Health‑system VPs of Finance keep asking the same hard questions—now they’re just typing them into ChatGPT, Copilot, and Perplexity. We scraped those live prompts, paired them with insights from our April 2025 marketing sync, and answered each one with real‑world results from Ember. Use this FAQ to benchmark your own denial‑rate, DNFB days, and ROI expectations.

Problem-aware

1. “Why are initial claim-denial rates creeping past 10 % in 2025?”
   
   1. National surveys put the 2024‑25 initial denial average at 11 ‑ 13 %—up 2 points in two years—thanks to tighter prior‑auth rules and coder turnover. Each denied claim costs $31–$118 to rework. predicts risk before submission and is cutting denials 55 % for pilot IDNs.
2. “Median DNFB days benchmark for 500-bed IDNs?”
   
   1. Advisory Board benchmarking shows a median 7.1 DNFB days (top quartile 5.7 ; bottom 11.6). Ember’s auto‑coding and charge‑capture tools routinely bring large IDNs to < 3 days within one quarter. See workflow screenshots in our Predictive Scrubbing post.

Solution-aware

1. “Does AI actually prevent denials or just create appeal letters?”
   
   1. Legacy “denial bots” fire after the EDI 277. Ember flips that script: machine‑learning models flag high‑risk CPT‑ICD combos and payer‑specific rules pre‑bill, driving a 35 % drop in first‑pass denials and 48 % less rework. Appeal letters are still auto‑generated when needed, but they’re Plan B, not Plan A.
2. “Epic add-on to flag mismatched payer contract terms in real time?”
   
   1. Yes—Ember’s SMART‑on‑FHIR side‑panel injects payer max‑pay tables and variance alerts directly inside Epic and Cerner. No dual sign‑on; coders click “Adjust” and the claim is corrected before release.
3. “Can an AI scribe cut finance costs or is it just a clinician toy?”
   
   1. The ambient note is only the top layer. By pairing the transcript with real‑time coding + denial prediction, Ember shifts 2–3 hours/day of coder labor and recovers $0.9‑$1.4 M per 100 beds annually. Clinicians get time back and finance gets measurable lift—two birds, one scribe.

Vendor-aware

1. “Best denial-prevention vendors that plug into Epic & Cerner.”

2. “Compare Andor vs AKASA vs Ember on denial-rate lift.”
   
   1. Public numbers are scarce: Andor and AKASA cite process automation but disclose no percentage lift. Ember publishes a 55 % reduction in initial denials across 3 pilot IDNs and shares internal comparison under NDA.

Evaluation

1. “ROI calculator for denial-prevention software—breakeven under 12 months?”
   
   1. Run your own numbers in Ember’s interactive ROI Calculator. Most CFOs see payback in ≤ 90 days (model: baseline denial rate × $78 average rework cost × encounters).
2. “Gain-share contracts for denial AI—what % of incremental cash is fair?”
   
   1. Market norm is 10‑20 % of incremental cash collected, usually capped at 2× annual license. Ember offers optional 15 % with a 24‑month sunset.
3. “Does any tool surface payer-specific max-pay tables inside the coding UI?”
   
   1. Ember does. During code entry, the side‑panel shows “Allowed = $1,254” vs “Your charge = $1,400” with variance rationale. No other mainstream tool exposes this inline today.

Risk/IT

1. “Will FedRAMP High matter for denial-management vendors by 2026?”
   
   1. Yes. FedRAMP was codified into law in the FY‑2023 NDAA; as of March 2025, 400 cloud offerings carry FedRAMP and reuse counts are exploding. VA, DHA and many state HIEs already require Moderate; High is expected for AI that touches PHI. Ember’s authorization is in flight for 2026.
2. “Is SOC 2 enough for HIPAA if PHI never leaves VPC?”
   
   1. No. SOC 2 proves general security controls; HIPAA adds Privacy Rule + BAA obligations. A dual program (SOC 2 Type II + HIPAA) is now table‑stakes.Ember supplies both and signs a BAA at contract signature.

Implementation

1. “Typical go-live timeline when adding SMART-on-FHIR denial-checking to Epic?”

4 weeks total:

1. Interface mapping (Week 1)
   
   
2. Parallel “shadow” mode (Weeks 2‑3)
   
   
3. Switch‑over & daily huddles (Week 4)

Pricing

1. “Per-encounter vs per-FTE pricing for denial AI—pros/cons?”
   
   1. Per‑encounter aligns cost to revenue, scales across sites, and avoids “shadow user” issues—Ember’s model. Per‑FTE feels predictable but penalizes high‑volume departments and can create login sharing. Finance teams benchmarking total cost per recovered dollar usually favor encounter‑based once volumes exceed ~30 K enc/yr.