title: Incidence rates of hospital-acquired urinary tract and bloodstream infections generated by automated compilation of electronically available healthcare data.
abstract: BACKGROUND
  Monitoring of hospital-acquired infection (HAI) by automated compilation of registry data may address the disadvantages of laborious, costly and potentially subjective and often random sampling of data by manual surveillance.
  
  
  OBJECTIVE
  To evaluate a system for automated monitoring of hospital-acquired urinary tract (HA-UTI) and bloodstream infections (HA-BSI) and to report incidence rates over a five-year period in a Danish hospital trust.
  
  
  METHODS
  Based primarily on electronically available data relating to microbiology results and antibiotic prescriptions, the automated monitoring of HA-UTIs and HA-BSIs was validated against data from six previous point-prevalence surveys (PPS) from 2010 to 2013 and data from a manual assessment (HA-UTI only) of one department of internal medicine from January 2010. Incidence rates (infections per 1000 bed-days) from 2010 to 2014 were calculated.
  
  
  RESULTS
  Compared with the PPSs, the automated monitoring showed a sensitivity of 88% in detecting UTI in general, 78% in detecting HA-UTI, and 100% in detecting BSI in general. The monthly incidence rates varied between 4.14 and 6.61 per 1000 bed-days for HA-UTI and between 0.09 and 1.25 per 1000 bed-days for HA-BSI.
  
  
  CONCLUSIONS
  Replacing PPSs with automated monitoring of HAIs may provide better and more objective data and constitute a promising foundation for individual patient risk analyses and epidemiological studies. Automated monitoring may be universally applicable in hospitals with electronic databases comprising microbiological findings, admission data, and antibiotic prescriptions.
authors.affiliation: Department of Clinical Microbiology, Lillebaelt Hospital, Vejle, Denmark; Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark. Electronic address: jacobredder@gmail.com.
authors.forename: J D
authors.initials: JD
authors.lastname: Redder
authors.affiliation: Department of Clinical Microbiology, Aarhus University Hospital, Denmark.
authors.forename: R A
authors.initials: RA
authors.lastname: Leth
authors.affiliation: Department of Clinical Microbiology, Lillebaelt Hospital, Vejle, Denmark; Institute of Regional Health Research, University of Southern Denmark, Odense, Denmark.
authors.forename: J K
authors.initials: JK
authors.lastname: Møller
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D001331:Automation; D002648:Child; D002675:Child, Preschool; D003428:Cross Infection / Q000453:epidemiology*; D003718:Denmark / Q000453:epidemiology; D001330:Electronic Data Processing; D057286:Electronic Health Records / Q000706:statistics & numerical data; D004812:Epidemiologic Methods; D005260:Female; D006801:Humans; D015994:Incidence; D007223:Infant; D007231:Infant, Newborn; D008297:Male; D008875:Middle Aged; D011446:Prospective Studies; D012189:Retrospective Studies; D018805:Sepsis / Q000453:epidemiology*; D014552:Urinary Tract Infections / Q000453:epidemiology*; D055815:Young Adult
keywords: Bloodstream infection; Computer algorithm; Electronic registry; Healthcare data; Hospital-acquired infection; Urinary tract infection