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

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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.
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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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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
Page 2 of 7
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.
Page 3 of 7
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 [2022].
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
Page 6 of 7
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
References
1. Centers for Disease Control and Prevention NCfHSMCoD-oCWOD,
released December 2018. Accessed 11 Nov 2019.
2. Miclette MA, Leff JA, Cuan I, Samet JH, Saloner B, Mendell G, et al. Closing
the gaps in opioid use disorder research, policy and practice: conference
proceedings. Addict Sci Clin Pract. 2018;13(1):22.
3. H.R.6—SUPPORT for Patients and Communities Act. https ://www.congr
ess.gov/bill/115th -congr ess/house -bill/6.
4. U.S. Department of Health and Human Services. National Institutes
of Health. U.S. National Library of Medicine. Common Data Element
Resource Portal. https ://www.nlm.nih.gov/cde/.
5. Opmeer BC. Electronic health records as sources of research data. JAMA.
2016;315(2):201–2.
6. Tai B, Wu LT, Clark HW. Electronic health records: essential tools in
integrating substance abuse treatment with primary care. Subst Abuse
Rehabil. 2012;3:1–8.
7. Lingren T, Sadhasivam S, Zhang X, Marsolo K. Electronic medical records
as a replacement for prospective research data collection in postopera-
tive pain and opioid response studies. Int J Med Inform. 2018;111:45–50.
8. Carrell D, Mardekian J, Cronkite D, Ramaprasan A, Hansen K, Gross DE,
et al. A fully automated algorithm for identifying patients with problem
prescription opioid use using electronic health record data. Drug Alcohol
Depend. 2017;171:e36.
9. Ghitza UE, Sparenborg S, Tai B. Improving drug abuse treatment delivery
through adoption of harmonized electronic health record systems. Subst
Abuse Rehabil. 2011;2011(2):125–31.
10. Tai B, McLellan AT. Integrating information on substance use dis-
orders into electronic health record systems. J Subst Abuse Treat.
2012;43(1):12–9.
11. U.S. Department of Health and Human Services. National Institutes of
Health. National Institute on Drug Abuse. Common Data Elements. https
://www.druga buse.gov/about -nida/organ izati on/cctn/ctn/resou rces/
commo n-data-eleme nts-cde.
12. Samuels EA, D’Onofrio G, Huntley K, Levin S, Schuur JD, Bart G, et al. A
quality framework for emergency department treatment of opioid use
disorder. Ann Emerg Med. 2019;73(3):237–47.
13. Services CfMM. CMS Measures Management System Blueprint (Blueprint
v15.0) 2019. 2020. https ://www.cms.gov/Medic are/Quali ty-Initi ative
s-Patie nt-Asses sment -Instr ument s/MMS/MMS-Bluep rint. Accessed 15
May 2020.
Page 7 of 7
Venkateshetal. Addict Sci Clin Pract (2020) 15:24
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14. U.S. Department of Health and Human Services. National Institutes of
Health. U.S. National Library of Medicine. Value Set Authority Center. https
://vsac.nlm.nih.gov/welco me.
15. U.S. Department of Health and Human Services. Centers for Medicare and
Medicaid Services. Data Element Library. https ://del.cms.gov/DELWe b/
pubNa vSear ch.
16. U.S. Department of Health and Human Services. Agency for Healthcare
Research and Quality. National Quality Measures Clearinghouse. https ://
www.ahrq.gov/profe ssion als/quali ty-patie nt-safet y/talki ngqua lity/resou
rces/initi ative s/nqmc.html.
17. The University of Washington. Alcohol and drug abuse institute. https ://
adai.washi ngton .edu/.
18. U.S. Department of Health and Human Services. National Institutes of
Health. National Human Genome Research Institute. Phenotypes and
Exposures (PhenX) Toolkit. https ://www.genom e.gov/Funde d-Progr ams-
Proje cts/Pheno types -and-Expos ures-PhenX .
19. Bodenreider O, Nguyen D, Chiang P, Chuang P, Madden M, Winnenburg
R, et al. The NLM value set authority center. Stud Health Technol Inform.
2013;192:1224.
20. Kirby JC, Speltz P, Rasmussen LV, Basford M, Gottesman O, Peissig PL,
et al. PheKB: a catalog and workflow for creating electronic phe-
notype algorithms for transportability. J Am Med Inf Assoc JAMIA.
