Assessing the readiness of digital data infrastructure for opioid use disorder research

Abstract

Background:
Gaps in electronic health record (EHR) data collection and the paucity of standardized clinical data elements (CDEs) captured from electronic and digital data sources have impeded research efforts aimed at understanding the epidemiology and quality of care for opioid use disorder (OUD). We identified existing CDEs and evaluated their validity and usability, which is required prior to infrastructure implementation within EHRs.

Methods:
We conducted (a) a systematic literature review of publications in Medline, Embase and the Web of Science using a combination of at least one term related to OUD and EHR and (b) an environmental scan of publicly available data systems and dictionaries used in national informatics and quality measurement of policy initiatives. Opioid-related data elements identified within the environmental scan were compared with related data elements contained within nine common health data code systems and each element was graded for alignment with match results categorized as "exact", "partial", or "none."

Results:
The literature review identified 5186 articles for title search, of which 75 abstracts were included for review and 38 articles were selected for full-text review. Full-text articles yielded 237 CDEs, only 12 (5.06%) of which were opioid-specific. The environmental scan identified 379 potential data elements and value sets across 9 data systems and libraries, among which only 84 (22%) were opioid-specific. We found substantial variability in the types of clinical data elements with limited overlap and no single data system included CDEs across all major data element types such as substance use disorder, OUD, medication and mental health. Relative to common health data code systems, few data elements had an exact match (< 1%), while 61% had a partial match and 38% had no matches.

Conclusions:
Despite the increasing ubiquity of EHR data standards and national attention placed on the opioid epidemic, we found substantial fragmentation in the design and construction of OUD related CDEs and little OUD specific CDEs in existing data dictionaries, systems and literature. Given the significant gaps in data collection and reporting, future work should leverage existing structured data elements to create standard workflow processes to improve OUD data capture in EHR systems.

Full text

Venkateshetal. Addict Sci Clin Pract (2020) 15:24
https://doi.org/10.1186/s13722-020-00198-3
RESEARCH
Assessing thereadiness ofdigital data
infrastructure foropioid use disorder research
Arjun Venkatesh1,2* , Caitlin Malicki1, Kathryn Hawk1, Gail D’Onofrio1, Jeremiah Kinsman1 and Andrew Taylor1
Abstract
Background: Gaps in electronic health record (EHR) data collection and the paucity of standardized clinical data ele-
ments (CDEs) captured from electronic and digital data sources have impeded research efforts aimed at understand-
ing the epidemiology and quality of care for opioid use disorder (OUD). We identified existing CDEs and evaluated
their validity and usability, which is required prior to infrastructure implementation within EHRs.
Methods: We conducted (a) a systematic literature review of publications in Medline, Embase and the Web of Sci-
ence using a combination of at least one term related to OUD and EHR and (b) an environmental scan of publicly
available data systems and dictionaries used in national informatics and quality measurement of policy initiatives.
Opioid-related data elements identified within the environmental scan were compared with related data elements
contained within nine common health data code systems and each element was graded for alignment with match
results categorized as “exact”, “partial”, or “none.”
Results: The literature review identified 5186 articles for title search, of which 75 abstracts were included for review
and 38 articles were selected for full-text review. Full-text articles yielded 237 CDEs, only 12 (5.06%) of which were
opioid-specific. The environmental scan identified 379 potential data elements and value sets across 9 data systems
and libraries, among which only 84 (22%) were opioid-specific. We found substantial variability in the types of clinical
data elements with limited overlap and no single data system included CDEs across all major data element types such
as substance use disorder, OUD, medication and mental health. Relative to common health data code systems, few
data elements had an exact match (< 1%), while 61% had a partial match and 38% had no matches.
Conclusions: Despite the increasing ubiquity of EHR data standards and national attention placed on the opioid
epidemic, we found substantial fragmentation in the design and construction of OUD related CDEs and little OUD
specific CDEs in existing data dictionaries, systems and literature. Given the significant gaps in data collection and
reporting, future work should leverage existing structured data elements to create standard workflow processes to
improve OUD data capture in EHR systems.
Keywords: Common data elements, Electronic health records, Opioid-related disorders, Emergency medicine
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adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and
the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material
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Background
e opioid epidemic, which is responsible for nearly
400,000 overdose deaths since 1999, has received
increased attention from researchers and policymakers
as a leading cause of injury-related death in the United
States [1]. Unfortunately, few evidence-based solutions
to the epidemic exist due to limited prior attention and
investments in research infrastructure for a condition
often stigmatized or marginalized [2]. e passage of the
Substance Use-Disorder Prevention that Promotes Opi-
oid Recovery and Treatment (SUPPORT) for Patients
and Communities Act, however, has generated marked
enthusiasm and support to address gaps in research,
Open Access
Addiction Science &
Clinical Practice
*Correspondence: arjun.venkatesh@yale.edu
1 Department of Emergency Medicine, Yale University School of Medicine,
464 Congress Ave, Suite 260, New Haven, CT 06519, USA
Full list of author information is available at the end of the article

