{"id":51,"date":"2019-02-13T10:00:44","date_gmt":"2019-02-13T18:00:44","guid":{"rendered":"http:\/\/www.sharpab.com\/Blog\/?p=51"},"modified":"2019-02-13T10:00:45","modified_gmt":"2019-02-13T18:00:45","slug":"ambulance-billing-accurate-forecasting","status":"publish","type":"post","link":"http:\/\/www.sharpab.com\/Blog\/general\/ambulance-billing-accurate-forecasting\/","title":{"rendered":"Ambulance Billing – Accurate Forecasting"},"content":{"rendered":"\n

AMBULANCE FORECASTING\nMETHODS<\/p>\n\n\n\n

One of the trickiest things in forecasting revenue for ambulance\nbilling is getting a methodology that combines the number of transports, the\npayer mix of the transports and the payer\u2019s average time of payment. <\/p>\n\n\n\n

When your software and your database allow you to easily access\nand combine that data the result can be an accurate forecast for your P&L\nand your cash flow. Below I will review a couple of methods to produce an\naccurate forecast starting with the most simple and expanding into a more\ndetailed and even more accurate method.<\/p>\n\n\n\n

All the methods described below are from the Sharp Ambulance\nBilling dashboard and reporting tools, but should be available in any quality\nbilling software on the market today. Unfortunately, I\u2019ve worked with a lot of\ninternal billing departments and external billing departments that fail to\nproduce even the most basic forecast for their clients.<\/p>\n\n\n\n

Many times the reports or forecasting tools that do produce\nsomething fail to take into account important factors that make a forecast\naccurate. Forecasting your cash flow may be less important for a government\nentity than it is for a private entity, but either should have a basic\nunderstanding of their revenue in order to determine they are getting all the\nrevenue available and in a timely fashion.<\/p>\n\n\n\n

Let us start with the most basic snapshot available on a good\ndashboard such as the one in Sharp Ambulance Billing that can provide you the\nability to do a fairly easy estimate that is still fairly accurate.<\/p>\n\n\n\n

The dashboard below provides some simple forecasting that for\nmany companies is enough to estimate revenue for the near\nfuture.  The number of runs is tracked because months with more runs\nwill most likely produce more revenue than those with less.   In\nthis dashboard (graphs and bar charts not shown) you can quickly see how many\nruns were done in a month and the associated charges.  The charges\nwill vary based on the mix of Level of Service.  <\/p>\n\n\n\n

The next is payments received for the runs in that\nmonth.  This is not the month the payments were collected but rather\nwhat payments were made against the transports in that\nmonth.  Combine that with the costs for that month and you can track\nhow profitable a month was.  <\/p>\n\n\n\n

Also provided are write-offs for contractual payers such as\nMedicare, Medicaid and other commercial contracts. This is the part of the\ncharges you cannot collect due to contractual obligations.  The data\nallows for a basic forecast in a month.  By using this data and the\naverage per run or the average percent paid of the charges (over the last 6-12\nmonths), you can produce a simple forecast.<\/p>\n\n\n\n

 A forecast can be\ngenerated by using one of two simple calculations (or running a report that\ndoes it for you).  Either taking the\ncharges times percent of charges normally paid, or by taking the average\ncollection per run multiplied by the number of runs to get the current months\nestimated revenue.  Either provides a\nfairly quick and fairly accurate forecast of the revenue the chosen month will\ngenerate in total over time.  The second chart\/report below can then\nexpand that into the estimate of when that cash\/revenue will arrive based on\nthe history of how it arrived before. <\/p>\n\n\n\n

\"\"
Basic Dashboard Forecast Data<\/figcaption><\/figure>\n\n\n\n

While the Dashboard above tells me April will generate $476 K,\nthe chart below shows me when that income was received, which changes based on\npayer mixes and their average payment times. And often there is a mix of\nsecondary payers, self-pay deductibles, co-insurance and co-payments and\/or\nslow paying insurance companies.  All\nthose variables combine to cause payments to trickle in over\ntime.   In the example below for April 2018 we collected a total\nso far of $476 K.  When it was collected\nis shown on the Cash Flow for DOS Month report, example below is   $84\nK was collected before the end of the month of April, $293 K was collected\nbefore the end of May and $50 K collected before the end of\nJune.  Meaning within 60 days of the end of April (or the month I am\nforecasting cash flow for) I received 81% of the cash that month would\neventually generate.   Then the next 3 months produced another\n9.1%.  The other 10% trickled in, some as late as Jan 2019. This\ncould be patients on payment plans, difficult to collect insurance, hospice,\nworkers compensation, VA and many other valid reasons these are not collected sooner.<\/p>\n\n\n\n

 Regardless of the numbers and timing the forecasting\nprocess in its most simple forms are the same.  Number of runs\nmultiplied by a  typical average payment per run (over time), and\npayments collected as percent of total charges (over time)  allows a\nfairly solid methods to predict an amount collected, or expected revenue from\nthat months transports.  Combine that and the average collected\npercent per month from the chart below and you have a fairly accurate cash\nflow forecast of that revenue. Combined with previous months and future months\nyou can accurately forecast a particular month\u2019s cash receipts.<\/p>\n\n\n\n

Below this chart I will address a way to make this kind of\nforecast even more accurate with the Sharp Ambulance Billing tools and\nhopefully tools you may have in your software.   <\/p>\n\n\n\n

\"\"
Date of Service cash collection <\/figcaption><\/figure>\n\n\n\n

How do you improve on this method?  The key to\nunderstanding the best way to improve on this method is to understand where the\ninaccuracy of this method is.  One of the\nconditions that can change your forecast month-to-month is if you\u2019re payer mix\nchanges fairly significantly month-to-month. \nA larger mix of lower self paid, no insurance accounts, or a larger mix\nof insurance such as Medicaid can often affect the amount of cash you will\nactually collect. <\/p>\n\n\n\n

