Clinical Documentation & Care Ops
Give clinicians their time back — ambient documentation, care coordination, and patient-risk signals, built for privacy and clinical review.
The problem
Clinicians spend more time on documentation than on patients, care coordination falls through the cracks, and risk is spotted too late. In healthcare, every AI output needs privacy controls and a clinician in the loop.
What it includes
The capabilities we build into this solution — engineered for production, not a demo.
Ambient documentation
Turn encounters into structured notes clinicians review and sign.
Care coordination
Summarize records and surface the next action across a care team.
Patient-risk signals
Flag patients at risk from records, labs, and history — early.
Patient engagement
Grounded assistants for scheduling, intake, and questions, with handoff.
Privacy & compliance
PHI controls, access logging, and deployment to meet HIPAA-grade needs.
Clinician-in-the-loop
AI drafts and flags; a clinician always makes the call.
How we'd build it
The same method behind everything we ship: de-risked, measured, and in production in one quarter.
Feasibility
We validate the use case on your real data, define the accuracy bar and ROI, and give you an honest go/no-go — usually in weeks.
Build & evaluate
We engineer the system and score it against a labelled set until it clears the bar — quality is a measured number, not a promise.
Deploy
We ship to production, integrated with your systems and behind the right human-approval gates, with monitoring from day one.
Operate & improve
We watch it in production, handle drift, and expand scope as trust builds — feasibility in 6 weeks, production in ~90 days.
What you get
- Structured clinical notes with clinician review
- Earlier, explainable patient-risk signals
- Less administrative load on clinical staff
- Privacy-first deployment with an audit trail
Production AI we've shipped
We'd build your solution with the same discipline behind these real, in-production engagements — described by sector under NDA.
A production document classifier — 5,000+ documents/month, built to a ≥95% accuracy target and live in 8 weeks.
Read the case studyBank-statement automation across 21 banks with a measured 0.24% transaction-direction error rate.
Read the case studyAI-at-POS recommendations running against live in-store transactions across every location.
Read the case studyWant to scope Clinical Documentation & Care Ops?
Tell us the use case. We'll come back with a feasibility view and an honest go/no-go — a working prototype in about six weeks.
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Location
Barcelona, Spain • Lisbon, Portugal • Sheridan, WY, USA (incorporation)
Why Choose CONE RED?
- AI R&D lab & venture builder — production systems, not slideware
- Feasibility in 6 weeks, production in ~90 days
- Accuracy measured and evaluated — never invented or guaranteed
- Real engagements across financial services, healthcare & retail
- End-to-end support from strategy to deployment
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