Best behavioral health EHR for billing is one of those searches that almost never gets a real answer, because almost nobody evaluating these systems is actually testing billing capability before they buy. This article is built from firsthand work: an attempt to build a cross-system accounts receivable benchmarking tool, direct product testing across five widely used systems, and live conversations with billers working inside them every day. What follows is what we actually found, not marketing copy from any vendor involved.
Why finding the best behavioral health EHR for billing is so hard
The root cause is simple. Almost nobody buys an EHR based on its billing or analytics capability, and that’s even more true in behavioral health than in general medicine. The decision maker is almost always a clinician, whether that’s a psychologist or an orthopedic surgeon, and the purchase decision runs on clinical workflow. Billing gets evaluated as a checkbox, not a real criterion, which means vendors have little incentive to invest in it.
That plays out in four consistent failure modes across nearly every system we’ve tested.
- Can’t get the data. Fields are missing entirely, or the reporting structure simply doesn’t expose the level of detail needed.
- A ton of work. Even when a report can be built, it rarely contains everything needed in one place, so someone ends up slicing, merging, and trying to join multiple reports, often without a reliable shared key to join them on.
- No real analysis. Plenty of vendors market “analytics,” but what’s actually delivered is reporting: raw data output, not insight.
- No resources. Even where the export exists, few practices or billing companies have staff capable of doing the data work required to turn it into something useful.
The net result is that a billing team can’t answer the questions it actually needs to answer, for itself or for the practice it serves, which makes it much harder to solve real problems or communicate clearly with anyone relying on that data.
Reporting and data extraction failures that show up almost everywhere
Try requesting one report with every field a biller actually needs: patient name, date of service, CPT code, modifiers, units, ICD codes, charges, place of service, primary insurance, plan, insurance collections, and insurance balance. Across the systems we tested, that report doesn’t exist. A typical result includes charges, CPT codes, and modifiers, but leaves out place of service or plan entirely.
The structural reason is straightforward: that much data doesn’t fit cleanly into a single flat file. It genuinely needs a relational database, and most of these systems simply don’t expose one to the practice. In practice, that means merging three to five separate reports by hand just to answer a single question, and we’ve seen this consume dozens of hours from skilled analysts, including expensive ones, just trying to extract usable data. Ongoing extraction at this level of manual effort tends to run 10 to 20 hours a month, on an ongoing basis, and joins are frequently impossible anyway because there’s no deterministic key shared across the reports being combined.
Customization is just as limited. In most systems, you can’t add a field, write a report from scratch, or pull from a different underlying table. You can only filter an existing canned report, and there are no saved templates, so the same report gets rebuilt from nothing every single month.
Whole categories of data are simply missing
- ERA and 835 data. Denial data lives in this file, and most systems don’t expose it, which is the root reason denials analytics is impossible in the majority of these tools.
- Biller and collector productivity data. The underlying data elements to track this often don’t exist at all.
- Transaction-level detail. Most billing systems can at least show, in a flat file, that a claim was paid and then had money taken back later. Several behavioral health systems can’t do even that, which makes recoupment reconciliation effectively impossible.
- Denial codes and descriptions. One system in this category has none at all, covered in detail further down.
AR reports are broken in ways that aren’t obvious until you rely on them
Some systems lock accounts receivable reporting to a single calendar month, so you can’t see all current outstanding AR at once, only what was outstanding as of a specific closed month. By the time that report is available, the practice has already moved on to caring about a different period entirely.
Aging buckets often carry no interior detail. A bucket showing dollars in the 31 to 60 day range gives no way to know whether those dollars sit closer to day 31 or day 59, and two practices with identical bucket totals can have meaningfully different actual AR health as a result. Some systems start the aging clock at the wrong point entirely, only beginning it once a claim is actually billed, so a claim can show as “current” at 90-plus days from the real date of service, making it impossible to reliably tell whether a claim is genuinely old.
Payer names rarely map cleanly either. Practices customize their own payer lists, so the same real payer can appear under different names across systems, and even across two practices using the identical system, which means any payer-level comparison requires a manual crosswalk before it means anything. Some systems don’t offer AR by payer at all, and can’t even show charges and AR in the same view, forcing constant manual back-and-forth between screens.
