Data and Information Management, 2020; 4(3): 148–170
Review Article
Open Access
Marcia Lei Zeng,* Yi Hong, Julaine Clunis, Shaoyi He, L.P. Coladangelo
Implications of Knowledge Organization
Systems for Health Information Exchange and
Communication during the COVID-19 Pandemic
https://doi.org/10.2478/dim-2020-0009
received May 21, 2020; accepted May 23, 2020.
Abstract: This article aims to review the important
roles of health knowledge organization systems (KOSs)
during the COVID-19 pandemic. Different types of
knowledge organization systems, including term lists,
synonym rings, thesauri, subject heading systems,
taxonomies, classification schemes, and ontologies
are widely recognized and applied in both modern
and traditional information systems. Apart from their
usage in the management of data, information, and
knowledge, KOSs are seen as valuable components for
large information architecture, content management,
findability improvement, and many other applications.
After introducing the challenges of information overload
and semantic conflicts, the article reviews the efforts
of major health KOSs, illustrates various health coding
schemes, explains their usages and implementations,
and reveals their implications for health information
exchange and communication during the COVID-19
pandemic. Some general examples of the applications,
services, and analysis powered by KOSs are presented at
the end. As revealed in this article, they have become even
more critical to aid the frontline endeavors to overcome
the obstacles due to information overload and semantic
conflicts that can occur during devastating historic and
worldwide events like the COVID-19 pandemic.
Keywords: knowledge organization systems, health
terminologies, health information exchange, COVID-19
pandemic
*Corresponding author: Marcia Lei Zeng, School of Information,
Kent State University, Kent, OH, USA. E-mail: mzeng@kent.edu
Yi Hong, Department of Product Development, DeepThink Health,
Inc., Richmond, CA, USA
Julaine Clunis, L.P. Coladangelo, School of Information, Kent State
University, Kent, OH, USA
Shaoyi He, Department of Information and Decision Sciences, Jack
H. Brown College of Business and Public Administration, California
State University, San Bernardino, CA, USA
1 Introduction: Dealing with
Information Overload and Semantic
Conflicts
During a pandemic period, one common challenge is
information overload. “Information overload” is a term
popularized by Alvin Toffler in his bestselling book Future
Shock (1970). It refers to the difficulty a person can have
in understanding an issue and making decisions that
can be caused by the presence of too much information.
During times of crisis such as a global pandemic,
users need access to immediate information. This is
compounded by the fact that news reports from around
the world are constantly being updated, providing users
with yet more new information. This information may
not be clearly delivered if the language or keywords
referenced carry different meanings in different contexts.
Moreover, one may also wish to trace the methods or
criteria for collecting data used in data-driven messages
and decision-making. There may even be doubts about
or perceived discrepancies between the communicated
messages and the data originally collected and analyzed.
The challenge could grow dramatically when a report
needs to be communicated across languages, regions,
and cultures, especially if statistics were generated based
on different contextual information and gathered from
diverse resources.
Therefore, while struggling to obtain information on
the new developments regarding the pandemic within
one’s own community or as they are occurring in other
places, one would inevitably face information overload,
or even misinformation, that would lead to confusion,
uncertainty, worries, fear, stress, or anxiety. When people
are in a new and difficult situation, especially in a medical
emergency, the availability of information that can be
useful would be very crucial for decision making. On one
hand, people may be easily overwhelmed by “the virtually
unlimited amount of information available, information
Open Access. © 2020 Marcia Lei Zeng et al., published by Sciendo.
Attribution-NonCommercial-NoDerivatives 3.0 License.
This work is licensed under the Creative Commons
Implications of Knowledge Organization Systems for Health Information
Exchange and Communication during the COVID-19 Pandemic
that is often poorly organized and of highly variable
quality and relevance” (Jadad, Haynes, Hunt, & Browman,
2000, p. 262). On the other hand, for some people, such
information overload could lead to cyberchondria, an
excessive anxiety caused by obsessively searching the
Web for medical information, when they are frightened by
the wide-spread misinformation of severity and fatality of
COVID-19 (Laato, Islam, A., Islam, M., & Whelan, 2020). In
a recent paper published in the Asian Journal of Psychiatry,
Sahoo et al. (2020) reported and discussed two cases in
which two people separately attempted self-harming due
to their apprehension of developing COVID-19 and were
brought to the emergency room. The paper pointed out that
such tragedies were the outcomes of information overload
that caused normal people to develop severe anxiety and
depression leading to self-harming. When discussing the
important aspects to be considered for dealing with the
impact of COVID-19 pandemic on mental health, Fiorillo
and Gorwood (2020) defined information overload as
an “infodemic” that was based on their observation
of the spreading of a large amount of fake news and/or
uncontrolled information about coronaviruses that moved
faster than the coronavirus itself. Information overload
as an infodemic has been given attention by more and
more organizations as well as researchers. For instance,
Shaw, Kim & Hua (2020) have discussed the impact of
governance, technology and citizen behavior on coping
with the COVID-19 infodemic in East Asia. The World
Health Organization (WHO) launched WHO Information
Network for Epidemics (EPI-WIN), a new information
platform to overcome the infodemic around COVID-19
(Zarocostas, 2020).
Another major challenge is called “semantic conflict,”
which can occur within any data and information
communication process. A relevant example is the
naming of a new disease, including the reuse of previous
names that may share some similarities, the adoption
of a known accepted name which may carry different
meanings at different times of history, and the inclusion
of names of particular groups of people, places, or
animals based on the cases reported earliest. During the
2009 outbreak of H1N1 influenza in humans, controversy
started early on regarding the usage of various of terms by
journalists, academics, and officials, such as “swine flu,”
“pig flu,” “Mexican flu,” “North American influenza,”
etc. This time, before getting its official name, “COVID19,” the disease was called “Wuhan SARS,” “Wuhan
Flu,” and “Wuhan coronavirus.” The virus was also
referred as “China virus” and “Wuhan virus” repeatedly
even after the official name was announced. A search
for three of these labels—“Chinese Coronavirus,” “China
Coronavirus,” and “Wuhan Coronavirus”—on Google
Scholar in mid-April retrieved more than 1,280 items.
Another search of these three names on Google showed
millions of related hits. This situation demonstrates that
disease-naming-based semantic conflicts are not only an
issue of the past, but also an issue of the present, and the
future, thus necessitating further investigation. These
semantic conflicts undoubtedly added more confusion to
an environment already overwhelmingly characterized by
heavy information overload.
Furthermore, the challenges of information overload
and semantic conflict directly impact the whole domain
of healthcare, which deals with the maintenance or
improvement of health via the prevention, diagnosis,
treatment, recovery, or cure of diseases, illnesses,
injuries, and other physical and mental impairments
in people. Healthcare extends beyond the delivery of
services to patients, encompassing many related sectors,
and is set within a larger picture of financing and
governance structures (Wikipedia, n.d.). At the frontlines
of healthcare, the need to make instantaneous decisions
at various levels is crucially based on the available and
truthful data and information.
To deal with information overload and eliminate
semantic conflicts, controlled, standardized, and
shared vocabularies have become critical to information
exchange and communication during the COVID-19
pandemic. Under the guidance of international and
national health organizations and government agencies,
KOSs, such as the International Classification of Diseases
(ICD) maintained by the WHO,SNOMED Clinical Terms
(SMOMED CT) of the International Health Terminology
Standards Development Organization (IHTSDO), Logical
Observation Identifiers Names and Codes (LOINC) of
the LOINC.org, and Medical Subject Headings (MeSH) of
the U.S. National Library of Medicine (NLM), responded
promptly to the WHO’s announcement of an official name
for the disease caused by the 2019 novel coronavirus,
“COVID-19,” as evidenced by their actions discussed in
Section 2.1 of this article, followed by the background
about the purposes and usages of the major health KOSs
in Section 2.2.
Even when a new concept is identified, and the related
terminologies or naming conventions are controlled by
national and international level institutions, semantic
conflicts can still occur through the way concepts are
classified and defined. Incorrect diagnoses and cause
of death is a well-known problem with international
morbidity and mortality statistics (O’Malley et al., 2005).
This will be a particular problem with COVID-19 where
cause of death is either being over- or under-attributed
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to COVID. That concern is the rationale behind the use
of a classification system, which is considered to be a
fundamental approach to organizing knowledge. Because
of this issue that many communities, professions, and
subject disciplines have developed different ways to
classify things and organize their knowledge, the highlevel effort to producing a unified classification system
such as International Classification of Diseases (ICD) is
critical, which provides a common language for reporting
and monitoring diseases and has been used worldwide
for morbidity and mortality statistics. By providing
classification notations to represent concepts (refer
to Figure 1 as an example), ICD greatly enhances the
consistency of coding across languages, cultures, and
healthcare systems (refer to Table 1 in Section 2.1.2). In
thesauri and other types of KOSs which are not primarily
in a hierarchical structure, the broader and narrower
hierarchical relationships provide contextual information
about a particular concept. Non-hierarchical but related
concepts are presented in associated relationships.
