EconPapers    
Economics at your fingertips  
 

Improving productivity using government data: The case of US Centers for Medicare & Medicaid's ‘Nursing Home Compare’

Marie-Laure Bougnol and José Dulà

Journal of the Operational Research Society, 2021, vol. 72, issue 5, 1075-1086

Abstract: The US Government’s Centers for Medicare & Medicaid Services (CMS) rates more than 15,000 nursing homes nationwide using a five-star scale. The outcomes are disseminated in various ways including a user friendly and informative web page. The ratings are generated using publicly available data. One objective of this work is to explore and extract these data in a replicable manner and to reveal how the government uses them to generate the star ratings. Another objective is to compare these ratings with classifications obtained with frontier analysis, a generalization of data envelopment analysis (DEA), using the same data and attributes. Frontier analysis can be made to generate results that closely parallel those from CMS. Frontier Analysis offers concrete benefits and advantages – derived mainly from its basis on linear programming – such as identification of peer performers, benchmarking, simplified sensitivity/scenario analyses, establishing star distributions, and incorporating management directives. Frontier Analysis provides transparency, simplicity, objectivity, and modeling flexibility. This work makes the case to governments to use quantitative methods such as frontier analysis to replace current highly specialized, complex, and esoteric practices while still attaining the objectives of effectively summarizing large amounts of data and information, simplifying the consumer’s decision-making process, and spotlighting excellence.

Date: 2021
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/01605682.2020.1724056 (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:72:y:2021:i:5:p:1075-1086

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/tjor20

DOI: 10.1080/01605682.2020.1724056

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 ().

 
Page updated 2025-03-20
Handle: RePEc:taf:tjorxx:v:72:y:2021:i:5:p:1075-1086