2016;23(6):1046–52.
21. Carr A. 2018. 2020. https ://news.nnlm.gov/psr-newsb its/nlm-vsac-launc
hes-inten siona l-defin ition -funct ional ity/. Accessed 15 May 2020.
22. Medicine NLo. VSAC authoring best practices. 2020. https ://www.nlm.nih.
gov/vsac/suppo rt/autho rguid eline s/bestp racti ces.html. Accessed 15 May
2020.
23. Connor JP, Gullo MJ, White A, Kelly AB. Polysubstance use: diag-
nostic challenges, patterns of use and health. Curr Opin Psychiatry.
2014;27(4):269–75.
24. Agency for healthcare research and quality. Phase 2 winner announce-
ment. https ://www.ahrq.gov/stepu pappc halle nge/phase 2-winne rs.html.
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... [18][19][20] Electronic health records (EHRs), which can facilitate systematic screening, guide clinician actions, and record results in structured data fields, have been underused for substance use. 21,22 Common data elements for alcohol and drug information have been defined and recommended for integration into EHRs 23 ; however, in most systems, this information is still gathered in social history fields that do not include validated screening questionnaires and are inconsistently used. ...
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Importance: Guidelines recommend that adult patients receive screening for alcohol and drug use during primary care visits, but the adoption of screening in routine practice remains low. Clinics frequently struggle to choose a screening approach that is best suited to their resources, workflows, and patient populations. Objective: To evaluate how to best implement electronic health record (EHR)-integrated screening for substance use by comparing commonly used screening methods and examining their association with implementation outcomes. Design, setting, and participants: This article presents the outcomes of phases 3 and 4 of a 4-phase quality improvement, implementation feasibility study in which researchers worked with stakeholders at 6 primary care clinics in 2 large urban academic health care systems to define and implement their optimal screening approach. Site A was located in New York City and comprised 2 clinics, and site B was located in Boston, Massachusetts, and comprised 4 clinics. Clinics initiated screening between January 2017 and October 2018, and 93 114 patients were eligible for screening for alcohol and drug use. Data used in the analysis were collected between January 2017 and October 2019, and analysis was performed from July 13, 2018, to March 23, 2021. Interventions: Clinics integrated validated screening questions and a brief counseling script into the EHR, with implementation supported by the use of clinical champions (ie, clinicians who advocate for change, motivate others, and use their expertise to facilitate the adoption of an intervention) and the training of clinic staff. Clinics varied in their screening approaches, including the type of visit targeted for screening (any visit vs annual examinations only), the mode of administration (staff-administered vs self-administered by the patient), and the extent to which they used practice facilitation and EHR usability testing. Main outcomes and measures: Data from the EHRs were extracted quarterly for 12 months to measure implementation outcomes. The primary outcome was screening rate for alcohol and drug use. Secondary outcomes were the prevalence of unhealthy alcohol and drug use detected via screening, and clinician adoption of a brief counseling script. Results: Patients of the 6 clinics had a mean (SD) age ranging from 48.9 (17.3) years at clinic B2 to 59.1 (16.7) years at clinic B3, were predominantly female (52.4% at clinic A1 to 64.6% at clinic A2), and were English speaking. Racial diversity varied by location. Of the 93,114 patients with primary care visits, 71.8% received screening for alcohol use, and 70.5% received screening for drug use. Screening at any visit (implemented at site A) in comparison with screening at annual examinations only (implemented at site B) was associated with higher screening rates for alcohol use (90.3%-94.7% vs 24.2%-72.0%, respectively) and drug use (89.6%-93.9% vs 24.6%-69.8%). The 5 clinics that used a self-administered screening approach had a higher detection rate for moderate- to high-risk alcohol use (14.7%-36.6%) compared with the 1 clinic that used a staff-administered screening approach (1.6%). The detection of moderate- to high-risk drug use was low across all clinics (0.5%-1.0%). Clinics with more robust practice facilitation and EHR usability testing had somewhat greater adoption of the counseling script for patients with moderate-high risk alcohol or drug use (1.4%-12.5% vs 0.1%-1.1%). Conclusions and relevance: In this quality improvement study, EHR-integrated screening was feasible to implement in all clinics and unhealthy alcohol use was detected more frequently when self-administered screening was used at any primary care visit. The detection of drug use was low at all clinics, as was clinician adoption of counseling. These findings can be used to inform the decision-making of health care systems that are seeking to implement screening for substance use. Trial registration: ClinicalTrials.gov Identifier: NCT02963948.
