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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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Detalles Bibliográficos
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
Descripción
Sumario: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.