Research, analysis, and insights on AI-driven revenue intelligence for rural hospitals, managed care organizations, and the Medicaid ecosystem.
How 30 years of tacit RCM expertise became structured data
Denial recovery has been bound by a structural constraint for thirty years: expertise that lives in a handful of senior heads. That constraint just came off the board. The engine carries 136 cited oversight findings across 7 states, backed by $1.5B+ in documented evidence — enough that a first-year RCM associate can file appeals stronger than what experts alone could write.
Why Your RCM Team Can't Do What You're Expecting Them To Do
Your billing team isn't underperforming. They're doing something that shouldn't be possible. 58 data points per claim, 27 million routing combinations, 9 payer portals - and payers use AI to deny while hospitals respond with spreadsheets. The math on why effort alone can't close the gap.
A Field Methodology for Recovering Underpaid Medicaid Revenue at Rural Hospitals
Field report from a Kansas rural hospital engagement where HRN recovered six figures of underpaid Medicaid revenue in fourteen days of operations. Documents the visibility-vs-recovery diagnostic reframe, the three-layer methodology, and the sprint cadence model that compresses the standard 90-day vendor cycle to 14 days. The proof-point counterpart to "When AI Makes the Problem Worse."
What the PHTI Report Means for Rural Health — and the Real Path to Solving Uncompensated Care
The Peterson Health Technology Institute just confirmed it on the record: AI is not lowering system-wide healthcare costs — it is raising them. Why generic AI RCM is making rural hospitals the third loser, why MCOs are paying for the same churn, and what an operator-led path forward actually looks like.
Medicaid complexity is structurally underpaying rural hospitals — and almost nobody is working the recovery side.
Why most rural hospitals are being underpaid by Medicaid for services they have already rendered, why generic AI doesn’t solve the problem, and what recovery actually looks like — written for CFOs and boards being asked to make survival decisions without a complete picture.
Why Managed Care Needs a New Intelligence Layer
Medical costs are outpacing capitation. Enrollment is churning. CMS is watching. What the data says about the Medicaid managed care margin crisis — and what MCOs can do about it.
Why New Mexico Hospitals Must Build Their Own Revenue Recovery Infrastructure
When every layer of a state's Medicaid infrastructure fails simultaneously, hospitals are structurally on their own. A state-level case study in systemic failure — and what it means for every hospital in America.
The Behavioral Science of Revenue Cycle Intelligence
How cognitive limitations — not staff limitations — drive rural hospital revenue loss. Explores the science of serial vs. parallel processing and what changes when AI can analyze 835 remittance data across all eight dimensions simultaneously.
Why Rural Hospital Revenue Recovery Requires a Different Kind of AI
Why generic healthcare AI fails at the rural hospital Medicaid problem, and what purpose-built, state-specific intelligence looks like in practice. Features Kansas and Nebraska case data with MCO-specific analysis.