The contextual information provided by KOSs ensures
consistency and helps to eliminate semantic conflicts.
In KOSs that do not use notations to represent
concepts, the dedicated record for a concept (which has
an assigned unique identifier) presents a standardized
preferred lexical label for each of the languages included
in the vocabulary. UNESCO Thesaurus,1 and the AGROVOC
thesaurus2 of the Food and Agricultural Organization
(FAO), both under United Nations (UN), are notable
examples (refer to Section 5.4). Each record also provides
terms and codes considered non-preferred but can lead
to a standardized preferred label (refer to the MeSH
record in Figure 2). The clarity provided by listing and
linking synonyms becomes important as naming is more
problematic early on and for selecting an official name
consensually. Synonym rings with alternate names could
then lead to the standardized terms, once established.
Preferred and non-preferred terms linked in synonym rings
have been widely used by search engines and information
retrieval systems to provide access to authoritative public
information such as news articles and reports that can
explain various factors for the causes, effects, risks, and
treatments, including recommendations for managing
personal decisions regarding a virulent infectious disease,
with terms and languages that are readily understood by
normal people. KOSs play a role in aiding the provision of
such accurate public information.
1 http://vocabularies.unesco.org/thesaurus/concept3505
2 http://aims.fao.org/aos/agrovoc/c_4ad07701
All KOSs’ fundamental structures and functional
requirements are defined by national and international
standards, with the main functions of eliminating
ambiguity, controlling synonyms or equivalents,
establishing explicit semantic relationships such as
hierarchical and associative relationships, and presenting
both relationships and properties of concepts in the
knowledge models (NISO Z39.19-2005, 2005; Zeng, 2008;
ISO 25964-1:2011, 2011; ISO 25964-2:2013, 2013). In addition
to providing a common framework and language for
healthcare domain practitioners (discussed in Section
2), various types of KOSs provide a structured way to
communicate complex concepts to the general public,
as evidenced by the selected cases of timely useful
information services (refer to Section 4 and Section
5). Similar to the case of COVID-19 (2019 – present),
such efforts can be seen during other recent epidemic
and pandemic periods including SARS ((Severe Acute
Respiratory Syndrome), 2002–2003),3 2009 H1N1
Influenza, (2009–2010),4 MERS ((Middle East Respiratory
Syndrome), 2012–present),5 and Ebola ((Ebola Virus
Disease), 2014–2016).6 In the next section, the selected
health KOSs demonstrate semantic structures that lead
to trustworthy and effective efforts to eliminate semantic
conflicts and make an impact on the global fight against
information overload during the COVID-19 pandemic.
2 Standardized Health KOSs and
Coding Guidance
2.1 Efforts of the Major Health KOSs during
the COVID-19 Pandemic
For the outbreak of a new viral disease, three most
important names have to be decided: (1) the disease, (2)
the virus, and (3) the species. The WHO is responsible for
the first (disease), expert virologists for the second (virus),
and the International Committee on Taxonomy of Viruses
(ICTV) for the third (species) (International Committee
on Taxonomy of Viruses, 2020). Establishing a name
for a new disease provides a shared understanding for
researchers and developers to discuss disease prevention,
spread, transmissibility, severity, and treatment. Viruses
are named based on their genetic structure to facilitate the
3 https://www.cdc.gov/sars/
4 https://www.cdc.gov/h1n1flu/
5 https://www.cdc.gov/coronavirus/mers/
6 https://www.cdc.gov/vhf/ebola/
Implications of Knowledge Organization Systems for Health Information
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Figure 1. ICD-10 emergency codes for COVID-19.
Image captured 2020-04-26. Source: https://icd.who.int/browse10/2019/en#/U07
development of diagnostic tests, vaccines, and medicines
(WHO, 2020a).
The following timeline reflects the recent efforts to
eliminate ambiguities and semantic conflicts through
naming of the disease led by the WHO and the actions of
the International Classification of Diseases, Tenth Revision
(ICD-10), as documented by the WHO’s press conferences
videos (WHO, 2020b) and the Centers for Disease Control
and Prevention (CDC, 2020). The next sub-section
provides a list of new codes and coding guidance from
standardized health KOSs, which are used in healthcare
workflows, in response to the WHO announcement of an
official name for the disease caused by the 2019 novel
coronavirus. ICD and other examples of standardized
KOS described below allow the world to compare and
share data in a consistent and standard way— between
institutions, across regions and countries, and over a
period of time. They facilitate the collection and storage
of data for analysis and evidence-based decisionmaking. Together they are contributing to the actions of
eliminating semantic conflicts and avoiding information
overload in real-world healthcare systems.
2.1.1 Naming and Classifying by WHO and ICD-10
2020-01-30. The WHO declared the 2019 Novel Coronavirus
(2019-nCoV) disease outbreak a public health emergency
of international concern.
• The term “2019-nCoV” was used instantly, e.g., in a
Science Jan. 31 article.8
7 DOI: 10.1126/science.367.6479.727
8 DOI:10.1126/science.367.6477.492
2020-01-31. As a result of the declaration, the WHO Family
of International Classifications (WHO-FIC) network’s
Classification and Statistics Advisory Committee (CSAC)
convened an emergency meeting to discuss the creation of
a specific code for this new type of coronavirus.
• The ICD-10 established a new emergency code
(“U07.1, 2019-nCoV, acute respiratory disease”).
At that time, the WHO Classification Team had noted
that the virus name “2019-nCoV” was temporary and
was likely to change (to be independent of date and
virus family, and for consistency with international
virus taxonomy).
2020-02-11. The WHO officially announced the name of the
disease, COVID-19, an acronym for “coronavirus disease
2019.”
• “Having a name matters to prevent the use of other
names that can be inaccurate or stigmatizing,” said
Director-General of the WHO, Tedros Adhanom
Ghebreyesus. “It also gives us a standard format
to use for any future coronavirus outbreaks.” The
WHO referenced guidelines set in 2015 ensure that
the name does not refer to a geographical location,
an animal, an individual or group of people, while
still being pronounceable and related to the disease
(WHO, 2015). Public health experts agree with the
choice not to name the disease after a geographic
region in China. Wendy Parmet, a law professor at
Northeastern University and public health expert,
told the TIME magazine that if the new name had
included a reference to Wuhan, it would have caused
a “tremendous stigmatization on the people of Wuhan
who are the victims” of the disease, (Mansoor, 2020;
WHO, 2020b).
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•
•
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The ICD-10 was updated with two emergency codes:
“U07.1 COVID-19, virus identified” was assigned to a
disease diagnosis of COVID-19 confirmed by laboratory
testing, and “U07.2 COVID-19, virus not identified”
was assigned to a clinical or epidemiological
diagnosis of COVID-19 where laboratory confirmation
would be inconclusive or not available (Figure 1).
The term “COVID-19” was used immediately
worldwide. For example, in an article published in
the journal Science on Feb. 14, the term used was
“COVID-19”.7
The same day, a study group of the International
Committee on Taxonomy of Viruses (ICTV) christened
the novel virus as “severe acute respiratory syndrome
coronavirus 2,” or SARS-CoV-2 (ICTV, 2020). ICTV
is the official body of the Virology Division of the
International Union of Microbiological Societies
responsible for naming and classifying viruses. SARSCoV-2 then became the official name used by WHO.
Meanwhile, WHO also began to refer the virus as “the
virus responsible for COVID-19” or “the COVID-19
virus” when communicating with the public (WHO,
2020b).
2.1.2 Coding Guidance Provided by the Standardized
Health KOSs
In response to the WHO’s naming of the disease COVID19 and the virus SARS-CoV-2, and the declaration of this
Novel Coronavirus Disease (COVID-19) as a “pandemic”
on March 11 (WHO, 2020b), the standardized health
KOSs immediately released their new codes and coding
guidance. The coding guidelines have been updated since
the first release (Table 1).
2.1.3 Supplementary Concept Record Added by the
Medical Subject Headings (MeSH)
In addition to the KOSs listed in Table 1 which are
directly used in the management of clinical practices and
healthcare delivery systems, the U.S. National Library of
Medicine (NLM)’s Medical Subject Headings (MeSH) added
a Supplementary Concept Record (SCR) for COVID-19 to
the 2020 MeSH Browser on February 13, 2020.9 MeSH is a
comprehensive controlled vocabulary for the biomedical
life science bibliographical databases (such as MEDLINE/
9 https://www.nlm.nih.gov/pubs/techbull/jf20/brief/jf20_mesh_
novel_coronavirus_disease.html
Figure 2. MeSH Supplementary Concept Record for COVID-19.
Image capture 2020-04-21. Source: https://meshb.nlm.nih.gov/
record/ui?ui=C000657245
PubMed). It is also used by ClinicalTrials.gov registry to
classify the diseases that are registered in ClinicalTrials.
