Provider Analytic Outliers, Trends, and Patterns Five Areas to Look Out For

November 12, 2025 / 5 min read

Among the many advantages of participating in the analysis of medical provider data are the opportunities to surface outliers, trends, and patterns. From a hierarchical perspective, the first level of anomalous behavior generally surfaces as an outlier in the data: something that stands out from all the rest. Depending on volume and propensity, an outlier may soon present as a trend in the data: something that we can deduce is happening with some degree of regularity. Similarly, the volume or propensity of a detected trend may result in the establishment of a behavioral pattern.

5 Trends Provider Scoring

In this article, we will present five distinct provider billing behaviors that have surfaced at sufficient levels to qualify them as patterns.

1. Ghost rides

Our first profiled behavior is known by the coined name, “ghost rides.” This condition is as simple as it is egregious. Ghost rides involve ambulance and/or medical transportation providers who bill for supposed services on dates where there exist no other billings by a treating provider, clinic, or facility. Though there may be some limited explanation as to why such billing is presented, the odds are low that there would not be at least some level of billing for that same patient, on that same date of service, by another provider—if even just for some form of patient intake.

More often, data analysis has revealed aggregated dollars in the millions where some sort of transportation services were billed, yet no other provider, clinic, or facility billed for the respective patients on the respective dates of service. This type of exposure vulnerability is scary because we would not immediately think of an ambulance or medical transportation provider billing for services not actually rendered, yet the data results are undeniable. Perhaps this scary scenario is exactly why the behavior has been called ghost rides.

2. Subjective primary diagnoses

The second behavior we’d like to profile involves providers billing subjective primary diagnoses. Subjective diagnoses are those identified as being vague, ambiguous, other, unspecified, and/or pain-related. For example, Diagnosis Code M54.2 is cervicalgia, a fancy medical designation for what amounts to neck pain. The application of a subjective diagnosis within, perhaps, the first thirty days of treatment may be appropriate. This scenario would assume that the provider would ostensibly perform additional assessments, run further tests, and/or secure diagnostic evidence to evaluate the cause and origin of the patient’s issue(s). However, once determined, those subjective patient diagnoses should be appropriately updated to specific ones. Typically, then an equally appropriate and updated treatment plan is designed to address the patient’s more accurate injury type/condition. Providers are required to apply the most specific diagnoses. Unfortunately for payers, without specific diagnoses determined and applied to patients, providers are able to generate and bill for a greater number of treatments and procedures. Such occurrences unnecessarily drive up claim costs. These are needless expenses that otherwise would not be possible when such treatments are assessed against more specific diagnoses.

3. Boilerplate billing

Third on our list is template—also known as boilerplate—billing. Due to the combined uniqueness of each patient, their history, condition type, and catalyst for their condition, it would be expected that variance in procedures/services should occur among patients with different injuries and/or functional deficit. Too high a level of consistency may indicate possible template treatment and/or boilerplate billing and warrants closer examination.

As such, when we find providers whose patient population largely have the same or similar treatment procedures or modalities applied to them—regardless of their varying diagnoses—we need to ask, why? To some extent, these types of template treatments have become so commonplace that they are seemingly just accepted by the industry. When we adopt such a mindset, we are essentially letting providers bill this way unchecked, while the providers engaged in this practice are laughing all the way to the bank.

We should never forget that the burden of proof lies with the provider presenting the bills. A respectable provider pedigree should never dissuade a good investigator or attorney from asking questions. If the provider believes they are justified in presenting the template-type billing across their patient population, compel them to explain why.

4. Dental billing

“Dental?” you ask? Yes—and that is exactly why we decided to profile this billing risk. Generally speaking, when one thinks of the Property & Casualty (P&C) space, dental claims do not often come to the top of mind. However, analysis has revealed that dental-related injury claims have a much higher financial exposure than one may imagine.

Within the context of our examination, in the last 3 years alone, claims for dental-related P&C exposure exceeded $100,000,000! Therefore, these types of exposure risks are something you may want to sink your teeth into. If nothing else, scrutinize and assess the legitimate nature of dental-related injuries claimed in your P&C books of business.

5. Rogue TINs (Tax Identification Numbers)

Rogue TINs are defined as those which, upon observation, are obviously incorrect, such as 000000000, 000000001, 999999999, 123456789, etc., and/or which contain prefixes such as 00, 07, 08, 09, and others which are invalid according to published information from the IRS. Such TINs are highly questionable and potentially illegal.

Could a rogue TIN be a way for a provider to receive significant income yet not report that income to state or federal tax agencies? For instance, if a provider’s legitimate TIN is 123456789 yet they transpose a few of the last numbers to 123456987, how does your company address that sudden change? Do you delay payment and contact the provider requesting clarification on the newly presented TIN, or do you simply enter the new, rogue TIN into your system and issue payment under that new number?

Respectfully, the protocol should be the former. Careful attention should be employed by carriers to ensure they are not issuing payments to rogue TINs.

Conclusion

Identifying these five patterns—ghost rides, subjective primary diagnoses, template billing, dental exposure risks, and rogue TINs—requires vigilant analysis and systematic evaluation of provider data. While recognizing these patterns is an important first step, insurers need robust analytical tools to efficiently surface these behaviors across their entire book of business.

Advanced analytical solutions can help SIUs quickly identify which providers warrant closer examination and why. By leveraging aggregated industry data and predictive analytics, insurers can move beyond manual analysis to systematically detect outliers, trends, and patterns of practice.

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