AI Solutions in Healthcare That Survive Security Review and Reach Production

Artificial intelligence is the umbrella term for software that performs tasks we would call intelligent. Machine learning is the subset that learns patterns from data instead of following rules a developer wrote. Deep learning is the subset of machine learning built on multi-layer neural networks, and it is what makes medical images, speech, and free-text clinical notes tractable. Most operational and risk work in healthcare is classic machine learning. Imaging and natural language processing are where deep learning earns the extra cost.

A proof of concept runs 4 to 8 weeks. A production MVP with one integration runs 3 to 6 months. Multi-system platforms run 6 to 12 months. EHR integration and security review, not model training, are what move those numbers.

Three shapes, and which one you need depends on how much is still unknown. A proof of concept runs $15,000 to $40,000 over 4 to 8 weeks and answers whether the use case is worth building at all. An MVP with one model in production and one integration runs $60,000 to $150,000 over 3 to 6 months. An enterprise-scale platform with several models and multiple EHR or device integrations starts at $200,000 and takes 6 to 12 months.

The error budget is smaller, because a wrong answer touches patient care rather than an ad click. The data is regulated, so the shortcuts available in other industries are closed. Adoption depends on clinicians trusting the output, which means the interface matters as much as the AI models behind it. The benefits are real, but they arrive later than the vendor slide suggests.

Enough labeled history for the decision you want to improve, plus a reliable path to it. Our health risk assessment work needed 14 days of activity data per user before a score meant anything. If your data is thin, we say so at discovery and propose a data collection phase instead of a model.

Yes. Epic, Cerner, and Allscripts through FHIR R4, plus HL7 messaging for lab, pharmacy, and clinical documentation feeds. Assume the integration and its approvals take longer than the model.

Encryption at rest and in transit, role-based access per user type, audit logging on every read and write, and de-identification before patient data reaches a training pipeline or a third-party model API. On the Dalth platform, HIPAA verification was part of the launch checklist.

You do. IP transfers to you, we sign an NDA before scoping if you want one, and you get the training pipeline and the runbook, not only the weights.

Toward smaller, better-scoped models sitting inside clinical workflows rather than one system that claims to run the hospital. The near future belongs to narrow tools with clear owners: documentation, triage, coding, imaging triage. Agentic systems will keep expanding into operations, and the compliance expectations around them will tighten in parallel. Build for that future by keeping a human approval step where it matters.

Then we say so. Plenty of what gets scoped as artificial intelligence turns out to be a reporting problem or a workflow problem wearing a model as a costume. We would rather lose the build and keep the relationship than take budget for a project we expect to stall at the security review. Contact us, and we will tell you which one you have.