A data-driven look at IDR offer strategy under the No Surprises Act, why the long-standing 1:1 offer-to-QPA playbook is losing ground, based on the 2023 CMS dispute data, and how payers should adapt.
Our CTIO on a real, measurable LLM use case: turning millions of dollars of manual document data entry into a controllable, multi-model extraction pipeline, and why the right answer often isn't the biggest model.
An NCQA mandate requires 80% directly-sourced REL data; the payer was collecting under 5%, with no governance to secure it. Trexin restructured their data-governance framework and built a 12-tactic roadmap to close health-equity gaps.
Most generative-AI projects don't stall on the technology. They stall because the unglamorous work that makes AI usable in production gets skipped. A practitioner's framework built on three things: the use case, the data, and governance.
Trexin served as data delivery lead building a large health payer's external data management system: governed, traceable data exchange that reduces the risk of sensitive-data disclosure, delivered on time and on budget.
Trexin led a behavioral-health provider's enterprise Data & Analytics Strategy: a 3-year roadmap of 28 capability projects to support a goal of doubling the business in four years.
Trexin stood up a production analytics environment in Microsoft Azure for a mid-sized manufacturer: self-service BI in about 90 days, aggregating 50+ source tables on a 5-minute refresh.
The post-2020 semiconductor shortage rippled across 169 industries. For one MedTech maker, the response wasn't waiting it out, it was modeling the scenarios and launching a non-affected device in five months.
A fast-growing senior-care provider's PACS imaging wasn't integrated with its EMR, hampering radiologists. Trexin closed the gap with RPA (UiPath + HL7): faster image access and a 20% increase in imaging throughput.
Growing 35%+ a year and opening 20 clinics, a senior-care provider needed automation to scale. Trexin's 8-week RPA pilot automated over 90% of tasks, cut errors 70%, and projected $2M+ ROI.
Most organizations accept that their data is valuable. Where they get stuck is turning it into something they can act on, and whether to treat data as a service they stand up in the cloud. A practitioner's view on when DaaS pays off, and what to weigh first.
A MedTech group's analytics ran on siloed data and Excel, producing late, two-number reports. Trexin re-architected it into a reusable 'analytics factory': MVP in 5 months, with data-prep effort down 60%.
Start with a fixed-scope AI Readiness Assessment: a fast, low-commitment way to find out where AI will actually pay off, and what it takes to get there.