What we found trying to build a real benchmarking tool
The premise was deliberately minimal: no logins from the practice, no system access, no integration work. A practice exports one file, uploads it, and gets a benchmark back. Even that turned out to be brutally hard, because the underlying reports are inconsistent enough that even the simplest possible RCM benchmark, an AR aging bucket distribution, is genuinely difficult to standardize across systems.
The original development estimate was 200 to 300 hours, and a large share of that time went into just trying to bring the number down. We tried joining multiple reports within a single system directly, for example pulling payer detail from one report and CPT detail from another inside the same platform to combine them into one view. It didn’t work. We backed all the way up to the crudest metric available, the aging bucket distribution itself, and even that failed, because there’s no way to know where inside a bucket the actual dollars sit.
The core finding, across every system tested: none of them produce a single export containing CPT, payer, charge, insurance balance, patient balance, and aging bucket together in one file. For any given system, the only real question is how close you can get with one report, and what the minimum join looks like to reconstruct everything else. The practical consequence is that you can meaningfully compare practices within the same system, ten TherapyNotes practices against each other, for instance, but comparing across systems requires heavy manual normalization that most practices and even most billing companies simply don’t have the resources to do.
Which is the best behavioral health EHR for billing? Five systems compared
Here’s what direct testing found across five commonly used systems, focused specifically on AR aging report viability.
| System | Native AR aging report | CPT in report | Payer in report | Buckets pre-built | Overall |
|---|---|---|---|---|---|
| TherapyNotes | Yes, Insurance Aging | Yes | Yes | Yes | Best |
| Valant | Yes, Insurance + Patient Aging | Yes | Yes | Yes | Strong |
| SimplePractice | Partial (Outstanding Claims) | Yes | Uncertain in CSV | No | Moderate |
| TheraNest | Yes (basic) | Unknown | Unknown | Yes | Needs verification |
| ICANotes | No | No | No | No | Skip |
TherapyNotes: the highest-rated of the group
TherapyNotes’ Insurance Aging Report tracks outstanding balances from payers, and clicking into any balance reveals patient, service type, primary payer, and further detail directly. Aging arrives pre-bucketed inside the report itself, with no manual derivation needed, and it exports either as a spreadsheet or as a saved or printed PDF. Patient-side balances live in a separate Patient Aging Report, and an Insurance Adjustment Report carries charge and paid-rate detail, with Claim History providing CPT information as well. The most reliable join strategy is combining Insurance Aging with Insurance Adjustment on patient, date, and service type. Of the five systems, this is also the one where a complete, dependable step-by-step export process can actually be documented.
Valant: strong, and structurally different from the rest
Valant offers both Insurance Aging and a Patient Aging Summary, with CPT captured in its Charges Report and a Fee Schedule organized by payer. Aging buckets are pre-built, and reconstructing a full picture takes two files. What makes Valant structurally interesting is its clearinghouse: Valant runs on Waystar, which maintains its own independent reporting portal. That gives Valant users a parallel data path to their own billing data, one that can offer better CPT, payer, and balance granularity than the EHR’s native reporting. It’s also worth asking Waystar directly about a combined, 835-based export that may carry all the needed fields in a single file.
SimplePractice: moderate, with its own dedicated coverage
SimplePractice lands in the moderate range here, largely because its AR aging is only partial, payer detail in CSV exports is uncertain, and aging has to be calculated manually from the date of service rather than arriving pre-built. SimplePractice’s specific billing and denial-management limitations are extensive enough that they deserve, and have, their own dedicated coverage rather than a short summary here.
TheraNest: unverified
TheraNest has a basic AR aging report with pre-built buckets, but field-level export content, CPT, payer, and patient balance alike, is unconfirmed across the board. This one genuinely needs hands-on testing with a real practice login before reliable export instructions could be written.
ICANotes: skip entirely for billing purposes
ICANotes has no meaningful AR aging path at all. Clearinghouse integration is weak enough that billing data doesn’t reliably flow back into the system in the first place, and there’s no evidence anywhere in public documentation of CPT-level AR aging exports.
Not sure if your A/R is normal for a practice your size? Run it through the free Mental Health AR Benchmark.
The export file problem nobody expects
Field availability turns out not to be the hardest part. What actually derails most export attempts is the shape the file comes out in. A lot of these exports are formatted for printing rather than for data use: grouped by patient or payer with the payer shown as a section header band instead of a normal column value, subtotal rows scattered throughout, blank spacer rows, and a repeating page header on every page. Those embedded subtotal and total rows will silently double-count results if they aren’t stripped out first, and credits or negative balances will wreck percentage math if they aren’t handled explicitly. Zero-dollar lines show up too and need to be filtered out.
