm not measuring the same thing.
So that's true to FAIR guiding principles,
and it's true to common data elements.
So common data elements -- a standardized,
precisely defined question
paired with a set of specific allowable responses,
used systematically across different sites,
studies or clinical trials,
ensure consistent data collection.
CDEs can be grouped into sets to form question surveys,
case report forms, or other instruments.
They are defined unambiguously in both human and machine
computable terms.
The benefits -- reduced time
and cost to develop data collection tools,
because you have the questions. You have the elements.
You have what the possible results can be.
So again, it could be gender at birth,
or it could be a lab test,
or it could be a question on a questionnaire.
Standardized and consistent data collection,
improved data quality and data sharing,
meeting those fair standards that we talked about
three slides ago allow for meta-analysis
and comparison of results from different studies.
If it's a lab result and you have a code and it's utilized,
then you can compare that sodium or that calcium result,
because you have the LOINC code
and they are identified as being the same.
If you have a question on a questionnaire and you --
it's coded, it has a metadata,
and you have content that the answers can be,
you can then compare the results for that question.
And so it allows that meta-analysis
and comparison of results.
Implementing CDE questions -- how is data released?
How is data shared?
How do you ensure the meaning of information across sites?
How can you use external sources of data?
Implementing CDE -- the purpose, to identify discrete,
defined items for data collection,
to promote consistent data collection in the field,
to eliminate unneeded or redundant data collection,
to promote consistent reporting and analysis,
to reduce the possibility of error
related to data translation and transformation,
and to facilitate data sharing.
Here's examples of clinical research typical data elements.
So from chapter 30 in the data management
and clinical trials book, on-study, demographic data,
eligibility criteria, family history, patient history,
surgical history, medication treatments,
laboratory results, radiology reports,
current symptoms, adverse events,
treatment response, disease status,
protocol treatment, non-protocol treatment,
toxicities, date of death,
cause of death are typical clinical research data elements.
The importance of common data elements
also leads to the ability to complete data management plans.
So during the design and development of a clinical trial,
you need to establish the data elements to be collected,
the design of the data collection,
and the design of the computer database.
You're building your common data elements for your study,
and you want to utilize common data elements
that already exist to make the data management
and sharing more effective and efficient.
The process of data management should result
in an error-free, valid, and statistically robust database.
Data management plans are developed to document processes
and procedures of how you're going to do that,
how you're going to handle the data,
how are -- for each of your trials,
what databases you're going to use, what data entry,
how you're tracking your quality,
what quality control measures you have,
how you're tracking adverse event reconciliation,
how you're going to deal with discrepancy management,
data transfer, extraction.
All the database management guidelines
will be in your data management plan.
The data management plan's comprised of multiple parts,
and here's examples --
so description of all data sources,
data and workflow diagrams, definitions of data elements,
planned data model during data collection,
any changes for specific data sharing --
if you're adding terminologies, adding codes, adding metadata --
observations and measurements, your data recording process,
your data processing processes, your data integrations,
your data quality control definitions
and acceptance criteria, plans for business continuity,
your security plan, your change management plan
to anything that's part of your data management process,
your data retention, archival, and disposal plan --
how long you're going to keep the data.
What are you going to do with the data
when you're ending your study?
Do you have to keep it for a period of time?
What is that period of time?
And then your data sharing plan --
how are you going to share your data?
So principles of data sharing in clinical trials --
so making the data sharing a reality,
consent for data sharing,
protection of trial participants,
data standards, rights,
types, management of access, data management
and repositories, discovery, and metadata --
all these are part of clinical trial data sharing.
All these rely on common data elements,
and so it's important that you identify
your common data elements that you're doing to utilize.
And anything that you create, make it a common data element
to be shared across trials by others.
So coding and metadata in clinical trials --
data and coding standards
should be built into any trial's data design
at the beginning of the trial.
Various data standards are available.
Those from CDISC should be considered
as offering the best starting point.
Metadata schema suitable
for describing all the repository data objects
linked to the clinical trials to be developed and implemented,
agreed by the stakeholders and repository managers
and widely disseminated.
Mechanisms to sustain metadata repositories
for portal search systems that connect them long-term,
so they can be utilized for data sharing
but across clinical trials.