Definitive drug testing and the value of state-specific claims edits

Definitive drug testing vs. presumptive drug testing

Before we get into the complexities of billing & tracking definitive drug testing, let’s break down the basics. Drug testing isn’t a one-size-fits-all process. With different methods designed for different needs, payers & providers typically distinguish between two main categories: presumptive and definitive testing. Each comes with its own level of testing accuracy, methodology, and layers of billing guidelines.

• Presumptive drug testing serves as an initial screen for potential substance use. Typically conducted on-site with rapid urine tests, it relies on immunoassay technology, which uses antibodies to detect drug molecules. While fast and efficient, it lacks the precision to confirm specific substances or concentrations.
• Definitive drug testing (a.k.a. confirmatory) gets specific. It is performed through a laboratory-based analysis to identify exact substances and their levels. Using gas chromatography/mass spectrometry (GC-MS) or liquid chromatography/mass spectrometry (LC-MS), these tests deliver high accuracy and detailed results essential for clinical decision-making. With greater precision, however, there comes a more complex billing and reimbursement process.

Distinguishing between these testing methods isn’t just a clinical necessity—it’s a key factor in how tests are properly billed and paid for, and can create complexities and wasteful spending down the line. 

CPT Codes 80305–80307, G0480–G0483, and G0659 consist of these primary categories of drug testing: presumptive and definitive.

When is definitive drug testing occurring most?

There are a variety of situations and provider types where definitive drug testing would typically be conducted. For example: 

• Following an initial presumptive screening for suspected use of illicit drugs, or if a presumptive test is not available. (Diagnosis code: Z79.899) 
• Routine drug tests in substance abuse treatment or rehabilitation centers to monitor patient adherence to treatment and recovery plans. (Diagnosis code: Z03.89)
• Pain management facilities confirming use or monitoring of medication for pain management (Diagnosis code: Z79.891)

Each one of these examples may have tests performed for different outcomes, but in the majority of cases, these definitive tests are performed to identify a focused selection of substances.

Decoding the billing complexities of definitive drug testing

Definitive drug testing coding is broken out into the amount of drug classes these tests are identifying. See the table of definitive drug testing codes below:

Billing for these isn’t always straightforward. Nuances to this billing include frequency of testing per calendar year, situations where presumptive screens are unavailable, or based on positive or inconclusive results of an initial screen. Navigating these nuances and coverage guidelines are critical to prevent overpayments for a plan. 

Sealing the cracks of CMS policy with state-specific claims edits

When looking at this at a state level, in New York for example, EMedNY policy clearly outlines they cover G0480 when no screening methods for the substances are available, which excludes other higher volume levels (G0481-83). When seeing these extended class codes - particularly G0483 rise in a NY plans’ Medicaid Managed Care or MCO data, it’s easy to question “why would so many patients be getting tested at a ‘G0483’ level?” 

Mark Starinsky, AHFI, CFE and Product Lead at Shift has seen many NY based labs billing at this level, “Even 10 years ago, I saw this rising while supporting other New York plans and it’s still around. This isn’t at the top of the list for NY plans, it’s not under control. Same codes, same labs, same problem.”

At a minimum, state plans follow CMS logic - if plans have a way to analyze EMedNY guidelines, additional claims edits can be created to control inaccurate billing and increase savings. 

Finding $233,000 in a plans data with Shift

Similar to other claims edits generated through policy scraping and generative AI, Shift used NLP and generative AI models to analyze EMedNY policy, fee schedules, and billing patterns related to definitive drug testing. This approach developed claim edit logic uncovering over $233,000 in potential overpayments across 18 months of paid claims data. These trends highlight clear opportunities to create state-specific claims edits that prevent future overpayments.

To learn more about this edit, or other emerging logic from state-level policy, reach out to schedule a meeting with the Shift team.