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How well does the minimum data set measure healthcare use? a validation study
BACKGROUND: To improve care, planners require accurate information about nursing home (NH) residents and their healthcare use. We evaluated how accurately measures of resident user status and healthcare use were captured in the Minimum Data Set (MDS) versus administrative data. METHODS: This retrosp...
Autores principales: | , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2018
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5896092/ https://www.ncbi.nlm.nih.gov/pubmed/29642929 http://dx.doi.org/10.1186/s12913-018-3089-7 |
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author | Doupe, Malcolm B. Poss, Jeff Norton, Peter G. Garland, Allan Dik, Natalia Zinnick, Shauna Lix, Lisa M. |
author_facet | Doupe, Malcolm B. Poss, Jeff Norton, Peter G. Garland, Allan Dik, Natalia Zinnick, Shauna Lix, Lisa M. |
author_sort | Doupe, Malcolm B. |
collection | PubMed |
description | BACKGROUND: To improve care, planners require accurate information about nursing home (NH) residents and their healthcare use. We evaluated how accurately measures of resident user status and healthcare use were captured in the Minimum Data Set (MDS) versus administrative data. METHODS: This retrospective observational cohort study was conducted on all NH residents (N = 8832) from Winnipeg, Manitoba, Canada, between April 1, 2011 and March 31, 2013. Six study measures exist. NH user status (newly admitted NH residents, those who transferred from one NH to another, and those who died) was measured using both MDS and administrative data. Rates of in-patient hospitalizations, emergency department (ED) visits without subsequent hospitalization, and physician examinations were also measured in each data source. We calculated the sensitivity, specificity, positive and negative predictive values (PPV, NPV), and overall agreement (kappa, κ) of each measure as captured by MDS using administrative data as the reference source. Also for each measure, logistic regression tested if the level of disagreement between data systems was associated with resident age and sex plus NH owner-operator status. RESULTS: MDS accurately identified newly admitted residents (κ = 0.97), those who transferred between NHs (κ = 0.90), and those who died (κ = 0.95). Measures of healthcare use were captured less accurately by MDS, with high levels of both under-reporting and false positives (e.g., for in-patient hospitalizations sensitivity = 0.58, PPV = 0.45), and moderate overall agreement levels (e.g., κ = 0.39 for ED visits). Disagreement was sometimes greater for younger males, and for residents living in for-profit NHs. CONCLUSIONS: MDS can be used as a stand-alone tool to accurately capture basic measures of NH use (admission, transfer, and death), and by proxy NH length of stay. As compared to administrative data, MDS does not accurately capture NH resident healthcare use. Research investigating these and other healthcare transitions by NH residents requires a combination of the MDS and administrative data systems. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12913-018-3089-7) contains supplementary material, which is available to authorized users. |
format | Online Article Text |
id | pubmed-5896092 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2018 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-58960922018-04-20 How well does the minimum data set measure healthcare use? a validation study Doupe, Malcolm B. Poss, Jeff Norton, Peter G. Garland, Allan Dik, Natalia Zinnick, Shauna Lix, Lisa M. BMC Health Serv Res Research Article BACKGROUND: To improve care, planners require accurate information about nursing home (NH) residents and their healthcare use. We evaluated how accurately measures of resident user status and healthcare use were captured in the Minimum Data Set (MDS) versus administrative data. METHODS: This retrospective observational cohort study was conducted on all NH residents (N = 8832) from Winnipeg, Manitoba, Canada, between April 1, 2011 and March 31, 2013. Six study measures exist. NH user status (newly admitted NH residents, those who transferred from one NH to another, and those who died) was measured using both MDS and administrative data. Rates of in-patient hospitalizations, emergency department (ED) visits without subsequent hospitalization, and physician examinations were also measured in each data source. We calculated the sensitivity, specificity, positive and negative predictive values (PPV, NPV), and overall agreement (kappa, κ) of each measure as captured by MDS using administrative data as the reference source. Also for each measure, logistic regression tested if the level of disagreement between data systems was associated with resident age and sex plus NH owner-operator status. RESULTS: MDS accurately identified newly admitted residents (κ = 0.97), those who transferred between NHs (κ = 0.90), and those who died (κ = 0.95). Measures of healthcare use were captured less accurately by MDS, with high levels of both under-reporting and false positives (e.g., for in-patient hospitalizations sensitivity = 0.58, PPV = 0.45), and moderate overall agreement levels (e.g., κ = 0.39 for ED visits). Disagreement was sometimes greater for younger males, and for residents living in for-profit NHs. CONCLUSIONS: MDS can be used as a stand-alone tool to accurately capture basic measures of NH use (admission, transfer, and death), and by proxy NH length of stay. As compared to administrative data, MDS does not accurately capture NH resident healthcare use. Research investigating these and other healthcare transitions by NH residents requires a combination of the MDS and administrative data systems. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1186/s12913-018-3089-7) contains supplementary material, which is available to authorized users. BioMed Central 2018-04-11 /pmc/articles/PMC5896092/ /pubmed/29642929 http://dx.doi.org/10.1186/s12913-018-3089-7 Text en © The Author(s). 2018 Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. |
spellingShingle | Research Article Doupe, Malcolm B. Poss, Jeff Norton, Peter G. Garland, Allan Dik, Natalia Zinnick, Shauna Lix, Lisa M. How well does the minimum data set measure healthcare use? a validation study |
title | How well does the minimum data set measure healthcare use? a validation study |
title_full | How well does the minimum data set measure healthcare use? a validation study |
title_fullStr | How well does the minimum data set measure healthcare use? a validation study |
title_full_unstemmed | How well does the minimum data set measure healthcare use? a validation study |
title_short | How well does the minimum data set measure healthcare use? a validation study |
title_sort | how well does the minimum data set measure healthcare use? a validation study |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5896092/ https://www.ncbi.nlm.nih.gov/pubmed/29642929 http://dx.doi.org/10.1186/s12913-018-3089-7 |
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