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Identifying Caregiver Availability Using Medical Notes With Rule-Based Natural Language Processing: Retrospective Cohort Study

BACKGROUND: Identifying caregiver availability, particularly for patients with dementia or those with a disability, is critical to informing the appropriate care planning by the health systems, hospitals, and providers. This information is not readily available, and there is a paucity of pragmatic a...

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Autores principales: Mahmoudi, Elham, Wu, Wenbo, Najarian, Cyrus, Aikens, James, Bynum, Julie, Vydiswaran, V G Vinod
Formato: Online Artículo Texto
Lenguaje:English
Publicado: JMIR Publications 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9539648/
https://www.ncbi.nlm.nih.gov/pubmed/35998328
http://dx.doi.org/10.2196/40241
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author Mahmoudi, Elham
Wu, Wenbo
Najarian, Cyrus
Aikens, James
Bynum, Julie
Vydiswaran, V G Vinod
author_facet Mahmoudi, Elham
Wu, Wenbo
Najarian, Cyrus
Aikens, James
Bynum, Julie
Vydiswaran, V G Vinod
author_sort Mahmoudi, Elham
collection PubMed
description BACKGROUND: Identifying caregiver availability, particularly for patients with dementia or those with a disability, is critical to informing the appropriate care planning by the health systems, hospitals, and providers. This information is not readily available, and there is a paucity of pragmatic approaches to automatically identifying caregiver availability and type. OBJECTIVE: Our main objective was to use medical notes to assess caregiver availability and type for hospitalized patients with dementia. Our second objective was to identify whether the patient lived at home or resided at an institution. METHODS: In this retrospective cohort study, we used 2016-2019 telephone-encounter medical notes from a single institution to develop a rule-based natural language processing (NLP) algorithm to identify the patient’s caregiver availability and place of residence. Using note-level data, we compared the results of the NLP algorithm with human-conducted chart abstraction for both training (749/976, 77%) and test sets (227/976, 23%) for a total of 223 adults aged 65 years and older diagnosed with dementia. Our outcomes included determining whether the patients (1) reside at home or in an institution, (2) have a formal caregiver, and (3) have an informal caregiver. RESULTS: Test set results indicated that our NLP algorithm had high level of accuracy and reliability for identifying whether patients had an informal caregiver (F(1)=0.94, accuracy=0.95, sensitivity=0.97, and specificity=0.93), but was relatively less able to identify whether the patient lived at an institution (F(1)=0.64, accuracy=0.90, sensitivity=0.51, and specificity=0.98). The most common explanations for NLP misclassifications across all categories were (1) incomplete or misspelled facility names; (2) past, uncertain, or undecided status; (3) uncommon abbreviations; and (4) irregular use of templates. CONCLUSIONS: This innovative work was the first to use medical notes to pragmatically determine caregiver availability. Our NLP algorithm identified whether hospitalized patients with dementia have a formal or informal caregiver and, to a lesser extent, whether they lived at home or in an institutional setting. There is merit in using NLP to identify caregivers. This study serves as a proof of concept. Future work can use other approaches and further identify caregivers and the extent of their availability.
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spelling pubmed-95396482022-10-08 Identifying Caregiver Availability Using Medical Notes With Rule-Based Natural Language Processing: Retrospective Cohort Study Mahmoudi, Elham Wu, Wenbo Najarian, Cyrus Aikens, James Bynum, Julie Vydiswaran, V G Vinod JMIR Aging Original Paper BACKGROUND: Identifying caregiver availability, particularly for patients with dementia or those with a disability, is critical to informing the appropriate care planning by the health systems, hospitals, and providers. This information is not readily available, and there is a paucity of pragmatic approaches to automatically identifying caregiver availability and type. OBJECTIVE: Our main objective was to use medical notes to assess caregiver availability and type for hospitalized patients with dementia. Our second objective was to identify whether the patient lived at home or resided at an institution. METHODS: In this retrospective cohort study, we used 2016-2019 telephone-encounter medical notes from a single institution to develop a rule-based natural language processing (NLP) algorithm to identify the patient’s caregiver availability and place of residence. Using note-level data, we compared the results of the NLP algorithm with human-conducted chart abstraction for both training (749/976, 77%) and test sets (227/976, 23%) for a total of 223 adults aged 65 years and older diagnosed with dementia. Our outcomes included determining whether the patients (1) reside at home or in an institution, (2) have a formal caregiver, and (3) have an informal caregiver. RESULTS: Test set results indicated that our NLP algorithm had high level of accuracy and reliability for identifying whether patients had an informal caregiver (F(1)=0.94, accuracy=0.95, sensitivity=0.97, and specificity=0.93), but was relatively less able to identify whether the patient lived at an institution (F(1)=0.64, accuracy=0.90, sensitivity=0.51, and specificity=0.98). The most common explanations for NLP misclassifications across all categories were (1) incomplete or misspelled facility names; (2) past, uncertain, or undecided status; (3) uncommon abbreviations; and (4) irregular use of templates. CONCLUSIONS: This innovative work was the first to use medical notes to pragmatically determine caregiver availability. Our NLP algorithm identified whether hospitalized patients with dementia have a formal or informal caregiver and, to a lesser extent, whether they lived at home or in an institutional setting. There is merit in using NLP to identify caregivers. This study serves as a proof of concept. Future work can use other approaches and further identify caregivers and the extent of their availability. JMIR Publications 2022-09-22 /pmc/articles/PMC9539648/ /pubmed/35998328 http://dx.doi.org/10.2196/40241 Text en ©Elham Mahmoudi, Wenbo Wu, Cyrus Najarian, James Aikens, Julie Bynum, V G Vinod Vydiswaran. Originally published in JMIR Aging (https://aging.jmir.org), 22.09.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Aging, is properly cited. The complete bibliographic information, a link to the original publication on https://aging.jmir.org, as well as this copyright and license information must be included.
spellingShingle Original Paper
Mahmoudi, Elham
Wu, Wenbo
Najarian, Cyrus
Aikens, James
Bynum, Julie
Vydiswaran, V G Vinod
Identifying Caregiver Availability Using Medical Notes With Rule-Based Natural Language Processing: Retrospective Cohort Study
title Identifying Caregiver Availability Using Medical Notes With Rule-Based Natural Language Processing: Retrospective Cohort Study
title_full Identifying Caregiver Availability Using Medical Notes With Rule-Based Natural Language Processing: Retrospective Cohort Study
title_fullStr Identifying Caregiver Availability Using Medical Notes With Rule-Based Natural Language Processing: Retrospective Cohort Study
title_full_unstemmed Identifying Caregiver Availability Using Medical Notes With Rule-Based Natural Language Processing: Retrospective Cohort Study
title_short Identifying Caregiver Availability Using Medical Notes With Rule-Based Natural Language Processing: Retrospective Cohort Study
title_sort identifying caregiver availability using medical notes with rule-based natural language processing: retrospective cohort study
topic Original Paper
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9539648/
https://www.ncbi.nlm.nih.gov/pubmed/35998328
http://dx.doi.org/10.2196/40241
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