Some exports are PDF-only and simply can’t be parsed at all; those have to be rejected outright with a request to re-export in a usable format. A more subtle problem is summary-versus-detail confusion: because system naming often doesn’t clearly distinguish the two, it’s common to accidentally export a pre-aggregated summary instead of the line-item detail report actually needed. On a genuinely clean flat CSV, field mapping itself is trivial, closer to an hour of work than ten. It’s the generic cleanup, computing age from date of service to report date, dropping subtotal rows, handling credits, ignoring zero-dollar lines, that takes real time, though it only has to be built once and then applies across every system afterward.
Even the core metrics are hard to compute reliably
Days in A/R can’t be computed from an aging file alone. It requires an average daily charges figure, and a single aging snapshot simply doesn’t contain that. The more defensible workaround is asking the practice directly for a trailing three-month average charges number; a thinner alternative is deriving a run rate from roughly the last ten days of charges in the file and annualizing it, though that window is easily skewed by intake timing. It’s generally safer not to trust a system’s own bucket boundaries at all, and instead compute age from date of service to report date independently, since bucket definitions differ meaningfully from system to system.
There’s also a real cold-start problem for any kind of benchmarking: the first handful of uploads can’t be meaningfully compared against each other, that’s noise dressed up as insight, and a sophisticated prospect will see through it immediately. Getting past that requires seeding comparisons from an existing book of business plus clearly sourced published figures. It’s worth knowing there’s no public, behavioral-health-specific AR benchmark to fall back on either. MGMA publishes broad benchmarks, but they’re paywalled, not specific to behavioral health, refreshed on a multi-year cycle, cost real money, and offer limited depth for any single specialty. A handful of RCM vendors publish generic figures, but a true upload-and-compare benchmark built specifically for behavioral health doesn’t exist publicly anywhere that we’ve found.
The PHI and compliance dimension of exporting your own data
AR aging detail files almost always contain client names, dates of service, payer information, account numbers, and often CPT and diagnosis codes as well. In mental health this carries extra weight, since substance use disorder data can fall under 42 CFR Part 2 on top of standard HIPAA protections, and that’s a meaningfully stricter standard. Any tool receiving these uploads is, functionally, receiving PHI, and the cleanest posture is to strip identifier columns immediately and retain only aggregate financial fields. Holding onto raw PHI instead pushes a vendor into full business associate agreement territory, along with real security-control obligations: encryption at rest, an audit trail, and an actual deletion process.
One more data gap worth flagging: many of these systems don’t capture the payer’s own claim number at all, only an internal, system-generated claim number that has no relationship to the number the payer actually uses. Without it, diagnosing a problem or calling a payer for status becomes considerably harder than it needs to be.
Case study: Sessions Health, one of the worst systems for billing
Sessions Health is barely documented publicly, which made this one worth examining directly through a working biller’s experience. The practice in question runs mostly individual therapy with some group work, no medication management, focused on depression and grief, including a meaningful number of kids struggling through the pandemic years. It moved from SimplePractice to Sessions Health in mid-2025 and bills through Availity alongside it. Total collections run roughly $146,000 to $150,000 a month, with 30 to 40 percent of the practice being cash or self-pay.
Denials: no codes, no descriptions, nothing
Sessions Health carries no CARC or RARC denial codes at all, and no denial description either, nothing like “provider out of network” or “coverage terminated.” The biller has to go out to Availity separately to get actual codes, and Availity only surfaces them for some health plans in the first place. Inside Sessions, the status field simply reads “rejected” or “denied,” occasionally with a vague blurb like “check the patient’s insurance,” and sometimes with nothing at all. Over time, the biller has essentially had to teach herself to reverse-engineer the likely cause behind a rejected claim with no real system support, since the platform itself never tells her what actually went wrong.
There is no denials analytics of any kind
There’s no way to run a report showing everything denied in a given month, and no way to group denials by reason, whether that’s eligibility, credentialing, or anything else. Denials management simply doesn’t exist as a capability here.
