We bridge the gap between AI potential and clinical reality. Using Six Sigma rigor, we help health systems go the “last mile” from technical validation to operational ROI.
The challenge isn’t the innovation. It’s the operating model.
Since 1970, administrative roles have grown to manage increasing regulatory and operational complexity. Adding AI without workflow redesign increases this load.
Source: BLS / Himmelstein & Woolhandler
Health systems operate on razor-thin margins compared to MedTech (18%) or SaaS (25%). Investments must demonstrate immediate, measurable ROI to be sustainable.
Source: Moody’s 2024 Outlook
Most AI initiatives stall before reaching production scale due to governance complexity, IT integration hurdles, and lack of operational alignment.
Source: Moody’s 2024 Outlook
Why are promised operational improvements rarely achieved? Here is the breakdown of the hidden costs and failure modes that most frequently derail ROI.
Derails: Predictive Analytics, AI Workflow
Medical data is notoriously messy. Your EMR codes differently than your Lab system. You budget for the AI, but spend extra hiring data engineers to map streams so the AI doesn’t hallucinate.
Models trained on 2023 data lose accuracy by 2025. This requires a permanent “ML-Ops” team to constantly re-validate, or clinicians stop trusting the tool.
Derails: All Technologies
Buying the tech is easy; connecting it to the EMR is hard. Vendors often charge substantial fees to “open the door” (API access) for third-party apps.
If integration is too expensive, staff manually type data from the smart device into the EMR. This increases labor time instead of reducing it.
Derails: IoT & RFID
The robot is a one-time cost, but instruments have “kill chips” preventing reuse. Recurring costs for disposables can run ~$2,000 per procedure.
Complex tools take longer to sterilize. If Central Sterile Supply gets backed up, the expensive robot sits idle, killing volume targets.
Derails: IoT & RFID
Hospitals are hostile to wireless signals (lead walls). You buy tags, but then need a massive network upgrade (cabling, APs) just to make them work.
Active RFID tags have batteries. If you have 10,000 tags, staff might need to replace 13 batteries daily. If they fall behind, the system goes dark.
Derails: Telemedicine & Patient Monitoring
Too many alerts cause clinicians to ignore them. You need to budget 20% of CapEx for educators to redesign workflows and set proper thresholds.
If an AI “predicts” an event and staff don’t react in time, liability increases. Digital records of “knowing” create new malpractice risks.
Hard questions to ask before you sign. Protect your ROI.
WHY:Forces them to admit if the model degrades (drifts) without constant retraining.
WHY:Vendors often assume your data is perfect. If it’s not (it never is), you want them to absorb the cost of cleaning it.
WHY:Don’t let them improve their product for free using your proprietary clinical data. You should get a discount or royalties.
WHY:Epic, Cerner, and others charge substantial fees to let 3rd party apps connect. Vendors often leave this blank.
WHY:‘Read-only’ means the nurse still has to manually type the result back into the chart. You need write-back capability for efficiency.
WHY:The software can be working perfectly, but if the link to the EMR breaks, it’s useless.
WHY:You need to know if a $2,000 tool will self-disable after exactly 10 uses, regardless of condition.
WHY:
Vendors often hook you with a cheap Year 1, then hike maintenance fees by 15% annually. Ask for a 5-year cap.
WHY:If a robot tool takes 4 hours to cool down and sterilize, you might need to buy double the inventory to keep up with your surgery schedule.
WHY:If they don’t survey your lead-lined walls first, they will inevitably blame ‘your network’ when devices don’t connect.
WHY:
Changing 5,000 batteries is a full-time job. Make sure you aren’t the one hiring the person to do it.
Traditional implementation methodologies fail because they treat AI like standard software. It isn't. It's a probabilistic tool that requires a deterministic operating model.
Our approach is disciplined, data-driven, and focused entirely on the metric that matters: Clinical Operational Improvement.
We don't start with software. We start with the clinical workflow. We map current state processes, identify bottlenecks, and quantify the baseline metrics.
We leverage our venture network to find and vet AI solutions. We look beyond the sales pitch to assess technical stability, EMR integration capability, and clinical safety.
This is the 'Last Mile.' We redesign the workflow to accommodate the AI, train the clinical staff, and manage the change management process using Lean principles.
We implement continuous monitoring systems to track drift, measure realized ROI, and expand successful pilots across the enterprise.
Real engagements. Real ROI.
A Level 1 Trauma Center was facing a 4-hour backlog on fracture reads during night shifts, leading to ED overcrowding and patient LWBS.
We implemented an AI fracture-detection solution and redesigned the workflow to allow “AI-positive” flags to trigger preliminary splinting orders.
An IDN was losing track of incidental lung nodules in ED CT scans, causing poor outcomes and revenue leakage.
We implemented NLP to scan radiology reports and route flagged patients directly to a Nurse Navigator for follow-up scheduling.
Real engagements. Real ROI.
We cut through the vendor noise. We perform deep technical and clinical due diligence to ensure you buy solutions that actually work.
Six Sigma workflow redesign. We map the "Before" and "After" states to ensure the AI augments the clinician rather than distracting them.
For startups, we provide capital and strategic guidance. For health systems, we structure governance models to manage AI risk.