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Related Concept Videos

Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
Random sampling is a method where each member of the population has an equal chance of being selected for the sample. It involves selecting individuals randomly, often using random number generators or lottery-type methods. For example, when analyzing the properties of a...
Cluster Sampling Method01:20

Cluster Sampling Method

Appropriate sampling methods ensure that samples are drawn without bias and accurately represent the population. Because measuring the entire population in a study is not practical, researchers use samples to represent the population of interest.
To choose a cluster sample, divide the population into clusters (groups) and then randomly select some of the clusters. All the members from these clusters are in the cluster sample. For example, if you randomly sample four departments from your...
Comparing the Survival Analysis of Two or More Groups01:20

Comparing the Survival Analysis of Two or More Groups

Survival analysis is a cornerstone of medical research, used to evaluate the time until an event of interest occurs, such as death, disease recurrence, or recovery. Unlike standard statistical methods, survival analysis is particularly adept at handling censored data—instances where the event has not occurred for some participants by the end of the study or remains unobserved. To address these unique challenges, specialized techniques like the Kaplan-Meier estimator, log-rank test, and Cox...
Classification of Illness01:17

Classification of Illness

The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe and...

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Related Experiment Video

Updated: May 30, 2026

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data
14:27

Identification of Disease-related Spatial Covariance Patterns using Neuroimaging Data

Published on: June 26, 2013

Identifying subgroups of complex patients with cluster analysis.

Sophia R Newcomer1, John F Steiner, Elizabeth A Bayliss

  • 1Institute for Health Research, Kaiser Permanente Colorado, Denver, USA. Sophia.r.newcomer@kp.org

The American Journal of Managed Care
|August 20, 2011
PubMed
Summary

Cluster analysis identified 10 patient subgroups with complex chronic conditions. These distinct groups, including those with mental illness and chronic pain, can guide targeted care management strategies for improved health outcomes.

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Last Updated: May 30, 2026

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12:27

Large-scale Reconstructions and Independent, Unbiased Clustering Based on Morphological Metrics to Classify Neurons in Selective Populations

Published on: February 15, 2017

Area of Science:

  • Health Services Research
  • Data Mining
  • Computational Biology

Background:

  • Complex patients with multiple chronic conditions represent a significant challenge for healthcare systems.
  • Identifying specific patient sub-populations is crucial for developing effective, targeted interventions.
  • Current care management strategies may not adequately address the heterogeneity of complex patient needs.

Purpose of the Study:

  • To demonstrate the utility of cluster analysis in identifying distinct patient subgroups within a high-cost population.
  • To reveal clinically relevant groupings of coexisting chronic conditions.
  • To inform the development of tailored care management strategies for complex patients.

Main Methods:

  • Retrospective cohort analysis of 15,480 adult members of an integrated health maintenance organization.
  • Inclusion criteria: 2+ chronic conditions and top 20% total cost of care for 2 consecutive years.
  • Agglomerative hierarchical clustering using Ward's minimum variance algorithm to identify subgroups based on coexisting conditions.

Main Results:

  • Ward's algorithm identified 10 clinically relevant patient clusters.
  • Clusters were characterized by "anchoring conditions" such as chronic pain, mental illness, obesity, cancer, cardiac disease, and diabetes.
  • Mental health diagnoses were highly prevalent across all identified clusters (28%-100%).

Conclusions:

  • Cluster analysis effectively identifies discrete patient groups with specific comorbid conditions.
  • These identified subgroups necessitate a spectrum of care management approaches.
  • Leveraging cluster analysis can facilitate the creation of targeted interventions to enhance patient health outcomes.