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Physician nurse care: A new use of UMLS to measure professional contribution: Are we talking about the same patient a new graph matching algorithm?

BACKGROUND: Physician and nurses have worked together for generations; however, their language and training are vastly different; comparing and contrasting their work and their joint impact on patient outcomes is difficult in light of this difference. At the same time, the EHR only includes the phys...

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Autores principales: Boyd, Andrew D., Lopez, Karen Dunn, Lugaresi, Camillo, Macieira, Tamara, Sousa, Vanessa, Acharya, Sabita, Balasubramanian, Abhinaya, Roussi, Khawllah, Keenan, Gail M., Lussier, Yves A., ‘John’ Li, Jianrong, Burton, Michel, Di Eugenio, Barbara
Formato: Online Artículo Texto
Lenguaje:English
Publicado: 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5909845/
https://www.ncbi.nlm.nih.gov/pubmed/29602435
http://dx.doi.org/10.1016/j.ijmedinf.2018.02.002
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author Boyd, Andrew D.
Lopez, Karen Dunn
Lugaresi, Camillo
Macieira, Tamara
Sousa, Vanessa
Acharya, Sabita
Balasubramanian, Abhinaya
Roussi, Khawllah
Keenan, Gail M.
Lussier, Yves A.
‘John’ Li, Jianrong
Burton, Michel
Di Eugenio, Barbara
author_facet Boyd, Andrew D.
Lopez, Karen Dunn
Lugaresi, Camillo
Macieira, Tamara
Sousa, Vanessa
Acharya, Sabita
Balasubramanian, Abhinaya
Roussi, Khawllah
Keenan, Gail M.
Lussier, Yves A.
‘John’ Li, Jianrong
Burton, Michel
Di Eugenio, Barbara
author_sort Boyd, Andrew D.
collection PubMed
description BACKGROUND: Physician and nurses have worked together for generations; however, their language and training are vastly different; comparing and contrasting their work and their joint impact on patient outcomes is difficult in light of this difference. At the same time, the EHR only includes the physician perspective via the physician-authored discharge summary, but not nurse documentation. Prior research in this area has focused on collaboration and the usage of similar terminology. OBJECTIVE: The objective of the study is to gain insight into interprofessional care by developing a computational metric to identify similarities, related concepts and differences in physician and nurse work. METHODS: 58 physician discharge summaries and the corresponding nurse plans of care were transformed into Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs). MedLEE, a Natural Language Processing (NLP) program, extracted “physician terms” from free-text physician summaries. The nursing plans of care were constructed using the HANDS(©) nursing documentation software. HANDS(©) utilizes structured terminologies: nursing diagnosis (NANDA-I), outcomes (NOC), and interventions (NIC) to create “nursing terms”. The physician’s and nurse’s terms were compared using the UMLS network for relatedness, overlaying the physician and nurse terms for comparison. Our overarching goal is to provide insight into the care, by innovatively applying graph algorithms to the UMLS network. We reveal the relationships between the care provided by each professional that is specific to the patient level. RESULTS: We found that only 26% of patients had synonyms (identical UMLS CUIs) between the two professions’ documentation. On average, physicians’ discharge summaries contain 27 terms and nurses’ documentation, 18. Traversing the UMLS network, we found an average of 4 terms related (distance less than 2) between the professions, leaving most concepts as unrelated between nurse and physician care. CONCLUSION: Our hypothesis that physician’s and nurse’s practice domains are markedly different is supported by the preliminary, quantitative evidence we found. Leveraging the UMLS network and graph traversal algorithms, allows us to compare and contrast nursing and physician care on a single patient, enabling a more complete picture of patient care. We can differentiate professional contributions to patient outcomes and related and divergent concepts by each profession.
