GLȲD(Σ): Revolutionizing Healthcare Data and Statistical Analysis

Posted by IntegrityM | | Data & Statistical Analysis

What if there was a healthcare data analysis solution that could save you a significant amount of time in the sampling and extrapolation process, resulting in substantial cost savings, and therefore greatly increasing your return on every dollar invested into this process?  More results with less time and costs!

Manually performing sampling and extrapolation can take a trained and experienced statistician 7 to 15 hours. The process is detailed and tedious, which can lead to errors. Enter GLȲD(Σ). GLȲD(Σ) is a statistical analysis solution that allows users at all levels to perform efficient, repeatable, and defendable samples and extrapolations. GLȲD(Σ) takes the manual steps out of the sampling and extrapolation process and performs the complex calculations in just minutes, not hours, which also increases return on investment (ROI).

Defendable Data Analytics for Healthcare Integrity Efforts

When sampling and extrapolation involves overpayments owed to the government, results are often challenged in Administrative Law Judge hearings. These challenges involve picking apart the statistical methodology behind the overpayments, leaving no room for even the smallest errors or inconsistencies. Administrative Law Judges have been known to dismiss overpayments if the appellant can prove an error exists in the sampling or extrapolation methodology and process.

Our statistical sampling and extrapolation software is transparent and offers users a consistent and accurate process to produce reliable results. GLȲD(Σ)’s sampling and extrapolation methodology was developed in accordance with industry best practices and has been defended in Medicare and Medicaid overpayment appeals.

To learn how GLȲD(Σ) can save your organization time on complex calculations,

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Data Sampling and Extrapolation Using GLȲD(Σ)

The purpose of sampling is to draw conclusions about a population by observing only a portion of that population. The results of the sample are projected, or extrapolated, to the population as a whole. For example, in program integrity, samples are used to review claims for benefits or services to determine whether payments were appropriate. Results of these reviews are then extrapolated to the population of claims to identify overpayments made to a provider, group of providers, or for a benefit overall. Whereas a sample may identify overpayments of hundreds or thousands of dollars, the extrapolated results may yield overpayments in the millions of dollars, also greatly increasing ROI.

GLȲD(Σ) is extremely intuitive and makes sampling and extrapolation accessible to a range of users. GLȲD(Σ) works with your data and does not require you to change any field names or formats. It allows you to enter the specifications for your desired sample or extrapolation and quickly produces results.

Powerful, Customizable Healthcare Data Analytics for Statisticians, Analysts, & Decision Makers

GLȲD(Σ) does not require users to have advanced degrees or knowledge of complex statistics, but it can appeal to users who do. This intuitive healthcare data analysis solution produces high-level results perfect for decision makers at any organization level. Organizations may have specific sampling or extrapolation requirements and GLȲD(Σ) can be easily customized to fit your organization’s needs. GLȲD(Σ) is a scalable solution that can be installed, but is also available as a software as a service option, supported by IntegrityM’s team of expert statisticians.

Contact IntegrityM for a Free Demo

For more information on GLȲD(Σ), or to schedule a free demonstration of our healthcare data analysis software, call us at (703) 683-9600 or contact us online.

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