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Related Concept Videos

Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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Structural Classification of Joints01:20

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Joints, also known as articulations, are classified based on their structural characteristics, i.e., based on whether the articulating surfaces of the adjacent bones are directly connected by fibrous connective tissue or cartilage, or whether the articulating surfaces contact each other within a fluid-filled joint cavity. These differences serve to divide the joints of the body into three structural classifications.
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Healthcare Agencies I01:18

Healthcare Agencies I

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Healthcare agencies provide healthcare services to people. In the United States, voluntary agencies are often non-profit centers sponsored by donations, grants, or fundraisers. One such organization is Meals on Wheels, which provides meals to the elderly and homebound. The American Heart Association and the American Lung Association are other non-profit community organizations. Doctors and nurses are frequently active members of these organizations, which offer health checks and educational...
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Issues And Trends In Healthcare Delivery System01:29

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
Cost Containment
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A complete procedure for testing a claim about a population proportion is provided here.
There are two methods of testing a claim about a population proportion: (1) Using the sample proportion from the data where a binomial distribution is approximated to the normal distribution and (2) Using the binomial probabilities calculated from the data.
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Comparing the Survival Analysis of Two or More Groups01:20

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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...
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Related Experiment Video

Updated: Jul 5, 2025

Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes
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Glycemic Impact on Knee Osteoarthritis Symptoms on Physical, Radiographic, and Inflammatory Markers among Individuals Aged 50 and Over with Diabetes

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Graphical Association Analysis for Identifying Variation in Provider Claims for Joint Replacement Surgery.

James Kemp1, Christopher Barker2, Norm Good3

  • 1University of New South Wales, Australia.

Studies in Health Technology and Informatics
|January 25, 2024
PubMed
Summary
This summary is machine-generated.

Unsupervised machine learning identifies unusual medical provider billing patterns for joint replacement surgery. This approach efficiently detects potentially fraudulent claims, reducing healthcare waste and improving billing integrity.

Keywords:
Unsupervised machine learningdata miningfraudnational health insuranceorthopedic procedures

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Area of Science:

  • Health economics
  • Medical data analysis
  • Machine learning applications

Background:

  • Detecting fraudulent or wasteful medical insurance claims is challenging due to data volume and manual effort.
  • Variations in provider billing behavior can indicate potential issues.

Purpose of the Study:

  • To apply unsupervised machine learning for identifying and ranking unusual medical provider claiming behaviors.
  • To develop interpretable models for analyzing claims related to unilateral joint replacement surgery.

Main Methods:

  • Utilized unsupervised machine learning on Australian Medicare Benefits Schedule data.
  • Constructed reference models for surgical procedures and compared individual provider claims.
  • Ranked providers based on additional fees beyond typical claims.

Main Results:

  • Identified providers with significantly unusual claiming patterns.
  • Outlying providers' claims could increase procedure costs by up to 192%.
  • The developed method is efficient and generalizable.

Conclusions:

  • Unsupervised machine learning provides an effective and interpretable method for detecting unusual medical billing.
  • This approach can be integrated into existing workflows to combat healthcare waste.
  • The model's generalizability allows application to various medical procedures.