| アブストラクト | 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. OBJECTIVES: (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. DESIGN: 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. SETTING: Thirty GPs in England, Wales and Scotland, participating in the Clinical Practice Research Datalink. PARTICIPANTS: All people presenting with ILI. INTERVENTIONS: None. MAIN OUTCOME MEASURES: 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. DATA SOURCES: 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. FUTURE WORK: 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. STUDY REGISTRATION: This study is registered as ISRCTN87130712 and UK Clinical Research Network 12827. FUNDING: 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. |
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| ジャーナル名 | Health technology assessment (Winchester, England) |
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| 投稿日 | 2015/10/31 |
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| 投稿者 | Venkatesan, Sudhir; Myles, Puja R; McCann, Gerard; Kousoulis, Antonis A; Hashmi, Maimoona; Belatri, Rabah; Boyle, Emma; Barcroft, Alan; van Staa, Tjeerd Pieter; Kirkham, Jamie J; Nguyen Van Tam, Jonathan S; Williams, Timothy J; Semple, Malcolm G |
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| 組織名 | Division of Epidemiology and Public Health, University of Nottingham, Nottingham,;UK.;Clinical Practice Research Datalink, Medicines and Healthcare products Regulatory;Agency, London, UK.;University of Manchester, Manchester, UK.;Institute of Translational Medicine, University of Liverpool, Liverpool, UK. |
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| Pubmed リンク | https://www.ncbi.nlm.nih.gov/pubmed/26514069/ |
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