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Semantic Similarity Search Approach to Extract Exemplars of Stigmatizing and Positive Language in Obstetric Clinical Notes: Exploratory Study.

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Title: Semantic Similarity Search Approach to Extract Exemplars of Stigmatizing and Positive Language in Obstetric Clinical Notes: Exploratory Study.
Authors: Scroggins JK; School of Nursing, University of North Carolina at Chapel Hill, 120 Medical Drive, Chapel Hill, NC, 27514, United States, 1 (919) 966-4260., Hulchafo II; School of Nursing, Columbia University, New York, NY, United States., Barcelona V; School of Nursing, Columbia University, New York, NY, United States., Davoudi A; VNS Health, New York, NY, United States., Moen H; Department of Computer Science, Aalto University, Espoo, Finland., Harkins S; School of Nursing, Columbia University, New York, NY, United States., Scharp D; Icahn School of Medicine at Mount Sinai, New York, NY, United States., Cato K; School of Nursing, University of Pennsylvania, Philadelphia, PA, United States., Tadiello M; Center for Community-Engaged Health Informatics and Data Science, Columbia University Irving Medical Center, New York, NY, United States., Topaz M; School of Nursing, Columbia University, New York, NY, United States.
Source: JMIR medical informatics [JMIR Med Inform] 2026 Sep 22; Vol. 14, pp. e85088. Date of Electronic Publication: 2026 Sep 22.
Publication Type: Journal Article
Language: English
Journal Info: Publisher: JMIR Publications Country of Publication: Canada NLM ID: 101645109 Publication Model: Electronic Cited Medium: Internet ISSN: 2291-9694 (Electronic) Linking ISSN: 22919694 NLM ISO Abbreviation: JMIR Med Inform Subsets: MEDLINE
Imprint Name(s): Original Publication: Toronto : JMIR Publications, [2013]-
MeSH Terms: Electronic Health Records*/statistics & numerical data , Obstetrics*/methods , Natural Language Processing* , Semantics* , Stereotyping*, Humans ; Female ; Pregnancy ; United States ; Language
Abstract: Background: Natural language processing can extract meaningful information from clinical notes. However, human annotation is time-consuming and costly, and scarce data poses a challenge.
Objective: This study aimed to explore a semantic similarity search approach to extract exemplars of stigmatizing and positive language in obstetric clinical notes.
Methods: We used electronic health record data from labor and birth admissions at 2 hospitals in the United States from 2017 to 2019. We used a semantic similarity search approach, which used 200 randomly selected true exemplars, stratified by language categories, as queries to search for similar exemplar candidates. We extracted the top 5 candidates with the highest cosine similarities, which were assessed for accuracy.
Results: We retrieved 1000 candidates. An average precision of 0.69 was achieved when candidates with cosine similarity thresholds of 0.75 or higher were included, at which point 68.8% (64/93) of exemplar candidates accurately represented true cases. At the 0.75 threshold, the proportion of true cases was higher for preferred language (41/56, 73.2%) and unilateral/authoritarian decisions (5/7, 71.4%). The proportion of true cases was lower for difficult patients (2/7, 28.6%) and marginalized identities (3/9, 33.3%).
Conclusions: The semantic similarity search approach shows promise in efficiently extracting exemplars while reducing the annotation burden, laying the groundwork for future applications in other domains.
