title: Data linkage between existing healthcare databases to support hospital epidemiology.
abstract: Enhancing the use of existing datasets within acute hospitals will greatly facilitate hospital epidemiology, surveillance, the monitoring of a variety of processes, outcomes and risk factors, and the provision of alert systems. Multiple overlapping data systems exist within National Health Service (NHS) hospitals in the UK, and many duplicate data recordings take place because of the lack of linkage and interfaces. This results in hospital-collected data not being used efficiently. The objective was to create an inventory of all existing systems, including administrative, management, human resources, microbiology, patient care and other platforms, to describe the data architecture that could contribute valuable information for a hospital epidemiology unit. These datasets were investigated as to how they could be used to generate surveillance data, key performance indicators and risk information that could be shared at board, clinical programme group, specialty and ward level. An example of an output of this integrated data platform and its application in influenza resilience planning and responsiveness is described. The development of metrics for staff absence and staffing levels may also be used as key indicators for risk-monitoring for infection prevention. This work demonstrates the value of such a data inventory and linkage and the importance of more sophisticated uses of existing NHS data, and innovative collaborative approaches to support clinical care, quality improvement, surveillance, emergency planning and research.
authors.affiliation: National Centre for Infection Prevention and Management, Imperial College, London, UK. l.garcia-alvarez@imperial.ac.uk
authors.forename: L
authors.initials: L
authors.lastname: García Álvarez
authors.forename: P
authors.initials: P
authors.lastname: Aylin
authors.forename: J
authors.initials: J
authors.lastname: Tian
authors.forename: C
authors.initials: C
authors.lastname: King
authors.forename: M
authors.initials: M
authors.lastname: Catchpole
authors.forename: S
authors.initials: S
authors.lastname: Hassall
authors.forename: K
authors.initials: K
authors.lastname: Whittaker-Axon
authors.forename: A
authors.initials: A
authors.lastname: Holmes
mesh_terms: D022126:Access to Information; D035843:Biomedical Research; D003428:Cross Infection / Q000453:epidemiology* / Q000517:prevention & control*; D003625:Data Collection; D016208:Databases, Factual; D006751:Hospital Information Systems; D006801:Humans; D017053:Infection Control; D053118:Influenza A Virus, H1N1 Subtype; D007251:Influenza, Human / Q000517:prevention & control / Q000628:therapy; D019451:Information Management; D011159:Population Surveillance / Q000379:methods; D006113:United Kingdom