Evaluation of Data Exchange Process for Interoperability and Impact on Electronic Laboratory Reporting Quality to a State Public Health Agency
Identifieur interne : 000970 ( Pmc/Corpus ); précédent : 000969; suivant : 000971Evaluation of Data Exchange Process for Interoperability and Impact on Electronic Laboratory Reporting Quality to a State Public Health Agency
Auteurs : Sripriya Rajamani ; Ann Kayser ; Emily Emerson ; Sarah SolarzSource :
- Online Journal of Public Health Informatics [ 1947-2579 ] ; 2018.
Abstract
Past and present national initiatives advocate for electronic exchange of health data and emphasize interoperability. The critical role of public health in the context of disease surveillance was recognized with recommendations for electronic laboratory reporting (ELR). Many public health agencies have seen a trend towards centralization of information technology services which adds another layer of complexity to interoperability efforts.
The study objective was to understand the process of data exchange and its impact on the quality of data being transmitted in the context of electronic laboratory reporting to public health. This was conducted in context of Minnesota Electronic Disease Surveillance System (MEDSS), the public health information system for supporting infectious disease surveillance in Minnesota. Data Quality (DQ) dimensions by Strong et al., was chosen as the guiding framework for evaluation.
The process of assessing data exchange for electronic lab reporting and its impact was a mixed methods approach with qualitative data obtained through expert discussions and quantitative data obtained from queries of the MEDSS system. Interviews were conducted in an open-ended format from November 2017 through February 2018. Based on these discussions, two high level categories of data exchange process which could impact data quality were identified: onboarding for electronic lab reporting and internal data exchange routing. This in turn comprised of ten critical steps and its impact on quality of data was identified through expert input. This was followed by analysis of data in MEDSS by various criteria identified by the informatics team.
All DQ metrics (Intrinsic DQ, Contextual DQ, Representational DQ, and Accessibility DQ) were impacted in the data exchange process with varying influence on DQ dimensions. Some errors such as improper mapping in electronic health records (EHRs) and laboratory information systems had a cascading effect and can pass through technical filters and go undetected till use of data by epidemiologists. Some DQ dimensions such as accuracy, relevancy, value-added data and interpretability are more dependent on users at either end of the data exchange spectrum, the relevant clinical groups and the public health program professionals. The study revealed that data quality is dynamic and on-going oversight is a combined effort by MEDSS Informatics team and review by technical and public health program professionals.
With increasing electronic reporting to public health, there is a need to understand the current processes for electronic exchange and their impact on quality of data. This study focused on electronic laboratory reporting to public health and analyzed both onboarding and internal data exchange processes. Insights gathered from this research can be applied to other public health reporting currently (e.g. immunizations) and will be valuable in planning for electronic case reporting in near future.
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DOI: 10.5210/ojphi.v10i2.9317
PubMed: 30349622
PubMed Central: 6194099
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PMC:6194099Le document en format XML
<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="en">Evaluation of Data Exchange Process for Interoperability and Impact
on Electronic Laboratory Reporting Quality to a State Public Health
Agency</title><author><name sortKey="Rajamani, Sripriya" sort="Rajamani, Sripriya" uniqKey="Rajamani S" first="Sripriya" last="Rajamani">Sripriya Rajamani</name><affiliation><nlm:aff id="aff1">Informatics Programs, School of Nursing<institution>University of Minnesota</institution>,<addr-line>Minneapolis, Minnesota</addr-line></nlm:aff></affiliation></author><author><name sortKey="Kayser, Ann" sort="Kayser, Ann" uniqKey="Kayser A" first="Ann" last="Kayser">Ann Kayser</name><affiliation><nlm:aff id="aff2">Minnesota Electronic Disease Surveillance System (MEDSS) Operations, Infectious Disease Epidemiology Prevention and Control Division,<institution>Minnesota Department of Health</institution>,<addr-line>St. Paul, Minnesota</addr-line></nlm:aff></affiliation></author><author><name sortKey="Emerson, Emily" sort="Emerson, Emily" uniqKey="Emerson E" first="Emily" last="Emerson">Emily Emerson</name><affiliation><nlm:aff id="aff2">Minnesota Electronic Disease Surveillance System (MEDSS) Operations, Infectious Disease Epidemiology Prevention and Control Division,<institution>Minnesota Department of Health</institution>,<addr-line>St. Paul, Minnesota</addr-line></nlm:aff></affiliation></author><author><name sortKey="Solarz, Sarah" sort="Solarz, Sarah" uniqKey="Solarz S" first="Sarah" last="Solarz">Sarah Solarz</name><affiliation><nlm:aff id="aff2">Minnesota Electronic Disease Surveillance System (MEDSS) Operations, Infectious Disease Epidemiology Prevention and Control Division,<institution>Minnesota Department of Health</institution>,<addr-line>St. Paul, Minnesota</addr-line></nlm:aff></affiliation></author></titleStmt><publicationStmt><idno type="wicri:source">PMC</idno><idno type="pmid">30349622</idno><idno type="pmc">6194099</idno><idno type="url">http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6194099</idno><idno type="RBID">PMC:6194099</idno><idno type="doi">10.5210/ojphi.v10i2.9317</idno><date when="2018">2018</date><idno type="wicri:Area/Pmc/Corpus">000970</idno><idno type="wicri:explorRef" wicri:stream="Pmc" wicri:step="Corpus" wicri:corpus="PMC">000970</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en" level="a" type="main">Evaluation of Data Exchange Process for Interoperability and Impact
on Electronic Laboratory Reporting Quality to a State Public Health
Agency</title><author><name sortKey="Rajamani, Sripriya" sort="Rajamani, Sripriya" uniqKey="Rajamani S" first="Sripriya" last="Rajamani">Sripriya Rajamani</name><affiliation><nlm:aff id="aff1">Informatics Programs, School of Nursing<institution>University of Minnesota</institution>,<addr-line>Minneapolis, Minnesota</addr-line></nlm:aff></affiliation></author><author><name sortKey="Kayser, Ann" sort="Kayser, Ann" uniqKey="Kayser A" first="Ann" last="Kayser">Ann Kayser</name><affiliation><nlm:aff id="aff2">Minnesota Electronic Disease Surveillance System (MEDSS) Operations, Infectious Disease Epidemiology Prevention and Control Division,<institution>Minnesota Department of Health</institution>,<addr-line>St. Paul, Minnesota</addr-line></nlm:aff></affiliation></author><author><name sortKey="Emerson, Emily" sort="Emerson, Emily" uniqKey="Emerson E" first="Emily" last="Emerson">Emily Emerson</name><affiliation><nlm:aff id="aff2">Minnesota Electronic Disease Surveillance System (MEDSS) Operations, Infectious Disease Epidemiology Prevention and Control Division,<institution>Minnesota Department of Health</institution>,<addr-line>St. Paul, Minnesota</addr-line></nlm:aff></affiliation></author><author><name sortKey="Solarz, Sarah" sort="Solarz, Sarah" uniqKey="Solarz S" first="Sarah" last="Solarz">Sarah Solarz</name><affiliation><nlm:aff id="aff2">Minnesota Electronic Disease Surveillance System (MEDSS) Operations, Infectious Disease Epidemiology Prevention and Control Division,<institution>Minnesota Department of Health</institution>,<addr-line>St. Paul, Minnesota</addr-line></nlm:aff></affiliation></author></analytic><series><title level="j">Online Journal of Public Health Informatics</title><idno type="eISSN">1947-2579</idno><imprint><date when="2018">2018</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><sec><title>Background</title><p>Past and present national initiatives advocate for electronic exchange of
health data and emphasize interoperability. The critical role of public
health in the context of disease surveillance was recognized with
recommendations for electronic laboratory reporting (ELR). Many public
health agencies have seen a trend towards centralization of information
technology services which adds another layer of complexity to
interoperability efforts.</p></sec><sec><title>Objectives</title><p>The study objective was to understand the process of data exchange and its impact on the quality of
data being transmitted in the context of electronic laboratory reporting to
public health. This was conducted in context of Minnesota Electronic
Disease Surveillance System (MEDSS), the public health information system
for supporting infectious disease surveillance in Minnesota. Data Quality
(DQ) dimensions by Strong et al., was chosen as the guiding framework for
evaluation.</p></sec><sec><title>Methods</title><p>The process of assessing data exchange for electronic lab reporting and its
impact was a mixed methods approach with qualitative data obtained through
expert discussions and quantitative data obtained from queries of the MEDSS
system. Interviews were conducted in an open-ended format from November 2017
through February 2018. Based on these discussions, two high level categories
of data exchange process which could impact data quality were identified:
onboarding for electronic lab reporting and internal data exchange routing.
