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Stratified sampling design based on data mining.

Yeonkook J Kim1, Yoonhwan Oh, Sunghoon Park

  • 1Technology Management, Economics and Policy Graduate Program, Seoul National University, Seoul, Korea.

Healthcare Informatics Research
|November 1, 2013
PubMed
Summary
This summary is machine-generated.

Data mining creates better healthcare provider sampling strata. This improves sampling efficiency by using variables like patient numbers and location density, outperforming traditional methods.

Keywords:
Data MiningDecision TreesSampling Studies

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Area of Science:

  • Health Services Research
  • Data Mining
  • Statistical Sampling

Background:

  • Stratified sampling is crucial for healthcare provider surveys.
  • Existing stratification methods may lack efficiency.
  • Identifying optimal stratification variables is key.

Purpose of the Study:

  • To develop data mining-based classification rules for defining strata in healthcare provider sampling.
  • To improve the efficiency of stratified sampling for healthcare providers.

Main Methods:

  • K-means clustering was used to group providers.
  • Decision trees generated stratification rules based on cluster labels.
  • Variance explained by new vs. conventional stratification was assessed using claims and provider data from South Korea.

Main Results:

  • Data mining yielded five strata for general surgery and five for ophthalmology.
  • Stratification variables included inpatients per specialist, location density, and number of beds.
  • The proposed method explained significantly more variance (22% surgery, 8% ophthalmology) than conventional methods (2% surgery, 0.2% ophthalmology).

Conclusions:

  • Data mining offers an effective alternative for designing stratified sampling in healthcare.
  • The proposed method utilizes readily available data for insurers and governments.
  • This approach enhances the efficiency of healthcare provider surveys.