eprintid: 10060190
rev_number: 29
eprint_status: archive
userid: 608
dir: disk0/10/06/01/90
datestamp: 2018-11-02 12:56:45
lastmod: 2021-12-06 00:05:39
status_changed: 2019-04-10 13:20:34
type: article
metadata_visibility: show
creators_name: Quan, TP
creators_name: Hope, R
creators_name: Clarke, T
creators_name: Moroney, R
creators_name: Butcher, L
creators_name: Knight, P
creators_name: Crook, D
creators_name: Hopkins, S
creators_name: Peto, T
creators_name: Johnson, AP
creators_name: Walker, AS
title: Using linked electronic health records to report healthcare-associated infections
ispublished: pub
divisions: UCL
divisions: B02
divisions: C10
divisions: D15
divisions: D65
divisions: J38
note: © 2018 Quan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. http://creativecommons.org/licenses/by/4.0/
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).

// AIMS:
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.
date: 2018-11-07
date_type: published
publisher: Public Library of Science (PLoS)
official_url: https://doi.org/10.1371/journal.pone.0206860
oa_status: green
full_text_type: pub
language: eng
primo: open
primo_central: open_green
article_type_text: Article
verified: verified_manual
elements_id: 1598252
doi: 10.1371/journal.pone.0206860
lyricists_name: Hopkins, Susan
lyricists_name: Walker, Ann
lyricists_id: SHOPK28
lyricists_id: ASWAL40
actors_name: Walker, Ann
actors_id: ASWAL40
actors_role: owner
full_text_status: public
publication: PLoS ONE
volume: 13
number: 11
article_number: e0206860
issn: 1932-6203
citation:        Quan, TP;    Hope, R;    Clarke, T;    Moroney, R;    Butcher, L;    Knight, P;    Crook, D;                 ... Walker, AS; + view all <#>        Quan, TP;  Hope, R;  Clarke, T;  Moroney, R;  Butcher, L;  Knight, P;  Crook, D;  Hopkins, S;  Peto, T;  Johnson, AP;  Walker, AS;   - view fewer <#>    (2018)    Using linked electronic health records to report healthcare-associated infections.                   PLoS ONE , 13  (11)    , Article e0206860.  10.1371/journal.pone.0206860 <https://doi.org/10.1371/journal.pone.0206860>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10060190/2/Quan_journal.pone.0206860.pdf