... This special series presents notable examples, such as Adam et al. [14] reporting on the feasibility and acceptability of a self-administered electronic screening tool for multiple substance use, and Bart et al. [15] presenting an outline of a clinical decision support for opioid use disorder to facilitate implementation in electronic health records. Electronic health records, as showed by Venkatesh et al. [16], could help opioid use disorder research, but the standardization of data collection and infrastructure is needed to combine different data sources and capitalize on digital data collection to advance clinical knowledge and research. ...
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Background Information technology can be used to advance addiction science and clinical practice. Main body This special issue, “Information technology (IT) interventions to advance treatment for opioid and other addictions” presents studies that expand our understanding of IT intervention efficacy, patients’ perspectives, and how IT can be used to improve substance use health care and research. This editorial introduces the topics addressed in the special issue and focuses on some of the challenges that the field is currently facing, such as attrition and treatment retention, transferability of intervention paradigms, and the challenge to keep pace with rapidly changing technologies. Conclusions Increasing treatment reach is particularly crucial in the addiction field. IT empowers researchers and clinicians to reach large portions of the population who might not otherwise access standard treatment modalities, because of geographical limitations, logistical constraints, stigma, or other reasons. The use of information technology may help reduce the substance use treatment gap and contribute to public health efforts to diminish the impact of substance use and other addictive behaviors on population health.
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Medication treatment for opioid use disorder (MOUD) is an effective evidence-based therapy for decreasing opioid-related adverse outcomes. Effective strategies for retaining persons on MOUD, an essential step to improving outcomes, are needed as roughly half of all persons initiating MOUD discontinue within a year. Data science may be valuable and promising for improving MOUD retention by using "big data" (e.g., electronic health record data, claims data mobile/sensor data, social media data) and specific machine learning techniques (e.g., predictive modeling, natural language processing, reinforcement learning) to individualize patient care. Maximizing the utility of data science to improve MOUD retention requires a three-pronged approach: (1) increasing funding for data science research for OUD, (2) integrating data from multiple sources including treatment for OUD and general medical care as well as data not specific to medical care (e.g., mobile, sensor, and social media data), and (3) applying multiple data science approaches with integrated big data to provide insights and optimize advances in the OUD and overall addiction fields.
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Study objective: We retrospectively evaluated the implementation of low-threshold emergency department (ED) buprenorphine treatment at 52 hospitals participating in the CA Bridge Program using the RE-AIM (reach, effectiveness, adoption, implementation, maintenance) framework. Methods: The CA Bridge model included low-threshold buprenorphine, connection to outpatient care, and harm reduction. Implementation began in March 2019. Participating hospitals reported aggregated clinical data monthly after program initiation. Outcomes included identification of opioid use disorder, buprenorphine administration, and linkage to outpatient addiction treatment. Multivariable models assessed associations between hospital location (rural versus urban) and teaching status (clinical teaching hospital versus community hospital) and outcomes in adopting the CA Bridge Program. Results: Reach: A diverse and geographically distributed group of 52 California hospitals were enrolled in 2 phases (March and August 2019); 12 (23%) were rural and 13 (25%) were teaching hospitals. Effectiveness: Over a 14-month implementation period, 12,009 opioid use disorder patient encounters were identified, including 7,179 (59.7%) where buprenorphine was administered and 4,818 (40.1%) where follow-up visits were attended. Adoption: In multivariable analysis, adoption did not differ significantly between rural and urban or teaching and nonteaching hospitals. Implementation: By program completion, all 52 (100%) hospitals treated opioid use disorder with buprenorphine; 45 (86.5%) administered buprenorphine after naloxone reversal; 41 (84.6%) offered buprenorphine for inpatients; 48 (92.3%) initiated buprenorphine in pregnant women; and 29 (55.8%) offered take-home naloxone. Maintenance: At 8-month follow-up, all 52 sites reported continued buprenorphine treatment. Conclusion: Low-threshold ED buprenorphine treatment implemented with a harm reduction approach and active navigation to outpatient addiction treatment was successful in achieving buprenorphine treatment for opioid use disorder in diverse California communities.