Denials are scattered, and some are genuinely invisible
A section called “Claims to Review” is usually where denials land, typically visible on the main screen, but denials sometimes aren’t caught there at all, requiring a manual check inside the individual patient’s record to see if anything came back rejected or denied. There’s also a separate “Claims Unpaid” section, filterable by the past 30, 60, or 90 days, and terminology is inconsistent between “denied” and “rejected” depending on where you’re looking. Because there’s no single place that reliably shows everything, real duplication happens: there’s no way to know whether a given claim was already worked through a different entry point, so some work gets done twice while other issues slip through entirely.
Appeals can’t be done inside the system at all
Filing an appeal in Sessions Health effectively means resubmitting the original claim, which the payer then denies again since they already have the first submission on file. Real appeals have to happen through Availity instead, completely outside Sessions’ system of record, which means Sessions has no knowledge an appeal ever happened and can’t track it, age it, or report on it. There’s a partial workaround, a menu option that flags a claim as “submitted externally,” and it works for both original submissions and appeals, but the actual status still has to be tracked manually back in Availity the whole time.
The home screen offers almost nothing useful
When asked directly what the single biggest unaddressed pain point was, this is what came up unprompted, which makes it the highest-signal answer from the entire conversation: a genuine wish for an organized home screen with real prioritization, alerts, and a clear “these specific things need attention today” view, rather than clutter with no direction.
What actually populates the home screen instead is a client-setup list, a claims-in-review list, and three numbers across the top: unpaid claims, revenue collected, and forecasted revenue. Other systems in this category do offer a genuinely prioritized worklist. Sessions Health, as of this testing, does not.
Workflow and time, quantified
Roughly 15 percent of this biller’s time goes to denials inside a standard 40-hour week. On a given day she gets through at least half the denial queue, sometimes all of it, and whatever’s left simply builds up, with smaller-dollar items typically falling off first depending on how busy intake is that particular month. There’s an explicit, practical triage happening constantly: weighing a claim likely to recover a meaningful amount against one with a low chance of recovering very little, described as a genuinely judgment-free calculation given the constraints of the system itself. Under real time pressure, the approach becomes trying to diagnose a claim quickly, submitting it, and hoping it clears. Write-offs get reviewed once a month with the practice’s accountant, timed to give remits a chance to come back first, and the reasons are usually either terminated insurance or a patient who’s stopped responding to outreach. Claims get worked oldest date-of-service first, since timely filing limits vary meaningfully by payer, sometimes 30 days, sometimes 60, sometimes 90. There’s a real timely-filing trap layered on top of all of it: a denial can still blow the filing deadline even after the fact, because if a claim never technically made it onto file in the first place, wrong payer, missing information, the filing clock never actually started running.
The financial logic is straightforward once laid out: a worse system means either something simply can’t be done at all, or it takes extra steps, like leaving the platform to work through Availity separately. Extra steps take more time to accomplish the same task. That means less gets done inside the same fixed 40-hour week. And that directly means less gets collected, because that’s simply how the math works. Tellingly, even the biller working inside Sessions Health day to day still said she preferred SimplePractice by comparison.
Other real patterns from the same interview
- The most common denial causes were a mistyped letter or number in the member ID, a forgotten telehealth modifier, and a payer claiming the patient’s insurance was cancelled, which sometimes turns out to be the payer’s own error once someone actually calls to check
- A “false termination” pattern shows up periodically: eligibility checked clean, the claim was billed, and it still came back denied as terminated when that wasn’t actually true
- The standard denial-checking sequence includes verifying insurance information, confirming the patient’s name is spelled correctly, checking the current address, and figuring out which parent’s insurance actually applies in cases involving divorced parents
- Some payers have started pushing back on telehealth now that in-person visits are an option again post-pandemic
- Payer consolidation adds real confusion, since a plan mid-merger into a larger insurer can make it genuinely hard to track which specific plans go where
- Stale insurance cards send claims to the wrong address when a patient has a newer card they never provided to the practice
- Cash and self-pay handling is entirely manual: the owner takes a card, enters it into Sessions, and bills the client directly, while the biller separately has to flag missing copays by hand
Want to see where your own claims stand? Schedule a brief call for a free billing performance audit, and we will show you exactly where your practice is losing money.
EDI and ERA enrollment: the configuration failure nobody audits
This gap goes unnoticed for a simple reason: nobody really owns it. It’s typically set up once at onboarding and never revisited afterward, even though enrollments aren’t static. New providers and new credentials require new enrollments, and existing ones need periodic renewal. There’s no alert anywhere when an enrollment lapses or was never completed correctly in the first place; the claim simply goes out on paper, or the remit comes back on paper, and nothing flags that anything is wrong. Most practices have genuinely never been told this number exists, let alone what it should be.