In this new Supplementary Concept Record, in addition
to the preferred lexical label “COVID-19” and a Unique
ID, more than 10 entry terms are provided (see Figure
2) to facilitate subject access to the related information
resources. It has mapped to other related MeSH descriptors
including “Coronavirus Infections,” “Pneumonia, Viral,”
and “Pandemics.” The Resource Description Framework
(RDF) Unique Identifier facilitates the creation of an RDF
record, downloadable in RDF/XML, RDF/N3, and JSON-LD
serialization formats (Figure 2).
Since the MeSH subject headings’ Unique IDs have
been used as one of the three major controlled identifiers
(together with that from ICD-10 and SNOMED CT) in
Wikipedia, searching with MeSH ID on the Web brings
dozens of results of multilingual Wikipedia entries aligned
with this identifier. There will be more discussions later
on in Section 5.5.
2.2 Purposes and Usages of the Major
Health KOSs
The efforts listed in the previous section necessitate
a discussion about the need for various types of
KOS. In healthcare systems, there are many parties,
including medical professionals, medical technicians
and paramedics, clinical laboratory technologists
and technicians, government agencies, international
organizations, academic researchers, medical center
Implications of Knowledge Organization Systems for Health Information
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Table 1
COVID-19 Coding Guidance
KOS
Code
U07.1*
ICD-10
International
Classification of Diseases
– Version 10.
(Guidance released on
2020-03-25: https://www.
who.int/classifications/
U07.1
icd/COVID-19-codingicd10.pdf)
Code Description
Coding guidance
COVID-19, virus identified
Positive test result; COVID-19 documented as
cause of death
*Use intervention/procedure codes to
capture any mechanical ventilation or
extracorporeal membrane oxygenation and
identify any admission to intensive care unit
COVID-19, virus not identified
Positive test result only, patient showing no
symptoms
o Clinically epidemiologically diagnosed
COVID-19
o Probable COVID-19
o Suspected COVID-19
CPT
Current Procedural
Terminology
(Guidance released on
2020-03-13;
https://www.amaassn.org/practicemanagement/cpt/covid19-coding-and-guida…)
U07.1 + codes for
symptoms
COVID-19, virus identified
Use additional code(s) for respiratory
disease (e.g. viral pneumonia J12.8) or signs
or symptoms of respiratory disease (e.g.
shortness of breath R06.0, cough R05) as
documented
U07.2; Z20.8
+ codes for
symptoms
Contact or suspected exposure
Suspected/probable cases. No other
etiology; history of travel
U07.2; Z20.8
+ codes for
symptoms
Contact or suspected exposure
Suspected/probable cases. Contact with
confirmed or probable case
U07.2 + codes for
symptoms
Suspected/probable cases. No other
etiology: hospitalization required
U07.2 + codes for
any symptoms
Suspected/probable cases. COVID-19
documented without any further information
regarding testing
87635
SARS-COV-2 COVID-19 AMP PRB
Effective March 13, 2020, for novel
coronavirus tests through infectious agent
detection by nucleic acid
86318
IMMUNOASSAY INFECTIOUS AGENT
ANTIBODY
Effective April 10, 2020, for novel
coronavirus tests through infectious agent
detection by nucleic acid
86328
IA NFCT AB SARSCOV2 COVID19
Effective April 10, 2020, for antibody tests
using a single step method immunoassay.
This testing method typically includes a strip
with all of the critical components for the
assay and is appropriate for a point of care
platform
86769
SARS-COV-2 COVID-19 ANTIBODY
Effective April 10, 2020, for antibody tests
using a multiple step method. For severe
acute respiratory syndrome coronavirus 2
(SARS-CoV-2) (Coronavirus disease (COVID19)) antibody testing using single step
method, use 86328
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Marcia Lei Zeng et al.
(continued) Table 1
COVID-19 Coding Guidance
KOS
Code
Code Description
Coding guidance
SNOMED CT
Systematized
Nomenclature of Medicine
– Clinical Terms
(Guidance released on
2020-03-09; https://
confluence.ihtsdotools.
org/display/snomed/
SNOMED+CT+COVID19+Related+Content)
840539006
COVID-19
Fully specified name (FSN) = Disease caused
by severe acute respiratory syndrome
coronavirus 2 (disorder)
840544004
Suspected COVID-19
FSN = Suspected disease caused by severe
acute respiratory coronavirus 2 (situation)
840534001
SARS-CoV-2 vaccination
FSN = Severe acute respiratory syndrome
coronavirus 2 vaccination (procedure)
840536004
Antigen of SARS-CoV-2
FSN = Antigen of severe acute respiratory
syndrome coronavirus 2 (substance)
840535000
Antibody to SARS-CoV-2
FSN = Antibody to severe acute respiratory
syndrome coronavirus 2 (substance)
840546002
Exposure to SARS-CoV-2
FSN = Exposure to severe acute respiratory
syndrome coronavirus 2 (event)
840533007
SARS-CoV-2
FSN = Severe acute respiratory syndrome
coronavirus 2 (organism)
94721-8
COVID-19 Evaluation note
These pre-released terms are not yet part of
an official LOINC release and therefore not
available as a direct download from LOINC
website
94723-4
Emergency department COVID-19 Initial
Evaluation form
For a complete list of COVID-19 related LOINC
codes, check https://loinc.org/prerelease/
94722-6
COVID-19 Initial Evaluation form
LOINC
Logical Observation
Identifiers Names and
Codes
(Special use codes and
terms pre-released in
mid-March; https://
clinicalarchitecture.com/
covid-19-updates/)
administrators, patients and families, and insurance
providers. During the COVID-19 or any pandemic, users
who want to find out the status, impacts, prevention, and
control of the disease need to find accurate information
while gaining a complete picture of its significance around
the world. Data and information have to be exchanged
among all of the above-mentioned parties. The ability
of KOS to reduce potential semantic conflicts assists in
connecting verified data, compatible data structures, and
explicit scientific information, which ensures the quality
and consistency of data and information communicated
in scholarly publications as well as to the general public.
As everything is increasingly digital, information and
data exchange become even more critical. For many years,
efforts have been given to produce clinical data standards
and health information exchange protocols. Each of these
KOSs has unique features and purposes, being used
in different workflows and at different decision levels,
appearing in electronic health records, clinical quality
reports, subject indexing, and in other forms. “Different
families of knowledge organization systems, including
thesauri, classification schemes, subject heading systems,
and taxonomies are widely recognized and applied in
both modern and traditional information systems” (W3C,
2009). The main functions of the standard KOSs, also
known as health terminology, as demonstrated above,
include eliminating ambiguity, controlling synonyms
or equivalents, and establishing explicit semantic
relationships. Embodied as (Web) services, they can
facilitate resource discovery and retrieval. They act
as semantic road maps and make possible a common
orientation for information professionals and future users
(whether human or machine) (Hodge, 2000; Tudhope &
Koch, 2006).
Electronic health records (EHRs) have become the
central feature in the healthcare domain of the 21st
century. In addition to the formal systems used, there
are an increasing number of open-source EHR systems
(Syzdykova, Malta, Zolfo, Diro, & Oliveira, 2017). In a
healthcare information system, semantic interoperability is
Implications of Knowledge Organization Systems for Health Information
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full ontology deliveries are available at the BioPortal11
terminology service, an authoritative, comprehensive
repository of biomedical ontologies. There exist specific
information systems in the world which may use different
terminologies or self-defined ones. Yet, in general, those
types of terminologies will also fall into these categories
according to the purposes and usages.
Figure 3. Variety of KOSs used in the world of EHR.
the ability to use digital health information across diverse
settings and clinical software as the increasing amounts of
health data from different locations make unique challenges
in connecting and analyzing these data as a unified set. It is
obvious that different systems that are modeled differently
will have difficulties in data transferring, cross-dataset
searching, and data reusing. Very often, this will also
impact data integrity and could cause serious problems.
For example, when two health information systems share
e-records, they will need to be very careful about data
transfer. For the data to be usable and exchangeable, those
systems have to communicate using standard language
and terminologies. This means that the codes from one
system must be mapped to the codes from the other system.
Though the data from multiple systems can be stored in one
place, if those codes cannot be mapped to one another, the
data would not be unlocked.
Standardized KOSs used in EHRs are illustrated in
Figure 3, based on the practices in the United States. These
include ICD, SNOMED CT (Systematized Nomenclature
of Medicine Clinical Terms), LOINC (Logical Observation
Identifiers Names and Codes), CPT (Current Procedural
Terminology), RxNorm, etc. (Figure 3).
Given the fact that multiple and diverse KOSs have
been widely used, it is necessary to describe them
based on their main purposes and usages. Since it is
the vocabulary that is used in the data exchange and
information communication, they will be referred to as
“terminology” in the following sub-sections. For most of
these terminologies, the full titles are also provided in
this section, in order to facilitate a better understanding
of their contents. A list of major health KOSs is provided
in the Appendix of this article. Sources of additional KOSs
mentioned below are provided in footnotes. All of them
are consolidated at the Unified Medical Language System
(UMLS)10 of the National Library of Medicine. Their
10 https://www.nlm.nih.gov/research/umls/index.html
2.2.1 WHY — Their Purposes and Responsibilities
First, health KOS may be categorized into administrative,
clinical, reference, and interface terminologies according
to their major responsibilities in application (Bronnert,
Masarie, Naeymi-Rad, Rose, & Aldin, 2012).
