How does the AI handle handwritten requisitions?
The AI uses computer vision and natural language processing trained on thousands of handwritten medical requisitions. It reads handwriting, interprets abbreviations (like common test code shorthand), and cross-references against your test catalog. When confidence is low on a field, it flags it for human review rather than guessing. Over time, it learns your specific providers' handwriting patterns and gets more accurate.
What LIS/LIMS systems do you integrate with?
We've integrated with every major LIS/LIMS platform — Sunquest, Cerner PathNet, Epic Beaker, Orchard Harvest, STARLIMS, LabWare, and several proprietary systems. The AI layer sits on top of your existing LIS, connecting to it via HL7, API, or database integration. We don't replace your LIS — we make it work better by automating the data entry and validation that happens before information enters the system.
How do we start — do we need to change our current systems?
No system changes required. We start with a 2-week AI Roadmap where our engineers observe your operation, map your document flows, and measure volumes. The first workflow runs alongside your existing process — AI processes orders in parallel while your team works normally. Once accuracy is validated (typically 2-3 weeks), we switch over. Your team starts managing exceptions instead of processing everything manually.
What happens when the AI encounters a format it hasn't seen before?
Every workflow has a confidence threshold. When the AI encounters an unfamiliar format, it routes the document to human review — same as your current process. But here's the difference: your team corrects the extraction, and the AI learns from that correction. After seeing 3-5 examples of a new format, it handles it automatically. New referring providers are typically fully automated within the first week.
What's the total cost for year 1?
Two options. Per Workflow: $25K-$75K per workflow, best for starting with one high-impact process like order intake. Dedicated Team: ~$22K/month for 2+ AI engineers embedded in your operation, building multiple workflows continuously. Most lab networks start with order intake automation, prove the ROI, then move to a dedicated team for result delivery and reconciliation. Every engagement starts with a free 30-minute assessment — no commitment.
How do we measure success?
We define success metrics with you before building anything. For lab networks, typical metrics include: orders processed per hour, error rate, result turnaround time, client follow-up call volume, billing denial rate, and revenue recovered. We set up dashboards that show before/after in real-time. If the numbers don't improve within 90 days, we have a problem — and we fix it on our dime.