{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,3]],"date-time":"2026-05-03T23:46:41Z","timestamp":1777852001735,"version":"3.51.4"},"reference-count":20,"publisher":"SAGE Publications","issue":"2","license":[{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T00:00:00Z","timestamp":1743465600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Health Informatics J"],"published-print":{"date-parts":[[2025,4]]},"abstract":"<jats:p>\n                    <jats:bold>Objective:<\/jats:bold>\n                    Free text fields embedded within electronic consultation (eConsult) orders serve as rich sources of descriptive information regarding common uses of this novel telehealth technology. Simple text mining and language processing may efficiently extract key insights that help inform providers and administrators.\n                    <jats:bold>Methods:<\/jats:bold>\n                    Text data from eConsult orders placed within a single academic medical center were extracted from the electronic health record and examined. N-gram frequencies were used to describe the content of eConsult clinical questions and care recommendations.\n                    <jats:bold>Results:<\/jats:bold>\n                    18,609 eConsults were ordered, with volumes ranging from 12 to 3839 orders across 28 subspecialties. Median character length for the clinical question was 189 and 1393 for specialist response text. Frequency count for top bigram varied greatly by specialty, with a high of 190 (\u201cthyroid nodule\u201d) in Endocrinology and a low of 6 (\u201cshoulder pain\u201d) in Orthopedics for clinical questions, and a high of 3139 (\u201cref range\u201d) in Endocrinology and a low of 6 (\u201csurgical oncology\u201d) in Medical Oncology for specialist response.\n                    <jats:bold>Discussion:<\/jats:bold>\n                    Descriptive word sequences from NLP may provide limited insight into common use cases for eConsult across many subspecialties, though pre-processing was required to generate meaningful results.\n                  <\/jats:p>","DOI":"10.1177\/14604582251345319","type":"journal-article","created":{"date-parts":[[2025,5,24]],"date-time":"2025-05-24T03:28:27Z","timestamp":1748057307000},"update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":0,"title":["Natural language processing to describe primary care requests for eConsult specialty care: A simple and practical application"],"prefix":"10.1177","volume":"31","author":[{"ORCID":"https:\/\/orcid.org\/0009-0009-4896-614X","authenticated-orcid":false,"given":"Stephanie","family":"Grim","sequence":"first","affiliation":[{"name":"University of Colorado Anschutz Medical Campus"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anne","family":"Fuhlbrigge","sequence":"additional","affiliation":[{"name":"University of Colorado Anschutz Medical Campus"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"John F.","family":"Thomas","sequence":"additional","affiliation":[{"name":"University of Colorado Anschutz Medical Campus"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rodger","family":"Kessler","sequence":"additional","affiliation":[{"name":"University of Colorado Anschutz Medical Campus"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2025,5,24]]},"reference":[{"key":"e_1_3_5_2_2","doi-asserted-by":"publisher","DOI":"10.1177\/1357633X15582108"},{"key":"e_1_3_5_3_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.22299"},{"key":"e_1_3_5_4_2","doi-asserted-by":"publisher","DOI":"10.1111\/jgs.15411"},{"key":"e_1_3_5_5_2","first-page":"157","article-title":"Natural Language processing for free-text classification in telehealth services: differences between diabetes and heart failure applications","volume":"279","author":"Wiesmuller F","year":"2021","unstructured":"Wiesmuller F, Hayn D, Kreiner K, et al. Natural Language processing for free-text classification in telehealth services: differences between diabetes and heart failure applications. Stud Health Technol Inf 2021; 279: 157\u2013164.","journal-title":"Stud Health Technol Inf"},{"key":"e_1_3_5_6_2","doi-asserted-by":"publisher","DOI":"10.1513\/AnnalsATS.201703-237OC"},{"key":"e_1_3_5_7_2","doi-asserted-by":"publisher","DOI":"10.1197\/jamia.M3028"},{"key":"e_1_3_5_8_2","doi-asserted-by":"publisher","DOI":"10.15265\/IY-2017-029"},{"key":"e_1_3_5_9_2","doi-asserted-by":"publisher","DOI":"10.1001\/jamanetworkopen.2023.1204"},{"key":"e_1_3_5_10_2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pdig.0000218"},{"key":"e_1_3_5_11_2","doi-asserted-by":"publisher","DOI":"10.9778\/cmajo.20200025"},{"key":"e_1_3_5_12_2","doi-asserted-by":"publisher","DOI":"10.1007\/s10916-021-01732-9"},{"key":"e_1_3_5_13_2","doi-asserted-by":"publisher","DOI":"10.1177\/1357633X16633553"},{"key":"e_1_3_5_14_2","doi-asserted-by":"publisher","DOI":"10.2214\/AJR.19.22270"},{"key":"e_1_3_5_15_2","doi-asserted-by":"publisher","DOI":"10.2196\/jmir.7921"},{"key":"e_1_3_5_16_2","doi-asserted-by":"publisher","DOI":"10.1186\/s12888-018-1701-3"},{"key":"e_1_3_5_17_2","first-page":"1","article-title":"Classifying electronic consults for triage status and question type","volume":"2020","author":"Ding X","year":"2020","unstructured":"Ding X, Barnett ML, Mehrotra A, et al. Classifying electronic consults for triage status and question type. Proc Conf Assoc Comput Linguist Meet 2020; 2020: 1\u20136.","journal-title":"Proc Conf Assoc Comput Linguist Meet"},{"key":"e_1_3_5_18_2","doi-asserted-by":"publisher","DOI":"10.1093\/jamia\/ocac092"},{"key":"e_1_3_5_19_2","unstructured":"American Association of Medical Colleges. Project core: coordinating optimal referral experiences. American Association of Medical Colleges (accessed 23 September 2023)."},{"key":"e_1_3_5_20_2","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00037"},{"key":"e_1_3_5_21_2","volume-title":"R: A language and environment for statistical computing","author":"R Foundation for Statistical Computing","year":"2021","unstructured":"R Foundation for Statistical Computing. R: A language and environment for statistical computing. 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