This in turn comprised of ten critical steps and its impact on quality of
data was identified through expert input. This was followed by analysis of
data in MEDSS by various criteria identified by the informatics team.</p></sec><sec><title>Results</title><p>All DQ metrics (Intrinsic DQ, Contextual DQ, Representational DQ, and
Accessibility DQ) were impacted in the data exchange process with varying
influence on DQ dimensions. Some errors such as improper mapping in
electronic health records (EHRs) and laboratory information systems had a
cascading effect and can pass through technical filters and go undetected
till use of data by epidemiologists. Some DQ dimensions such as accuracy,
relevancy, value-added data and interpretability are more dependent on users
at either end of the data exchange spectrum, the relevant clinical groups
and the public health program professionals. The study revealed that data
quality is dynamic and on-going oversight is a combined effort by MEDSS
Informatics team and review by technical and public health program
professionals.</p></sec><sec><title>Conclusion</title><p>With increasing electronic reporting to public health, there is a need to
understand the current processes for electronic exchange and their impact on
quality of data. This study focused on electronic laboratory reporting to
public health and analyzed both onboarding and internal data exchange
processes. Insights gathered from this research can be applied to other
public health reporting currently (e.g. immunizations) and will be valuable
in planning for electronic case reporting in near future.</p></sec></div></front><back><div1 type="bibliography"><listBibl><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Overhage, Jm" uniqKey="Overhage J">JM Overhage</name></author><author><name sortKey="Grannis, S" uniqKey="Grannis S">S Grannis</name></author><author><name sortKey="Mcdonald, Cj" uniqKey="Mcdonald C">CJ McDonald</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Nguyen, Tq" uniqKey="Nguyen T">TQ Nguyen</name></author><author><name sortKey="Thorpe, L" uniqKey="Thorpe L">L Thorpe</name></author><author><name sortKey="Makki, Ha" uniqKey="Makki H">HA Makki</name></author><author><name sortKey="Mostashari, F" uniqKey="Mostashari F">F Mostashari</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Dixon, Be" uniqKey="Dixon B">BE Dixon</name></author><author><name sortKey="Grannis, Sj" uniqKey="Grannis S">SJ Grannis</name></author><author><name sortKey="Revere, D" uniqKey="Revere D">D Revere</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Revere, D" uniqKey="Revere D">D Revere</name></author><author><name sortKey="Hills, Rh" uniqKey="Hills R">RH Hills</name></author><author><name sortKey="Dixon, Be" uniqKey="Dixon B">BE Dixon</name></author><author><name sortKey="Gibson, Pj" uniqKey="Gibson P">PJ Gibson</name></author><author><name sortKey="Grannis, Sj" uniqKey="Grannis S">SJ Grannis</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Lai, Pt" uniqKey="Lai P">PT Lai</name></author><author><name sortKey="Johns, Je" uniqKey="Johns J">JE Johns</name></author><author><name sortKey="Kirbiyik, U" uniqKey="Kirbiyik U">U Kirbiyik</name></author><author><name sortKey="Dixon, Be" uniqKey="Dixon B">BE Dixon</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Dixon, Be" uniqKey="Dixon B">BE Dixon</name></author><author><name sortKey="Zhang, Z" uniqKey="Zhang Z">Z Zhang</name></author><author><name sortKey="Lai, Pts" uniqKey="Lai P">PTS Lai</name></author><author><name sortKey="Kirbiyik, U" uniqKey="Kirbiyik U">U Kirbiyik</name></author><author><name sortKey="Williams, J" uniqKey="Williams J">J Williams</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Rajeev, D" uniqKey="Rajeev D">D Rajeev</name></author><author><name sortKey="Staes, Cj" uniqKey="Staes C">CJ Staes</name></author><author><name sortKey="Evans, Rs" uniqKey="Evans R">RS Evans</name></author><author><name sortKey="Mottice, S" uniqKey="Mottice S">S Mottice</name></author><author><name sortKey="Rolfs, R" uniqKey="Rolfs R">R Rolfs</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Dixon, Be" uniqKey="Dixon B">BE Dixon</name></author><author><name sortKey="Siegel, Ja" uniqKey="Siegel J">JA Siegel</name></author><author><name sortKey="Oemig, Tv" uniqKey="Oemig T">TV Oemig</name></author><author><name sortKey="Grannis, Sj" uniqKey="Grannis S">SJ Grannis</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Dixon, Be" uniqKey="Dixon B">BE Dixon</name></author><author><name sortKey="Vreeman, Dj" uniqKey="Vreeman D">DJ Vreeman</name></author><author><name sortKey="Grannis, Sj" uniqKey="Grannis S">SJ Grannis</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Dixon, Be" uniqKey="Dixon B">BE Dixon</name></author><author><name sortKey="Gibson, Pj" uniqKey="Gibson P">PJ Gibson</name></author><author><name sortKey="Grannis, Sj" uniqKey="Grannis S">SJ Grannis</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Overhage, Jm" uniqKey="Overhage J">JM Overhage</name></author><author><name sortKey="Suico, J" uniqKey="Suico J">J Suico</name></author><author><name sortKey="Mcdonald, Cj" uniqKey="Mcdonald C">CJ McDonald</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct><analytic><author><name sortKey="Dixon, Be" uniqKey="Dixon B">BE Dixon</name></author><author><name sortKey="Duke, J" uniqKey="Duke J">J Duke</name></author><author><name sortKey="Grannis, S" uniqKey="Grannis S">S