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<front>
<journal-meta>
<journal-id journal-id-type="publisher-id">JDS</journal-id>
<journal-title-group><journal-title>Journal of Data Science</journal-title></journal-title-group>
<issn pub-type="epub">1683-8602</issn><issn pub-type="ppub">1680-743X</issn><issn-l>1680-743X</issn-l>
<publisher>
<publisher-name>School of Statistics, Renmin University of China</publisher-name>
</publisher>
</journal-meta>
<article-meta>
<article-id pub-id-type="publisher-id">JDS1063</article-id>
<article-id pub-id-type="doi">10.6339/22-JDS1063</article-id>
<article-categories><subj-group subj-group-type="heading">
<subject>Data Science in Action</subject></subj-group></article-categories>
<title-group>
<article-title>Integration of Social Determinants of Health Data into the Largest, Not-for-Profit Health System in South Florida</article-title>
</title-group>
<contrib-group>
<contrib contrib-type="author">
<contrib-id contrib-id-type="orcid">https://orcid.org/0000-0003-0007-2293</contrib-id>
<name><surname>Rojas</surname><given-names>Lourdes M.</given-names></name><email xlink:href="mailto:LourdesRoja@baptisthealth.net">LourdesRoja@baptisthealth.net</email><xref ref-type="aff" rid="j_jds1063_aff_001">1</xref><xref ref-type="fn" rid="cor1">∗</xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Vincent</surname><given-names>Gregory L.</given-names></name><xref ref-type="aff" rid="j_jds1063_aff_001">1</xref>
</contrib>
<contrib contrib-type="author">
<name><surname>Parris</surname><given-names>Don</given-names></name><xref ref-type="aff" rid="j_jds1063_aff_001">1</xref>
</contrib>
<aff id="j_jds1063_aff_001"><label>1</label><institution>Baptist Health South Florida – Center for Advanced Analytics</institution>, <country>USA</country></aff>
</contrib-group>
<author-notes>
<corresp id="cor1"><label>∗</label>Email: <ext-link ext-link-type="uri" xlink:href="mailto:LourdesRoja@baptisthealth.net">LourdesRoja@baptisthealth.net</ext-link>.</corresp>
</author-notes>
<pub-date pub-type="ppub"><year>2023</year></pub-date><pub-date pub-type="epub"><day>30</day><month>8</month><year>2022</year></pub-date><volume>21</volume><issue>4</issue><fpage>735</fpage><lpage>744</lpage><supplementary-material id="S1" content-type="archive" xlink:href="jds1063_s001.zip" mimetype="application" mime-subtype="x-zip-compressed">
<caption>
<title>Supplementary Material</title>
<p>Supplement 1 contains an example of how to download and clean the American Community Survey data.</p>
<p>Supplement 2 contains the full Data Dictionary for Information Technology, that was referenced in Table 2. With this Data Dictionary, the reader can find links to download the publicly-available data sets.</p>
</caption>
</supplementary-material><history><date date-type="received"><day>30</day><month>12</month><year>2021</year></date><date date-type="accepted"><day>4</day><month>8</month><year>2022</year></date></history>
<permissions><copyright-statement>2023 The Author(s). Published by the School of Statistics and the Center for Applied Statistics, Renmin University of China.</copyright-statement><copyright-year>2023</copyright-year>
<license license-type="open-access" xlink:href="https://creativecommons.org/licenses/by/4.0/">
<license-p>Open access article under the <ext-link ext-link-type="uri" xlink:href="https://creativecommons.org/licenses/by/4.0/">CC BY</ext-link> license.</license-p></license></permissions>
<abstract>
<p>Social determinants of health (SDOH) are the conditions in which people are born, grow, work, and live. Although evidence suggests that SDOH influence a range of health outcomes, health systems lack the infrastructure to access and act upon this information. The purpose of this manuscript is to explain the methodology that a health system used to: 1) identify and integrate publicly available SDOH data into the health systems’ Data Warehouse, 2) integrate a HIPAA compliant geocoding software (via DeGAUSS), and 3) visualize data to inform SDOH projects (via Tableau). First, authors engaged key stakeholders across the health system to convey the implications of SDOH data for our patient population and identify variables of interest. As a result, fourteen publicly available data sets, accounting for &gt;30,800 variables representing national, state, county, and census tract information over 2016–2019, were cleaned and integrated into our Data Warehouse. To pilot the data visualization, we created county and census tract level maps for our service areas and plotted common SDOH metrics (e.g., income, education, insurance status, etc.). This practical, methodological integration of SDOH data at a large health system demonstrated feasibility. Ultimately, we will repeat this process system wide to further understand the risk burden in our patient population and improve our prediction models – allowing us to become better partners with our community.</p>
</abstract>
<kwd-group>
<label>Keywords</label>
<kwd>data visualization</kwd>
<kwd>health system</kwd>
<kwd>social determinants of health</kwd>
</kwd-group>
</article-meta>
</front>
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