{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,29]],"date-time":"2026-08-29T05:15:36Z","timestamp":1787980536903,"version":"build-2784847793"},"reference-count":33,"publisher":"Oxford University Press (OUP)","issue":"4","license":[{"start":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T00:00:00Z","timestamp":1696377600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100006545","name":"National Institute on Minority Health and Health Disparities","doi-asserted-by":"publisher","award":["R01MD015844-01"],"award-info":[{"award-number":["R01MD015844-01"]}],"id":[{"id":"10.13039\/100006545","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2023,10,4]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Objectives<\/jats:title>\n                    <jats:p>To develop and test a scalable, performant, and rule-based model for identifying 3 major domains of social needs (residential instability, food insecurity, and transportation issues) from the unstructured data in electronic health records (EHRs).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Materials and Methods<\/jats:title>\n                    <jats:p>We included patients aged 18 years or older who received care at the Johns Hopkins Health System (JHHS) between July 2016 and June 2021 and had at least 1 unstructured (free-text) note in their EHR during the study period. We used a combination of manual lexicon curation and semiautomated lexicon creation for feature development. We developed an initial rules-based pipeline (Match Pipeline) using 2 keyword sets for each social needs domain. We performed rule-based keyword matching for distinct lexicons and tested the algorithm using an annotated dataset comprising 192 patients. Starting with a set of expert-identified keywords, we tested the adjustments by evaluating false positives and negatives identified in the labeled dataset. We assessed the performance of the algorithm using measures of precision, recall, and F1 score.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>The algorithm for identifying residential instability had the best overall performance, with a weighted average for precision, recall, and F1 score of 0.92, 0.84, and 0.92 for identifying patients with homelessness and 0.84, 0.82, and 0.79 for identifying patients with housing insecurity. Metrics for the food insecurity algorithm were high but the transportation issues algorithm was the lowest overall performing metric.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Discussion<\/jats:title>\n                    <jats:p>The NLP algorithm in identifying social needs at JHHS performed relatively well and would provide the opportunity for implementation in a healthcare system.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>The NLP approach developed in this project could be adapted and potentially operationalized in the routine data processes of a healthcare system.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1093\/jamiaopen\/ooad085","type":"journal-article","created":{"date-parts":[[2023,9,21]],"date-time":"2023-09-21T16:19:46Z","timestamp":1695313186000},"source":"Crossref","is-referenced-by-count":19,"title":["Application of natural language processing to identify social needs from patient medical notes: development and assessment of a scalable, performant, and rule-based model in an integrated healthcare delivery system"],"prefix":"10.1093","volume":"6","author":[{"given":"Geoffrey M","family":"Gray","sequence":"first","affiliation":[{"name":"Center for Pediatric Data Science and Analytic Methodology, Johns Hopkins All Children\u2019s Hospital , St. Petersburg, FL, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ayah","family":"Zirikly","sequence":"additional","affiliation":[{"name":"Department of Computer Science, Whiting School of Engineering, Johns Hopkins University , Baltimore, MD, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Luis M","family":"Ahumada","sequence":"additional","affiliation":[{"name":"Center for Pediatric Data Science and Analytic Methodology, Johns Hopkins All Children\u2019s Hospital , St. Petersburg, FL, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masoud","family":"Rouhizadeh","sequence":"additional","affiliation":[{"name":"Department of Pharmaceutical Outcomes and Policy, University of Florida College of Pharmacy , Gainesville, FL, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Thomas","family":"Richards","sequence":"additional","affiliation":[{"name":"Department of Health Policy and Management, Center for Population Health Information Technology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Christopher","family":"Kitchen","sequence":"additional","affiliation":[{"name":"Department of Health Policy and Management, Center for Population Health Information Technology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Iman","family":"Foroughmand","sequence":"additional","affiliation":[{"name":"Department of