title: Development of processes allowing near real-time refinement and validation of triage tools during the early stage of an outbreak in readiness for surge: the FLU-CATs Study.
abstract: BACKGROUND
  During pandemics of novel influenza and outbreaks of emerging infections, surge in health-care demand can exceed capacity to provide normal standards of care. In such exceptional circumstances, triage tools may aid decisions in identifying people who are most likely to benefit from higher levels of care. Rapid research during the early phase of an outbreak should allow refinement and validation of triage tools so that in the event of surge a valid tool is available. The overarching study aim is to conduct a prospective near real-time analysis of structured clinical assessments of influenza-like illness (ILI) using primary care electronic health records (EHRs) during a pandemic. This abstract summarises the preparatory work, infrastructure development, user testing and proof-of-concept study.
  
  
  OBJECTIVE
  (1) In preparation for conducting rapid research in the early phase of a future outbreak, to develop processes that allow near real-time analysis of general practitioner (GP) assessments of people presenting with ILI, management decisions and patient outcomes. (2) As proof of concept: conduct a pilot study evaluating the performance of the triage tools 'Community Assessment Tools' and 'Pandemic Medical Early Warning Score' to predict hospital admission and death in patients presenting with ILI to GPs during inter-pandemic winter seasons.
  
  
  METHODS
  Prospective near real-time analysis of structured clinical assessments and anonymised linkage to data from EHRs. User experience was evaluated by semistructured interviews with participating GPs.
  
  
  METHODS
  Thirty GPs in England, Wales and Scotland, participating in the Clinical Practice Research Datalink.
  
  
  METHODS
  All people presenting with ILI.
  
  
  METHODS
  None.
  
  
  METHODS
  Study outcome is proof of concept through demonstration of data capture and near real-time analysis. Primary patient outcomes were hospital admission within 24 hours and death (all causes) within 30 days of GP assessment. Secondary patient outcomes included GP decision to prescribe antibiotics and/or influenza-specific antiviral drugs and/or refer to hospital - if admitted, the need for higher levels of care and length of hospital stay.
  
  
  METHODS
  Linked anonymised data from a web-based structured clinical assessment and primary care EHRs.
  
  
  RESULTS
  In the 24 months to April 2015, data from 704 adult and 159 child consultations by 30 GPs were captured. GPs referred 11 (1.6%) adults and six (3.8%) children to hospital. There were 13 (1.8%) deaths of adults and two (1.3%) of children. There were too few outcome events to draw any conclusions regarding the performance of the triage tools. GP interviews showed that although there were some difficulties with installation, the web-based data collection tool was quick and easy to use. Some GPs felt that a minimal monetary incentive would promote participation.
  
  
  CONCLUSIONS
  We have developed processes that allow capture and near real-time automated analysis of GP's clinical assessments and management decisions of people presenting with ILI.
  
  
  CONCLUSIONS
  We will develop processes to include other EHR systems, attempt linkage to data on influenza surveillance and maintain processes in readiness for a future outbreak.
  
  
  BACKGROUND
  This study is registered as ISRCTN87130712 and UK Clinical Research Network 12827.
  
  
  BACKGROUND
  The National Institute for Health Research Health Technology Assessment programme. MGS is supported by the UK NIHR Health Protection Research Unit in Emerging and Zoonotic Infections.
authors.affiliation: Division of Epidemiology and Public Health, University of Nottingham, Nottingham, UK.
authors.forename: Sudhir
authors.initials: S
authors.lastname: Venkatesan
authors.affiliation: Division of Epidemiology and Public Health, University of Nottingham, Nottingham, UK.
authors.forename: Puja R
authors.initials: PR
authors.lastname: Myles
authors.affiliation: Clinical Practice Research Datalink, Medicines and Healthcare products Regulatory Agency, London, UK.
authors.forename: Gerard
authors.initials: G
authors.lastname: McCann
authors.affiliation: Clinical Practice Research Datalink, Medicines and Healthcare products Regulatory Agency, London, UK.
authors.forename: Antonis A
authors.initials: AA
authors.lastname: Kousoulis
authors.affiliation: Clinical Practice Research Datalink, Medicines and Healthcare products Regulatory Agency, London, UK.
authors.forename: Maimoona
authors.initials: M
authors.lastname: Hashmi
authors.affiliation: Clinical Practice Research Datalink, Medicines and Healthcare products Regulatory Agency, London, UK.
authors.forename: Rabah
authors.initials: R
authors.lastname: Belatri
authors.affiliation: Clinical Practice Research Datalink, Medicines and Healthcare products Regulatory Agency, London, UK.
authors.forename: Emma
authors.initials: E
authors.lastname: Boyle
authors.affiliation: Clinical Practice Research Datalink, Medicines and Healthcare products Regulatory Agency, London, UK.
authors.forename: Alan
authors.initials: A
authors.lastname: Barcroft
authors.affiliation: University of Manchester, Manchester, UK.
authors.forename: Tjeerd Pieter
authors.initials: TP
authors.lastname: van Staa
authors.affiliation: Institute of Translational Medicine, University of Liverpool, Liverpool, UK.
authors.forename: Jamie J
authors.initials: JJ
authors.lastname: Kirkham
authors.affiliation: Division of Epidemiology and Public Health, University of Nottingham, Nottingham, UK.
authors.forename: Jonathan S
authors.initials: JS
authors.lastname: Nguyen Van Tam
authors.affiliation: Clinical Practice Research Datalink, Medicines and Healthcare products Regulatory Agency, London, UK.
authors.forename: Timothy J
authors.initials: TJ
authors.lastname: Williams
authors.affiliation: Institute of Translational Medicine, University of Liverpool, Liverpool, UK.
authors.forename: Malcolm G
authors.initials: MG
authors.lastname: Semple
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000818:Animals; D002648:Child; D002675:Child, Preschool; D002985:Clinical Protocols; D000068240:Data Anonymization; D057286:Electronic Health Records / Q000458:organization & administration; D005260:Female; D006801:Humans; D007223:Infant; D053118:Influenza A Virus, H1N1 Subtype / Q000302:isolation & purification; D007251:Influenza, Human / Q000453:epidemiology* / Q000401:mortality / Q000635:transmission; D008297:Male; D008875:Middle Aged; D017063:Outcome Assessment, Health Care; D058873:Pandemics; D010865:Pilot Projects; D011320:Primary Health Care; D011446:Prospective Studies; D014218:Triage / Q000379:methods*; D006113:United Kingdom / Q000453:epidemiology; D015047:Zoonoses / Q000453:epidemiology