ArticlePloS one2019
A cluster-based approach for integrating clinical management of Medicare beneficiaries with multiple chronic conditions.
Article in PloS one, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers, 2 of them syntheses that pooled it.
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Who cites it
14 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- A systematic review and meta-analysis of disease clusters in multimorbidity.Nature communications · 2025Pooled it
- Social determinants of multimorbidity patterns: A systematic review.Frontiers in public health · 2023Pooled it
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- Impediments to transforming the healthcare delivery system: shifting the paradigm from provider centric to patient centric.Frontiers in health services · 2026Article
- Managing Hypertension in Older Adults.Current hypertension reports · 2024Review
- Epidemiology, mortality, and health service use of local-level multimorbidity patterns in South Spain.Nature communications · 2023Article
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- Exploring Patient Multimorbidity and Complexity Using Health Insurance Claims Data: A Cluster Analysis Approach.JMIR medical informatics · 2022Article
- Changing the research paradigm for digital transformation in healthcare delivery.Frontiers in digital health · 2022Article
- Remote Patient Management May Reduce All-Cause Mortality in Patients With Heart-Failure and Renal Impairment.Frontiers in medicine · 2022Article
- Simulation-derived best practices for clustering clinical data.Journal of biomedical informatics · 2021Article
- Abandonment at the Transition from Hospital to Home: Family Caregivers' Experiences.Ethiopian journal of health sciences · 2021Article
- Use of Latent Class Analysis and k-Means Clustering to Identify Complex Patient Profiles.JAMA network open · 2020Article
- Unsupervised machine learning and prognostic factors of survival in chronic lymphocytic leukemia.Journal of the American Medical Informatics Association : JAMIA · 2020Article
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Authors and funding
6 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundApproximately 28% of adults have ≥3 chronic conditions (CCs), accounting for two-thirds of U.S. healthcare costs, and often having suboptimal outcomes. Despite Institute of Medicine recommendations in 2001 to integrate guidelines for multiple CCs, progress is minimal. The vast number of unique combinations of CCs may limit progress. METHODS AND
findingsTo determine whether major CCs segregate differentially in limited groups, electronic health record and Medicare paid claims data were examined in one accountable care organization with 44,645 Medicare beneficiaries continuously enrolled throughout 2015. CCs predicting clinical outcomes were obtained from diagnostic codes. Agglomerative hierarchical clustering defined 13 groups having similar within group patterns of CCs and named for the most common CC. Two groups, congestive heart failure (CHF) and kidney disease (CKD), included 23% of beneficiaries with a very high CC burden (10.5 and 8.1 CCs/beneficiary, respectively). Five groups with 54% of beneficiaries had a high CC burden ranging from 7.1 to 5.9 (descending order: neurological, diabetes, cancer, cardiovascular, chronic pulmonary). Six groups with 23% of beneficiaries had an intermediate-low CC burden ranging from 4.7 to 0.4 (behavioral health, obesity, osteoarthritis, hypertension, hyperlipidemia, 'other'). Hypertension and hyperlipidemia were common across groups, whereas 80% of CHF segregated to the CHF group, 85% of CKD to CKD and CHF groups, 82% of cancer to Cancer, CHF, and CKD groups, and 85% of neurological disorders to Neuro, CHF, and CKD groups. Behavioral health diagnoses were common only in groups with a high CC burden. The number of CCs/beneficiary explained 36% of the variance (R2 = 0.36) in claims paid/beneficiary.
conclusionsIdentifying a limited number of groups with high burdens of CCs that disproportionately drive costs may help inform a practical number of integrated guidelines and resources required for comprehensive management. Cluster informed guideline integration may improve care quality and outcomes, while reducing costs.
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