Health Policy and Management, Center for Population Health Information Technology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Elham","family":"Hatef","sequence":"additional","affiliation":[{"name":"Department of Health Policy and Management, Center for Population Health Information Technology, Johns Hopkins Bloomberg School of Public Health , Baltimore, MD, United States"},{"name":"Division of General Internal Medicine, Department of Medicine, Johns Hopkins School of Medicine , Baltimore, MD, United States"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2023,10,4]]},"reference":[{"issue":"2","key":"2023100419175891700_ooad085-B1","doi-asserted-by":"crossref","first-page":"222","DOI":"10.1089\/pop.2020.0043","article-title":"Assessing the impact of social needs and social determinants of health on health care utilization: using patient- and community-level data","volume":"24","author":"Hatef","year":"2021","journal-title":"Popul Health Manag"},{"issue":"6","key":"2023100419175891700_ooad085-B2","doi-asserted-by":"crossref","first-page":"890","DOI":"10.3122\/jabfm.2019.06.190138","article-title":"The association between neighborhood socioeconomic and housing characteristics with hospitalization: results of a national study of veterans","volume":"32","author":"Hatef","year":"2019","journal-title":"J Am Board Fam Med"},{"issue":"6","key":"2023100419175891700_ooad085-B3","doi-asserted-by":"crossref","first-page":"811","DOI":"10.1016\/j.amepre.2018.12.012","article-title":"The impact of social determinants of health on hospitalization in the veterans health administration","volume":"56","author":"Hatef","year":"2019","journal-title":"Am J Prev Med"},{"issue":"3","key":"2023100419175891700_ooad085-B4","first-page":"14","article-title":"Assessing the availability of data on social and behavioral determinants in structured and unstructured electronic health records: a retrospective analysis of a multilevel health care system","volume":"7","author":"Hatef","year":"2019","journal-title":"JMIR Med Inf"},{"issue":"3","key":"2023100419175891700_ooad085-B5","doi-asserted-by":"crossref","first-page":"1600","DOI":"10.1111\/1475-6773.12730","article-title":"Food insecurity and health care expenditures in the United States, 2011-2013","volume":"53","author":"Berkowitz","year":"2018","journal-title":"Health Serv. Res"},{"issue":"1","key":"2023100419175891700_ooad085-B6","doi-asserted-by":"crossref","first-page":"71","DOI":"10.1111\/j.1525-1497.2005.00278.x","article-title":"Housing instability and food insecurity as barriers to health care among low-income Americans","volume":"21","author":"Kushel","year":"2006","journal-title":"J Gen Intern Med"},{"issue":"1","key":"2023100419175891700_ooad085-B7","doi-asserted-by":"crossref","first-page":"ooac006","DOI":"10.1093\/jamiaopen\/ooac006","article-title":"Development and assessment of a natural language processing model to identify residential instability in electronic health records\u2019 unstructured data: a comparison of 3 integrated healthcare delivery systems","volume":"5","author":"Hatef","year":"2022","journal-title":"JAMIA Open"},{"issue":"8","key":"2023100419175891700_ooad085-B8","doi-asserted-by":"crossref","first-page":"1181","DOI":"10.7326\/M22-0147","article-title":"When there is value in asking: an argument for social risk screening in clinical practice","volume":"175","author":"Byhoff","year":"2022","journal-title":"Ann Intern Med"},{"issue":"10","key":"2023100419175891700_ooad085-B9","doi-asserted-by":"crossref","first-page":"1419","DOI":"10.1093\/jamia\/ocy068","article-title":"Opportunities and challenges in developing deep learning models using electronic health records data: a systematic review","volume":"25","author":"Xiao","year":"2018","journal-title":"J Am Med Inf Assoc"},{"key":"2023100419175891700_ooad085-B10","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1016\/j.cosrev.2022.100511","article-title":"Neural natural language processing for unstructured data in electronic health records: a review","volume":"46","author":"Li","year":"2022","journal-title":"Comput Sci Rev"},{"key":"2023100419175891700_ooad085-B11","doi-asserted-by":"crossref","first-page":"4","DOI":"10.1016\/j.tacc.2021.02.007","article-title":"Natural language processing in medicine: a review","volume":"38","author":"Locke","year":"2021","journal-title":"Trends Anaesth Crit Care"},{"issue":"2","key":"2023100419175891700_ooad085-B12","doi-asserted-by":"crossref","first-page":"463","DOI":"10.1016\/j.jaci.2019.12.897","article-title":"Artificial intelligence approaches using natural language processing to advance EHR-based clinical research","volume":"145","author":"Juhn","year":"2020","journal-title":"J Allergy Clin Immunol"},{"issue":"1","key":"2023100419175891700_ooad085-B13","doi-asserted-by":"crossref","first-page":"10","DOI":"10.1186\/s13326-019-0198-0","article-title":"Moonstone: a novel natural language processing system for inferring