The cost shows up as more paper EOBs instead of electronic remits, meaningfully slower payment, real difficulty figuring out what hasn’t been paid or resolved, and lost checks that arrive at the bank with no matching remittance data attached. Since billing capacity is largely fixed, that added difficulty translates directly into less productive work and lower collections overall. It’s also worth being direct about the ceiling here: without structured remittance data, no denial analysis is possible on those claims at all, no matter how good a system’s other reporting might be. Regardless of which system a practice runs on, this enrollment number needs to be high, and checking it is the first fix worth making, ahead of any decision about switching platforms entirely.
Billing problems that go well beyond reporting
We have never seen a behavioral health EHR or practice management system with genuine denials management built in. Most don’t even carry basic information about why an individual claim was denied, which is a strikingly basic thing to be missing from billing software.
Eligibility and benefits checking is consistently weak across the board. Some systems can’t check certain plans at all. Others require a painfully manual process, clicking into each patient individually, that still only returns a simple yes-or-no answer rather than real benefit detail. Some gate meaningfully better eligibility access behind a higher pricing tier. None of the systems we’ve reviewed are genuinely strong at full benefits verification, which happens to be the single most time-consuming part of this work.
Some systems block direct access to their own embedded clearinghouse, which makes billing meaningfully harder, generates more denials, and results in less revenue collected overall. Some can’t handle appeals from inside the system at all, forcing billers out to a separate clearinghouse to file them. That isn’t just added work. It creates a genuine information silo: appeal status lives somewhere the EHR can’t see, which means claims can get lost or miss timely filing deadlines entirely, and that’s a direct, quantifiable revenue loss rather than a mere inconvenience.
What this actually costs the practice owner
It’s genuinely difficult to run a practice effectively without real information. Every decision made without it is a guess with a lower probability of success than it should have. The real question worth asking is simple: what does an owner actually need to know in order to succeed, and is that information available anywhere right now?
Without something like this, a practice is running blind in the most literal sense: no analysis, nothing proactive, no ability to group similar claims into one solvable class, and often no real understanding of why claims aren’t getting paid. The clearest illustration is a familiar one: fifty claims failing to pay for a single payer because of one provider’s credentialing gap looks, without the right data, like fifty separate unrelated problems rather than a single fixable cause, so they get worked one at a time, forever, instead of solved once.
The patient side compounds all of it. A longer insurance A/R cycle means the patient gets billed later than they should. Worse, unpaid visits can pile up quietly until a patient receives one large bill covering many sessions at once. A poor verification of benefits at intake often means the practice wasn’t collecting enough from the patient upfront, or never had the conversation about financial responsibility at all. Having to tell a patient they owe for ten visits because their insurance didn’t pay is exactly the kind of conversation that damages trust and can drive a patient away entirely, over what was ultimately a billing data problem rather than anything clinical.
A biller who genuinely understands behavioral health billing tends to catch these patterns specifically because they know to look for them, rather than assuming a bad month is simply normal variation. Left unaddressed, the alternative pattern is billing staff working only the easiest claims, thin staffing, and no real performance tracking, which shows up as rising A/R, rising write-offs, or larger patient bills. At the extreme, a claim can simply never get submitted at all if patient information is incomplete, with nothing in the system flagging that gap and no redundant check on how charge data was entered in the first place. The standard worth holding any billing process to is straightforward: nothing falls through the cracks, and no filing deadline gets missed.
The false friction ceiling that keeps this hidden
There’s a widespread belief that some baseline amount of revenue loss is simply an unavoidable cost of doing business, and it shows up everywhere from solo practices to billion-dollar hospital systems. In our experience, the real number tends to run well above what most people assume, and because almost nobody is actually measuring it precisely, even a genuinely serious problem gets waved off as normal.
In one prior analytics engagement, a predictive-analytics application ingested real claims data and quantified how much more a comparable provider should be collecting for the same procedure, same payer, and same circumstances. For a $15 million practice, that gap came out to roughly $3.1 million a year, a figure well above what that organization’s own finance leadership had assumed going in. That’s one specific, described engagement rather than a universal benchmark, but the underlying pattern holds broadly: almost nobody can tell you with real precision how much a given provider should actually be collecting, so almost everyone assumes they’re within normal range.