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Venkateshetal. Addict Sci Clin Pract (2020) 15:24
surveillance, and care for opioid use disorder (OUD)
using increasingly available electronic and digital data
sources such as electronic health records (EHRs) [3].
While the National Institute of Health encourages the use
of common data elements (CDEs) “to improve data qual-
ity and opportunities for comparison and combination
of data from multiple studies and with electronic health
records” [4], numerous challenges still exist in identify-
ing and incorporating OUD-specific CDEs into research
initiatives [5, 6]. Prior work has identified numerous gaps
in EHRs or data standards that preclude high-quality
OUD research, including single site-specific definitions
that cannot be generalized for observational studies or
surveillance as well as the use of disparate data EHR data
systems between vendors when capturing and storing
health data [7, 8]. Additionally, fragmented CDEs that
are not easily translated across settings or data systems
that are inherently designed for select types or structured
or clinically oriented data prevent the effective develop-
ment of quality measurement or surveillance systems [9,
10]. For example a common National Institutes of Health
(NIH) CDE is derived from the Timeline Followback
Method Assessment, which collects information about
opioid use in the past week [11]. However, this CDE does
not map to any existing data standard or system which
are inherently designed for more structured data or hier-
archies of data terms in ontologies not specific to a single
question.
e creation and inclusion of opioid relevant CDEs in
clinical data registries and EHRs would both enable and
improve the quality of substance use disorder research
and the evaluation of interventions to improve outcomes
[6]. For example, improving EHR data infrastructure for
OUD data elements could provide the building blocks
for future quality measures, performance benchmark-
ing, and answering important research questions, such
as “how many providers provide naloxone or administer
buprenorphine for OUD?” or “what proportion of emer-
gency department (ED) patients with OUD have low back
pain?” [12], which would improve our understanding of
the scope of this issue, as well as evaluate interventions.
We therefore aimed to identify and categorize existing
CDEs in relation to OUD and assess their alignment with
common data standards, which is required prior to infra-
structure implementation.
Methods
is study included the parallel conduct of an environ-
mental scan and a literature review. e former was
designed to capture data elements and concepts used in
national informatics and quality measurement initiatives,
while the latter encompassed CDEs published in peer-
reviewed literature. is comprehensive study design was
based on the current structure and availability of relevant
data, with input from a multidisciplinary committee
of experts, and allowed for inclusion of a diverse set of
data standards ranging from diagnostic codes originally
intended for billing purposes to EHR standards for clini-
cal information. e Yale University Institutional Review
Board (IRB) determined that review and approval were
not required, as the project did not involve human sub-
jects research.
Environmental scan
We conducted an environmental scan of publicly avail-
able data systems, data elements and data dictionaries
used in several public and private initiatives to identify
OUD data elements suitable for capture in the EHR. e
environmental scan was conducted in concert with guid-
ance of the Centers for Medicare and Medicaid Service’s
MMS Blueprint, a guidance document for quality meas-
ure development in which environmental scans are simi-
larly applied to diverse data types for similar purposes to
this work [13].
Data sources
We searched publicly available data system and diction-
ary websites for opioid-related data sets and elements
including the Value Set Authority Center (VSAC) [14],
Centers for Medicare and Medicaid (CMS) Data Element
Library (DEL) [15], National Quality Measures Clear-
inghouse (NQMC) [16], the NIH CDEs [4], the Univer-
sity of Washington Alcohol and Drug Abuse Institute
(ADAI) Library Instruments [17], the National Human
Genome Research Institute (NHGRI): PhenX Toolkit [18]
and the National Institute of Drug Abuse (NIDA) CDEs
[11]. Each source contains fairly unique CDE informa-
tion including: clinical concepts within the VSAC, qual-
ity measure specific data instruments within the DEL,
primarily patient reported outcome survey instruments
within the NIH CDE, human readable data element spec-
ifications within the NQMC, and consensus measure-
ment protocols within PhenX.
Search strategy
For VSAC, CMS DEL, NQMC, NIH CDEs, University
of Washington ADAI, and PhenX researchers searched
“opioid,” along with relevant keywords such as heroin,
buprenorphine, naloxone, Narcan and methadone. We
found that expanding search terms beyond opioid did not
return any additional value sets that were not found using
opioid only. Given the relevance of NIDA CDEs to sub-
stance use disorder [11], we manually reviewed all 204
CDEs for any referencing opioids.