An administrative terminology is developed to help
administrative functions of healthcare, such as medical
billing, reimbursement, classification of information, and
other secondary data aggregation. Common standardized
ones are ICD, Current Procedural Terminology (CPT), and
Healthcare Common Procedure Coding System (HCPCS).
WHO’s ICD has been translated into 43 languages and it
is used by all member States. Most countries (117) use it to
report mortality data, a primary indicator of health status,
according to ICD.12
A clinical terminology is developed for electronic
exchange and aggregation of clinical data through
encoding specific clinical entities involved in clinical
workflow. Among the common standards is RxNorm,
a normalized naming system for generic and branded
drugs. It is a tool for supporting semantic interoperation
between drug terminologies and pharmacy knowledge
base systems. The purpose of RxNorm is to address the
communication problems when the systems used in
hospitals, pharmacies, and other organizations record
and process drug information with different sets of drug
names. Another universal standard is Logical Observation
Identifiers Names and Codes (LOINC) developed for
identifying laboratory observations.
A reference terminology is concept-based. It
identifies semantic relationships between concepts,
maintains a common reference point in the health
information systems, and enables organization and
aggregation of clinical information. This kind of
terminology, e.g., Systematized Nomenclature of Medicine
Clinical Terms (SNOWMED CT), represents a large
number and range of possible concepts in a consistent
11 http://bioportal.bioontology.org/
12 https://www.who.int/classifications/icd/revision/icd11faq/en/
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manner and allows health information systems to get
values from clinical data coded at the point of care. In
general, reference terms are useful for decision support
and aggregate reporting and are therefore more general
than the highly detailed descriptions of actual patient
conditions.
An interface terminology is a code set to ensure
interoperability. Also known as colloquial terminologies,
application terminologies, or entry terminologies,
they are collections of healthcare related terms that
support documentation of patient information. Since
administrative codes and descriptors do not use the
same language as different clinical, administrative,
and colloquial terms that clinicians are using daily in
healthcare, it makes it difficult for clinicians, information
management professionals, and patients to find the terms
they need when performing simple text searches. An
interface terminology, e.g., Intelligent Medical Objects
(IMO)’s Clinical Interface Terminology (CIT), 13 enables the
mapping of the clinically relevant terms to the standard
administrative and clinical terminologies and bridges
the gaps. With interface terminology in place within an
EHR, clinicians are able to find the right diagnosis and
procedure terms to document and code, generating more
comprehensive and accurate coding within their normal
workflow.
2.2.2 WHERE — Their Major Usages
Secondly, from the perspective of usage, these common
health KOSs may also be categorized into medical billing
standards, clinically specific standards, pharmacy
terminology standards, nursing terminology standards,
and messaging standards. They play a big role in healthcare
information systems to facilitate data normalization.
Medical billing terminology standards are used
by all healthcare organizations to support medical
billing, including ICD for diagnosis and procedure
reimbursement, Current Procedural Terminology (CPT)
for billing and reimbursement of outpatient procedures
and interventions, and Diagnosis Related Group (DRG)
classification14 for billing a patient’s hospital stay in
the inpatient setting. They are mandated by the Health
Insurance Portability and Accountability Act (HIPAA)15 to
code a patient’s medical history.
13 https://www.imohealth.com/imo-precision-sets
14 https://hmsa.com/portal/PROVIDER/zav_pel.fh.DIA.650.htm
15 https://aspe.hhs.gov/report/health-insurance-portability-andaccountabil…
Clinical terminology standards are used to describe
health conditions and problems, supporting meaningful
electronic exchange and aggregation of clinical data for
better patient care. The most common examples include
Systematized Nomenclature of Medicine Clinical Terms
(SNOMED CT) and Logical Observation Identifiers Names
and Codes (LOINC), while RadLex radiology lexicon and
other clinically specific standards tend to have a specific
clinical or workflow emphasis.
Pharmacy terminology domain is well-represented
in many commonly used solutions and databases in
pharmacy management and drug interaction software
such as National Drug File - Reference Terminology (NDFRT),16 National Drug Code (NDC),17 First Databank (FDB),18
Multum MediSource Lexicon (Multum),19 Medi-Span Drug
Data (Medi-Span),20 and United States Pharmacopeia
(USP) Compendial Nomenclature.21 The interoperability
standard recommended for pharmacy terminology is
the open-source RxNorm of the U.S. National Library of
Medicine that provides normalized names for clinical
drugs, which has links to many existing drug vocabularies
to facilitate interoperability of drug information.
Nursing terminology standards have been
developed to describe the nursing process, document
nursing care with interventions and outcomes, and
facilitate aggregation of data for comparisons at local,
regional, national, and international levels. The Nursing
Outcomes Classification (NOC)22 and Nursing Intervention
classifications (NIC)23 are used to describe nursing
practice in both home and acute care. The International
Classification for Nursing Practice (ICNP)24 and NANDA
International (NANDA-I)25 are being used internationally to
describe nursing diagnoses, interventions, and outcomes.
Messaging standards enable health information
systems to communicate using the industry standards
of health information exchange. Digital Imaging
16 https://www.nlm.nih.gov/research/umls/sourcereleasedocs/current/NDFRT/
17 https://www.fda.gov/drugs/drug-approvals-and-databases/national-drug-co…
18 https://www.fdbhealth.com/
19 https://www.cerner.com/solutions/drug-database
20 http://www.wolterskluwercdi.com/drug-data/
21 https://www.usp.org/health-quality-safety/compendial-nomenclature
22 https://www.nlm.nih.gov/research/umls/sourcereleasedocs/current/NOC/
23 https://www.nlm.nih.gov/research/umls/sourcereleasedocs/current/NIC/
24 http://www.who.int/classifications/icd/adaptations/icnp/en
25 http://www.nanda.org/
Implications of Knowledge Organization Systems for Health Information
Exchange and Communication during the COVID-19 Pandemic
and Communications in Medicine (DICOM)26 is the
international standard to transmit, store, retrieve, print,
process, and display medical imaging information.
The Health Level Seven (HL7)27 standard is a set of
international standards dedicated to providing a
comprehensive framework and related standards for the
exchange, integration, sharing and retrieval of electronic
health information that supports clinical practice and
the management, delivery and evaluation of health
services. HL7 requires using standardized terminologies
to represent health data. Besides developing its own
standardized code sets to identify administrative data
such as gender code, data type, and status codes, HL7 has
employed existing standardized health KOSs to support
unambiguously health information exchange.
In general, health KOS standards are essential parts of the
framework supporting unique applications and services for
healthcare research and practice. Initiatives to understand
the evolution and spread of viruses are necessary for public
health measures as well as for surveillance and tracking.
As viruses evolve rapidly, it becomes necessary to make
inferences about their epidemic history from genomic data,
and this is possible if the right applications can be created
to quickly harness the genomic data. No matter whether it
is during a pandemic or not, data integration will be a vital
component in the delivery of quality services as data are
ingested, captured, and collected from multiple sources.
The integration and interoperability of these resources are
key to enabling applications that will answer questions
currently elude us.
3 Schemas for Web-based
Information Management and
Communication
When dealing with millions of published and unpublished
resources from various sources, it is necessary that search
engines, authors, users, databases, and programs are
all able to reference conceptual terms from the same
language. Search engines, for example, rely on the content
markup embedded in the websites to improve the display
of search results, making it easier for people to find the
right web pages. Again, to eliminate semantic conflicts
and handle information overload, shared vocabularies
26 https://www.dicomstandard.org
27 http://www.hl7.org/
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are necessary for webmasters and website developers to
provide semantically enriched structured data regarding
the contents of their web resources.
One such common and extensive vocabulary is
Schema.org. Schema.org is an open, community-based,
collaboratively developed vocabulary to promote the
structuring of data on the Internet, originally founded
by Google, Microsoft, Yahoo, and Yandex (Schema.org
Community Group, n.d.-a.). The extensible vocabulary
covers different types of entities, relationships between
entities, and is used by over ten million sites to markup
web pages and email messages. This markup ability is
especially important for the purposes of web indexing
and exposing web-based information to search engines,
as the vocabulary can be used to structure the contents of
websites as HTML Microdata.
