Mining routinely collected acute data to reveal non-linear relationships between nurse staffing levels and outcomes

Alison Leary; Elaine Maxwell; Geoffrey Punshon

doi:10.1136/bmjopen-2016-011177

Mining routinely collected acute data to reveal non-linear relationships between nurse staffing levels and outcomes

Alison Leary, Elaine Maxwell, Geoffrey Punshon

Adult Nursing and Midwifery

Research output: Contribution to journal › Article › peer-review

20 Citations (Scopus)

Abstract

Report of a study of routinely collected data in one NHS hospital that revealed hitherto unrecognised patterns between nurse staffing and patient outcomes

Original language	English
Journal	BMJ Open
DOIs	https://doi.org/10.1136/bmjopen-2016-011177
Publication status	Published - 16 Dec 2016

Keywords

Data science
Nurse staffing
Math modelling
Data mining

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10.1136/bmjopen-2016-011177Licence: CC BY 4.0

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title = "Mining routinely collected acute data to reveal non-linear relationships between nurse staffing levels and outcomes",

abstract = "Report of a study of routinely collected data in one NHS hospital that revealed hitherto unrecognised patterns between nurse staffing and patient outcomes",

keywords = "Data science, Nurse staffing, Math modelling, Data mining",

author = "Alison Leary and Elaine Maxwell and Geoffrey Punshon",

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AU - Punshon, Geoffrey

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N2 - Report of a study of routinely collected data in one NHS hospital that revealed hitherto unrecognised patterns between nurse staffing and patient outcomes

AB - Report of a study of routinely collected data in one NHS hospital that revealed hitherto unrecognised patterns between nurse staffing and patient outcomes

KW - Data science

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KW - Math modelling

KW - Data mining

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DO - 10.1136/bmjopen-2016-011177

M3 - Article

SN - 2044-6055

JO - BMJ Open

JF - BMJ Open

ER -

Mining routinely collected acute data to reveal non-linear relationships between nurse staffing levels and outcomes

Abstract

Keywords

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