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Abstract Drug overdose deaths involving opioids have surged in recent years and the economic cost of the opioid epidemic is estimated to be over $500 billion annually. In the midst of calls for declaring a national emergency, health policy decision makers are considering the best ways to allocate resources to curb the epidemic. On June 9, 2017, 116 invited health researchers, clinicians, policymakers, health system leaders, and other stakeholders met at the University of Pennsylvania to discuss approaches to address the gaps in evidence-based substance use disorder policy and practice, with an emphasis on the opioid epidemic. The conference was sponsored by the Center for Health Economics of Treatment Interventions for Substance Use Disorder, HCV, and HIV (CHERISH), a NIDA-funded National Center of Excellence, and hosted by the Leonard Davis Institute of Health Economics of the University of Pennsylvania. The conference aims were to: (1) foster new relationships between researchers and policymakers through a collaborative work process and (2) generate evidence-based policy recommendations to address the opioid epidemic. The conference concluded with an interactive work session during which attendees self-identified as researchers or policymakers and were divided equally among 13 tables. These groups met to develop and present policy recommendations based on an opioid use disorder case study. Thirteen policy recommendations emerged across four themes: (1) quality of treatment, (2) continuity of care, (3) opioid prescribing and pain management, and (4) consumer engagement. This conference serves as a proposed model to develop equitable, working relationships among researchers, clinicians, and policymakers.
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Purpose of Review: Polysubstance use is common, particularly among some age groups and sub-cultures. It is also associated with elevated risk of psychiatric and physical health problems. We review recent research findings, comment on changes to polysubstance diagnoses, report on contemporary clinical and epidemiological polysubstance trends, and examine the efficacy of preventive and treatment approaches. Recent Findings: Approaches to describing polysubstance use profiles are becoming more sophisticated. Models over the past 18 months that employ Latent Class Analysis typically report a no use or limited range cluster (alcohol/tobacco/marijuana), a moderate range cluster (limited range, plus amphetamine derivatives) and an extended range cluster (moderate range, plus nonmedical use of prescription drugs and other illicit drugs). Prevalence rates vary as a function of the population surveyed. Wider-ranging polysubstance users carry higher risk of comorbid psychopathology, health problems and deficits in cognitive functioning. Summary: Wide-ranging polysubstance use is more prevalent in sub-cultures such as ‘ravers’ (dance club attendees), and those already dependent on substances. Health risks are elevated in these groups. Research into prevention and treatment of polysubstance use is underdeveloped. There may be benefit in targeting specific polysubstance use and/or risk profiles in prevention and clinical research.
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While substance use problems are considered to be common in medical settings, they are not systematically assessed and diagnosed for treatment management. Research data suggest that the majority of individuals with a substance use disorder either do not use treatment or delay treatment-seeking for over a decade. The separation of substance abuse services from mainstream medical care and a lack of preventive services for substance abuse in primary care can contribute to under-detection of substance use problems. When fully enacted in 2014, the Patient Protection and Affordable Care Act 2010 will address these barriers by supporting preventive services for substance abuse (screening, counseling) and integration of substance abuse care with primary care. One key factor that can help to achieve this goal is to incorporate the standardized screeners or common data elements for substance use and related disorders into the electronic health records (EHR) system in the health care setting. Incentives for care providers to adopt an EHR system for meaningful use are part of the Health Information Technology for Economic and Clinical Health Act 2009. This commentary focuses on recent evidence about routine screening and intervention for alcohol/drug use and related disorders in primary care. Federal efforts in developing common data elements for use as screeners for substance use and related disorders are described. A pressing need for empirical data on screening, brief intervention, and referral to treatment (SBIRT) for drug-related disorders to inform SBIRT and related EHR efforts is highlighted.