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spelling pubmed-59098452018-05-01 Physician nurse care: A new use of UMLS to measure professional contribution: Are we talking about the same patient a new graph matching algorithm? Boyd, Andrew D. Lopez, Karen Dunn Lugaresi, Camillo Macieira, Tamara Sousa, Vanessa Acharya, Sabita Balasubramanian, Abhinaya Roussi, Khawllah Keenan, Gail M. Lussier, Yves A. ‘John’ Li, Jianrong Burton, Michel Di Eugenio, Barbara Int J Med Inform Article BACKGROUND: Physician and nurses have worked together for generations; however, their language and training are vastly different; comparing and contrasting their work and their joint impact on patient outcomes is difficult in light of this difference. At the same time, the EHR only includes the physician perspective via the physician-authored discharge summary, but not nurse documentation. Prior research in this area has focused on collaboration and the usage of similar terminology. OBJECTIVE: The objective of the study is to gain insight into interprofessional care by developing a computational metric to identify similarities, related concepts and differences in physician and nurse work. METHODS: 58 physician discharge summaries and the corresponding nurse plans of care were transformed into Unified Medical Language System (UMLS) Concept Unique Identifiers (CUIs). MedLEE, a Natural Language Processing (NLP) program, extracted “physician terms” from free-text physician summaries. The nursing plans of care were constructed using the HANDS(©) nursing documentation software. HANDS(©) utilizes structured terminologies: nursing diagnosis (NANDA-I), outcomes (NOC), and interventions (NIC) to create “nursing terms”. The physician’s and nurse’s terms were compared using the UMLS network for relatedness, overlaying the physician and nurse terms for comparison. Our overarching goal is to provide insight into the care, by innovatively applying graph algorithms to the UMLS network. We reveal the relationships between the care provided by each professional that is specific to the patient level. RESULTS: We found that only 26% of patients had synonyms (identical UMLS CUIs) between the two professions’ documentation. On average, physicians’ discharge summaries contain 27 terms and nurses’ documentation, 18. Traversing the UMLS network, we found an average of 4 terms related (distance less than 2) between the professions, leaving most concepts as unrelated between nurse and physician care. CONCLUSION: Our hypothesis that physician’s and nurse’s practice domains are markedly different is supported by the preliminary, quantitative evidence we found. Leveraging the UMLS network and graph traversal algorithms, allows us to compare and contrast nursing and physician care on a single patient, enabling a more complete picture of patient care. We can differentiate professional contributions to patient outcomes and related and divergent concepts by each profession. 2018-02-09 2018-05 /pmc/articles/PMC5909845/ /pubmed/29602435 http://dx.doi.org/10.1016/j.ijmedinf.2018.02.002 Text en https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/BY/4.0/ (https://creativecommons.org/licenses/by/4.0/) ).
spellingShingle Article
Boyd, Andrew D.
Lopez, Karen Dunn
Lugaresi, Camillo
Macieira, Tamara
Sousa, Vanessa
Acharya, Sabita
Balasubramanian, Abhinaya
Roussi, Khawllah
Keenan, Gail M.
Lussier, Yves A.
‘John’ Li, Jianrong
Burton, Michel
Di Eugenio, Barbara
Physician nurse care: A new use of UMLS to measure professional contribution: Are we talking about the same patient a new graph matching algorithm?
title Physician nurse care: A new use of UMLS to measure professional contribution: Are we talking about the same patient a new graph matching algorithm?
title_full Physician nurse care: A new use of UMLS to measure professional contribution: Are we talking about the same patient a new graph matching algorithm?
title_fullStr Physician nurse care: A new use of UMLS to measure professional contribution: Are we talking about the same patient a new graph matching algorithm?
title_full_unstemmed Physician nurse care: A new use of UMLS to measure professional contribution: Are we talking about the same patient a new graph matching algorithm?
title_short Physician nurse care: A new use of UMLS to measure professional contribution: Are we talking about the same patient a new graph matching algorithm?
title_sort physician nurse care: a new use of umls to measure professional contribution: are we talking about the same patient a new graph matching algorithm?
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5909845/
https://www.ncbi.nlm.nih.gov/pubmed/29602435
http://dx.doi.org/10.1016/j.ijmedinf.2018.02.002
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