(© Jihye Kim Scroggins, Ismael Ibrahim Hulchafo, Veronica Barcelona, Anahita Davoudi, Hans Moen, Sarah Harkins, Danielle Scharp, Kenrick Cato, Michele Tadiello, Maxim Topaz. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)
References: Artif Intell Med. 2023 Dec;146:102701. (PMID: 38042599)
J Med Internet Res. 2026 Mar 25;28:e82997. (PMID: 41880603)
JAMA Netw Open. 2021 Jul 1;4(7):e2117052. (PMID: 34259849)
Matern Child Health J. 2024 Mar;28(3):578-586. (PMID: 38147277)
PLoS One. 2023 Dec 15;18(12):e0292356. (PMID: 38100453)
Obstet Gynecol. 2023 Oct 1;142(4):795-803. (PMID: 37678895)
Health Aff (Millwood). 2022 Feb;41(2):203-211. (PMID: 35044842)
J Am Med Inform Assoc. 2019 Nov 1;26(11):1314-1322. (PMID: 31294792)
J Obstet Gynecol Neonatal Nurs. 2025 Jan;54(1):112-122.e4. (PMID: 39577837)
Issues Ment Health Nurs. 2009 Mar;30(3):199. (PMID: 19291498)
Int J Med Inform. 2017 Oct;106:25-31. (PMID: 28870380)
Nurs Inq. 2023 Jul;30(3):e12557. (PMID: 37073504)
Nurs Outlook. 2026 May-Jun;74(3):102757. (PMID: 41962499)
JAMA Netw Open. 2022 Jan 4;5(1):e2144967. (PMID: 35084481)
JMIR Med Inform. 2020 Nov 27;8(11):e22508. (PMID: 33245284)
Behav Med. 2019 Apr-Jun;45(2):86-101. (PMID: 31343961)
AMIA Annu Symp Proc. 2018 Dec 05;2018:1552-1560. (PMID: 30815201)
JMIR Med Inform. 2021 May 26;9(5):e23099. (PMID: 34037527)
NPJ Digit Med. 2025 Jan 19;8(1):45. (PMID: 39828800)
J Biomed Inform. 2014 Aug;50:162-72. (PMID: 24859155)
J Am Med Inform Assoc. 2025 Feb 01;32(2):308-317. (PMID: 39569431)
J Am Med Inform Assoc. 2014 May-Jun;21(3):406-13. (PMID: 24001514)
Contributed Indexing: Keywords: bias; electronic health records; health communication; natural language processing; stigmatizing language
Entry Date(s): Date Created: 20260922 Date Completed: 20260922 Latest Revision: 20260924
Update Code: 20260924
PubMed Central ID: PMC13597023
DOI: 10.2196/85088
PMID: 42771877
Database: MEDLINE
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  Data: Semantic Similarity Search Approach to Extract Exemplars of Stigmatizing and Positive Language in Obstetric Clinical Notes: Exploratory Study.
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  Data: <searchLink fieldCode="AU" term="%22Scroggins+JK%22">Scroggins JK</searchLink>; School of Nursing, University of North Carolina at Chapel Hill, 120 Medical Drive, Chapel Hill, NC, 27514, United States, 1 (919) 966-4260.<br /><searchLink fieldCode="AU" term="%22Hulchafo+II%22">Hulchafo II</searchLink>; School of Nursing, Columbia University, New York, NY, United States.<br /><searchLink fieldCode="AU" term="%22Barcelona+V%22">Barcelona V</searchLink>; School of Nursing, Columbia University, New York, NY, United States.<br /><searchLink fieldCode="AU" term="%22Davoudi+A%22">Davoudi A</searchLink>; VNS Health, New York, NY, United States.<br /><searchLink fieldCode="AU" term="%22Moen+H%22">Moen H</searchLink>; Department of Computer Science, Aalto University, Espoo, Finland.<br /><searchLink fieldCode="AU" term="%22Harkins+S%22">Harkins S</searchLink>; School of Nursing, Columbia University, New York, NY, United States.<br /><searchLink fieldCode="AU" term="%22Scharp+D%22">Scharp D</searchLink>; Icahn School of Medicine at Mount Sinai, New York, NY, United States.<br /><searchLink fieldCode="AU" term="%22Cato+K%22">Cato K</searchLink>; School of Nursing, University of Pennsylvania, Philadelphia, PA, United States.<br /><searchLink fieldCode="AU" term="%22Tadiello+M%22">Tadiello M</searchLink>; Center for Community-Engaged Health Informatics and Data Science, Columbia University Irving Medical Center, New York, NY, United States.<br /><searchLink fieldCode="AU" term="%22Topaz+M%22">Topaz M</searchLink>; School of Nursing, Columbia University, New York, NY, United States.