Grannis</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Dixon, Be" uniqKey="Dixon B">BE Dixon</name></author><author><name sortKey="Rosenman, M" uniqKey="Rosenman M">M Rosenman</name></author><author><name sortKey="Xia, Y" uniqKey="Xia Y">Y Xia</name></author><author><name sortKey="Grannis, Sj" uniqKey="Grannis S">SJ Grannis</name></author></analytic></biblStruct><biblStruct><analytic><author><name sortKey="Dixon, Be" uniqKey="Dixon B">BE Dixon</name></author><author><name sortKey="Siegel, Ja" 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sortKey="Nahm, M" uniqKey="Nahm M">M Nahm</name></author><author><name sortKey="Hammond, We" uniqKey="Hammond W">WE Hammond</name></author></analytic></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct><biblStruct></biblStruct></listBibl></div1></back></TEI><pmc article-type="research-article"><pmc-dir>properties open_access</pmc-dir>
<front><journal-meta><journal-id journal-id-type="nlm-ta">Online J Public Health Inform</journal-id><journal-id journal-id-type="iso-abbrev">Online J Public Health Inform</journal-id><journal-id journal-id-type="publisher-id">OJPHI</journal-id><journal-title-group><journal-title>Online Journal of Public Health Informatics</journal-title></journal-title-group><issn pub-type="epub">1947-2579</issn><publisher><publisher-name>University of Illinois at Chicago Library</publisher-name></publisher></journal-meta><article-meta><article-id pub-id-type="pmid">30349622</article-id><article-id pub-id-type="pmc">6194099</article-id><article-id pub-id-type="publisher-id">ojphi-10-e204</article-id><article-id pub-id-type="doi">10.5210/ojphi.v10i2.9317</article-id><article-categories><subj-group subj-group-type="heading"><subject>Research Article</subject></subj-group></article-categories><title-group><article-title>Evaluation of Data Exchange Process for Interoperability and Impact
on Electronic Laboratory Reporting Quality to a State Public Health
Agency</article-title></title-group><contrib-group><contrib contrib-type="author"><name><surname>Rajamani</surname><given-names>Sripriya</given-names></name><xref ref-type="aff" rid="aff1"><sup>1</sup></xref><xref ref-type="corresp" rid="cor1"><sup>*</sup></xref></contrib><contrib contrib-type="author"><name><surname>Kayser</surname><given-names>Ann</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Emerson</surname><given-names>Emily</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><contrib contrib-type="author"><name><surname>Solarz</surname><given-names>Sarah</given-names></name><xref ref-type="aff" rid="aff2"><sup>2</sup></xref></contrib><aff id="aff1"><label>1</label>Informatics Programs, School of Nursing<institution>University of Minnesota</institution>,<addr-line>Minneapolis, Minnesota</addr-line></aff><aff id="aff2"><label>2</label>Minnesota Electronic Disease Surveillance System (MEDSS) Operations, Infectious Disease Epidemiology Prevention and Control Division,<institution>Minnesota Department of Health</institution>,<addr-line>St. Paul, Minnesota</addr-line></aff></contrib-group><author-notes><corresp id="cor1"><label>*</label>Correspondence: <email xlink:href="sripriya@umn.edu">sripriya@umn.edu</email></corresp></author-notes><pub-date pub-type="epub"><day>21</day><month>9</month><year>2018</year></pub-date><pub-date pub-type="collection"><year>2018</year></pub-date><volume>10</volume><issue>2</issue><elocation-id>e204</elocation-id><permissions><copyright-statement>This is an Open Access article. Authors own copyright of their
articles appearing in the Journal of Public Health Informatics. Readers may copy
articles without permission of the copyright owner(s), as long as the author and
OJPHI are acknowledged in the copy and the copy is used for educational,
not-for-profit purposes.</copyright-statement><copyright-year>2018</copyright-year><copyright-holder>2018 the author(s)</copyright-holder></permissions><abstract><sec><title>Background</title><p>Past and present national initiatives advocate for electronic exchange of
health data and emphasize interoperability. The critical role of public
health in the context of disease surveillance was recognized with
recommendations for electronic laboratory reporting (ELR). Many public
health agencies have seen a trend towards centralization of information
technology services which adds another layer of complexity to
interoperability efforts.</p></sec><sec><title>Objectives</title><p>The study objective was to understand the process of data exchange and its impact on the quality of
data being transmitted in the context of electronic laboratory reporting to
public health. This was conducted in context of Minnesota Electronic
Disease Surveillance System (MEDSS), the public health information system
for supporting infectious disease surveillance in Minnesota. Data Quality
(DQ) dimensions by Strong et al., was chosen as the guiding framework for
evaluation.</p></sec><sec><title>Methods</title><p>The process of assessing data exchange for electronic lab reporting and its
impact was a mixed methods approach with qualitative data obtained through
expert discussions and quantitative data obtained from queries of the MEDSS
system. Interviews were conducted in an open-ended format from November 2017
through February 2018. Based on these discussions, two high level categories
of data exchange process which could impact data quality were identified:
onboarding for electronic lab reporting and internal data exchange routing.