social risk from clinical narratives","volume":"10","author":"Conway","year":"2019","journal-title":"J Biomed Semant"},{"key":"2023100419175891700_ooad085-B14","first-page":"1456","article-title":"Identifying patients with significant problems related to social determinants of health with natural language processing","volume":"264","author":"Dorr","year":"2019","journal-title":"Stud Health Technol Inform"},{"issue":"12","key":"2023100419175891700_ooad085-B15","doi-asserted-by":"crossref","first-page":"2716","DOI":"10.1093\/jamia\/ocab170","article-title":"Extracting social determinants of health from electronic health records using natural language processing: a systematic review","volume":"28","author":"Patra","year":"2021","journal-title":"J Am Med Inf Assoc"},{"key":"2023100419175891700_ooad085-B16","author":"Arons","year":"2018"},{"key":"2023100419175891700_ooad085-B17","first-page":"221","article-title":"Enrich classifications in psychiatry with textual data: an ontology for psychiatry including social concepts","volume":"210","author":"Richard","year":"2015","journal-title":"Stud Health Technol Inform"},{"key":"2023100419175891700_ooad085-B18","author":"Bureau TUSC","year":"2023"},{"key":"2023100419175891700_ooad085-B19","author":"Bureau TUSC","year":"2023"},{"key":"2023100419175891700_ooad085-B20","author":"Centers. NAoCH","year":"2023"},{"issue":"1","key":"2023100419175891700_ooad085-B21","doi-asserted-by":"crossref","first-page":"8","DOI":"10.1056\/NEJMp1512532","article-title":"Accountable health Communities - addressing social needs through Medicare and Medicaid","volume":"374","author":"Alley","year":"2016","journal-title":"N Engl J Med"},{"issue":"8","key":"2023100419175891700_ooad085-B22","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1111\/jgs.15411","article-title":"The value of unstructured electronic health record data in geriatric syndrome case identification","volume":"66","author":"Kharrazi","year":"2018","journal-title":"J Am Geriatr Soc"},{"issue":"1","key":"2023100419175891700_ooad085-B23","doi-asserted-by":"crossref","first-page":"248","DOI":"10.1186\/s12877-017-0645-7","article-title":"Comparing clinician descriptions of frailty and geriatric syndromes using electronic health records: a retrospective cohort study","volume":"17","author":"Anzaldi","year":"2017","journal-title":"BMC Geriatr"},{"key":"2023100419175891700_ooad085-B24"},{"key":"2023100419175891700_ooad085-B25","author":"Honnibal","year":"2017"},{"key":"2023100419175891700_ooad085-B26"},{"issue":"11","key":"2023100419175891700_ooad085-B27","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1145\/2934664","article-title":"Apache spark: a unified engine for big data processing","volume":"59","author":"Zaharia","year":"2016","journal-title":"Commun ACM"},{"issue":"1","key":"2023100419175891700_ooad085-B28","doi-asserted-by":"crossref","first-page":"107","DOI":"10.1145\/1327452.1327492","article-title":"Mapreduce: simplified data processing on large clusters","volume":"51","author":"Dean","year":"2008","journal-title":"Commun ACM"},{"key":"2023100419175891700_ooad085-B29","author":"Alsentzer","year":"2019"},{"key":"2023100419175891700_ooad085-B30","author":"Kexin","year":"2019"},{"key":"2023100419175891700_ooad085-B31","first-page":"1135","author":"Ribeiro","year":"2016"},{"issue":"5","key":"2023100419175891700_ooad085-B32","doi-asserted-by":"crossref","first-page":"21m03173","DOI":"10.4088\/PCC.21m03173","article-title":"Unemployment, homelessness, and other societal outcomes among US veterans with schizophrenia relapse: a retrospective cohort study","volume":"24","author":"Lin","year":"2022","journal-title":"Prim Care Companion CNS Disord"},{"issue":"5","key":"2023100419175891700_ooad085-B33","doi-asserted-by":"crossref","first-page":"1359","DOI":"10.1007\/s11606-020-06447-0","article-title":"The impact of social determinants of health on medication adherence: a systematic review and meta-analysis","volume":"36","author":"Wilder","year":"2021","journal-title":"J Gen Intern Med"}],"container-title":["JAMIA Open"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/academic.oup.com\/jamiaopen\/article-pdf\/6\/4\/ooad085\/51862725\/ooad085.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/jamiaopen\/article-pdf\/6\/4\/ooad085\/51862725\/ooad085.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,10,4]],"date-time":"2023-10-04T15:18:20Z","timestamp":1696432700000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/jamiaopen\/article\/doi\/10.1093\/jamiaopen\/ooad085\/7289086"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,10,4]]},"references-count":33,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,10,4]]}},"URL":"https:\/\/doi.org\/10.1093\/jamiaopen\/ooad085","relation":{},"ISSN":["2574-2531"],"issn-type":[{"value":"2574-2531","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2023,12,1]]},"published":{"date-parts":[[2023,10,4]]},"article-number":"ooad085"}}