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Venkateshetal. Addict Sci Clin Pract (2020) 15:24
Inclusion/exclusion
For the analysis, we included all data elements consid-
ered relevant to OUD research based on a review by
two research investigators (CM, AT) and any disagree-
ments in relevance were reviewed by a third investiga-
tor (AKV) and resolved by consensus discussion. In
general, due to limited specificity of CDEs, the process
was inclusive of most data elements and only data ele-
ments solely specific to another substance use disorder
such as tobacco or alcohol without any OUD relevance
were excluded.
Analysis
Opioid-related data elements identified for each data
system and library were compared with related data
elements contained within the following common
health data code systems: Current Procedural Termi-
nology (CPT), International Classification of Diseases,
9th Revision (ICD9), International Classification of
Diseases, 10th Revision (ICD10), Systematized Nomen-
clature of Medicine Clinical Terms (SNOMEDCT),
Logical Observation Identifiers Names and Codes
(LOINC), National Drug File –Reference Terminol-
ogy (NDFRT), Healthcare Common Procedure Cod-
ing System (HCPCS), Centers for Disease Control and
Prevention Race and Ethnicity Code Set (CDCREC)
and RXNORM. Data elements were graded by a study
investigator, with match results categorized as “exact”,
“partial”, or “none.” To ensure accuracy, matching was
reviewed by a second study investigator and any disa-
greements were resolved by a third investigator.
Literature review
For the literature review, we constructed a comprehen-
sive search strategy built upon clinical experience, prior
systematic reviews in substance use disorder literature,
and input from a professional librarian.
Sources
We conducted a search of relevant publications in
Medline and Embase using OVID, as well as the Web
of Science.
Search strategy
Searches included a combination of at least one term
related to opioid use disorders and electronic medical
records. Opioid related search terms included analge-
sics, opioid-related disorders, opiate alkaloids, or types
of opioids (opioid OR opiate OR heroin OR nalox-
one OR narcan OR evzio OR percocet OR endocet
OR primlev OR oxycontin OR oxycodone OR roxico-
done OR xtampza OR oxaydo OR buprenorphine OR
buprenex OR butrans OR probuphine OR suboxone
OR belbuca). Electronic medical record search terms
included medical records, EHR OR Electronic Health
Record* OR Electronic Medical Record* OR Electronic
Data Element* OR Electronic Phenotype* OR value set
authority center* OR VSAC OR ontology OR SNOMED
OR ICD9 OR ICD10 OR data standard* OR HL7 OR
Health Level 7 OR FHIR OR common data element*
OR medical record* OR clinical data element*. Search
terms were similar for Web of Science and adapted for
their terms/indexes.
Inclusion/exclusion
Using Covidence, we systematically reviewed our
search results to identify publications for review
and analysis, the results of which are presented in a
PRISMA flow chart in Fig. 1. In summary, the search
returned 5186 references (1070 in Medline, 3653 in
Embase and 1103 in Web of Science), and after remov-
ing duplicates (n = 157) and articles that did not include
relevant content related to both opioid use disorders
and electronic medical records (n = 4954), a total of 75
full text articles remained for further analysis. Of these,
37 studies were excluded primarily due to lack of rel-
evant outcomes or non-peer-reviewed publication type
and a total of 38 studies were included for review and
analysis.
Fig. 1 PRISMA flow diagram of literature review