In terms of improving the dissemination of healthrelated information on the Web, the vocabulary also
specifically supports the embedding and structuring of
health and medical information through a schema type
MedicalEntity and its subtypes (Schema.org Community
Group, n.d.-b.). Citing both the existence of high-quality
health information on the Web and the difficulty users
experience in finding and navigating such information,
developers looked for a way to allow webmasters and
publishers to mark up the medical-related content on
their websites. Developers worked with experts from
the U.S. National Center for Biotechnology Information
(NCBI), physicians from Harvard, Duke, and other
institutions and from health websites, as well as
contributors from the W3C Healthcare and Lifesciences
group to address the markup of the implicit structure in
health-related information. The goal was to bridge the
gap between medical knowledge found in high-quality
information on the Web and the keywords to be used
in web searches through search engines. The resulting
schema models medical entities such as conditions, signs
and symptoms, risk factors, therapies, studies and trials,
and guidelines. Although not intended for clinical data
exchange or to supplant existing medical vocabularies,
the schema covers information meant for consumers and
professionals, and provides ways to annotate entities with
codes from existing controlled medical vocabularies (e.g.,
MeSH, SNOMED, ICD, RxNorm, UMLS). The Health and
Lifesciences Section28 within Schema.org enumerates an
extension of defined classes and properties for structuring
medical information.
Most recently, developers and community leaders of
Schema.org quickly responded to the global pandemic
28 https://health-lifesci.schema.org/
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by releasing the versions 7.0 through 7.04 within a
month (Schema.org Community Group, 2020). Some of
the following examples of changes to the vocabulary
demonstrate the vocabulary’s flexibility in addressing
medical and health related properties and the urgent need
for structuring public and official information regarding
COVID-19. For example, in the creation of new types
such as SpecialAnnouncement (Thing > CreativeWork
> SpecialAnnouncement),29 the motivating scenario is
the coronavirus pandemic, and the initial vocabulary
is oriented to this emergency situation. Note that
terms in Schema.org for classes/types are named with
capital letters with no spaces, or upper camel case (e.g.,
MedicalEntity) and properties are named in lower camel
case (e.g., medicineSystem).
• 2020-03-17, Version 7.0
○ Un-used
medical health properties with
“inappropriately general” names were removed.
○ Motivated by the need to structure urgent,
crisis-related information during the COVID-19
pandemic, a new type SpecialAnnouncement and
its properties were created.
○ A
subtype
of
MedicalClinic,
labelled
CovidTestingFacility, was implemented to
describe locations where testing for COVID-19
is available. This also allowed reuse of existing
Schema.org structured data, such as contact
information, address, and operating hours, for
existing described instances.
○
A property, hasDriveThroughService, was created
to indicate the presence of drive-through services,
like drive-through virus testing.
○
Due to work at home policies and the migration
to online work settings, a VirtualLocation type
was developed, including a way to indicate that
an event has moved from a physical location to
an online or virtual location.
• 2020-03-22, Version 7.01
○ SchoolDistrict was added as a subtype of
AdministrativeArea.
○ A
property, datePosted, was included in
SpecialAnnouncement, which has the dates
showing changes or revisions done by official
announcements as they are developed in real
time.
• 2020-03-31, Version 7.02
○ Another property, announcementLocation, was
added in SpecialAnnouncement, to clarify the
spatial coverage for an announcement.
•
In addition to structuring information that will be published
on the Web, indexed by search engines, and disseminated
to the public, one of the notable developments to the
Schema.org vocabulary mentioned in the timeline above
is the creation of a type of StructuredValue which encodes
CDC data fields as properties of a CDCPMDRecord. A
CDCPMDRecord (which stands for CDC Patient Module
Denominator Record) is defined as “a data structure
representing a record in a CDC tabular data format
used for hospital data reporting” (Bradley, 2020). The
properties of this class ensure consistency with CDC
records by corresponding with required CDC data fields,
and have been prefixed with “cvd” to differentiate these
properties with other properties that may occur elsewhere
in the Schema.org vocabulary. For instance, where a
CDC record has a data field for a number of available
inpatient hospital beds (“numbeds”), a corresponding
property cvdNumBeds is used in Schema.org to structure
such data, and to distinguish it from the property
numberOfBeds that already exists for describing hotel
rooms, apartments, etc. The prefixing of “cvd” keeps data
regarding the CDCPMDRecord type (14 properties as of the
beginning of April)30 relatively self-contained (Brickley,
2020). Although developers of Schema.org note that data
encoded in this way may not be published on the public
Web or made available to search engines, it does mitigate
future semantic conflicts by ensuring that consistency
between data reported to the CDC under its reporting
requirements and Schema.org encoded records if they
were made available later on the Web as linked datasets.
29 https://schema.org/SpecialAnnouncement
30 https://schema.org/docs/cdc-covid.html
•
2020-04-06, Version 7.03
Due to the requirements of medical and
government authorities to aggregate data from
different medical facilities and the U.S. CDC
definition of data fields to facilitate exchange
of COVID-19-related data, a 1:1 Schema.
org representation of the required CDC CSV
data reporting format, as well as schema
documentation for its implementation, was
developed (Brickley, 2020).
○ Developers issued a clarification that the
property webFeed can be used to describe a
SpecialAnnouncement.
2020-04-16, Version 7.04
○ Developers
clarified that an Educational
Organization was also a subtype of Civic Structure,
in order to be a value for an announcementLocation
of a SpecialAnnouncement.
○
Implications of Knowledge Organization Systems for Health Information
Exchange and Communication during the COVID-19 Pandemic
Based on the swift and evolving response from its
development community, Schema.org’s actions during
the COVID-19 pandemic filled gaps caused by the dramatic
increase in the amount of information on the Web and
satisfied the need for managing and communicating that
information through effectively structured data. These
efforts to organize and classify relevant information and
resources directly contribute to addressing the challenges
presented by information overload and semantic conflict.
4 Ontologies for COVID-19 Research
Another significant set of KOS products are the ontologies
being created or experimented with regarding to COVID19 research and development. In the fields of computer
science and knowledge organization, an ontology
has been defined as “an explicit specification of a
conceptualization” (Gruber, 1995, p. 908), in which the
basic terms for concepts and their relationships within
a particular domain are defined, as well as the rules
for combining and associating terms and relationships
to represent more complex concepts (Neches et al.,
1991). In contrast to the health standards introduced
in the previous sections in this article, ontologies are
used for specific-use scenarios and situations. In the
case of COVID-19, ontologies are being used to present
interoperability solutions by linking equivalencies and
associations between terms in different vocabularies and
data encoded from different standards as well as semantic
solutions to make data processible by both humans and
machines. These innovative usages can be seen in the
creation of knowledge graphs, automatic reasoning, and
using artificial intelligence to help tackle the information
regarding coronaviruses. The following selections
provide a handful of examples that demonstrate the
ways in which ontologies can power the organization and
exchange of massive amount of information over Webbased applications and collected through automated
systems.
4.1 Infectious Disease Ontology
Infectious Disease Ontology (IDO) Core is a set of
interoperable ontology modules covering entities and
relations generally relevant to the infectious disease domain
(Babcock, Beverley, Cowell, & Smith, 2020). This core is
then extended to focus on specific diseases and pathogens.
IDO is itself related to and extended from the Ontology for
General Medical Science (OGMS), where IDO Core describes
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relationships among and distinguishes between entities
of an infectious disease, sign or symptom of the infectious
disease, infectious disease diagnosis, infectious disease
course, and infection. Extensions of IDO Core relevant
to COVID-19 include IDO Virus (VIDO), the Coronavirus
Infectious Disease Ontology (CIDO), and IDO-COVID-19
Infectious Disease Ontology, with VIDO and IDO-COVID-19
being currently in development. IDO Core serves as an
important backbone in reducing semantic conflicts and
promoting interoperability among heterogeneous bodies
of research data as well as among ontologies designed for
specific kinds of infectious diseases.
4.2 Coronavirus Infectious Disease Ontology
One important extension of IDO is CIDO, the Coronavirus
Infectious Disease Ontology (He, 2020), an expansive,
open-source, community-developed biomedical ontology
aimed at providing human- and machine-readable
annotation and representing numerous aspects of
coronavirus infectious diseases in general. With over
3,700 classes of entities and 82 properties, the ontology
covers terms for disease causes, transmission, diagnosis,
prevention, and treatment, with properties for integrating
other forms of medical vocabulary and relevant coding
systems such as matching terms with their Chinese labels
and ways of linking terms from MeSH, SNOMED, RxNorm,
UMLS, and Unique Ingredient Identifiers (UNII) from
the U.S. Food and Drug Administration (FDA) Substance
Registration System.
4.3 COVID-19 Surveillance Ontology
(COVID19)
COVID-19 Surveillance Ontology is a small application
ontology (Liyanage, de Lusignan, & Williams, 2020)
intended to support surveillance of COVID-19 in primary
care settings. Classes of entities include terms for
classifying means of exposure, methods of testing and
diagnosis, and subsequent courses of action such as
isolation and contact tracing. The ontology is envisioned
to facilitate COVID-19 case monitoring, including
related respiratory conditions and data gathered from
computerized medical systems.
