Evidence map›Paper›PMID 27986733›Full record

ArticleBMJ open2016

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

Alison Leary, Rob Cook, Sarahjane Jones, Judith Smith, Malcolm Gough, Elaine Maxwell, Geoffrey Punshon, Mark Radford

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In one paragraph

Article in BMJ open, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Observational
  6. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Alison LearyLondon South Bank University, London, UK.
Rob CookWolfram Research Europe, Oxford UK.
Sarahjane JonesBirmingham City University, Birmingham, UK.
Judith SmithUniversity Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
Malcolm GoughUniversity Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.
Elaine MaxwellLondon South Bank University, London, UK.
Geoffrey PunshonLondon South Bank University, London, UK.
Mark RadfordUniversity Hospitals Coventry and Warwickshire NHS Trust, Coventry, UK.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesNursing is a safety critical activity but not easily quantified. This makes the building of predictive staffing models a challenge. The aim of this study was to determine if relationships between registered and non-registered nurse staffing levels and clinical outcomes could be discovered through the mining of routinely collected clinical data. The secondary aim was to examine the feasibility and develop the use of 'big data' techniques commonly used in industry for this area of healthcare and examine future uses.

settingThe data were obtained from 1 large acute National Health Service hospital trust in England. Routinely collected physiological, signs and symptom data from a clinical database were extracted, imported and mined alongside a bespoke staffing and outcomes database using Mathmatica V.10. The physiological data consisted of 120 million patient entries over 6 years, the bespoke database consisted of 9 years of daily data on staffing levels and safety factors such as falls. PRIMARY AND SECONDARY OUTCOMES: To discover patterns in these data or non-linear relationships that would contribute to modelling. To examine feasibility of this technique in this field.

resultsAfter mining, 40 correlations (p<0.00005) emerged between safety factors, physiological data (such as the presence or absence of nausea) and staffing factors. Several inter-related factors demonstrated step changes where registered nurse availability appeared to relate to physiological parameters or outcomes such as falls and the management of symptoms. Data extraction proved challenging as some commercial databases were not built for extraction of the massive data sets they contain.

conclusionsThe relationship between staffing and outcomes appears to exist. It appears to be non-linear but calculable and a data-driven model appears possible. These findings could be used to build an initial mathematical model for acute staffing which could be further tested.

Indexed as

Data MiningQuality Indicators, Health CareDatabases, FactualEnglandHumansNursing Staff, HospitalPatient Safetydata miningnursingsafetystaffing

Identifiers

PMID27986733
PMCPMC5223722

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.