The signal in the noise: Robust detection of performance “outliers” in health services
Nathan C. Proudlove,
Mhorag Goff,
Kieran Walshe and
Ruth Boaden
Journal of the Operational Research Society, 2019, vol. 70, issue 7, 1102-1114
Abstract:
To make the increasing amounts of data about the performance of public sector organisations digestible by decision makers, composite indicators are commonly constructed, from which a natural step is rankings and league tables. However, how much credence should be given to the results of such approaches? Studying English NHS maternity services (N = 130 hospital trusts), we assembled and used a set of 38 indicators grouped into four baskets of aspects of service delivery. In the absence of opinion on how the indicators should be aggregated, we focus on the uncertainty this brings to the composite results. We use a large two-stage Monte Carlo simulation to generate possible aggregation weights and examine the discrimination in the composite results. We find that positive and negative “outliers” can be identified robustly, of particular value to decision makers for investigation for learning or intervention, however results in between should be treated with great caution.
Date: 2019
References: Add references at CitEc
Citations: View citations in EconPapers (1)
Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2018.1487816 (text/html)
Access to full text is restricted to subscribers.
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:taf:tjorxx:v:70:y:2019:i:7:p:1102-1114
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20
DOI: 10.1080/01605682.2018.1487816
Access Statistics for this article
Journal of the Operational Research Society is currently edited by Tom Archibald
More articles in Journal of the Operational Research Society from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().