This in turn comprised of ten critical steps and its impact on quality of
data was identified through expert input. This was followed by analysis of
data in MEDSS by various criteria identified by the informatics team.</p></sec><sec><title>Results</title><p>All DQ metrics (Intrinsic DQ, Contextual DQ, Representational DQ, and
Accessibility DQ) were impacted in the data exchange process with varying
influence on DQ dimensions. Some errors such as improper mapping in
electronic health records (EHRs) and laboratory information systems had a
cascading effect and can pass through technical filters and go undetected
till use of data by epidemiologists. Some DQ dimensions such as accuracy,
relevancy, value-added data and interpretability are more dependent on users
at either end of the data exchange spectrum, the relevant clinical groups
and the public health program professionals. The study revealed that data
quality is dynamic and on-going oversight is a combined effort by MEDSS
Informatics team and review by technical and public health program
professionals.</p></sec><sec><title>Conclusion</title><p>With increasing electronic reporting to public health, there is a need to
understand the current processes for electronic exchange and their impact on
quality of data. This study focused on electronic laboratory reporting to
public health and analyzed both onboarding and internal data exchange
processes. Insights gathered from this research can be applied to other
public health reporting currently (e.g. immunizations) and will be valuable
in planning for electronic case reporting in near future.</p></sec></abstract><kwd-group kwd-group-type="author"><title>Keywords: </title><kwd>public health informatics</kwd><kwd>public health surveillance</kwd><kwd>disease notification</kwd><kwd>communicable diseases</kwd><kwd>electronic laboratory reporting</kwd><kwd>electronic health records</kwd></kwd-group></article-meta></front><body><sec sec-type="intro"><title>Introduction</title><p>Past [<xref rid="r1" ref-type="bibr">1</xref>] and present [<xref rid="r2" ref-type="bibr">2</xref>] national initiatives that promote
electronic health records (EHRs), also advocate for the electronic exchange of data
across various healthcare sectors using nationally recommended standards [<xref rid="r3" ref-type="bibr">3</xref>]. The critical role of public health, in the
context of disease surveillance is recognized by these regulations, with
recommendations for electronic laboratory reporting (ELR). ELR refers to the
electronic transmission of labs related to reportable conditions to public health
[<xref rid="r4" ref-type="bibr">4</xref>]. The emphasis on interoperability in
recent legislations [<xref rid="r5" ref-type="bibr">5</xref>] and roadmaps [<xref rid="r6" ref-type="bibr">6</xref>] is facilitating the focus on electronic
movement of data across healthcare settings. Many public health agencies have seen a
trend towards centralization of information technology services which adds another
layer of complexity to interoperability efforts. Given this landscape, it is
essential to understand the process of data exchange and its impact on quality of
data being transmitted, as this is a crucial step in interoperability. In addition,
this holds broad implications for future priority transactions such as electronic
case reporting to public health.</p><p>Initial research around ELR focused on comparison of paper-based reports to
electronic transmissions and found predominantly positive impact of ELR [<xref rid="r7" ref-type="bibr">7</xref>,<xref rid="r8" ref-type="bibr">8</xref>] on
specifically two metrics of data quality: timeliness and completeness. Subsequent
studies have assessed the role of intermediaries such as Health Information
Exchanges (HIE) [<xref rid="r9" ref-type="bibr">9</xref>-<xref rid="r11" ref-type="bibr">11</xref>] to facilitate ELR and reported
better completeness of data with HIE support. Presently, studies have begun to focus
on provider reporting of notifiable diseases [<xref rid="r12" ref-type="bibr">12</xref>,<xref rid="r13" ref-type="bibr">13</xref>],
as moving to electronic case notification [<xref rid="r14" ref-type="bibr">14</xref>-<xref rid="r16" ref-type="bibr">16</xref>] along with ELR will be
great progress to support overall public health disease surveillance. Challenges in
adoption and use of recommended codes [<xref rid="r17" ref-type="bibr">17</xref>-<xref rid="r19" ref-type="bibr">19</xref>] and need for an
informatics savvy workforce [<xref rid="r20" ref-type="bibr">20</xref>] were
identified as some of the issues in the move towards ELR [<xref rid="r21" ref-type="bibr">21</xref>].</p><p>A recurring theme across these studies was assessing the quality of data, including
exploring new venues to measure [<xref rid="r22" ref-type="bibr">22</xref>-<xref rid="r24" ref-type="bibr">24</xref>] and improve [<xref rid="r25" ref-type="bibr">25</xref>] it. Timeliness and completeness were the
two dimensions of data quality (DQ) which were often evaluated. Metrics from DQ
frameworks published in literature can be used as guidance in identifying additional
parameters for assessment. Data quality assessment framework by Kahn et al. [<xref rid="r26" ref-type="bibr">26</xref>], identifies three DQ categories:
conformance, completeness and plausibility, along with verification and validation
as two DQ assessment contexts. DQ framework by Strong et al., proposes a broad
conceptualization of the quality of data from perspective of data consumers. It
defines high quality data as one that is fit for use and emphasizes context around
data production and usage.</p><p>Strong’s framework proposes four DQ categories (Intrinsic DQ, Contextual DQ,
Representational DQ, Accessibility DQ) comprising of fifteen DQ dimensions [<xref rid="r27" ref-type="bibr">27</xref>,<xref rid="r28" ref-type="bibr">28</xref>].
These include Intrinsic DQ (Accuracy, Objectivity, Believability, Reputation);
Contextual DQ (Relevancy, Value-Added, Timeliness, Completeness, Amount of data);
Representational DQ (Interpretability, Ease of understanding, Concise
representation, Consistent representation); Accessibility DQ (Accessibility, Access
security). The strength of this framework is the breadth of DQ characteristics. Data
quality is a multi-dimensional concept dependent on multitude of factors and
adoption of data standards does facilitate DQ, but does not guarantee it [<xref rid="r29" ref-type="bibr">29</xref>]. Good quality data that meet many of DQ
dimensions are critical for public health surveillance purposes. With increasing
electronic data exchange and emphasis on interoperability, it is essential to
understand impact of various facets of data exchange on various dimensions of
DQ.</p><p>The Minnesota Electronic Disease Surveillance System (MEDSS) [<xref rid="r30" ref-type="bibr">30</xref>] is the public health information system for supporting
infectious disease surveillance at a state level for Minnesota and operational since
2008. It holds data on reportable conditions and receives ELRs submitted to the
state public health agency. MEDSS is used for case management, contact tracing and
to support outbreak investigations. Its scope has expanded to include non-infectious
diseases such as blood lead surveillance and birth defects. It’s a
person-centric surveillance system which currently holds ~1,279,986 events across
infectious diseases, lead and community and family health programs. Approximately
153,880 lab tests/results were reported electronically for 2017 across six health