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4.4 WHO COVID-19 Rapid Version CRF
Semantic Data Model (COVIDCRFRAPID)
This data model (Bonino, 2020) allows semantic
referencing of data gathered from the “Global COVID-19:
Clinical platform: Novel coronavirus (COVID-19): Rapid
version form,” a clinical characterization case record form
(clinical CRF) developed by the WHO to standardize the
collection of clinical data regarding characteristics of
the disease and its treatment (WHO, 2020c). The model
enumerates the medical and biological terminology that
appears on the WHO CRF and maps those terms to their
semantic equivalents in other KOSs.
4.5 Cochrane PICO Ontology and Linked Data
Vocabulary
Not-for-profit organization Cochrane originally developed
its PICO Ontology to characterize four areas addressed by
clinical studies (Population, Intervention, Comparison,
and Outcome), which is supported by the approximately
400,000 terms of the Cochrane Linked Data Vocabulary
(Cochrane, 2020). This vocabulary is linked to existing
health vocabularies—including MeSH, SNOMED-CT,
Medical Dictionary for Regulatory Activities (MedDRA),
and RxNorm—to support semantic standardization,
and now includes a term for a Condition, COVID-19. The
PICO Ontology and the Cochrane Linked Data Vocabulary
provide part of the data architecture on the back-end to
enable front-end applications like the recent Cochrane
COVID-19 Study Register,31 which provides up-to-date
information on clinical trials and published research
(Wilton, 2020).
This small subset of ontologies discussed above reflects
some of the immediate efforts to directly respond to the
organization and classification of the overwhelmingly
large amount of information that has proliferated as
a result of both clinical data collection efforts and
research efforts to combat the virus. Many other ontology
development projects exist worldwide, and some may
be extended or revised to integrate new entities and
relationships regarding COVID-19. For instance, the Gene
Ontology has compiled and included in their knowledge
base functions of human proteins used by the SARS-CoV-2
virus to enter a human cell and those possibly targeted by
the virus after cell entry, as well as semantic annotations
of relevant human and viral genes (Gene Ontology
31 https://covid-19.cochrane.org/
Consortium, 2020). Moreover, experimental application
of a COVID-19 ontology with tools for descriptive logic
reasoning has been proposed in the context of detecting
“fake news” to identify inconsistencies between trusted
medical sources and sources of unknown veracity (Groza,
2020). In addition to the open source ontologies and novel
approaches involving ontological modeling introduced
here, there may be many other proprietary or local
ontologies or ontology-related studies being used and
developed in research labs and beyond.
Additionally, other KOSs can be used as the basis
for, or converted into, ontological models. Such thesauri,
classifications, and taxonomies converted into OWL
ontologies can be found in the BioPortal ontology
repository. A search conducted on April 25, 2020 for the
ontological class “Coronavirus” led to 19 ontologies
containing this term, which included various entries on the
family of Coronaviruses of which COVID-19 is a part, as well
as other human and animal viruses. A search for “COVID19” brought up ontologies such as the Experimental Factor
Ontology (EFO) and the Vaccine Ontology (VO), in addition
to the COVID-19 Surveillance Ontology and WHO’s Rapid
version CRF data model described earlier. As information
continues to augment regarding COVID-19 disease and
the SARS-CoV-2 virus, more ontologies and semantic
applications will adapt and expand their classes, entities,
and related properties to structure emerging biological
and medical data about the pandemic.
5 Applications, Services, and Databased Analysis Empowered by KOS
Data-driven decision-making has become increasingly
important in our daily lives as well as in business,
research, and clinical settings. The ever-increasing
data volumes and complexities will become even more
overwhelming for users already overburdened by
information overload. Interesting studies on information
overload and decision making in various situations
have been continuously conducted. When examining
the scope of research on health information overload in
consumers, Khaleel et al. identified predictors of health
information overload, such as low education level,
health literacy, and socioeconomic status; and found that
videotaped consultations and written materials can be
used as interventions for information overload (Khaleel
et al., 2020). Bawden and Robinson (2020) came up with
some ideas for avoiding information overload, such as
filtering, withdrawing, queuing, and satisficing, as well as
Implications of Knowledge Organization Systems for Health Information
Exchange and Communication during the COVID-19 Pandemic
designing better information systems, managing personal
information effectively, and promoting digital and media
literacy. Consequently, it is therefore crucial to develop
strategies that avoid or negate the harmful impacts of
information overload at this very difficult time of the
COVID-19 pandemic. Ortutay and Klepper (2020, April 24)
suggested the following strategies for individuals to avoid
information overload: (1) look for the source; (2) check the
websites of the CDC and the WHO; (3) act like a journalist;
(4) pause before reposting the news; and (5) do not believe
everything you see.
The challenges of information overload could
increase dramatically when a report or a message needs
to be communicated across languages, regions, and
cultures, especially when the statistics are generated
based on different types of contextual information
from diverse resources. To illustrate, Nielsen, Fletcher,
Newman, Brennen, and Howard (2020) investigated and
compared the information overload in terms of accessing
and rating news and information about coronavirus
by people in six countries based on the data from news
media, blogs, and social media. Baniamin, Rahman,
and Hasan (2020) further tried to answer the question:
“Why are some countries more successful than others
in the COVID-19 pandemic?” Their findings show that
the better performing countries have done well with the
following aspects: consideration of people’s attitudes,
demographic profile, citizen trust, culture, magnitude of
policy learning, state structure, and technological and
administrative readiness.
It should be noted in all the examples above that welldesigned and purposeful technology plays a key role in
reducing the negative impacts of information overload.
Data can allow for assessment of trends and patterns and
be used to make inferences from contextual information
that humans may lose sight of. Studies have shown that
various applications and services such as knowledge
graphs, recommendation engines, and decision support
systems can provide remarkable support as aids in
decision-making and positively impact decision quality.
The field of healthcare—in particular the clinical processes
of diagnosis treatment, monitoring and prognosis—is
largely driven and shaped by knowledge. Thus, there is a
critical need to integrate vast amounts of structured, semistructured, and weakly structured data and a tremendous
volume of unstructured information in addition to
enhancing the functioning of workflows, processes and
guidelines, thereby minimizing costs and inefficiencies to
improve research and practice (Holzinger & Jurisica, 2014).
New methods and applications that support knowledge
discovery and interpretation of complex data in integrated
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and interactive ways are needed and are being developed.
Keselman et al. (2010) noted that a proliferation of disaster
health information results in information overload
among responsible professionals, requiring efforts be
made to manage and organize information in ways that
support decision making using semantic technology,
data mining and machine learning approaches, graphs,
and integration of systems. As such clinical health and
public health has seen the development of multiple tools
to harness voluminous data and perform surveillance,
tracking, analysis, prediction and modeling for various
healthcare crises. The next sections provide a review of
some tools being used to support the efforts of eliminating
information overload during the COVID-19 pandemic.
5.1 Health Dashboards
5.1.1 HealthMap
HealthMap (https://www.healthmap.org/en/) is a health
surveillance system supporting public health by focusing
on event-based monitoring of infectious diseases. The
tool continually aggregates reports on new and ongoing
infectious disease outbreaks which it then extracts,
categorizes, filters, and integrates in an effort to facilitate
knowledge management and early detection (Brownstein,
Freifeld, Reis, & Mandl, 2008). The system serves as a
source for libraries, local governments, governments and
multinational agencies, as well as normal users. HealthMap
uses text mining algorithms to perform characterization
of the sources from which it draws data. This includes
identifying disease and location, ascertaining the
relevance, grouping reports, and removing duplicates
(Freifeld, Mandl, Reis, & Brownstein, 2008). To perform
extraction of locations and disease names it relies on
various KOSs dealing with pathogens and geographic
names to classify and tag the data. Because multiple
sources are being integrated, HealthMap often ascribes
a reliability score to the information shown by giving
increased weight to official sources of information and
less to media reports. Currently HealthMap is being used
to track the spread of COVID-19 and support public health
official efforts to fight the disease. The website provides
multiple faceted filters, allowing a user to look for
information about the outbreaks and alerts by location,
time, diseases, species, and sources. Each of them is
supported by pre-defined vocabularies, name authorities,
and taxonomies; hence the information is exchanged
without possible semantic conflict.
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5.1.2 Nextstrain
Nextstrain (https://nextstrain.org/) is an open-source
initiative that uses pathogen genome data in its provision
of real-time views of the spread and evolution of viruses.
It consists of data curation analysis and visualization
components (Hadfield et al., 2018) and is facilitated
by collaborations with subject matter experts. This
system relies on taxonomies from the National Center
for Biotechnology Information (NCBI) of the United
States National Library of Medicine and data from the
Virus Pathogen Resource (a searchable database of virus
genomes) which itself stores genomic, proteomic, and
annotation data from various ontologies and protein
databases. Nextstrain is unique in that it tracks and is able
to recreate mutations as well as convey the geographical
spread of the virus and the underlying genomic data that
is supporting that. Nextstrain is being used by researchers
to trace the virus back to its origin point and date in
time and space and to perform analysis of its genomic
variations in order to facilitate treatment, containment,
and development of vaccines.
