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    Allowables: Predicting Expected Medical Billing Collections

    Posted by Carl Mays on Wed, Jul 08, 2009 @ 08:36 PM

    medical billing allowables

    This is the fifth and final article in my series on allowables. Now that we have covered how to understand your allowables, set your fee schedules, calculate your yields and value your AR, we are ready to discuss how to predict your practice's cash flow month-to-month.

    In its simplest form, predicting collections can be done by taking your practice's average charges per month over the past year and multiplying by your weighted average practice yield. This calculation provides your average expected monthly collections. For instance, if you practice's average charges per month are $500,000 and your weighted average practice yield is 30%, then your average expected month collections should be around $150,000 ($500,000 X 0.3 = $150,000).

    This does a good job of telling you're your average monthly collections and helping your understand if the collections your have budgeted for the year are supported by your charge volume. It does not, however, help you predict the month-to-month variations that can make managing a practice's cash flow difficult. These variations are primarily driven by changes in charge volume from month-to-month.

    In order to capture the month-to-month variations it is necessary to add another element to your calculations; the distribution of the average month's payments by date of service. In other words, which month's patient encounters generated this month's collections? Once you know this you can apply your practice's average weighted yield to the portion of each preceding month's charges that will impact the current month's collections. This is easiest to see with an example:

    Let's assume your weighted average practice yield is 30% and your collection distribution is:

    • 15% of this month's collections come from this month's dates of service (month N);
    • 40% of this month's collections come from last month's date of service (month N-1);
    • 25% of this month's collections come from dates of service from two months ago (month N-2);
    • 10% of this month's collections come from dates of service from three months ago (month N-3);
    • 10% of this month's collections come from dates of service of 4+ months ago (month N-4+).

    With this information in hand (which a good billing system or billing provider should be able to provide) you are ready to build a predictive collections model. If you use excel then you can build the model so that on one row your enter the practice's charges by month and then directly below you calculate the collections for the month. If we take the data from above, the calculation for each month would be (where n equals the current month):

     ((month N charges x 0.3 x 0.15) + (month N-1 charges x 0.3 x 0.4) + (month N-2 charges x 0.3 x 0.25) + (month N-3 charges x 0.3 x 0.1))/0.9  = Month N expected collections.

    A couple of items of note:

    • The faster your collections the more the current month's collections are dependent on the current month's charges.
    • In order to simplify the calculation it is helpful to limit the calculation to the current month and the three previous months. This is what I did above and it is the reason that I divided the answer by 0.9. The current month and the preceding 3 months account for 90% of the current month's collections. When I divide the answer by 0.9 (90%), I take this 90% answer and extrapolate it to 100%.

    Once you have constructed an excel spreadsheet with the formula's outlines above you can quite accurately predict your month-to-month collections and account for the impact of seasonal and vacation driven changes in your charge volume. In addition, with the collection prediction in place your can quickly spot billing issues before they have a chance to propagate.

    Copyright 2009, Carl Mays II and the ClaimCare Medical Billing Company

    Tags: medical billing education, medical allowables, medical billing, improving medical billing

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