systems and four reference labs. Many healthcare systems are currently on a waiting
list for either onboarding/move to electronic exchange or upgrade to better version
of reporting standard.</p><p>Nationally recommended standards for ELR [<xref rid="r4" ref-type="bibr">4</xref>]
comprise of HL7 2.5.1 for message format and LOINC [<xref rid="r31" ref-type="bibr">31</xref>] and SNOMED [<xref rid="r32" ref-type="bibr">32</xref>] codes for
representation of lab tests and results respectively. With increasing demands for
electronic data exchange for incoming data to MEDSS from clinical sectors and for
outgoing data to Centers for Disease Control and Prevention (CDC), new informatics
tools to support data validation and exchange were implemented. The objective of
this study was to assess the data exchange process and to understand its impact on
the quality of data in MEDSS. The overarching goal is to utilize findings for
improvements in informatics tools and processes to enhance the value of MEDSS by
providing good quality data to support various public health purposes including
disease surveillance.</p></sec><sec sec-type="methods"><title>Methods</title><p>The process of assessing data exchange for electronic lab reporting and its impact
was a mixed methods approach with qualitative data obtained through expert
discussions and quantitative data obtained from queries of the MEDSS system. Various
subject matter experts (n=9) were identified spanning across the informatics team
that supports MEDSS operations, public health program professionals who are users of
the MEDSS system and its data, and the Information Technology (IT) team which
supports the data exchange process. The focus included both on-boarding (process of
shifting to electronic exchange for either new reporting or migration/upgrade to
different standard) and on-going submissions. ELR is unique in that reporting can
occur from either EHR or from LIMS (Laboratory Information Management System) and
can occur from healthcare delivery organization or from reference laboratories and
these were taken into consideration. Interviews were conducted in an
open-ended/discussion format and were done over time frame of November 2017 through
February 2018. Based on these discussions, two high level categories of data
exchange process which could impact data quality were identified: onboarding for
electronic lab reporting and internal data exchange routing.</p><p><xref ref-type="fig" rid="f1">Figure 1</xref> displays the ELR onboarding process and
includes the testing and validation suite of tools offered in public domain by the
National Institute of Standards and Technology (NIST) [<xref rid="r33" ref-type="bibr">33</xref>]. The six identified key processes that influence quality of
data are numbered A through F (A - mapping of tests and results to appropriate
codes, B - NIST test bed for testing of messages, C - submit test HL7 messages, D -
solicit HL7 messages with test cases (e.g. specific tests, seasonal diseases), E -
technical review, F - program review). <xref ref-type="fig" rid="f2">Figure 2</xref>displays the internal data exchange routing process which includes the PHIN
Messaging System (PHIN MS) [<xref rid="r34" ref-type="bibr">34</xref>], a CDC
provided software that serves as a transport mechanism for effective movement of
messages. This part comprises of four main components numbered G through J (G - PHIN
MS, H - Lab code list database validation, I – Rhapsody® Integration
Engine [<xref rid="r35" ref-type="bibr">35</xref>] rules, J - mapping in MEDSS).</p><fig id="f1" fig-type="figure" orientation="portrait" position="float"><label>Figure 1</label><caption><p>Overview of ELR Onboarding Process</p></caption><graphic xlink:href="ojphi-10-e204-g001"></graphic></fig><fig id="f2" fig-type="figure" orientation="portrait" position="float"><label>Figure 2</label><caption><p>Overview of Internal Data Exchange Routing Process</p></caption><graphic xlink:href="ojphi-10-e204-g002"></graphic></fig><p>The potential influence of the ten identified critical steps in the data exchange
process and its impact on quality of data was identified through expert input using
Strong’s DQ framework as a guidance. This was followed by analysis of data in
MEDSS by criteria identified by the informatics team. Evaluation of messages not
mapped to any disease program in MEDSS was identified as a priority. Next,
assessment of completeness of race and ethnicity fields before and after
implementation of demographic data import feature in ELR was completed. Using Influenza
reporting as a scenario, the number of non-reportable tests that get submitted and
added to data in MEDSS was examined. Finally, the number of incoming messages which
get rejected due to errors was examined to quantify the need for additional technical
assistance.</p></sec><sec sec-type="results"><title>Results</title><p>The process of exchanging data electronically is iterative and is initiated with
numerous rounds of message testing and varying gradation of technical assistance
based on data submitter need and capabilities. Each step in the process was deemed
critical in its impact on the quality of data which moves across clinical sector and
public health. <xref rid="t1" ref-type="table">Table 1</xref> lists the six
identified key processes for ELR onboarding, relevant sub-processes/notes and their
influence including both DQ metric and DQ dimension. All DQ metrics (Intrinsic DQ,
Contextual DQ, Representational DQ, and Accessibility DQ) were impacted with varying
influence on DQ dimensions. Some errors such as improper mapping on EHR end had a
cascading effect and can pass through technical filters and go undetected till use
of data by epidemiologists. Some DQ dimensions such as accuracy, relevancy,
value-added data and interpretability are more dependent on users at either end of
the data exchange spectrum, the relevant clinical groups and the public health
program professionals.</p><table-wrap id="t1" orientation="portrait" position="float"><label>Table 1</label><caption><title>Onboarding for Electronic Lab Reporting and Data Quality</title></caption><table frame="hsides" rules="groups"><col width="62.02%" span="1"></col><col width="37.98%" span="1"></col><tbody><tr><td valign="middle" align="center" style="border-left: solid 1.50pt; border-top: solid 1.50pt; border-right: dotted 1pt; border-bottom: solid 1.50pt; background-color:rgb(217,217,217)" scope="row" rowspan="1" colspan="1"><bold>Data Exchange Process for ELR Onboarding</bold></td><td valign="middle" align="center" style="border-left: dotted 1pt; border-top: solid 1.50pt; border-right: solid 1.50pt; border-bottom: solid 1.50pt; background-color:rgb(217,217,217)" rowspan="1" colspan="1"><bold>Data Quality Metric and Data Quality Dimension Impact</bold></td></tr><tr><td valign="middle" align="justify" style="border-left: solid 1.50pt; border-top: solid 1.50pt; border-right: dotted 1pt; border-bottom: dotted 1pt" scope="row" rowspan="1" colspan="1">A. Mapping of Tests and Results to Appropriate
Codes<break></break> - Completed in the
clinical healthcare space (EHR system and LIMS)</td><td valign="middle" align="center" style="border-left: dotted 1pt; border-top: solid 1.50pt; border-right: solid 1.50pt; border-bottom: dotted 1pt" rowspan="1" colspan="1">Intrinsic DQ (Accuracy, Objectivity)</td></tr><tr><td valign="middle" align="justify" style="border-left: solid 1.50pt; border-top: dotted 1pt; border-right: dotted 1pt; border-bottom: dotted 1pt" scope="row" rowspan="1" colspan="1">B. Test messages using NIST Test
