Gemma Housley
Accurate identification of hospital admissions from care homes: development and validation of an automated algorithm
Housley, Gemma; Lewis, Sarah; Usman, Adeela; Gordon, Adam L.; Shaw, Dominick E.
Authors
Professor SARAH LEWIS SARAH.LEWIS@NOTTINGHAM.AC.UK
PROFESSOR OF MEDICAL STATISTICS
Adeela Usman
Adam L. Gordon
Dominick E. Shaw
Abstract
Background: measuring the complex needs of care home residents is crucial for resource allocation. Hospital patient administration systems (PAS) may not accurately identify admissions from care homes.
Objective: to develop and validate an accurate, practical method of identifying care home resident hospital admission using routinely collected PAS data.
Method: admissions data between 2011 and 2012 (n = 103,105) to an acute Trust were modelled to develop an automated tool which compared the hospital PAS address details with the Care Quality Commission’s (CQC) database, producing a likelihood of care home residency. This tool and the Nuffield method (CQC postcode match only) were validated against a manual check of a random sample of admissions (n = 2,000). A dataset from a separate Trust was analysed to assess generalisability.
Results:the hospital PAS was inaccurate; none of the admissions from a care home identified on manual check had a care home source of admission recorded on the PAS. Both methods performed well; the automated tool had a higher positive predictive value than the Nuffield method (100% 95% confidence interval (CI) 98.23–100% versus 87.10% 95%CI 82.28–91.00%), meaning those coded as care home residents were more likely to actually be from a care home. Our automated tool had a high level of agreement 99.2% with the second Trust’s data (Kappa 0.86 P < 0.001).
Conclusions: care home status is not routinely or accurately captured. Automated matching offers an accurate, repeatable, scalable method to identify care home residency and could be used as a tool to benchmark how care home residents use acute hospital resources across the National Health Service.
Citation
Housley, G., Lewis, S., Usman, A., Gordon, A. L., & Shaw, D. E. (2018). Accurate identification of hospital admissions from care homes: development and validation of an automated algorithm. Age and Ageing, 47(3), 387-391. https://doi.org/10.1093/ageing/afx182
Journal Article Type | Article |
---|---|
Acceptance Date | Nov 20, 2017 |
Online Publication Date | Dec 18, 2017 |
Publication Date | May 1, 2018 |
Deposit Date | Jan 3, 2018 |
Publicly Available Date | Jan 3, 2018 |
Journal | Age and Ageing |
Print ISSN | 0002-0729 |
Electronic ISSN | 1468-2834 |
Publisher | Oxford University Press |
Peer Reviewed | Peer Reviewed |
Volume | 47 |
Issue | 3 |
Pages | 387-391 |
DOI | https://doi.org/10.1093/ageing/afx182 |
Keywords | care homes, algorithm, secondary care, informatics, patient admission, older people |
Public URL | https://nottingham-repository.worktribe.com/output/900375 |
Publisher URL | https://academic.oup.com/ageing/advance-article/doi/10.1093/ageing/afx182/4757114 |
Contract Date | Jan 3, 2018 |
Files
Housley 2017 Age and Ageing.pdf
(103 Kb)
PDF
Publisher Licence URL
https://creativecommons.org/licenses/by-nc-nd/4.0/
Copyright Statement
Copyright information regarding this work can be found at the following address: http://creativecommons.org/licenses/by-nc-nd/4.0
You might also like
Downloadable Citations
About Repository@Nottingham
Administrator e-mail: discovery-access-systems@nottingham.ac.uk
This application uses the following open-source libraries:
SheetJS Community Edition
Apache License Version 2.0 (http://www.apache.org/licenses/)
PDF.js
Apache License Version 2.0 (http://www.apache.org/licenses/)
Font Awesome
SIL OFL 1.1 (http://scripts.sil.org/OFL)
MIT License (http://opensource.org/licenses/mit-license.html)
CC BY 3.0 ( http://creativecommons.org/licenses/by/3.0/)
Powered by Worktribe © 2025
Advanced Search