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Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach
BACKGROUND: Although the trend of progressing morbidity is widely recognized, there are numerous challenges when studying multimorbidity and patient complexity. For multimorbid or complex patients, prone to fragmented care and high health care use, novel estimation approaches need to be developed. O...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
JMIR Publications
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9016510/ https://www.ncbi.nlm.nih.gov/pubmed/35377334 http://dx.doi.org/10.2196/34274 |
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author | Nicolet, Anna Assouline, Dan Le Pogam, Marie-Annick Perraudin, Clémence Bagnoud, Christophe Wagner, Joël Marti, Joachim Peytremann-Bridevaux, Isabelle |
author_facet | Nicolet, Anna Assouline, Dan Le Pogam, Marie-Annick Perraudin, Clémence Bagnoud, Christophe Wagner, Joël Marti, Joachim Peytremann-Bridevaux, Isabelle |
author_sort | Nicolet, Anna |
collection | PubMed |
description | BACKGROUND: Although the trend of progressing morbidity is widely recognized, there are numerous challenges when studying multimorbidity and patient complexity. For multimorbid or complex patients, prone to fragmented care and high health care use, novel estimation approaches need to be developed. OBJECTIVE: This study aims to investigate the patient multimorbidity and complexity of Swiss residents aged ≥50 years using clustering methodology in claims data. METHODS: We adopted a clustering methodology based on random forests and used 34 pharmacy-based cost groups as the only input feature for the procedure. To detect clusters, we applied hierarchical density-based spatial clustering of applications with noise. The reasonable hyperparameters were chosen based on various metrics embedded in the algorithms (out-of-bag misclassification error, normalized stress, and cluster persistence) and the clinical relevance of the obtained clusters. RESULTS: Based on cluster analysis output for 18,732 individuals, we identified an outlier group and 7 clusters: individuals without diseases, patients with only hypertension-related diseases, patients with only mental diseases, complex high-cost high-need patients, slightly complex patients with inexpensive low-severity pharmacy-based cost groups, patients with 1 costly disease, and older high-risk patients. CONCLUSIONS: Our study demonstrated that cluster analysis based on pharmacy-based cost group information from claims-based data is feasible and highlights clinically relevant clusters. Such an approach allows expanding the understanding of multimorbidity beyond simple disease counts and can identify the population profiles with increased health care use and costs. This study may foster the development of integrated and coordinated care, which is high on the agenda in policy making, care planning, and delivery. |
format | Online Article Text |
id | pubmed-9016510 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | JMIR Publications |
record_format | MEDLINE/PubMed |
spelling | pubmed-90165102022-04-20 Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach Nicolet, Anna Assouline, Dan Le Pogam, Marie-Annick Perraudin, Clémence Bagnoud, Christophe Wagner, Joël Marti, Joachim Peytremann-Bridevaux, Isabelle JMIR Med Inform Original Paper BACKGROUND: Although the trend of progressing morbidity is widely recognized, there are numerous challenges when studying multimorbidity and patient complexity. For multimorbid or complex patients, prone to fragmented care and high health care use, novel estimation approaches need to be developed. OBJECTIVE: This study aims to investigate the patient multimorbidity and complexity of Swiss residents aged ≥50 years using clustering methodology in claims data. METHODS: We adopted a clustering methodology based on random forests and used 34 pharmacy-based cost groups as the only input feature for the procedure. To detect clusters, we applied hierarchical density-based spatial clustering of applications with noise. The reasonable hyperparameters were chosen based on various metrics embedded in the algorithms (out-of-bag misclassification error, normalized stress, and cluster persistence) and the clinical relevance of the obtained clusters. RESULTS: Based on cluster analysis output for 18,732 individuals, we identified an outlier group and 7 clusters: individuals without diseases, patients with only hypertension-related diseases, patients with only mental diseases, complex high-cost high-need patients, slightly complex patients with inexpensive low-severity pharmacy-based cost groups, patients with 1 costly disease, and older high-risk patients. CONCLUSIONS: Our study demonstrated that cluster analysis based on pharmacy-based cost group information from claims-based data is feasible and highlights clinically relevant clusters. Such an approach allows expanding the understanding of multimorbidity beyond simple disease counts and can identify the population profiles with increased health care use and costs. This study may foster the development of integrated and coordinated care, which is high on the agenda in policy making, care planning, and delivery. JMIR Publications 2022-04-04 /pmc/articles/PMC9016510/ /pubmed/35377334 http://dx.doi.org/10.2196/34274 Text en ©Anna Nicolet, Dan Assouline, Marie-Annick Le Pogam, Clémence Perraudin, Christophe Bagnoud, Joël Wagner, Joachim Marti, Isabelle Peytremann-Bridevaux. Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 04.04.2022. https://creativecommons.org/licenses/by/4.0/This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on https://medinform.jmir.org/, as well as this copyright and license information must be included. |
spellingShingle | Original Paper Nicolet, Anna Assouline, Dan Le Pogam, Marie-Annick Perraudin, Clémence Bagnoud, Christophe Wagner, Joël Marti, Joachim Peytremann-Bridevaux, Isabelle Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach |
title | Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach |
title_full | Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach |
title_fullStr | Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach |
title_full_unstemmed | Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach |
title_short | Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach |
title_sort | exploring patient multimorbidity and complexity using health insurance claims data: a cluster analysis approach |
topic | Original Paper |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9016510/ https://www.ncbi.nlm.nih.gov/pubmed/35377334 http://dx.doi.org/10.2196/34274 |
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