Validation of a method for identifying nursing home admissions using administrative claims

<p>Abstract</p> <p>Background</p> <p>Currently there is no standard algorithm to identify whether a subject is residing in a nursing home from administrative claims. Our objective was to develop and validate an algorithm that identifies nursing home admissions at the re...

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Main Authors: Hsu Van Doren, Sato Masayo, Zuckerman Ilene H, Hernandez Jose J
Format: Article
Language:English
Published: BMC 2007-12-01
Series:BMC Health Services Research
Online Access:http://www.biomedcentral.com/1472-6963/7/202
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spelling doaj-d6fea149fd074300836a5dd77a6097762020-11-25T01:32:30ZengBMCBMC Health Services Research1472-69632007-12-017120210.1186/1472-6963-7-202Validation of a method for identifying nursing home admissions using administrative claimsHsu Van DorenSato MasayoZuckerman Ilene HHernandez Jose J<p>Abstract</p> <p>Background</p> <p>Currently there is no standard algorithm to identify whether a subject is residing in a nursing home from administrative claims. Our objective was to develop and validate an algorithm that identifies nursing home admissions at the resident-month level using the MarketScan Medicare Supplemental and Coordination of Benefit (COB) database.</p> <p>Methods</p> <p>The computer algorithms for identifying nursing home admissions were created by using provider type, place of service, and procedure codes from the 2000 – 2002 MarketScan Medicare COB database. After the algorithms were reviewed and refined, they were compared with a detailed claims review by an expert reviewer. A random sample of 150 subjects from the claims was selected and used for the validity analysis of the algorithms. Contingency table analysis, comparison of mean differences, correlations, and t-test analyses were performed. Percentage agreement, sensitivity, specificity, and Kappa statistics were analyzed.</p> <p>Results</p> <p>The computer algorithm showed strong agreement with the expert review (99.9%) for identification of the first month of nursing home residence, with high sensitivity (96.7%), specificity (100%) and a Kappa statistic of 0.97. Weighted Pearson correlation coefficient between the algorithm and the expert review was 0.97 (<it>p </it>< 0.0001).</p> <p>Conclusion</p> <p>A reliable algorithm indicating evidence of nursing home admission was developed and validated from administrative claims data. Our algorithm can be a useful tool to identify patient transitions from and to nursing homes, as well as to screen and monitor for factors associated with nursing home admission and nursing home discharge.</p> http://www.biomedcentral.com/1472-6963/7/202
collection DOAJ
language English
format Article
sources DOAJ
author Hsu Van Doren
Sato Masayo
Zuckerman Ilene H
Hernandez Jose J
spellingShingle Hsu Van Doren
Sato Masayo
Zuckerman Ilene H
Hernandez Jose J
Validation of a method for identifying nursing home admissions using administrative claims
BMC Health Services Research
author_facet Hsu Van Doren
Sato Masayo
Zuckerman Ilene H
Hernandez Jose J
author_sort Hsu Van Doren
title Validation of a method for identifying nursing home admissions using administrative claims
title_short Validation of a method for identifying nursing home admissions using administrative claims
title_full Validation of a method for identifying nursing home admissions using administrative claims
title_fullStr Validation of a method for identifying nursing home admissions using administrative claims
title_full_unstemmed Validation of a method for identifying nursing home admissions using administrative claims
title_sort validation of a method for identifying nursing home admissions using administrative claims
publisher BMC
series BMC Health Services Research
issn 1472-6963
publishDate 2007-12-01
description <p>Abstract</p> <p>Background</p> <p>Currently there is no standard algorithm to identify whether a subject is residing in a nursing home from administrative claims. Our objective was to develop and validate an algorithm that identifies nursing home admissions at the resident-month level using the MarketScan Medicare Supplemental and Coordination of Benefit (COB) database.</p> <p>Methods</p> <p>The computer algorithms for identifying nursing home admissions were created by using provider type, place of service, and procedure codes from the 2000 – 2002 MarketScan Medicare COB database. After the algorithms were reviewed and refined, they were compared with a detailed claims review by an expert reviewer. A random sample of 150 subjects from the claims was selected and used for the validity analysis of the algorithms. Contingency table analysis, comparison of mean differences, correlations, and t-test analyses were performed. Percentage agreement, sensitivity, specificity, and Kappa statistics were analyzed.</p> <p>Results</p> <p>The computer algorithm showed strong agreement with the expert review (99.9%) for identification of the first month of nursing home residence, with high sensitivity (96.7%), specificity (100%) and a Kappa statistic of 0.97. Weighted Pearson correlation coefficient between the algorithm and the expert review was 0.97 (<it>p </it>< 0.0001).</p> <p>Conclusion</p> <p>A reliable algorithm indicating evidence of nursing home admission was developed and validated from administrative claims data. Our algorithm can be a useful tool to identify patient transitions from and to nursing homes, as well as to screen and monitor for factors associated with nursing home admission and nursing home discharge.</p>
url http://www.biomedcentral.com/1472-6963/7/202
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