5.1.3 Johns Hopkins COVID-19 Dashboard
In response to the COVID-19 pandemic, the Johns
Hopkins University developed an interactive web-based
dashboard that could visualize and track cases in real
time (https://coronavirus.jhu.edu/us-map). It aggregates
local government and media reports around the world
and confirms the data with local health authorities before
publishing. Researchers, public health authorities, and
the general public are using the tool to track the outbreak
as it happens. In the US it reports data at the city level and
in China at province level. It uses a semi-automated living
data stream strategy to aggregate data from multiple
sources. This tool relies strongly on federal information
processing standards and geographic identifiers from
KOSs created by The American National Standards
Institute (ANSI), US Census Bureau, US Department
of Education, and the US Geological Survey (USGS) to
organize the information presented (Dong, Du & Gardner,
2020).
5.1.4 FAPDA— Food and Agriculture Policy Decision
Analysis Tool
The FAPDA is a FAO initiative that aims to promote
evidence-based decision making by providing up-to-
date information on national policy decisions and policy
frameworks, through the FAPDA policy database. The
FAPDA database (http://www.fao.org/in-action/fapda/
fapda-home/en/) is a global and comprehensive database
which contains over 10,000 national policy decisions
and 2,000 national policy frameworks for 100 countries
around the world. In the current exceptional situation,
a timely tool is provided for discovering policy measures
and urgent actions directly related to COVID-19. The
search interface provides faceted filters, which enable
assessment of the data from different points of views, or
from different aspects. Similar to HealthMap, facets of
What, Where, When, Who, and How enable access points
for browsing or searching of the policies with different
traits. Behind each of the facets, there is a KOS which
supports browsing and searching with standardized
terms and semantic relationships. The Policy Decision
Classification allows for the presentation of consumeroriented, producer-oriented, and trade-oriented policies.
Combined with schemes which support the food security
dimension (access_to, availability_of, and utilization_of
food), as well as the commodities, the policy decisions
found can match with carefully crafted queries.
As exhibited above, in recent weeks the mobilization
of the research community in response to the novel
coronavirus has led to the development of multiple tools
and applications to organize and harness knowledge.
Without these efforts and the support of the KOSs much
of this knowledge would remain hidden. Integrating these
data sources has allowed for reasoning and analysis of
the information on a scale that supports clinical decision
making and local and international policy making.
5.2 Knowledge Graphs
Another type of application that benefits from the existence
of KOS is knowledge graphs. On the Web knowledge
graphs are essential components of information systems
that need to access semantically structured data and
support knowledge discovery. They are highly curated
representations of knowledge and their function is
most often seen to support semantic knowledge in web
searches. Knowledge graphs describe real world entities
and the relationships between them, defining classes and
relations of entities in a schema. They enable the creation
of relationships between arbitrary entities and can cover
a wide variety of domains (Paulheim, 2017). Knowledge
graphs drawn from multiple data points and present
connections between data points to enable multiple
Implications of Knowledge Organization Systems for Health Information
Exchange and Communication during the COVID-19 Pandemic
perspectives in one glance and infer meaning, tailoring
the information returned to the unique context of the
search. Some of the most well-known implementations
of knowledge graphs are those which can be seen when
running Web searches. As an example, in a search for
H1N1 which is an influenza virus, a number of data points
are made available to users in the graph, including a
general description of the virus as well as the virus family,
class, higher classification and other pieces of data
pulled from multiple sources. Currently a quick search for
COVID-19 in a web browser will have the knowledge graph
returning multiple data points including a quick statistic
summary starting with your location and then extending
it to worldwide.
Of note, the Cochrane COVID-19 Study Register
(https://covid-19.cochrane.org/) is a knowledge graph
technology built in direct response to COVID-19. In their
efforts to produce evidence-based systematic reviews
they utilize a flexible knowledge graph based linked data
architecture. Systematic reviews are a highly knowledgebound, domain-specific, time-intensive, and complicated
task requiring oversight by subject matter experts to
complete. A knowledge graph can support and simplify
this process by capturing evidential statements about the
contents of documents and describing the evidence at
the right place within the content using structured linked
data. A model for the knowledge graph implementation
includes (1) a model for describing clinical questions,
(2) a linked data vocabulary to describe and construct
the questions, (3) a content model for description, (4) an
annotation model to capture provenance, (5) a knowledge
graph implementation to store the linked data vocabulary,
the content metadata, and the evidence, and (6) a tool to
enable all this (Wilton, 2020). At multiple steps of this
process one can see its reliance on various types of KOS
(from the simple to the complex) and the process itself is
an integrated KOS.
5.3 Electronic Health Records
KOSs are often used for organizing the contents of
electronic health records (EHRs), as discussed in Section
2.2. This is necessary because of heterogeneity of resources
and the need to enable semantic interoperability (Park,
Kim, & Min, 2012). Their use can improve population
and health outcomes through the collection of data that
can be shared across multiple organizations. The KOSs
supporting these allow for the collection of standardized,
systematic data that can be used for surveillance
data submission, and electronic laboratory reporting.
163
Information from these records is what providers use
to transfer public and population health data to public
health officials and organizations which they in turn use
to monitor, prevent, and manage disease. In the current
pandemic, tools built into the EHRs are supporting the
work of clinicians through screening, patient education,
updates for clinicians, decision support, ambulatory
orders, reporting and analytics, secure communication,
and telemedicine (Reeves et al., 2020).
Information found in EHRs is also being used in new
ways to facilitate clinical research through secondary use
of the data collected. The quality and context of secondary
data is often questioned. KOSs can be used to improve
the consistency and completeness of the data as well as
to enable archiving and sharing the data after clinical
studies are completed. Published studies have shown that
they are helping clinicians assess risk factors for mortality
(Zhou et al., 2020), transmission potential in pregnant
women and asymptomatic carriers (Chen et al., 2020; Bai
et al., 2020), and decipher clinical, epidemiological and
diagnostic features of both adult and child patients (Qiu et
al., 2020; Ng et al., 2020; Wu & McGoogan, 2020).
5.4 Multilingual Vocabulary and Concept
Hubs
Information overload and semantic conflict challenges
are multiplied when communication involves many
languages, let alone more than one. Similar to other
KOSs for information organization and retrieval use,
multilingual thesauri are structured collections of
concepts, terms, definitions and relationships. They differ
from monolingual thesauri in that they cover many world
languages and are often core resources for many countries
and regions. Another feature of multilingual thesauri is
that their development and maintenance are dependent
on collaborative editing and community contributions.
Centralized vocabulary and concept hubs are typically
provided by international organizations. The FAO of the
UN, for example, has a well-established multilingual
thesaurus, AGROVOC, since early 1980s. Each concept in
AGROVOC has corresponding terms used to express those
concepts in various languages. As of April 2020, AGROVOC
consists of over 37,000 concepts and more than 750,000
terms in up to 38 languages.32 The copyright for AGROVOC
content that occurs in official FAO languages — Arabic,
Chinese, English, French, Russian and Spanish — is
retained by the FAO and licensed under the international
32 http://aims.fao.org/standards/agrovoc/concept-scheme
164
Marcia Lei Zeng et al.
Table 2
Wikipedia and Wikidata Entries Related to COVID-19 (Data Collected on May 20, 2020)
Creative Commons Attribution License (CC-BY), while
content in other languages rests with the institutions
that authored it.33 Following a request for more specific
terminology on COVID-19, AGROVOC added new concepts
related to the current world health crisis in an additional
release on April 10 (the concepts coronavirus and
pandemics were already present in the thesaurus). These
include entries for “coronavirus disease” (alternative
term COVID-19) and “severe acute respiratory syndrome
coronavirus 2” (alternative SARS-Cov-2). Other concepts
were added for MERS, SARS, movement restrictions, and
supply chain disruptions. As of April 10, the lexical labels
for these concepts were available in 16 languages.34
De-centralized vocabularies and concept hubs are
also growing with the advancement of Wikimedia. As
the COVID-19 pandemic became a dominant topic in
news and social media, Wikipedia and Wikidata have
had entries established and updated continuously. For
example, the Wikipedia entry “Coronavirus disease 2019”
had entries in 128 languages (as of the middle of May);
the concept is matched to ICD-10, MeSH, and SNOMED
CT. This concept’s entry in Wikidata (as a name authority)
has the label “COVID-19” with the identifier Q84263196,
includes scope notes, has many synonyms or equivalents
(e.g., 19 in English), and is mapped to 21 identifiers found
in other controlled terminologies, news topic IDs, Google
Knowledge Graph ID, etc. The data is updated frequently,
while requests for changes in entry titles were noted in
33 http://aims.fao.org/vest-registry/vocabularies/agrovoc
34 http://aims.fao.org/activity/blog/AGROVOC_Covid-19
March and April. For example, Wikipedia changed the
article’s entry title from “2019–20 coronavirus pandemic”
to “COVID-19 pandemic” in May 2020.
