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A User-friendly and Powerful R Analysis of Large-scale Datasets
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Converting Lab Report Files into Usable Data.

David Wesley

    Journal of Insurance Medicine (New York, N.Y.)
    |September 2, 2016
    PubMed
    Summary

    Medical directors can leverage existing laboratory results to predict outcomes for new preferred risk programs. This practical approach transforms raw data into actionable insights for strategic decision-making.

    Area of Science:

    • Health Informatics
    • Biostatistics
    • Risk Management

    Background:

    • Medical directors often face the challenge of analyzing historical laboratory data.
    • Existing datasets may not be readily structured for predictive analysis.
    • Accurate data analysis is crucial for evaluating new insurance programs.

    Purpose of the Study:

    • To demonstrate a practical method for organizing company laboratory results.
    • To illustrate how to prepare data for predictive analysis in insurance.
    • To support medical directors in analyzing laboratory data for risk assessment.

    Main Methods:

    • Utilizing historical laboratory results from a company.
    • Developing a dataset suitable for statistical analysis.
    • Applying predictive modeling techniques to the prepared data.
    Keywords:
    Laboratory resultsPythonRStudioStata, Rdata wranglingtext data

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    Main Results:

    • A structured dataset was created from experienced laboratory results.
    • The process facilitated the prediction of insured distributions for a new program.
    • Demonstrated the feasibility of using internal lab data for external risk assessment.

    Conclusions:

    • Companies can effectively marshal internal laboratory results into valuable datasets.
    • This methodology aids in predicting outcomes for new insurance products.
    • Practical data preparation is key for informed decision-making by medical directors.