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  Data: <searchLink fieldCode="JN" term="%22101645109%22">JMIR medical informatics</searchLink> [JMIR Med Inform] 2026 Sep 22; Vol. 14, pp. e85088. <i>Date of Electronic Publication: </i>2026 Sep 22.
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  Data: <i>Publisher: </i><searchLink fieldCode="PB" term="%22JMIR+Publications%22">JMIR Publications </searchLink><i>Country of Publication: </i>Canada <i>NLM ID: </i>101645109 <i>Publication Model: </i>Electronic <i>Cited Medium: </i>Internet <i>ISSN: </i>2291-9694 (Electronic) <i>Linking ISSN: </i><searchLink fieldCode="IS" term="%2222919694%22">22919694 </searchLink><i>NLM ISO Abbreviation: </i>JMIR Med Inform <i>Subsets: </i>MEDLINE
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  Data: <i>Original Publication</i>: Toronto : JMIR Publications, [2013]-
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  Data: <searchLink fieldCode="MM" term="%22Electronic+Health+Records%22">Electronic Health Records*</searchLink>/<searchLink fieldCode="MM" term="%22Electronic+Health+Records+statistics+%26+numerical+data%22">statistics & numerical data</searchLink> <br /><searchLink fieldCode="MM" term="%22Obstetrics%22">Obstetrics*</searchLink>/<searchLink fieldCode="MM" term="%22Obstetrics+methods%22">methods</searchLink> <br /><searchLink fieldCode="MM" term="%22Natural+Language+Processing%22">Natural Language Processing*</searchLink> <br /><searchLink fieldCode="MM" term="%22Semantics%22">Semantics*</searchLink> <br /><searchLink fieldCode="MM" term="%22Stereotyping%22">Stereotyping*</searchLink><br /><searchLink fieldCode="MH" term="%22Humans%22">Humans</searchLink> ; <searchLink fieldCode="MH" term="%22Female%22">Female</searchLink> ; <searchLink fieldCode="MH" term="%22Pregnancy%22">Pregnancy</searchLink> ; <searchLink fieldCode="MH" term="%22United+States%22">United States</searchLink> ; <searchLink fieldCode="MH" term="%22Language%22">Language</searchLink>
– Name: Abstract
  Label: Abstract
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  Data: Background: Natural language processing can extract meaningful information from clinical notes. However, human annotation is time-consuming and costly, and scarce data poses a challenge.<br />Objective: This study aimed to explore a semantic similarity search approach to extract exemplars of stigmatizing and positive language in obstetric clinical notes.<br />Methods: We used electronic health record data from labor and birth admissions at 2 hospitals in the United States from 2017 to 2019. We used a semantic similarity search approach, which used 200 randomly selected true exemplars, stratified by language categories, as queries to search for similar exemplar candidates. We extracted the top 5 candidates with the highest cosine similarities, which were assessed for accuracy.<br />Results: We retrieved 1000 candidates. An average precision of 0.69 was achieved when candidates with cosine similarity thresholds of 0.75 or higher were included, at which point 68.8% (64/93) of exemplar candidates accurately represented true cases. At the 0.75 threshold, the proportion of true cases was higher for preferred language (41/56, 73.2%) and unilateral/authoritarian decisions (5/7, 71.4%). The proportion of true cases was lower for difficult patients (2/7, 28.6%) and marginalized identities (3/9, 33.3%).<br />Conclusions: The semantic similarity search approach shows promise in efficiently extracting exemplars while reducing the annotation burden, laying the groundwork for future applications in other domains.<br /> (© Jihye Kim Scroggins, Ismael Ibrahim Hulchafo, Veronica Barcelona, Anahita Davoudi, Hans Moen, Sarah Harkins, Danielle Scharp, Kenrick Cato, Michele Tadiello, Maxim Topaz. Originally published in JMIR Medical Informatics (https://medinform.jmir.org).)