These multilingual vocabulary and concept hubs
have become central resources for ordinary users during
the COVID-19 pandemic, particularly for the purpose of
communicating across different languages and cultures
worldwide. By using a unique identifier for each concept,
the collaborative controlled vocabularies provide a
significant number of synonyms and equivalent terms used
by different populations and agents. The mapped identifiers
greatly enhance semantic interoperability among different
commonly used KOSs as well as special vocabularies used
by institutions and branches of the media.
6 Conclusion
Classification
systems,
taxonomies,
controlled
glossaries, thesauri, subject headings, ontologies, and
other types of KOS have existed for many years. The health
and medical fields, as well as other disciplines, have
used KOSs to organize, represent, and conceptualize the
specialized information that is vital to understanding
their respective domains. KOSs provide a common
framework and language for domain practitioners and
a structured way to communicate complex concepts to
the general public. The Digital Age has only brought
more attention to KOSs in the overall global information
infrastructure, as they increasingly form the backbone
of automated and Web-based systems and services. As
Implications of Knowledge Organization Systems for Health Information
Exchange and Communication during the COVID-19 Pandemic
165
Figure 4. Data-Information-Knowledge-Wisdom (DIKW) and basic strategy
Source: Image generated based on Ackoff (1989) and Zeleny (1987).
shown earlier, they have become even more critical to
aid the frontline endeavors to overcome the obstacles
of information overload and semantic conflicts that can
occur during special historic and worldwide events like
the COVID-19 pandemic.
This review of the efforts, usages, and functions
of the significant KOSs discussed here reveals their
essential roles in supporting health data exchange
and information management, as well as ensuring
consistency and interoperability of data collection and
reuse among various providers and care settings. They
also support many other services and applications, as
they have been developed and routinely updated for
administrative, reference, and interface purposes. This
attention to consistency, responsiveness, and systematic
maintenance also demonstrates their flexibility and
swiftness to respond to a flood of new information, such
as that experienced during the COVID-19 pandemic. In
a digital information environment, these standardized
health KOSs increasingly play a larger and more important
role in healthcare information systems to facilitate data
normalization, which is a fundamental requirement for any
subsequent data analysis and information management.
Additionally, both health KOSs and non-health- oriented
KOSs which have a medical and health component
vocabulary facilitate the dissemination of important
and accurate information, in multiple languages, to
audiences as diverse as healthcare providers, medical
researchers, public health and government officials, the
news media, and the general public. They also enable
search and retrieval systems, interactive maps and charts,
repositories and databases supporting research efforts,
policy and decision making by governments, businesses,
and medical facilities to have far reaching applications on
patients and healthcare consumers.
Ultimately, the networking environment in which
we live also means that we are in a big-data world. The
use cases of KOSs presented here have been examined
in context of their abilities to solve two major problems
occurred as a result of this big-data world: first, dealing
with information overload; second, resolving semantic
conflicts. The fundamental approaches of KOS, including
eliminating ambiguity, managing semantic relationships,
and modeling ontological classes and their properties,
enable effective and efficient management of information
and knowledge. In categorizing and contextualizing vast
amounts of developing information and by controlling
or eliminating the pitfalls of semantic conflicts, KOSs
ultimately underpin how experts and ordinary citizens
understand the complexities of rapidly unfolding, global
situations like those occurred during the COVID-19
pandemic.
166
Marcia Lei Zeng et al.
The authors would like to conclude this article
by
revisiting
the
Data-Information-KnowledgeWisdom (DIKW) pyramid (Figure 4) used in knowledge
management (Ackoff 1989). The DIKW pyramid represents
the most basic strategy for understanding a world that is
far beyond the capacity of our brain: the ability to filter,
winnow, and otherwise reduce the chaos and confusion of
the world to something more meaningful, building from
a state of knowing “nothing,” to understanding “how,”
“what,” and “why” (Zeleny 1987). As supported by the
examples discussed in this article, this sense-making
hierarchy is bolstered by the foundation provided by
KOSs. Especially in challenging times, KOSs provide the
crucial infrastructure to transform disparate data and
overloaded information into real, actionable knowledge
as the basis of a wise decision-making.
Acknowledgments: The authors would like to thank the
three anonymous reviewers for providing their valuable
feedback and guidance.
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Implications of Knowledge Organization Systems for Health Information
Exchange and Communication during the COVID-19 Pandemic
169
Appendix
List of Common Health KOS Standards (in alphabetical order)
KOS
Full title
Description
Provider
URL
CPT
Current Procedural
Terminology
Offer doctors and health care professionals a uniform
language for coding medical services and procedures to
streamline reporting, increase accuracy and efficiency.
CPT codes are also used for administrative management
purposes such as claims processing and developing
guidelines for medical care review.
American
Medical
Association
https://www.ama-assn.
org/amaone/cpt-currentprocedural-terminology
FDB
First Databank Drug
Database
Provide clinical and descriptive drug knowledge that’s
integrated into healthcare information systems around
the world.
First Databank
https://www.fdbhealth.
com/
ICD
International
Classification of
Diseases
A diagnostic classification standard for all clinical and
World Health
research purposes. ICD defines the universe of diseases, Organization
disorders, injuries and other related health conditions.
(WHO)
(Previous name: International Statistical Classification of
Diseases and Related Health Problems)
https://www.who.int/
classifications/icd/en/
ICNP
International
Classification for
Nursing Practice
Provide an agreed set of terms that can be used to
record the observations and interventions of nurses
across the world.
International
Council of
Nurses (ICN)
https://www.icn.ch/whatwe-doprojectsehealthicnpdownload/icnp-download
LOINC
Logical Observation The international standard for identifying health
Identifiers Names
measurements, observations, and documents, which
and Codes
may help the receiving facility to better understand the
results and make appropriate treatment choices based
upon the laboratory results.
Regenstrief
Institute, Inc.
https://loinc.org
MediSpan
Medi-Span drug
databases
Used across the healthcare continuum to help inform
medication-related decisions.
Wolters Kluwer
https://www.
wolterskluwercdi.com/
drug-data/why-medispan/
MeSH
Medical Subject
Headings
A controlled and hierarchically organized vocabulary,
which is used for indexing, cataloging, and searching of
biomedical and health-related information.
National Library
of Medicine
(NLM)
https://www.nlm.nih.gov/
mesh/meshhome.html
Multum
Multum MediSource A foundational database with comprehensive drug
Lexicon
product and disease nomenclature information. It
includes drug names, drug product information,
disease names, coding systems such as ICD-9-CM
and NDC, generic names, brand names, and common
abbreviations.
Cerner
Corporation
https://www.cerner.com/
solutions/drug-database
NANDA-I
NANDA International Facilitate the development, refinement, dissemination,
and use of standardized nursing diagnostic
terminology
International
Nursing
Knowledge
Association
https://www.nanda.org/
NDC
National Drug Code
Food and Drug
Administration
(FDA)
https://www.fda.gov/
drugs/drug-approvals-anddatabases/national-drugcode-directory
Serves as a universal product identifier for drugs. FDA
publishes the listed NDC numbers and the information
submitted as part of the listing information in the NDC
Directory which is updated daily.
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Marcia Lei Zeng et al.
KOS
Full title
Description
Provider
NDF-RT
National Drug
File - Reference
Terminology (NDFRT)
An extension of the VHA National Drug File (NDF). It
organizes the drug list into a formal representation,
which NDF-RT combines the NDF hierarchical drug
classification with a multi-category reference model.
Used for modeling drug characteristics including
ingredients, chemical structure, dose form, physiologic
effect, mechanism of action, pharmacokinetics, and
related diseases.
U.S. Department https://www.nlm.nih.
of Veterans
gov/research/umls/
Affairs
sourcereleasedocs/
current/NDFRT/index.html
NIC
Nursing Intervention A comprehensive, research-based, standardized
Classification
classification of interventions that nurses perform.
University of
Iowa College of
Nursing
http://www.nursing.
uiowa.edu/cncce/
nursing-interventionsclassification-overview
NOC
Nursing Outcomes
Classification
A standardized classification system of patient
outcomes for evaluating the effects of nursing
interventions.
University of
Iowa College of
Nursing
http://www.nursing.
uiowa.edu/cncce/nursingoutcomes-classificationoverview
RadLex
RadLex radiology
lexicon
Provide medical imaging terms which are not found in
other medical terminologies. It may unify terms used in
radiology reports, bridge the terminology gap among
radiologists, and promote radiological knowledge
sharing.
RSNA
http://radlex.org/
RxNorm
Provide normalized names for clinical drugs and links its National Library
names to many of the drug vocabularies commonly used of Medicine
in pharmacy management and drug interaction software, (NLM)
including those in First Databank, NDF-RT, Micromedex,
Gold Standard Drug Database, Multum, and U.S.
Pharmacopeia (USP) Compendial Nomenclature.
https://www.nlm.nih.gov/
research/umls/rxnorm/
index.html
SNOMED
CT
Systematized
Nomenclature of
Medicine – Clinical
Terms
A global common language for clinical terms. The most
comprehensive clinical terminology in use around the
world.
http://www.snomed.org/
SNOMED
International
URL
