Cargando…
Initial development of tools to identify child abuse and neglect in pediatric primary care
BACKGROUND: Child abuse and neglect (CAN) is prevalent, associated with long-term adversities, and often undetected. Primary care settings offer a unique opportunity to identify CAN and facilitate referrals, when warranted. Electronic health records (EHR) contain extensive information to support hea...
Autores principales: | , , , , , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10656827/ https://www.ncbi.nlm.nih.gov/pubmed/37978498 http://dx.doi.org/10.1186/s12911-023-02361-7 |
_version_ | 1785148078870757376 |
---|---|
author | Hanson, Rochelle F. Zhu, Vivienne Are, Funlola Espeleta, Hannah Wallis, Elizabeth Heider, Paul Kautz, Marin Lenert, Leslie |
author_facet | Hanson, Rochelle F. Zhu, Vivienne Are, Funlola Espeleta, Hannah Wallis, Elizabeth Heider, Paul Kautz, Marin Lenert, Leslie |
author_sort | Hanson, Rochelle F. |
collection | PubMed |
description | BACKGROUND: Child abuse and neglect (CAN) is prevalent, associated with long-term adversities, and often undetected. Primary care settings offer a unique opportunity to identify CAN and facilitate referrals, when warranted. Electronic health records (EHR) contain extensive information to support healthcare decisions, yet time constraints preclude most providers from thorough EHR reviews that could indicate CAN. Strategies that summarize EHR data to identify CAN and convey this to providers has potential to mitigate CAN-related sequelae. This study used expert review/consensus and Natural Language Processing (NLP) to develop and test a lexicon to characterize children who have experienced or are at risk for CAN and compared machine learning methods to the lexicon + NLP approach to determine the algorithm’s performance for identifying CAN. METHODS: Study investigators identified 90 CAN terms and invited an interdisciplinary group of child abuse experts for review and validation. We then used NLP to develop pipelines to finalize the CAN lexicon. Data for pipeline development and refinement were drawn from a randomly selected sample of EHR from patients seen at pediatric primary care clinics within a U.S. academic health center. To explore a machine learning approach for CAN identification, we used Support Vector Machine algorithms. RESULTS: The investigator-generated list of 90 CAN terms were reviewed and validated by 25 invited experts, resulting in a final pool of 133 terms. NLP utilized a randomly selected sample of 14,393 clinical notes from 153 patients to test the lexicon, and .03% of notes were identified as CAN positive. CAN identification varied by clinical note type, with few differences found by provider type (physicians versus nurses, social workers, etc.). An evaluation of the final NLP pipelines indicated 93.8% positive CAN rate for the training set and 71.4% for the test set, with decreased precision attributed primarily to false positives. For the machine learning approach, SVM pipeline performance was 92% for CAN + and 100% for non-CAN, indicating higher sensitivity than specificity. CONCLUSIONS: The NLP algorithm’s development and refinement suggest that innovative tools can identify youth at risk for CAN. The next key step is to refine the NLP algorithm to eventually funnel this information to care providers to guide clinical decision making. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-023-02361-7. |
format | Online Article Text |
id | pubmed-10656827 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106568272023-11-17 Initial development of tools to identify child abuse and neglect in pediatric primary care Hanson, Rochelle F. Zhu, Vivienne Are, Funlola Espeleta, Hannah Wallis, Elizabeth Heider, Paul Kautz, Marin Lenert, Leslie BMC Med Inform Decis Mak Research BACKGROUND: Child abuse and neglect (CAN) is prevalent, associated with long-term adversities, and often undetected. Primary care settings offer a unique opportunity to identify CAN and facilitate referrals, when warranted. Electronic health records (EHR) contain extensive information to support healthcare decisions, yet time constraints preclude most providers from thorough EHR reviews that could indicate CAN. Strategies that summarize EHR data to identify CAN and convey this to providers has potential to mitigate CAN-related sequelae. This study used expert review/consensus and Natural Language Processing (NLP) to develop and test a lexicon to characterize children who have experienced or are at risk for CAN and compared machine learning methods to the lexicon + NLP approach to determine the algorithm’s performance for identifying CAN. METHODS: Study investigators identified 90 CAN terms and invited an interdisciplinary group of child abuse experts for review and validation. We then used NLP to develop pipelines to finalize the CAN lexicon. Data for pipeline development and refinement were drawn from a randomly selected sample of EHR from patients seen at pediatric primary care clinics within a U.S. academic health center. To explore a machine learning approach for CAN identification, we used Support Vector Machine algorithms. RESULTS: The investigator-generated list of 90 CAN terms were reviewed and validated by 25 invited experts, resulting in a final pool of 133 terms. NLP utilized a randomly selected sample of 14,393 clinical notes from 153 patients to test the lexicon, and .03% of notes were identified as CAN positive. CAN identification varied by clinical note type, with few differences found by provider type (physicians versus nurses, social workers, etc.). An evaluation of the final NLP pipelines indicated 93.8% positive CAN rate for the training set and 71.4% for the test set, with decreased precision attributed primarily to false positives. For the machine learning approach, SVM pipeline performance was 92% for CAN + and 100% for non-CAN, indicating higher sensitivity than specificity. CONCLUSIONS: The NLP algorithm’s development and refinement suggest that innovative tools can identify youth at risk for CAN. The next key step is to refine the NLP algorithm to eventually funnel this information to care providers to guide clinical decision making. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12911-023-02361-7. BioMed Central 2023-11-17 /pmc/articles/PMC10656827/ /pubmed/37978498 http://dx.doi.org/10.1186/s12911-023-02361-7 Text en © This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Hanson, Rochelle F. Zhu, Vivienne Are, Funlola Espeleta, Hannah Wallis, Elizabeth Heider, Paul Kautz, Marin Lenert, Leslie Initial development of tools to identify child abuse and neglect in pediatric primary care |
title | Initial development of tools to identify child abuse and neglect in pediatric primary care |
title_full | Initial development of tools to identify child abuse and neglect in pediatric primary care |
title_fullStr | Initial development of tools to identify child abuse and neglect in pediatric primary care |
title_full_unstemmed | Initial development of tools to identify child abuse and neglect in pediatric primary care |
title_short | Initial development of tools to identify child abuse and neglect in pediatric primary care |
title_sort | initial development of tools to identify child abuse and neglect in pediatric primary care |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10656827/ https://www.ncbi.nlm.nih.gov/pubmed/37978498 http://dx.doi.org/10.1186/s12911-023-02361-7 |
work_keys_str_mv | AT hansonrochellef initialdevelopmentoftoolstoidentifychildabuseandneglectinpediatricprimarycare AT zhuvivienne initialdevelopmentoftoolstoidentifychildabuseandneglectinpediatricprimarycare AT arefunlola initialdevelopmentoftoolstoidentifychildabuseandneglectinpediatricprimarycare AT espeletahannah initialdevelopmentoftoolstoidentifychildabuseandneglectinpediatricprimarycare AT walliselizabeth initialdevelopmentoftoolstoidentifychildabuseandneglectinpediatricprimarycare AT heiderpaul initialdevelopmentoftoolstoidentifychildabuseandneglectinpediatricprimarycare AT kautzmarin initialdevelopmentoftoolstoidentifychildabuseandneglectinpediatricprimarycare AT lenertleslie initialdevelopmentoftoolstoidentifychildabuseandneglectinpediatricprimarycare |