Using linked electronic health records to report healthcare-associated infections

T. Phuong Quan, Russell Hope, Tiphanie Clarke, Ruth Moroney, Lisa Butcher, Peter Knight, Derrick Crook, Susan Hopkins, Timothy E.A. Peto, Alan Johnson, A. Sarah Walker

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)

Abstract

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.

Original languageEnglish
Article numbere0206860
JournalPLoS ONE
Volume13
Issue number11
DOIs
Publication statusPublished - Nov 2018

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