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Venkateshetal. Addict Sci Clin Pract (2020) 15:24
Analysis
We conducted a systematic assessment of included stud-
ies based on expert review, abstraction and curation by
two study investigators. Each CDE identified in the man-
uscript was abstracted into a standardized data collection
tool and classified each data element as related to diag-
nosis, medication, patient demographics, or vital signs.
Given the heterogeneity of underlying studies as well
as the purpose of this exploratory literature review, no
meta-analysis was considered necessary or feasible.
Results
Environmental scans of data dictionaries and databases
on seven publicly available websites identified 379 CDEs,
including 175 CDEs captured using the search term “opi-
oid” and 204 contained within the NIDA CDEs. Based on
manual review, only 84 (22%) of all CDEs identified were
opioid-specific, while 93 (25%) were related to substance
use disorder (SUD) and 202 (53%) were categorized as
“other” (Table1). e majority of opioid-specific CDEs
were found in VSAC, which focused on intravenous
drug use, pain medications and urine screening, and
the Washington ADAI, which included clinical instru-
ments such as the Clinical Opiate Withdrawal Scale and
Opioid Craving Scale. When comparing 305 CDEs with
related data elements contained within 9 common health
data code systems in VSAC (e.g., CPT, ICD10, LOINC,
etc.) for a combined total of 2745 potential matches,
61% had a partial match, 38% had no matches and less
than 1% had an exact match with VSAC data code sys-
tems (Table2). Overall, we found substantial variability
in the types of clinical data elements available in each
major data system with limited overlap (Fig. 2). Many
CDE groups were dominated by one data category (e.g.
NQMC) and few capture data elements from a wide vari-
ety of data categories well (e.g. NIDA CDEs). Notably, the
NQMC included many CDEs specific to pain and quality
of life but virtually none specific to mental health, which
is captured by the NIDA CDEs, and no medications
which are uniquely captured by the CMS DEL. No single
data system includes CDEs across all major data element
types such as SUD, OUD, medication and mental health.
A comprehensive summary of categorized data elements
is available in Additional file1: Appendix S1.
e literature review identified 38 articles for analysis
(Additional file2: Appendix S2), which described obser-
vational research, expert consensus/review publica-
tions and a limited set of experimental studies. e vast
majority of studies were not directly reporting CDEs but
rather included outcomes or cohort definitions that were
descriptive of a CDE and suitable for consideration for
future data infrastructure work.
Overall, the literature review identified a total of 237
CDEs that could potentially be OUD related, of which
225 (95%) were diagnosis-based and not opioid specific.
ese included descriptions of CDEs for other SUD such
as alcohol use as well as diagnosis codes for concomi-
tant mental health conditions. No standard or consistent
diagnostic CDE definitions were used across the stud-
ies further indicating the lack of consensus or standard
vocabularies for OUD CDEs.
Discussion
is environmental scan and literature review revealed
several notable gaps in the digital data infrastructure
necessary for EHRs to support research on OUD. First,
despite the increasing ubiquity of EHR data standards,
we found substantial fragmentation in the design and
construction of OUD-related CDEs. Value sets that
are posted and curated within the NLM VSAC increas-
ingly represent a centralized set or list of potential CDEs
that define clinical concepts to support effective and
Table 1 Opioid specicity ofclinical data elements identied inenvironmental scan
Category Data code systems
VSAC n (%) CMS DEL n (%) NQMC n (%) NIH CDE n (%) ADAI n (%) PhenX n (%) NIDA CDEs n (%) Total n (%)
Opioid specific 27 (36) 9 (100) 1 (3) 12 (63) 24 (67) 0 (0) 11 (5) 84 (22)
SUD nonspecific 16 (22) 0 (0) 0 (0) 2 (11) 12 (33) 2 (100) 61 (30) 93 (25)
Other 31 (42) 0 (0) 34 (97) 5 (26) 0 (0) 0 (0) 132 (65) 202 (53)
Total 74 (100) 9 (100) 35 (100) 19 (100) 36 (100) 2 (100) 204 (100) 379 (100)
Table 2 Summary ofVSAC matches bydata code system
Data code system Exact n (%) Partial n (%) None n (%) Total n
NIDA CDE 9 (1) 1275 (69) 552 (30) 1836
CMS DEL 0 (0) 59 (73) 22 (27) 81
NQMC 0 (0) 0 (0) 315 (100) 315
NIH CDE 0 (0) 91 (53) 80 (47) 171
ADAI 0 (0) 252 (78) 72 (22) 324
PhenX 0 (0) 14 (78) 4 (22) 18
Total 9 (1) 1691 (61) 1045 (38) 2745

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Venkateshetal. Addict Sci Clin Pract (2020) 15:24
interoperable health information exchange [19]. How-
ever, the value sets we identified are often limited to a
single data type or data, which limits use across data
systems and in turn exacerbates gaps in CDE capture of
clinical concepts. For example, while diagnostic codes of
OUD and medication-based value sets that could be used
to identify OUD are independently present in the VSAC,
the lack of data integration results in multiple OUD defi-
nitions of poor sensitivity and/or specificity. For OUD
research initiatives to yield broadly generalizable results,
future work must either develop validated cross walks
between data sources (e.g. linking specific SNOMED
concepts to ICD-10 diagnostic codes) or more likely,
hybrid definitions that integrate multiple datatypes to
characterize a clinical concept such as “opioid overdose”
in a manner that leverages the strengths and accommo-
dates the limitations of disparate electronic data systems
and ontologies [20–22].
Second, we found little OUD-specific CDEs in exist-
ing data dictionaries and systems. Given high rates of
co-occurrence, many substance use disorder CDEs are
OUD-relevant [23], yet few CDEs effectively capture
OUD-specific data needed for most research initiatives.
For example, most NIDA CDEs relevant to OUD were
initially developed or designed to assess SUD more
broadly or for other substances such as alcohol [23].
In addition, while many medication CDEs exist related
to opioids, few distinguished between opioid prescrib-
ing outside the hospital-based setting and within the
hospital setting. Even fewer CDEs distinguish between
the prescribing of opioids for episodic or acute condi-
tions and chronic purposes. is is an important dis-
tinction for the development of future opioid related
quality measures and research, as the gaps in current
data infrastructure preclude many important observa-
tional or epidemiological analyses impossible without
the opioid drug and OUD element specificity needed
by investigators. Additionally, while we recognize that
the number of opioid-specific CDEs is limited by the
pool of data included in this review, when matching the
NIDA CDEs—which specifically includes data relevant
Fig. 2 Distribution of clinical data element type by data system

