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Discovering associations between problem list and practice setting
BACKGROUND: The Health Information Technology for Economic and Clinical Health Act (HITECH) has greatly accelerated the adoption of electronic health records (EHRs) with the promise of better clinical decisions and patients’ outcomes. One of the core criteria for “Meaningful Use” of EHRs is to have...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448189/ https://www.ncbi.nlm.nih.gov/pubmed/30943957 http://dx.doi.org/10.1186/s12911-019-0779-y |
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author | Wang, Liwei Wang, Yanshan Shen, Feichen Rastegar-Mojarad, Majid Liu, Hongfang |
author_facet | Wang, Liwei Wang, Yanshan Shen, Feichen Rastegar-Mojarad, Majid Liu, Hongfang |
author_sort | Wang, Liwei |
collection | PubMed |
description | BACKGROUND: The Health Information Technology for Economic and Clinical Health Act (HITECH) has greatly accelerated the adoption of electronic health records (EHRs) with the promise of better clinical decisions and patients’ outcomes. One of the core criteria for “Meaningful Use” of EHRs is to have a problem list that shows the most important health problems faced by a patient. The implementation of problem lists in EHRs has a potential to help practitioners to provide customized care to patients. However, it remains an open question on how to leverage problem lists in different practice settings to provide tailored care, of which the bottleneck lies in the associations between problem list and practice setting. METHODS: In this study, using sampled clinical documents associated with a cohort of patients who received their primary care at Mayo Clinic, we investigated the associations between problem list and practice setting through natural language processing (NLP) and topic modeling techniques. Specifically, after practice settings and problem lists were normalized, statistical χ(2) test, term frequency-inverse document frequency (TF-IDF) and enrichment analysis were used to choose representative concepts for each setting. Then Latent Dirichlet Allocations (LDA) were used to train topic models and predict potential practice settings using similarity metrics based on the problem concepts representative of practice settings. Evaluation was conducted through 5-fold cross validation and Recall@k, Precision@k and F1@k were calculated. RESULTS: Our method can generate prioritized and meaningful problem lists corresponding to specific practice settings. For practice setting prediction, recall increases from 0.719 (k = 2) to 0.931 (k = 10), precision increases from 0.882 (k = 2) to 0.931 (k = 10) and F1 increases from 0.790 (k = 2) to 0.931 (k = 10). CONCLUSION: To our best knowledge, our study is the first attempting to discover the association between the problem lists and hospital practice settings. In the future, we plan to investigate how to provide more tailored care by utilizing the association between problem list and practice setting revealed in this study. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-019-0779-y) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-6448189 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-64481892019-04-15 Discovering associations between problem list and practice setting Wang, Liwei Wang, Yanshan Shen, Feichen Rastegar-Mojarad, Majid Liu, Hongfang BMC Med Inform Decis Mak Research BACKGROUND: The Health Information Technology for Economic and Clinical Health Act (HITECH) has greatly accelerated the adoption of electronic health records (EHRs) with the promise of better clinical decisions and patients’ outcomes. One of the core criteria for “Meaningful Use” of EHRs is to have a problem list that shows the most important health problems faced by a patient. The implementation of problem lists in EHRs has a potential to help practitioners to provide customized care to patients. However, it remains an open question on how to leverage problem lists in different practice settings to provide tailored care, of which the bottleneck lies in the associations between problem list and practice setting. METHODS: In this study, using sampled clinical documents associated with a cohort of patients who received their primary care at Mayo Clinic, we investigated the associations between problem list and practice setting through natural language processing (NLP) and topic modeling techniques. Specifically, after practice settings and problem lists were normalized, statistical χ(2) test, term frequency-inverse document frequency (TF-IDF) and enrichment analysis were used to choose representative concepts for each setting. Then Latent Dirichlet Allocations (LDA) were used to train topic models and predict potential practice settings using similarity metrics based on the problem concepts representative of practice settings. Evaluation was conducted through 5-fold cross validation and Recall@k, Precision@k and F1@k were calculated. RESULTS: Our method can generate prioritized and meaningful problem lists corresponding to specific practice settings. For practice setting prediction, recall increases from 0.719 (k = 2) to 0.931 (k = 10), precision increases from 0.882 (k = 2) to 0.931 (k = 10) and F1 increases from 0.790 (k = 2) to 0.931 (k = 10). CONCLUSION: To our best knowledge, our study is the first attempting to discover the association between the problem lists and hospital practice settings. In the future, we plan to investigate how to provide more tailored care by utilizing the association between problem list and practice setting revealed in this study. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12911-019-0779-y) contains supplementary material, which is available to authorized users. BioMed Central 2019-04-04 /pmc/articles/PMC6448189/ /pubmed/30943957 http://dx.doi.org/10.1186/s12911-019-0779-y Text en © The Author(s). 2019 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Wang, Liwei Wang, Yanshan Shen, Feichen Rastegar-Mojarad, Majid Liu, Hongfang Discovering associations between problem list and practice setting |
title | Discovering associations between problem list and practice setting |
title_full | Discovering associations between problem list and practice setting |
title_fullStr | Discovering associations between problem list and practice setting |
title_full_unstemmed | Discovering associations between problem list and practice setting |
title_short | Discovering associations between problem list and practice setting |
title_sort | discovering associations between problem list and practice setting |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6448189/ https://www.ncbi.nlm.nih.gov/pubmed/30943957 http://dx.doi.org/10.1186/s12911-019-0779-y |
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