Bed<break></break> - Ability to map content
to HL7 fields<break></break> - Capability to
submit data in R (required) fields</td><td valign="middle" align="center" style="border-left: dotted 1pt; border-top: dotted 1pt; border-right: solid 1.50pt; border-bottom: dotted 1pt" rowspan="1" colspan="1">Contextual DQ (Completeness)</td></tr><tr><td valign="middle" align="justify" style="border-left: solid 1.50pt; border-top: dotted 1pt; border-right: dotted 1pt; border-bottom: dotted 1pt" scope="row" rowspan="1" colspan="1">C. Submit HL7 test messages to
MEDSS<break></break> - Capability to submit
data in R (required) fields<break></break> -
Complete RE (required, but may be empty)
and<break></break> O
(optional) fields</td><td valign="middle" align="center" style="border-left: dotted 1pt; border-top: dotted 1pt; border-right: solid 1.50pt; border-bottom: dotted 1pt" rowspan="1" colspan="1">Contextual DQ (Completeness, Value-added data)</td></tr><tr><td valign="middle" align="justify" style="border-left: solid 1.50pt; border-top: dotted 1pt; border-right: dotted 1pt; border-bottom: dotted 1pt" scope="row" rowspan="1" colspan="1">D. Solicit HL7 messages with specific tests, seasonal
diseases<break></break> - Checking for
message formats and codes which may not be present in current HL7
test feeds</td><td valign="middle" align="center" style="border-left: dotted 1pt; border-top: dotted 1pt; border-right: solid 1.50pt; border-bottom: dotted 1pt" rowspan="1" colspan="1">Contextual DQ (Completeness, Relevancy)</td></tr><tr><td valign="middle" align="justify" style="border-left: solid 1.50pt; border-top: dotted 1pt; border-right: dotted 1pt; border-bottom: dotted 1pt" scope="row" rowspan="1" colspan="1">E. Technical
review<break></break> - HL7 format
checks<break></break> - Review of LOINC
codes<break></break> - Review of SNOMED
codes<break></break> - Review of
LOINC-SNOMED pairs<break></break> - Mapping
of code pairs with appropriate disease</td><td valign="middle" align="center" style="border-left: dotted 1pt; border-top: dotted 1pt; border-right: solid 1.50pt; border-bottom: dotted 1pt" rowspan="1" colspan="1">Contextual DQ (Completeness), Representational DQ (Consistent
representation, Interpretability)</td></tr><tr><td valign="middle" align="justify" style="border-left: solid 1.50pt; border-top: dotted 1pt; border-right: dotted 1pt; border-bottom: solid 1.50pt" scope="row" rowspan="1" colspan="1">F. Program
Review<break></break> - Confirm mapping of
code pairs with diseases<break></break> -
Check for positive and negative test
results<break></break> - Check for odd
messages</td><td valign="middle" align="center" style="border-left: dotted 1pt; border-top: dotted 1pt; border-right: solid 1.50pt; border-bottom: solid 1.50pt" rowspan="1" colspan="1">Intrinsic DQ (Objectivity), Contextual DQ (Completeness),
Representational DQ (Interpretability)</td></tr></tbody></table></table-wrap><p><xref rid="t2" ref-type="table">Table 2</xref> lists the six identified key processes
related to on-going production submissions using the internal data exchange routing
and their influence on data quality. Similar to the on-boarding process, all DQ
metrics (Intrinsic DQ, Contextual DQ, Representational DQ, and Accessibility DQ)
were impacted with varying influence on DQ dimensions. The three steps labelled H.
(Lab Code List Database Validation), I. (Rhapsody Integration Engine Rules) and J.
(Mapping in MEDSS) were deemed critical with high level of need for on-going
maintenance. Laboratory tests are constantly evolving along with new lab codes
(LOINC) and organisms detected (SNOMED) and their combinations to determine disease
changing, some processes (H. I. J.) require frequent review. The analysis also
revealed the need for collaboration and some processes are dependent on coordination
across MEDSS informatics team, information technology (IT) staff and public health
program professionals.</p><table-wrap id="t2" orientation="portrait" position="float"><label>Table 2</label><caption><title>Data Quality Impact of Internal Data Exchange Routing Process</title></caption><table frame="hsides" rules="groups"><col width="63.98%" span="1"></col><col width="36.02%" span="1"></col><tbody><tr><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 0.50pt; border-right: solid 2.25pt; background-color:rgb(166,166,166)" scope="row" rowspan="1" colspan="1"><bold>Internal Data Exchange Routing Process</bold></td><td valign="middle" align="center" style="border-left: solid 2.25pt; border-top: solid 0.50pt; border-right: solid 0.50pt; background-color:rgb(166,166,166)" rowspan="1" colspan="1"><bold>Influence on Data Quality Metric and Data Quality
Dimension</bold></td></tr><tr><td valign="middle" align="justify" style="border-left: solid 0.50pt; border-top: solid 2.25pt; border-right: solid 2.25pt; border-bottom: solid 2.25pt; background-color:rgb(255,255,255)" scope="row" rowspan="1" colspan="1">G. PHIN-MS
Transport<break></break> -
Secure messaging platform for transport of messages</td><td valign="middle" align="center" style="border-left: solid 2.25pt; border-top: solid 2.25pt; border-right: solid 0.50pt; border-bottom: solid 2.25pt; background-color:rgb(255,255,255)" rowspan="1" colspan="1">Accessibility DQ<break></break>(Access Security)</td></tr><tr><td valign="middle" align="justify" style="border-left: solid 0.50pt; border-top: solid 2.25pt; border-right: solid 2.25pt; border-bottom: solid 2.25pt; background-color:rgb(255,255,255)" scope="row" rowspan="1" colspan="1">H. Lab Code List Database
Validation<break></break> -
Check to ensure that message contains approved code pairs or rules
for
exemption<break></break> -
Update codes and code pairs based on new tests and results</td><td valign="middle" align="center" style="border-left: solid 2.25pt; border-top: solid 2.25pt; border-right: solid 0.50pt; border-bottom: solid 2.25pt; background-color:rgb(255,255,255)" rowspan="1" colspan="1">Contextual DQ (Completeness), Representational DQ (Consistent
representation)</td></tr><tr><td valign="middle" align="justify" style="border-left: solid 0.50pt; border-top: solid 2.25pt; border-right: solid 2.25pt; border-bottom: solid 2.25pt; background-color:rgb(255,255,255)" scope="row" rowspan="1" colspan="1">I. Rhapsody Integration Engine
Rules<break></break> - Fixes
format of incoming messages as per
rules<break></break> -
Converts messages into MEDSS accepted format</td><td valign="middle" align="center" style="border-left: solid 2.25pt; border-top: solid 2.25pt; border-right: solid 0.50pt; border-bottom: solid 2.25pt; background-color:rgb(255,255,255)" rowspan="1" colspan="1"> Representational DQ (Consistent
representation)</td></tr><tr><td valign="middle" align="justify" style="border-left: solid 0.50pt; border-top: solid 2.25pt; border-right: solid 2.25pt; border-bottom: double 0.50pt; background-color:rgb(255,255,255)" scope="row" rowspan="1" colspan="1">J. Mapping in
MEDSS<break></break> -
Assignment of messages to diseases</td><td valign="middle" align="center" style="border-left: solid 2.25pt; border-top: solid 2.25pt; border-right: solid 0.50pt; border-bottom: double 0.50pt; background-color:rgb(255,255,255)" rowspan="1" colspan="1">Contextual DQ (Relevancy), Representational DQ
(Interpretability)</td></tr></tbody></table></table-wrap><p>The results from analysis of data in MEDSS by various criteria identified by the
informatics team is presented in <xref rid="t3" ref-type="table">Table 3</xref>.
