Increasing Cost Estimate Accuracy by 25%

Client Overview

Mindful Health Solutions (MHS) is a leading behavioral health group with clinics across California, Texas, Georgia, and Washington. Offering services like psychotherapy, transcranial magnetic stimulation (TMS), and esketamine, MHS manages over 16,000 patient appointments monthly across a diverse insurance landscape.

The Problem: Accurate Care Was Undercut by Inaccurate Bills

MHS built a serious clinical offering — psychotherapy, TMS, esketamine — across four states and 16,000 monthly appointments. But the quality of that care was getting undermined before a single session started, at the moment a patient learned what they'd owe.

Despite running established tools like Phreesia and NextGen, MHS's front-office teams couldn't consistently surface accurate copays, deductibles, or the carve-out logic specific to behavioral health coverage. By July 2024, baseline eligibility accuracy had fallen to just 72%. Every point below reliable meant more claim denials, more surprise bills landing on patients after the fact, and more hours of front-office staff time spent chasing down financial details that should have been correct the first time. For a multi-state, PE-backed organization trying to scale, that wasn't a tooling gap — it was a ceiling on how fast MHS could grow without growing its administrative burden right alongside it.

Sohar's Approach: Fix the Data Layer, Not the Tech Stack

MHS didn't need to rip out NextGen or Waystar to fix this. The tools weren't the problem — the eligibility data flowing into them was. So Sohar's approach was to wrap a more accurate verification layer around MHS's existing systems, rather than asking a 230-person, multi-state organization to re-platform in order to get better answers.

That meant working closely with MHS's RCM, operations, and IT teams to design an integration that respected how the business already ran:

• A dedicated sandbox and secure credentialing so testing never touched live patient data.
• Custom workflows timed to MHS's existing 3-day pre-visit request window, rather than forcing a new operational rhythm.

• Claims-informed validation that improved copay and coinsurance accuracy using real downstream outcomes, not just point-in-time requests.

• Daily exports built to slot directly into MHS's existing intake operations.

Rather than declaring victory at go-live, Sohar and MHS treated accuracy as something to keep earning: historical claims were analyzed to benchmark the starting 72% baseline, and per-plan adjustments continued based on audit findings as real data came in. By mid-2025, verification accuracy had stabilized at 90% — not through a single fix, but through continuous, claims-informed tuning.

The Outcome

The shift moved MHS from fragmented, manually patched eligibility processes to a unified verification layer that its front-office teams could actually trust. Automation now handles 94% of eligibility requests, eliminating roughly 1,000 manual requests a week. Member obligation estimates reach 95% accuracy, and benefits-request accuracy overall improved by 25% — which for patients means fewer surprises after the visit, and for MHS means a front office no longer absorbing the cost of chasing down answers that should have been right from the start.

How It Works, If You're Curious

MHS's results were powered by Sohar's Verification product, embedded into their intake systems through a structured, API-led integration alongside their existing NextGen and Waystar setup. But the real shift wasn't a new tool — it was treating eligibility accuracy as something to continuously earn through real claims data, not something a single integration could solve once and walk away from.