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  • Comparison of revenue for various CPT codes for a given time-period
  • Comparison of underpayments for various payers for a given time-period
  • Comparison of denials for various payers for a given time-period
  • A single key dat

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    Average medical practice may lose as much as 11% of its revenue due to underpayments. But underpayment recovery potential averages only 5% of revenue and involves costly appeal process. To avoid unrecoverable losses, some providers discontinue servicing patients insured by the worst performing payers. Unfortunately, such a drastic loss reduction measure may boomerang and increase losses depending on complexity of referral relationships. This article outlines limitations of traditional database queries used to identify payer candidates for contract termination and demonstrates alternative decision choices with superior performance in terms of revenue and risk management, facilitated with On Line Analytical Processing (OLAP) technology.

    First Order SQL Queries for Accounts Receivable Analysis

    Traditional accounts receivable analysis includes identification of payers that systematically underpay and refuse denial appeals. Such analysis is based on simple queries, designed to identify the best CPT code or the worst payer in absolute terms:

    • Comparison of revenue for various CPT codes for a given time-period
    • Comparison of underpayments for various payers for a given time-period
    • Comparison of denials for various payers for a given time-period

    A single key dat

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    d by the worst performing payers. Unfortunately, such a drastic loss reduction measure may boomerang and increase losses depending on complexity of referral relationships. This article outlines limitations of traditional database queries used to identify payer candidates for contract termination and demonstrates alternative decision choices with superior performance in terms of revenue and risk management, facilitated with On Line Analytical Processing (OLAP) technology.

    First Order SQL Queries for Accounts Receivable Analysis

    Traditional accounts receivable analysis includes identification of payers that systematically underpay and refuse denial appeals. Such analysis is based on simple queries, designed to identify the best CPT code or the worst payer in absolute terms:

    • Comparison of revenue for various CPT codes for a given time-period
    • Comparison of underpayments for various payers for a given time-period
    • Comparison of denials for various payers for a given time-period

    A single key dat

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    es for contract termination and demonstrates alternative decision choices with superior performance in terms of revenue and risk management, facilitated with On Line Analytical Processing (OLAP) technology.

    First Order SQL Queries for Accounts Receivable Analysis

    Traditional accounts receivable analysis includes identification of payers that systematically underpay and refuse denial appeals. Such analysis is based on simple queries, designed to identify the best CPT code or the worst payer in absolute terms:

    • Comparison of revenue for various CPT codes for a given time-period
    • Comparison of underpayments for various payers for a given time-period
    • Comparison of denials for various payers for a given time-period

    A single key dat

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    Traditional accounts receivable analysis includes identification of payers that systematically underpay and refuse denial appeals. Such analysis is based on simple queries, designed to identify the best CPT code or the worst payer in absolute terms:

    • Comparison of revenue for various CPT codes for a given time-period
    • Comparison of underpayments for various payers for a given time-period
    • Comparison of denials for various payers for a given time-period

    A single key dat

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  • Comparison of revenue for various CPT codes for a given time-period
  • Comparison of underpayments for various payers for a given time-period
  • Comparison of denials for various payers for a given time-period
  • A single key database indexing is a standard measure to improve time performance of such queries. It builds an ordered relationship within the data elements based on the value of the selected metric. But single key indexing precludes implementation of more complex queries like "who is the payer that underpays the most for the best CPT code," or "who is the worst referring physician for my worst payer?" and require complex SQL programming skills because of the need to store and process intermediate results. Therefore, ranking the data elements along a single attribute, forces a limited choice for management decision:

    • Ignore the problem,
    • Renegotiate the contract with the payer, or
    • Stop serving patients insured by the worst payer.

    But to find more subtle solutions the office manager requires the ability to aggregate and drill into data and formulate queries in real time, in response to observed results to the previous queries. Specifically, a low frequency under performing payer with a high degree of underpayment may not b

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