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Published in: BMC Health Services Research 1/2017

Open Access 01-12-2017 | Research article

Comparison of methods to identify long term care nursing home residence with administrative data

Authors: James S. Goodwin, Shuang Li, Jie Zhou, James E. Graham, Amol Karmarkar, Kenneth Ottenbacher

Published in: BMC Health Services Research | Issue 1/2017

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Abstract

Background

To compare different methods for identifying a long term care (LTC) nursing home stay, distinct from stays in skilled nursing facilities (SNFs), to the method currently used by the Center for Medicare and Medicaid Services (CMS). We used national and Texas Medicare claims, Minimum Data Set (MDS), and Texas Medicaid data from 2011-2013.

Methods

We used Medicare Part A and B and MDS data either alone or in combination to identify LTC nursing home stays by three methods. One method used Medicare Part A and B data; one method used Medicare Part A and MDS data; and the current CMS method used MDS data alone. We validated each method against Texas 2011 Medicare-Medicaid linked data for those with dual eligibility.

Results

Using Medicaid data as a gold standard, all three methods had sensitivities > 92% to identify LTC nursing home stays of more than 100 days in duration. The positive predictive value (PPV) of the method that used both MDS and Medicare Part A data was 84.65% compared to 78.71% for the CMS method and 66.45% for the method using Part A and B Medicare. When the patient population was limited to those who also had a SNF stay, the PPV for identifying LTC nursing home was highest for the method using Medicare plus MDS data (88.1%).

Conclusions

Using both Medicare and MDS data to identify LTC stays will lead to more accurate attribution of CMS nursing home quality indicators.
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Metadata
Title
Comparison of methods to identify long term care nursing home residence with administrative data
Authors
James S. Goodwin
Shuang Li
Jie Zhou
James E. Graham
Amol Karmarkar
Kenneth Ottenbacher
Publication date
01-12-2017
Publisher
BioMed Central
Published in
BMC Health Services Research / Issue 1/2017
Electronic ISSN: 1472-6963
DOI
https://doi.org/10.1186/s12913-017-2318-9

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