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Unusual Results01:16

Unusual Results

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Unusual results are those that have a very low chance of occurring. Unusual results can be identified using probabilities and the range rule of thumb. In problems involving probability, unusual results can be observed in 2 instances – an unusually high number of successes or an unusually low number of successes.
According to the range rule of thumb, any value above or below two standard deviations, 2σ  from the mean, μ  is considered unusual.
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Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
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In the ever-evolving field of public health, statistical analysis serves as a cornerstone for understanding and managing disease outbreaks. By leveraging various statistical tools, health professionals can predict potential outbreaks, analyze ongoing situations, and devise effective responses to mitigate impact. For that to happen, there are a few possible stages of the analysis:
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An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
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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.
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Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
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Explainable unsupervised anomaly detection for healthcare insurance data.

Hannes De Meulemeester1, Frank De Smet2,3, Johan van Dorst2

  • 1Department of Electrical Engineering, ESAT-STADIUS, KU Leuven, Kasteelpark Arenberg 10, B-3001 Leuven, Belgium. hannes.demeulemeester@gmail.com.

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This study introduces a machine learning workflow to detect healthcare waste and fraud. It uses advanced anomaly detection and explanations to help insurers identify unusual provider behavior efficiently.

Keywords:
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Area of Science:

  • Health Informatics
  • Data Science
  • Machine Learning

Background:

  • Healthcare waste and fraud pose significant challenges for insurers.
  • Big data analytics and machine learning offer potential solutions for detection.
  • Acquiring labeled data for training fraud detection models is difficult and expensive.

Purpose of the Study:

  • To develop a machine learning workflow for detecting healthcare waste and fraud.
  • To assist investigators in identifying practitioners with unusual resource utilization.
  • To improve the efficiency of combating waste and fraud in health insurance.

Main Methods:

  • Combined categorical embeddings, unsupervised anomaly detection, and Shapley additive explanations (SHAP).
  • Applied techniques to high-cardinality categorical variables and anomaly detection.
  • Utilized SHAP for model interpretability in healthcare insurance anomaly detection.

Main Results:

  • Categorical embeddings significantly improved performance over standard methods.
  • Unsupervised anomaly detection techniques generally outperformed traditional methods.
  • The workflow successfully identified a novel anomalous trend among general practitioners.

Conclusions:

  • The proposed workflow effectively detects healthcare providers with atypical behavior.
  • It aids expert investigators in making informed decisions regarding potential fraud and overconsumption.
  • This approach enhances the fight against waste and fraud in health insurance.