Evidence map›Paper›PMID 30656778›Full record

ArticleHealth economics2019

Can productivity growth measures identify best performing hospitals? Evidence from the English National Health Service.

María José Aragón Aragón, Adriana Castelli, Martin Chalkley, James Gaughan

Abstract read
In one paragraph

Article in Health economics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing papers in PubMed
–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

2 citing papers in PubMed.

  1. Assessing Productivity Development of Public Hospitals: A Case Study of Shanghai, China.International journal of environmental research and public health · 2020
    Article
  2. Article
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

4 authors.

María José Aragón AragónCentre for Health Economics, University of York, York, UK.ORCID 0000-0002-3787-6220
Adriana CastelliCentre for Health Economics, University of York, York, UK.ORCID 0000-0002-2546-419X
Martin ChalkleyCentre for Health Economics, University of York, York, UK.ORCID 0000-0002-1091-8259
James GaughanCentre for Health Economics, University of York, York, UK.

Funding

NIHR Policy Research Programme 071/0081
6 · The paper itself

Abstract

Health-care systems around the world face limited financial resources, and England is no exception. The ability of the health-care system in England to operate within its financial resources depends in part on continually increasing its productivity. One means of achieving this is to identify and disseminate throughout the system the most efficient processes. We examine the annual productivity growth achieved by 151 hospitals over five financial years, using the same methods developed to measure productivity of the National Health Service as a whole. We consider whether there are hospitals that consistently achieve higher than average productivity growth. These could act as examples of good practice for others to follow and provide a means of increasing system performance. We find that the productivity growth of some hospitals over the whole period exhibits better than average performance, but there is little or no evidence of consistency in the performance of these hospitals over adjacent years. Even the best performers exhibit periods of very poor performance and vice versa. We therefore conclude that accepted methods of measuring productivity growth for the health system as a whole do not appear suitable for identifying good performance at the hospital level.

Indexed as

Efficiency, OrganizationalState MedicineEconomics, HospitalEnglandHospitalsHumansLongitudinal StudiesEnglandgrowthhospitalNHSproductivity

Identifiers

PMID30656778
PMCPMC6590216

What OpenQuestion holds

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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.