Our work in Healthcare & Life Sciences

Trexin CTIO Ton Roelandse joined the MedCity Pivot Podcast to discuss the real-world potential of AI in healthcare and the guardrails needed to keep it from causing harm.

Trexin's own SaaS platform manages the federal IDR process under the No Surprises Act end to end, built on a solution already proven in production at a healthcare payer.

Trexin managed a Microsoft Azure + UiPath document-automation pilot for a large health insurer, projected $1.2M in first-year savings at a success rate 12% above target.

A large health insurer took back control of an underperforming, third-party AI prior-authorization model, and Trexin built the strategy and roadmap behind $6.25M in annual savings.

Facing roughly 12,000 dispute emails a month under the No Surprises Act, a health insurer asked Trexin to overhaul IDR processing. The withdrawal process alone saved about $100k a month, and win rates climbed as high as 32%.

Tightening PFAS regulation and supplier exits threatened a MedTech maker's supply chain. Trexin ran a structured outreach across 3,000+ suppliers (3,085 contacted, 1,971 reaching complete status) to inventory 'forever chemicals' for compliance and continuity.

Third-party breaches are a top CISO worry, one healthcare-payer breach ran to $3B in costs. Trexin ran a 'friendly' risk assessment of a multistate payer's subsidiary, scoring maturity and finding 6,000 vulnerabilities (3,000 high), 87% mitigable in the Azure migration.

A global medical-device maker's supplier data was siloed and inconsistent, breaking reporting. Trexin built an 'MDM-lite' solution on the tools they already owned (faster ingestion, automated auditing, change-history tracking) with no new license fees.

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.

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.

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.

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.

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%.

A large health payer's tangle of warehouses couldn't support modern analytics. Trexin's 12-week assessment (56 leaders, 4 health plans) designed a future-state platform (data lake, sandboxes, analytic workbenches, and an AI/ML library) with an incremental build roadmap.

A malpractice insurer estimated $4M a year from catching high-risk claims at first notice of loss, but review was manual and expert-bound. Trexin's machine-learning model learned the rules from history; within a year the Client was on track to take down $4.3M.

A non-profit wanted to prove the economics of a procedure it advocated. Trexin's Actionable Analytics Jumpstart answered it in under 30 days, and showed the study should measure survival, not just cost, since untreated patients died too quickly for cost offsets to appear.

A state Medicaid plan set a $4MM cost-of-care savings target. Trexin's machine-learning model learned the experts' member-reassignment rules at over 99% accuracy, cutting cycle time an estimated 8–12 weeks and driving more than $1MM of the goal.

A safety-net hospital wanted to know whether severe-infection patients were being triaged to the right level of care, without a multi-year prospective study. Trexin's data-science analysis of billing and discharge data found mis-triage in 1 in 8 patients, tied to worse outcomes.

Risk-adjustment models exist to remove uncertainty, but current models capture only 10–15% of the variation. The 80%+ they miss is exactly where better care and value hide.