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Using linked electronic health records to report healthcare-associated infections

BACKGROUND: Reporting of strategic healthcare-associated infections (HCAIs) to Public Health England is mandatory for all acute hospital trusts in England, via a web-based HCAI Data Capture System (HCAI-DCS). AIM: Investigate the feasibility of automating the current, manual, HCAI reporting using li...

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Autores principales: Quan, T. Phuong, Hope, Russell, Clarke, Tiphanie, Moroney, Ruth, Butcher, Lisa, Knight, Peter, Crook, Derrick, Hopkins, Susan, Peto, Timothy E. A., Johnson, Alan P., Walker, A. Sarah
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Public Library of Science 2018
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6221334/
https://www.ncbi.nlm.nih.gov/pubmed/30403746
http://dx.doi.org/10.1371/journal.pone.0206860
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author Quan, T. Phuong
Hope, Russell
Clarke, Tiphanie
Moroney, Ruth
Butcher, Lisa
Knight, Peter
Crook, Derrick
Hopkins, Susan
Peto, Timothy E. A.
Johnson, Alan P.
Walker, A. Sarah
author_facet Quan, T. Phuong
Hope, Russell
Clarke, Tiphanie
Moroney, Ruth
Butcher, Lisa
Knight, Peter
Crook, Derrick
Hopkins, Susan
Peto, Timothy E. A.
Johnson, Alan P.
Walker, A. Sarah
author_sort Quan, T. Phuong
collection PubMed
description BACKGROUND: Reporting of strategic healthcare-associated infections (HCAIs) to Public Health England is mandatory for all acute hospital trusts in England, via a web-based HCAI Data Capture System (HCAI-DCS). AIM: Investigate the feasibility of automating the current, manual, HCAI reporting using linked electronic health records (linked-EHR), and assess its level of accuracy. METHODS: All data previously submitted through the HCAI-DCS by the Oxford University Hospitals infection control (IC) team for methicillin-resistant and methicillin-susceptible Staphylococcus aureus (MRSA, MSSA), Clostridium difficile, and Escherichia coli, through March 2017 were downloaded and compared to outputs created from linked-EHR, with detailed comparisons between 2013–2017. FINDINGS: Total MRSA, MSSA, E. coli and C. difficile cases entered by the IC team vs linked-EHR were 428 vs 432, 795 vs 816, 2454 vs 2450 and 3365 vs 3393 respectively. From 2013–2017, most discrepancies (32/37 (86%)) were likely due to IC recording errors. Patient and specimen identifiers were completed for >98% of cases by both methods, with very high agreement (>97%). Fields relating to the patient at the time the specimen was taken were complete to a similarly high level (>99% IC, >97% linked-EHR), and agreement was fairly good (>80%) except for the main and treatment specialties (57% and 54% respectively) and the patient category (55%). Optional, organism-specific data-fields were less complete, by both methods. Where comparisons were possible, agreement was reasonably high (mostly 70–90%). CONCLUSION: Basic factual information, such as demographic data, is almost-certainly better automated, and many other data fields can potentially be populated successfully from linked-EHR. Manual data collection is time-consuming and inefficient; automated electronic data collection would leave healthcare professionals free to focus on clinical rather than administrative work.
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spelling pubmed-62213342018-11-19 Using linked electronic health records to report healthcare-associated infections Quan, T. Phuong Hope, Russell Clarke, Tiphanie Moroney, Ruth Butcher, Lisa Knight, Peter Crook, Derrick Hopkins, Susan Peto, Timothy E. A. Johnson, Alan P. Walker, A. Sarah PLoS One Research Article BACKGROUND: Reporting of strategic healthcare-associated infections (HCAIs) to Public Health England is mandatory for all acute hospital trusts in England, via a web-based HCAI Data Capture System (HCAI-DCS). AIM: Investigate the feasibility of automating the current, manual, HCAI reporting using linked electronic health records (linked-EHR), and assess its level of accuracy. METHODS: All data previously submitted through the HCAI-DCS by the Oxford University Hospitals infection control (IC) team for methicillin-resistant and methicillin-susceptible Staphylococcus aureus (MRSA, MSSA), Clostridium difficile, and Escherichia coli, through March 2017 were downloaded and compared to outputs created from linked-EHR, with detailed comparisons between 2013–2017. FINDINGS: Total MRSA, MSSA, E. coli and C. difficile cases entered by the IC team vs linked-EHR were 428 vs 432, 795 vs 816, 2454 vs 2450 and 3365 vs 3393 respectively. From 2013–2017, most discrepancies (32/37 (86%)) were likely due to IC recording errors. Patient and specimen identifiers were completed for >98% of cases by both methods, with very high agreement (>97%). Fields relating to the patient at the time the specimen was taken were complete to a similarly high level (>99% IC, >97% linked-EHR), and agreement was fairly good (>80%) except for the main and treatment specialties (57% and 54% respectively) and the patient category (55%). Optional, organism-specific data-fields were less complete, by both methods. Where comparisons were possible, agreement was reasonably high (mostly 70–90%). CONCLUSION: Basic factual information, such as demographic data, is almost-certainly better automated, and many other data fields can potentially be populated successfully from linked-EHR. Manual data collection is time-consuming and inefficient; automated electronic data collection would leave healthcare professionals free to focus on clinical rather than administrative work. Public Library of Science 2018-11-07 /pmc/articles/PMC6221334/ /pubmed/30403746 http://dx.doi.org/10.1371/journal.pone.0206860 Text en © 2018 Quan et al http://creativecommons.org/licenses/by/4.0/ This is an open access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
spellingShingle Research Article
Quan, T. Phuong
Hope, Russell
Clarke, Tiphanie
Moroney, Ruth
Butcher, Lisa
Knight, Peter
Crook, Derrick
Hopkins, Susan
Peto, Timothy E. A.
Johnson, Alan P.
Walker, A. Sarah
Using linked electronic health records to report healthcare-associated infections
title Using linked electronic health records to report healthcare-associated infections
title_full Using linked electronic health records to report healthcare-associated infections
title_fullStr Using linked electronic health records to report healthcare-associated infections
title_full_unstemmed Using linked electronic health records to report healthcare-associated infections
title_short Using linked electronic health records to report healthcare-associated infections
title_sort using linked electronic health records to report healthcare-associated infections
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6221334/
https://www.ncbi.nlm.nih.gov/pubmed/30403746
http://dx.doi.org/10.1371/journal.pone.0206860
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