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Emergency clinicians are on the front lines of responding to the opioid epidemic and are leading innovations to reduce opioid overdose deaths through safer prescribing, harm reduction, and improved linkage to outpatient treatment. Currently, there are no nationally recognized quality measures or best practices to guide emergency department quality improvement efforts, implementation science researchers, or policymakers seeking to reduce opioid-associated morbidity and mortality. To address this gap, in May 2017, the National Institute on Drug Abuse's Center for the Clinical Trials Network convened experts in quality measurement from the American College of Emergency Physicians’ (ACEP's) Clinical Emergency Data Registry, researchers in emergency and addiction medicine, and representatives from federal agencies, including the National Institute on Drug Abuse and the Centers for Medicare & Medicaid Services. Drawing from discussions at this meeting and with experts in opioid use disorder treatment and quality measure development, we developed a multistakeholder quality improvement framework with specific structural, process, and outcome measures to guide an emergency medicine agenda for opioid use disorder policy, research, and clinical quality improvement.
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Background and aim: Many clinical research studies claim to collect data that are also captured in the electronic medical record (EMR). We evaluate the potential for EMR data to replace prospective research data collection. Methods: Using a dataset of 358 surgical patients enrolled in a prospective study, we examined the completeness and agreement of EMR and study entries for several variables, including the patient's stay in the post-operative care unit (PACU), surgical pain relief and pain medication side effects. Results: For all variables with a completeness percentage, values were greater than 96%. For the adverse event variables, we found slight to substantial agreement (Cohen's kappa), ranging from 0.19 (nausea) to 0.48 (respiratory depression) to 0.73 (emesis). Conclusion: The potential to use EMR data as a replacement for prospective research data collection shows promise, but for now, should be evaluated on a variable-by-variable basis.
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Objective Health care generated data have become an important source for clinical and genomic research. Often, investigators create and iteratively refine phenotype algorithms to achieve high positive predictive values (PPVs) or sensitivity, thereby identifying valid cases and controls. These algorithms achieve the greatest utility when validated and shared by multiple health care systems. Materials and Methods We report the current status and impact of the Phenotype KnowledgeBase (PheKB, http://phekb.org ), an online environment supporting the workflow of building, sharing, and validating electronic phenotype algorithms. We analyze the most frequent components used in algorithms and their performance at authoring institutions and secondary implementation sites. Results As of June 2015, PheKB contained 30 finalized phenotype algorithms and 62 algorithms in development spanning a range of traits and diseases. Phenotypes have had over 3500 unique views in a 6-month period and have been reused by other institutions. International Classification of Disease codes were the most frequently used component, followed by medications and natural language processing. Among algorithms with published performance data, the median PPV was nearly identical when evaluated at the authoring institutions (n = 44; case 96.0%, control 100%) compared to implementation sites (n = 40; case 97.5%, control 100%). Discussion These results demonstrate that a broad range of algorithms to mine electronic health record data from different health systems can be developed with high PPV, and algorithms developed at one site are generally transportable to others. Conclusion By providing a central repository, PheKB enables improved development, transportability, and validity of algorithms for research-grade phenotypes using health care generated data.
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To the Editor Dr Angus outlined a perspective in which randomized clinical trials (RCTs) are fused with “big data,” referring to increasing digitization of patient and health care information in electronic health records (EHRs).1 As a member of the work group on research data in relation to EHRs in our medical center, I was concerned about the optimistic expectations of using EHRs as a major source of information for clinical research.
The Value Set Authority Center (VSAC) at the National Library of Medicine (NLM) provides downloadable access to all official versions of vocabulary value sets contained in the Clinical Quality Measures (CQMs) used in the certification criteria for electronic health record systems ("Meaningful Use" incentive program). Each value set consists of the numerical values (codes) and human-readable names (descriptions), drawn from standard vocabularies such as LOINC, RxNorm and SNOMED CT<sup>®</sup>, that are used to define clinical data elements used in clinical quality measures (e.g., patients with diabetes, tricyclic antidepressants). The content of the VSAC will gradually expand to incorporate value sets for other use cases, as well as for new measures and updates to existing measures.
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For reasons of safety and effectiveness, many forces in health care, especially the Affordable Care Act of 2010, are pressing for improved identification and management of substance use disorders within mainstream health care. Thus, standard information about patient substance use will have to be collected and used by providers within electronic health record systems (EHRS). Although there are many important technical, legal, and patient confidentiality issues that must be dealt with to achieve integration, this article focuses upon efforts by the National Institute on Drug Abuse and other federal agencies to develop a common set of core questions to screen, diagnose, and initiate treatment for substance use disorders as part of national EHRS. This article discusses the background and rationale for these efforts and presents the work to date to identify the questions and to promote information sharing among health care providers.