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Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients

Outside medical records (OMRs) accompanying referred patients are frequently sent as faxes from external healthcare providers. Accessing useful and relevant information from these OMRs in a timely manner is a challenging task due to a combination of the presence of machine-illegible information and...

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Autores principales: Moon, Sungrim, Liu, Sijia, Chen, David, Wang, Yanshan, Wood, Douglas L., Chaudhry, Rajeev, Liu, Hongfang, Kingsbury, Paul
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Springer International Publishing 2019
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982748/
https://www.ncbi.nlm.nih.gov/pubmed/35415427
http://dx.doi.org/10.1007/s41666-019-00044-5
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author Moon, Sungrim
Liu, Sijia
Chen, David
Wang, Yanshan
Wood, Douglas L.
Chaudhry, Rajeev
Liu, Hongfang
Kingsbury, Paul
author_facet Moon, Sungrim
Liu, Sijia
Chen, David
Wang, Yanshan
Wood, Douglas L.
Chaudhry, Rajeev
Liu, Hongfang
Kingsbury, Paul
author_sort Moon, Sungrim
collection PubMed
description Outside medical records (OMRs) accompanying referred patients are frequently sent as faxes from external healthcare providers. Accessing useful and relevant information from these OMRs in a timely manner is a challenging task due to a combination of the presence of machine-illegible information and the limited system interoperability inherent in healthcare. Little research has been done on investigating information in OMRs. This paper evaluated overlapping and non-overlapping medical concepts captured from digitally faxed OMRs for patients transferring to the Department of Cardiovascular Medicine and from clinical consultant notes generated at the Mayo Clinic. We used optical character recognition (OCR) techniques to make faxed OMRs machine-readable and used natural language processing (NLP) techniques to capture clinical concepts from both machine-readable OMRs and Mayo clinical notes. We measured the level of overlap in medical concepts between OMRs and Mayo clinical narratives in the quantitative approaches and assessed the salience of concepts specific to Cardiovascular Medicine by calculating the ratio of those mentioned concepts relative to an independent clinical corpus. Among the concepts collected from the OMRs, 11.19% of those were also present in the Mayo clinical narratives that were generated within the 3 months after their initial encounter at the Mayo Clinic. For those common concepts, 73.97% were identified in initial consultant notes (ICNs) and 26.03% were captured over subsequent follow-up consultant notes (FCNs). These findings implied that information collected from the OMRs is potentially informative for patient care, but some valuable information (additionally identified in FCNs) collected from the OMRs is not fully used in an earlier stage of the care process. The concepts collected from the ICNs have the highest salience to Cardiovascular Medicine (0.112) compared to concepts in OMRs and concepts in FCNs. Additionally, unique concepts captured in ICNs (unseen in OMRs or FCNs) carried the most salient information (0.094), which demonstrated that ICNs provided the most informative concepts for the care of transferred patients.
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spelling pubmed-89827482022-04-11 Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients Moon, Sungrim Liu, Sijia Chen, David Wang, Yanshan Wood, Douglas L. Chaudhry, Rajeev Liu, Hongfang Kingsbury, Paul J Healthc Inform Res Research Article Outside medical records (OMRs) accompanying referred patients are frequently sent as faxes from external healthcare providers. Accessing useful and relevant information from these OMRs in a timely manner is a challenging task due to a combination of the presence of machine-illegible information and the limited system interoperability inherent in healthcare. Little research has been done on investigating information in OMRs. This paper evaluated overlapping and non-overlapping medical concepts captured from digitally faxed OMRs for patients transferring to the Department of Cardiovascular Medicine and from clinical consultant notes generated at the Mayo Clinic. We used optical character recognition (OCR) techniques to make faxed OMRs machine-readable and used natural language processing (NLP) techniques to capture clinical concepts from both machine-readable OMRs and Mayo clinical notes. We measured the level of overlap in medical concepts between OMRs and Mayo clinical narratives in the quantitative approaches and assessed the salience of concepts specific to Cardiovascular Medicine by calculating the ratio of those mentioned concepts relative to an independent clinical corpus. Among the concepts collected from the OMRs, 11.19% of those were also present in the Mayo clinical narratives that were generated within the 3 months after their initial encounter at the Mayo Clinic. For those common concepts, 73.97% were identified in initial consultant notes (ICNs) and 26.03% were captured over subsequent follow-up consultant notes (FCNs). These findings implied that information collected from the OMRs is potentially informative for patient care, but some valuable information (additionally identified in FCNs) collected from the OMRs is not fully used in an earlier stage of the care process. The concepts collected from the ICNs have the highest salience to Cardiovascular Medicine (0.112) compared to concepts in OMRs and concepts in FCNs. Additionally, unique concepts captured in ICNs (unseen in OMRs or FCNs) carried the most salient information (0.094), which demonstrated that ICNs provided the most informative concepts for the care of transferred patients. Springer International Publishing 2019-01-28 /pmc/articles/PMC8982748/ /pubmed/35415427 http://dx.doi.org/10.1007/s41666-019-00044-5 Text en © The Author(s) 2019 https://creativecommons.org/licenses/by/4.0/Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/ (https://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.
spellingShingle Research Article
Moon, Sungrim
Liu, Sijia
Chen, David
Wang, Yanshan
Wood, Douglas L.
Chaudhry, Rajeev
Liu, Hongfang
Kingsbury, Paul
Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients
title Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients
title_full Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients
title_fullStr Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients
title_full_unstemmed Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients
title_short Salience of Medical Concepts of Inside Clinical Texts and Outside Medical Records for Referred Cardiovascular Patients
title_sort salience of medical concepts of inside clinical texts and outside medical records for referred cardiovascular patients
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8982748/
https://www.ncbi.nlm.nih.gov/pubmed/35415427
http://dx.doi.org/10.1007/s41666-019-00044-5
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