Evaluation for cases which are not mapped to any disease program and assigned to
“other/unknown” category yielded 952 cases. Assessment of messages for
these cases noted an absence of LOINC and/or SNOMED codes and their combination pair
for disease assignment. Next, the analysis focused on submission of non-reportable
respiratory diseases along with reportable conditions (Influenza) due to issues with
special lab test panel, and this identified 366 cases. This was followed by
evaluating the number of incoming messages which get rejected due to errors and
there currently isn’t any process that keeps track of it. The corresponding
impact on data quality metrics due to these identified issues are also presented in
<xref rid="t3" ref-type="table">Table 3</xref>. An enhancement was implemented
in January 2018 to import demographic data (race, ethnicity) from ELR feeds and this
evaluation presented in <xref rid="t4" ref-type="table">Table 4</xref>. Of the total
of 3,651 electronic lab messages received from January through February 2018, data
on Race was present in 2,310 messages and 1680 messages received in that time frame
had data on Ethnicity. Comparison of this new data with already existing race and
ethnicity data in MEDSS obtained through case reporting and follow-up investigations
revealed 270 number of messages wherein race from ELR feed was different than one
currently recorded in MEDSS.</p><table-wrap id="t3" orientation="portrait" position="float"><label>Table 3</label><caption><title>Identified Issues, Data Quality Impact and Correlations with Data
Exchange Processes</title></caption><table frame="hsides" rules="groups"><col width="25.88%" span="1"></col><col width="19.98%" span="1"></col><col width="28.98%" span="1"></col><col width="25.16%" span="1"></col><tbody><tr><td valign="middle" align="center" style="border-left: solid 1pt; border-top: solid 1pt; border-right: solid 0.50pt; border-bottom: solid 1pt; background-color:rgb(191,191,191)" scope="row" rowspan="1" colspan="1"><bold>Identified Issue</bold></td><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 1pt; border-right: solid 0.50pt; border-bottom: solid 1pt; background-color:rgb(191,191,191)" rowspan="1" colspan="1"><bold># of cases (time frame)</bold></td><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 1pt; border-right: solid 0.50pt; border-bottom: solid 1pt; background-color:rgb(191,191,191)" rowspan="1" colspan="1"><bold>Data Exchange Process</bold></td><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 1pt; border-right: solid 1pt; border-bottom: solid 1pt; background-color:rgb(191,191,191)" rowspan="1" colspan="1"><bold>DQ Impact</bold></td></tr><tr><td valign="middle" colspan="4" align="center" style="border-left: solid 1pt; border-top: solid 1pt; border-right: solid 1pt; border-bottom: solid 1pt; background-color:rgb(242,242,242)" scope="col" rowspan="1"><bold>Non-assignment of Messages to Diseases</bold></td></tr><tr><td valign="middle" align="center" style="border-left: solid 1pt; border-top: solid 1pt; border-right: solid 0.50pt; border-bottom: solid 0.50pt" scope="row" rowspan="1" colspan="1">Lack of LOINC and/or SNOMED codes</td><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 1pt; border-right: solid 0.50pt; border-bottom: solid 0.50pt" rowspan="1" colspan="1">952 <italic>(currently)</italic></td><td rowspan="2" valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 1pt; border-right: solid 0.50pt; border-bottom: solid 0.50pt" colspan="1">▪ Testing during ELR onboarding<break></break>▪ Validation
checks with Lab Code List Database</td><td rowspan="2" valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 1pt; border-right: solid 1pt; border-bottom: solid 0.50pt" colspan="1">Contextual DQ (Completeness, Value-added data), Representational DQ
(Consistent representation)</td></tr><tr><td valign="middle" align="center" style="border-left: solid 1pt; border-top: solid 0.50pt; border-right: solid 0.50pt; border-bottom: solid 1pt" scope="row" rowspan="1" colspan="1">LOINC – SNOMED pair missing / not mapped</td><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 0.50pt; border-right: solid 0.50pt; border-bottom: solid 1pt" rowspan="1" colspan="1">952 <italic>(currently)</italic></td></tr><tr><td valign="middle" colspan="4" align="center" style="border-left: solid 1pt; border-top: solid 1pt; border-right: solid 1pt; border-bottom: solid 1pt; background-color:rgb(242,242,242)" scope="col" rowspan="1"><bold>Submission of non-reportable Diseases</bold></td></tr><tr><td valign="middle" align="center" style="border-left: solid 1pt; border-top: solid 1pt; border-right: solid 0.50pt; border-bottom: solid 1pt" scope="row" rowspan="1" colspan="1">Presence of numerous non-reportable respiratory
pathogens (e.g. adeno virus, corona virus)</td><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 1pt; border-right: solid 0.50pt; border-bottom: solid 1pt" rowspan="1" colspan="1">366 <italic>(over 1 year)</italic></td><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 1pt; border-right: solid 0.50pt; border-bottom: solid 1pt" rowspan="1" colspan="1">▪ Testing during ELR onboarding<break></break>▪ Screening
with Rhapsody integration engine rules</td><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 1pt; border-right: solid 1pt; border-bottom: solid 1pt" rowspan="1" colspan="1"> Contextual
DQ (Relevancy)</td></tr><tr><td valign="middle" colspan="4" align="center" style="border-left: solid 1pt; border-top: solid 1pt; border-right: solid 1pt; border-bottom: solid 1pt; background-color:rgb(242,242,242)" scope="col" rowspan="1"> <bold>Missing
Messages due to Rejections</bold></td></tr><tr><td valign="middle" align="center" style="border-left: solid 1pt; border-top: solid 1pt; border-right: solid 0.50pt; border-bottom: solid 1pt" scope="row" rowspan="1" colspan="1">Rejection of messages due to format and code issues</td><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 1pt; border-right: solid 0.50pt; border-bottom: solid 1pt" rowspan="1" colspan="1">? approx. few/day <italic>(not tracked)</italic></td><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 1pt; border-right: solid 0.50pt; border-bottom: solid 1pt" rowspan="1" colspan="1">▪ Validation checks with Lab Code List
Database<break></break>▪ Screening with Rhapsody integration engine
rules</td><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 1pt; border-right: solid 1pt; border-bottom: solid 1pt" rowspan="1" colspan="1"> Contextual
DQ (Value-added data)</td></tr></tbody></table></table-wrap><table-wrap id="t4" orientation="portrait" position="float"><label>Table 4</label><caption><title>Demographic Data from Electronic Lab Reports and Influence on Data