Another factor that changes monthly is often the mix of service\nlevel (ALS versus BLS) mix can raise or lower the forecast.  While\nthe service level mix is reflected in the total charges for the month, it may\naffect the average per run in the forecast.<\/p>\n\n\n\n

Therefore the next step in providing a more accurate forecast is\na report that takes each run individually and looks at the previous history to\ncreate a very detailed calculation run by run. \nThat detail is then totaled into a summary of the data.  The information can be displayed the same as\nthe previous forecast, but it does the calculations using detailed data not an\noverview of the past. <\/p>\n\n\n\n

Run by run this more accurate and detail forecast adds current\nmonth (forecasted month) data and uses the history of the same type of run with\nthe same payer to predict collections to the most accurate level possible.   <\/p>\n\n\n\n

While Sharp has several methods to produce such a report they\nall have one thing in common – they take the actual payer and service\nlevel run by run and produce expected payment revenue.  <\/p>\n\n\n\n

For example one of the more accurate ones that Sharp makes\navailable uses the 6-9 month history of those payers to conclude how much\nshould be expected to be collected.  Of course, you need to have 6-9\nmonths of history to use this report. \nTherefore, the first less accurate reports are usually a starting point\nuntil such history is available.  <\/p>\n\n\n\n

These more accurate reports look at primary payers and secondary\npayers.   This process is unique to many reports we use for\nforecasting.  For example the collection rate from a Medicare primary\nand Blue Cross Secondary might be 100% of the Medicare\nallowable.  Whereas the collection rate of a Medicare Primary and\nMedicaid in states like California are limited to 80% of the Medicare allowable\nas Medicaid will pay zero and will not allow the patient to be billed the other\n20%. <\/p>\n\n\n\n

Then there is the Medicare Primary and Patient\nsecondary.  That is where the historical data from a 6-9 month period\nmight tell us a more accurate depiction, for example we only collected 92% of\nthat allowable.  If reduced from 100% because too many patients are\ndifficult to collect their co-pays from if at all.<\/p>\n\n\n\n

While there are many other ways to make reports more and more\naccurate they all require historical data, and sophisticated software.   Even with the tools it takes someone with\nthe expertise and the ability to understand the software tools and how that\ndata is created to insure it is accurate. <\/p>\n\n\n\n

Also can that software and data produce accurate reports\ncustomized to the uniqueness of each ambulance company? Many times I have found\nsoftware that could possibly produce these types of reports and information but\nthe users were simply not capable of making the tool do that. That is where the\nexpertise of your billers and people knowledgeable of the software\u2019s database\nand reports can combine to make more accurate forecasts.<\/p>\n\n\n\n

If you need this type of information today and are not getting\nit I would suggest finding a way.  Either\nfind out how to use your software to produce that type of accurate reporting or\nbegin to look for new software or even a new billing service. <\/p>\n\n\n\n

It is most important that if you are reviewing reports for this\ninformation that you fully understand how that data you are looking at is\ncalculated.  How a forecast is calculated\nmust be known if you are going to rely on that forecast when making business\ndecisions..  I often have asked a\ncustomer, how is that data calculated? \nThe same report can produce very different information across software,\neven something as simple as the average revenue per run.  One company report I reviewed only used the\npaid runs, which produced a significantly better result per run then including\nruns never paid.  But which was more\nuseful when multiplying times run performed in a month.  The lower one was more accurate, because every\nmonth had a percent of unpaid runs, especially in the 911 business.  The first one was higher and might make your\nbilling results look better, but it was not good data for the purpose of forecasting.\n<\/p>\n\n\n\n

In running your business it is vital to have at least the\nsimplest level of information for forecasting (that was discussed at the\nbeginning of this article). It requires good data, good software and a very\ngood understanding of the data and how the software uses it for reporting.  Are you capable of getting this from your\nsoftware?  Are the users of your software\ncapable of getting this information?  <\/p>\n","protected":false},"excerpt":{"rendered":"

AMBULANCE FORECASTING METHODS One of the trickiest things in forecasting revenue for ambulance billing is getting a methodology that combines the number of transports, the payer mix of the transports and the payer\u2019s average time of payment.  When your software and your database allow you to easily access and combine that data the result can … Continue reading Ambulance Billing – Accurate Forecasting<\/span><\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-51","post","type-post","status-publish","format-standard","hentry","category-general"],"_links":{"self":[{"href":"http:\/\/www.sharpab.com\/Blog\/wp-json\/wp\/v2\/posts\/51"}],"collection":[{"href":"http:\/\/www.sharpab.com\/Blog\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"http:\/\/www.sharpab.com\/Blog\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"http:\/\/www.sharpab.com\/Blog\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"http:\/\/www.sharpab.com\/Blog\/wp-json\/wp\/v2\/comments?post=51"}],"version-history":[{"count":2,"href":"http:\/\/www.sharpab.com\/Blog\/wp-json\/wp\/v2\/posts\/51\/revisions"}],"predecessor-version":[{"id":55,"href":"http:\/\/www.sharpab.com\/Blog\/wp-json\/wp\/v2\/posts\/51\/revisions\/55"}],"wp:attachment":[{"href":"http:\/\/www.sharpab.com\/Blog\/wp-json\/wp\/v2\/media?parent=51"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"http:\/\/www.sharpab.com\/Blog\/wp-json\/wp\/v2\/categories?post=51"},{"taxonomy":"post_tag","embeddable":true,"href":"http:\/\/www.sharpab.com\/Blog\/wp-json\/wp\/v2\/tags?post=51"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}