title: The Problem with Big Data: Operating on Smaller Datasets to Bridge the Implementation Gap.
abstract: Big datasets have the potential to revolutionize public health. However, there is a mismatch between the political and scientific optimism surrounding big data and the public's perception of its benefit. We suggest a systematic and concerted emphasis on developing models derived from smaller datasets to illustrate to the public how big data can produce tangible benefits in the long term. In order to highlight the immediate value of a small data approach, we produced a proof-of-concept model predicting hospital length of stay. The results demonstrate that existing small datasets can be used to create models that generate a reasonable prediction, facilitating health-care delivery. We propose that greater attention (and funding) needs to be directed toward the utilization of existing information resources in parallel with current efforts to create and exploit "big data."
authors.affiliation: School of Mathematics, University of Leeds , Leeds , UK.
authors.forename: Richard P
authors.initials: RP
authors.lastname: Mann
authors.affiliation: Faculty of Medicine and Health, University of Leeds , Leeds , UK.
authors.forename: Faisal
authors.initials: F
authors.lastname: Mushtaq
authors.affiliation: Faculty of Medicine and Health, University of Leeds, Leeds, UK; Big Data and Analytics Unit, Institute of Global Health Innovation, Imperial College London, London, UK.
authors.forename: Alan D
authors.initials: AD
authors.lastname: White
authors.affiliation: Faculty of Medicine and Health, University of Leeds , Leeds , UK.
authors.forename: Gabriel
authors.initials: G
authors.lastname: Mata-Cervantes
authors.affiliation: Faculty of Medicine and Health, University of Leeds , Leeds , UK.
authors.forename: Tom
authors.initials: T
authors.lastname: Pike
authors.affiliation: Big Data and Analytics Unit, Institute of Global Health Innovation, Imperial College London , London , UK.
authors.forename: Dalton
authors.initials: D
authors.lastname: Coker
authors.affiliation: Leeds Teaching Hospitals NHS Trust , Leeds , UK.
authors.forename: Stuart
authors.initials: S
authors.lastname: Murdoch
authors.affiliation: Leeds Teaching Hospitals NHS Trust , Leeds , UK.
authors.forename: Tim
authors.initials: T
authors.lastname: Hiles
authors.affiliation: Leeds Teaching Hospitals NHS Trust , Leeds , UK.
authors.forename: Clare
authors.initials: C
authors.lastname: Smith
authors.affiliation: Leeds Teaching Hospitals NHS Trust , Leeds , UK.
authors.forename: David
authors.initials: D
authors.lastname: Berridge
authors.affiliation: Leeds Teaching Hospitals NHS Trust , Leeds , UK.
authors.forename: Suzanne
authors.initials: S
authors.lastname: Hinchliffe
authors.affiliation: Faculty of Medicine and Health, University of Leeds, Leeds, UK; Leeds Teaching Hospitals NHS Trust, Leeds, UK.
authors.forename: Geoff
authors.initials: G
authors.lastname: Hall
authors.affiliation: Faculty of Medicine and Health, University of Leeds, Leeds, UK; Leeds Teaching Hospitals NHS Trust, Leeds, UK.
authors.forename: Stephen
authors.initials: S
authors.lastname: Smye
authors.affiliation: Faculty of Medicine and Health, University of Leeds , Leeds , UK.
authors.forename: Richard M
authors.initials: RM
authors.lastname: Wilkie
authors.affiliation: Faculty of Medicine and Health, University of Leeds, Leeds, UK; Leeds Teaching Hospitals NHS Trust, Leeds, UK.
authors.forename: J Peter A
authors.initials: JP
authors.lastname: Lodge
authors.affiliation: Faculty of Medicine and Health, University of Leeds , Leeds , UK.
authors.forename: Mark
authors.initials: M
authors.lastname: Mon-Williams
keywords: big data; health economics; length of stay; small data; surgery