How billing companies compound these same problems
System limitations and billing company behavior are tightly linked. Bad systems make good work genuinely hard, most billing companies then don’t put in the extra work required to compensate for that, and the owner ends up with no real visibility into either half of the problem.
The default pattern looks like this: working claims one at a time, opening a specific patient and date of service, figuring out what’s happening with that one claim, working it, and repeating that cycle endlessly, which is a major reason these problems persist instead of getting fixed at the source. A billing company that isn’t specifically focused on behavioral health is at a real disadvantage here, since coding errors, eligibility problems, credentialing gaps, payer-specific visit limits, or incorrectly billed unlicensed clinicians are all things a general medical biller may simply never learn to recognize, especially when the system itself is actively hiding the evidence.
Even billing companies that genuinely want to solve problems find it far harder, sometimes close to impossible, when the underlying system won’t give them the data to work with. The standard that actually matters is research, analyze, solve, and report, proactive rather than purely reactive, and a lot of billing companies simply don’t operate that way even in situations where the system isn’t the limiting factor at all.
Why practice owners rarely catch any of this themselves
Most mental health clinicians are genuinely skilled at reading people, not at systems-level operational thinking, and that’s exactly as it should be given what the work actually requires. Owners are focused on helping patients, not the often-painful mechanics running underneath the billing side of the business.
It’s genuinely hard to believe how bad the underlying process really is until it’s shown directly, since most owners aren’t naturally inclined to dig into this level of operational detail on their own. What most owners actually want is refreshingly simple: a clear, weekly sense of what to expect in payment, what was billed, what wasn’t, what got recouped, and why. It doesn’t need to be simple for anyone else, just for them, and with real trust in a billing team, the painful underlying detail doesn’t need to be something an owner personally wades through. Even well-intentioned transparency can miss that mark, though: a live, shared spreadsheet from a billing company can still leave an owner feeling completely overwhelmed rather than genuinely informed.
What to ask before you decide anything
Telling someone outright that their billing is bad and they should switch companies rarely lands well. It’s far more effective to help an owner see the actual chain for themselves: a system limitation creates a process limitation, which then shows up as a staffing or team limitation.
- Can the system show you all the denial codes and denial reasons, not just a status?
- Can you give me a list of denials grouped by issue, like credentialing or eligibility, rather than one undifferentiated list?
- Can you produce this specific report, right now?
Watch closely for a tracks-covering answer. If someone claims they can do all of it but never actually demonstrates it with a real, system-generated report rather than something typed by hand into a spreadsheet, treat that claim as unproven. If they drag their feet producing proof, it’s usually because the proof doesn’t exist yet and they’re scrambling to build it now that you’ve asked. If they genuinely can’t produce it at all, that’s the clearest signal available: real money is being lost, and it’s time to make a change, whether the biller in question is in-house or outsourced.
When you do get screenshots, don’t accept ones showing vague denial language with no codes and no analytics behind them; that plain text isn’t a standardized denial description and can be misleading or simply wrong. Ask instead for real pattern examples: which specific issue is generating a lot of the denials, which payer is consistently slow on credentialing and only surfaces it after the fact, which payer tends to claim a patient’s insurance termed just to buy time.
So what’s the best behavioral health EHR for billing?
Based on everything above, TherapyNotes and Valant come out ahead on raw billing and reporting capability, SimplePractice lands in the middle with real, documented gaps, TheraNest remains genuinely unverified, and ICANotes and Sessions Health are difficult to recommend for billing purposes at all. But even the strongest system in this comparison still can’t get all the fields a biller actually needs into one file, still can’t do real denial pattern analysis on its own, and still relies entirely on someone actually watching enrollment, eligibility, and reconciliation instead of assuming the software is handling it.
That’s the real answer underneath the question: the best behavioral health EHR for billing is still just a starting point, not a finished solution, and the practices getting genuinely strong results are the ones layering real expertise and proactive tracking on top of whatever system they’re using, not the ones hoping the software will solve this on its own.
Wondering how your own system’s billing and reporting gaps stack up against what’s described here? Schedule a brief call and we’ll walk through what your current setup is likely hiding.
Wondering how much your claims are actually leaving on the table? Schedule a brief call for a free billing performance audit, and we will walk through your denials, A/R and credentialing status with you and show you exactly where your practice is losing money.