Quality</title></caption><table frame="hsides" rules="groups"><col width="28.82%" span="1"></col><col width="25.05%" span="1"></col><col width="46.13%" span="1"></col><tbody><tr><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 0.50pt; border-right: solid 0.50pt; border-bottom: solid 0.50pt" scope="row" rowspan="1" colspan="1"><bold>Data Imported from ELR</bold></td><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 0.50pt; border-right: solid 0.50pt; border-bottom: solid 0.50pt" rowspan="1" colspan="1"><bold>Number <italic>(Jan – Feb 2018)</italic></bold></td><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 0.50pt; border-right: solid 0.50pt; border-bottom: solid 0.50pt" rowspan="1" colspan="1"><bold>Data Quality Enhancement</bold></td></tr><tr><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 0.50pt; border-right: solid 0.50pt; border-bottom: solid 0.50pt" scope="row" rowspan="1" colspan="1">Race Data</td><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 0.50pt; border-right: solid 0.50pt; border-bottom: solid 0.50pt" rowspan="1" colspan="1">2,310 / 3,651 (63%)</td><td rowspan="2" valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 0.50pt; border-right: solid 0.50pt; border-bottom: solid 0.50pt" colspan="1">Contextual DQ (Completeness, Value-added data)</td></tr><tr><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 0.50pt; border-right: solid 0.50pt; border-bottom: solid 0.50pt" scope="row" rowspan="1" colspan="1">Ethnicity Data</td><td valign="middle" align="center" style="border-left: solid 0.50pt; border-top: solid 0.50pt; border-right: solid 0.50pt; border-bottom: solid 0.50pt" rowspan="1" colspan="1">1,680 / 3,651 (46%)</td></tr></tbody></table></table-wrap></sec><sec sec-type="discussion"><title>Discussion</title><p>Federal regulations and incentives have offered the needed momentum towards
electronic reporting to public health. But, there are differences in public health
measure reporting [<xref rid="r36" ref-type="bibr">36</xref>] with ELR lagging
behind immunization reporting due to complexities around multitude of labs
associated with reportable conditions, slow adoption of recommended codes and
multiple entities/professionals involved in exchange such as clinical labs,
reference labs, ordering provider, infection control practitioner and disease
epidemiologists. Another key factor to consider is that ELR can be generated from
EHRs or from laboratory information systems (LIS) in reference labs or in healthcare
settings. This study also portrays the need for constant updates to the various
validation tools to ensure errors are not being propagated across the data exchange
chain.</p><p>This research points to the complexity of the data exchange process by illustrating
the numerous stakeholders involved and the critical role each one plays in moving
towards interoperability. It also pointed to the need for all data exchange partners
to be informed of evolution of standards, both message formats (e.g. HL7) and codes
(e.g. LOINC, SNOMED). Some of these exchange mechanisms require technical assistance
for either submitter (e.g. labs, providers) and the receiver (e.g. public health) or
both of them. National projects such as Digital Bridge [<xref rid="r37" ref-type="bibr">37</xref>] and APHL Informatics Messaging Services (AIMS) [<xref rid="r38" ref-type="bibr">38</xref>] are aimed to assist in data exchange across
jurisdictional boundaries in public health. The data exchange process could be set
such that messages get rejected if they fail any of the checks, but will require
manual intervention by public health or the data reporters to understand quality
issues around rejection and fix them.</p><p>The study also presents various testing tools (NIST test bed) and validation engines
(Rhapsody, lab code list validation database) that help to automate quality checks
and monitor various DQ dimensions. Approaches from other public health reporting
such as immunizations wherein provider quality reports [<xref rid="r39" ref-type="bibr">39</xref>] are generated could be tried in the context of ELR.
Likewise open source software tools have been proposed to support data quality
checks for both immunization reporting [<xref rid="r39" ref-type="bibr">39</xref>]
and ELR [<xref rid="r23" ref-type="bibr">23</xref>,<xref rid="r40" ref-type="bibr">40</xref>]. Implementation and maintenance of these tools require both
financial and technical resources. Importantly, there needs to be overarching
guidance and support from national organizations such as CDC to ensure
standardization and to facilitate sharing of tools/resources across
jurisdictions.</p><p>The study revealed that data quality is dynamic and on-going oversight is a
collaborative effort by MEDSS informatics team, technical and public health program
professionals. Overall, maintenance of good data quality in context of ELR needs a
multipronged approach with automated tools, data exchange partners education,
technical assistance, regular updates of codes/tools, organizational commitment and
national guidelines along with support by informaticians/data quality analysts.</p><p>This research depicts the details of processes, people and technology and the need
for all the parts to align to make an electronic data exchange truly meaningful by
providing good quality to data that fits the purpose (public health surveillance in
this case). It highlights the benefits of standardization of data exchange processes
which can be applied to other public health transactions. Many public health
agencies have seen a trend towards centralization of information technology services
which adds another layer of complexity to interoperability efforts. It underscores
the value of a public health informatician to be part of electronic exchange of data
across various sectors (clinical care, labs) and public health. Finally, this study
presents a compelling picture of the interoperability endeavor as a team effort and
underscores the critical role an informatics team can play in facilitating the data
exchange process.</p></sec><sec><title>Limitations</title><p>The study has some limitations and focus on some dimensions of data quality by Strong
et al., is one of them. Some DQ aspects such as accessibility are not integrated
with exchange process and hence were excluded. The research emphasis was determined
by criteria outlined by MEDSS informatics team, and was limited based on available
data during study period. Some metrics were not tracked and certain tool
enhancements were implemented recently by IT support team and thus evaluation was
limited. Another limitation is that currently a large volume of ELR submitters are
reference labs which are not required to collect race and ethnicity data and hence
completeness of those data fields through ELR is limited. Some DQ errors are
attributed to frequency of upgrade of codes/validation engine that are driven by
organizational resources (finances, trained personnel) / institutional priorities
and beyond the scope of this study.</p></sec><sec sec-type="conclusions"><title>Conclusion</title><p>With the growing demands for electronic reporting with public health, there is a need
to understand the current processes for supporting electronic exchange and their
impact on quality of data. This study focused on electronic laboratory reporting to
public health and analyzed both onboarding and internal data exchange processes.
Insights gathered from this research can be applied to other public health reporting
currently (e.g. immunizations) and will be valuable in planning for electronic case
reporting in near future. The study has potential implications in promoting data
quality along with electronic exchange to support public health surveillance.</p></sec></body><back><ack><title>Acknowledgements</title><p>The authors would like to thank the members of the MEDSS technical team for
discussions around the data exchange processes and various public health program
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