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Venkateshetal. Addict Sci Clin Pract (2020) 15:24
to substance use—there were still very few (n = 11) data
elements specific to opioids.
ird, we found that traditional resource sources such
as peer-reviewed publications contain few CDEs ready
to use for existing data systems. Most research regard-
ing structured data and patient-reported outcomes
has utilized non-electronic data sources such as chart
review or surveys, or low-fidelity sources such as insur-
ance claims, and has also acknowledged notable limita-
tions in data definitions due to the paucity of standard
CDEs and definitions. Future data infrastructure efforts
will need to rely on non-traditional data sources to
identify CDEs and federal and state informatics initia-
tives to identify standards and be flexible to adapt non-
electronic tools to electronic applications [24].
Conclusions
Despite the increasing ubiquity of EHR data standards,
we found substantial fragmentation in the design and
construction of OUD related CDEs and little OUD spe-
cific CDEs in existing data dictionaries, systems and lit-
erature. Future work should leverage existing structured
data elements to create standard workflow processes to
improve OUD data capture in EHR systems.
Supplementary information
Supplementary information accompanies this paper at https ://doi.
org/10.1186/s1372 2-020-00198 -3.
Additional le1.Summary of categorized data elements identified by
environmental scan mapped to common data elements of Value Set
Authority Center.
Additional le2.Detailed results of literature review.
Abbreviations
ADAI: Alcohol and Drug Abuse Institute; CDCREC: Centers for Disease Control
and Prevention Race and Ethnicity Code Set; CDE: Common data element;
CMS: Centers for Medicare and Medicaid; CPT: Current Procedural Terminol-
ogy; DEL: Data element library; ED: Emergency department; EHR: Electronic
health record; HCPCS: Healthcare Common Procedure Coding System; ICD10:
International Classification of Diseases, 10th Revision; ICD9: International
Classification of Diseases, 9th Revision; LOINC: Logical Observation Identifiers
Names and Codes; NDFRT: National Drug File – Reference Terminology; NHGRI:
National Human Genome Research Institute; NIDA: National Institute of Drug
Abuse; NIH: National Institutes of Health; NQMC: National Quality Measures
Clearinghouse; OUD: Opioid use disorder; SNOMED CT: Systematized Nomen-
clature of Medicine Clinical Terms; SUD: Substance use disorder; VSAC: Value
Set Authority Center.
Acknowledgements
Not applicable.
Authors’ contributions
Concept and design: AV, GD, AT, KH. Acquisition, analysis, or interpretation of
data: AV, AT, CM. Drafting of the manuscript: AV, AT, CM, JK. Critical revision of
the manuscript for important intellectual content: All. Statistical analysis: AT.
Obtained funding: AV. Supervision: AV, AT. All authors read and approved the
final manuscript.
Funding
This work was supported by the HHS Office of the Secretary Patient Centered
Outcomes Research Trust Fund (PCORTF) under IDDA# ASPE-2018-001 and
NIDA UG1DA015831-18S2. In addition, Dr. Venkatesh was supported by KL2
TR000140 from the National Center for Advancing Translational Sciences of
the NIH. The contents of this work are solely the responsibility of the authors
and do not necessarily represent the official view of NIH.
Availability of data and materials
All data generated or analyzed during this study are included in this published
article and its Additional files 1, 2.
Ethics approval and consent to participate
Not applicable.
Consent for publication
Not applicable.
Competing interests
The authors declare that they have no competing interests.
Author details
1 Department of Emergency Medicine, Yale University School of Medicine, 464
Congress Ave, Suite 260, New Haven, CT 06519, USA. 2 Center for Outcomes
Research and Evaluation, Yale New Haven Hospital, New Haven, CT, USA.
Received: 22 January 2020 Accepted: 2 July 2020
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