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  Data: Artif Intell Med. 2023 Dec;146:102701. (PMID: <searchLink fieldCode="PM" term="%2238042599%22">38042599)</searchLink><br />J Med Internet Res. 2026 Mar 25;28:e82997. (PMID: <searchLink fieldCode="PM" term="%2241880603%22">41880603)</searchLink><br />JAMA Netw Open. 2021 Jul 1;4(7):e2117052. (PMID: <searchLink fieldCode="PM" term="%2234259849%22">34259849)</searchLink><br />Matern Child Health J. 2024 Mar;28(3):578-586. (PMID: <searchLink fieldCode="PM" term="%2238147277%22">38147277)</searchLink><br />PLoS One. 2023 Dec 15;18(12):e0292356. (PMID: <searchLink fieldCode="PM" term="%2238100453%22">38100453)</searchLink><br />Obstet Gynecol. 2023 Oct 1;142(4):795-803. (PMID: <searchLink fieldCode="PM" term="%2237678895%22">37678895)</searchLink><br />Health Aff (Millwood). 2022 Feb;41(2):203-211. (PMID: <searchLink fieldCode="PM" term="%2235044842%22">35044842)</searchLink><br />J Am Med Inform Assoc. 2019 Nov 1;26(11):1314-1322. (PMID: <searchLink fieldCode="PM" term="%2231294792%22">31294792)</searchLink><br />J Obstet Gynecol Neonatal Nurs. 2025 Jan;54(1):112-122.e4. (PMID: <searchLink fieldCode="PM" term="%2239577837%22">39577837)</searchLink><br />Issues Ment Health Nurs. 2009 Mar;30(3):199. (PMID: <searchLink fieldCode="PM" term="%2219291498%22">19291498)</searchLink><br />Int J Med Inform. 2017 Oct;106:25-31. (PMID: <searchLink fieldCode="PM" term="%2228870380%22">28870380)</searchLink><br />Nurs Inq. 2023 Jul;30(3):e12557. (PMID: <searchLink fieldCode="PM" term="%2237073504%22">37073504)</searchLink><br />Nurs Outlook. 2026 May-Jun;74(3):102757. (PMID: <searchLink fieldCode="PM" term="%2241962499%22">41962499)</searchLink><br />JAMA Netw Open. 2022 Jan 4;5(1):e2144967. (PMID: <searchLink fieldCode="PM" term="%2235084481%22">35084481)</searchLink><br />JMIR Med Inform. 2020 Nov 27;8(11):e22508. (PMID: <searchLink fieldCode="PM" term="%2233245284%22">33245284)</searchLink><br />Behav Med. 2019 Apr-Jun;45(2):86-101. (PMID: <searchLink fieldCode="PM" term="%2231343961%22">31343961)</searchLink><br />AMIA Annu Symp Proc. 2018 Dec 05;2018:1552-1560. (PMID: <searchLink fieldCode="PM" term="%2230815201%22">30815201)</searchLink><br />JMIR Med Inform. 2021 May 26;9(5):e23099. (PMID: <searchLink fieldCode="PM" term="%2234037527%22">34037527)</searchLink><br />NPJ Digit Med. 2025 Jan 19;8(1):45. (PMID: <searchLink fieldCode="PM" term="%2239828800%22">39828800)</searchLink><br />J Biomed Inform. 2014 Aug;50:162-72. (PMID: <searchLink fieldCode="PM" term="%2224859155%22">24859155)</searchLink><br />J Am Med Inform Assoc. 2025 Feb 01;32(2):308-317. (PMID: <searchLink fieldCode="PM" term="%2239569431%22">39569431)</searchLink><br />J Am Med Inform Assoc. 2014 May-Jun;21(3):406-13. (PMID: <searchLink fieldCode="PM" term="%2224001514%22">24001514)</searchLink>
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