AI implementation strategies today frequently suffer from a narrow focus, prioritizing speed and cost-reduction for just one or two disjointed initiatives. While individual efficiencies are valuable, this approach leaves legacy systemic fragmentation in place, and rebuilds the same clinical logic separately for every new use case.
True transformation lies in building an AI pipeline for health organizations that accomplishes what was previously impossible: aligning and unifying data and workflows, compressing clinical knowledge engineering from person-years into about a month, and delivering precision high-value care cost effectively.
In this video, Rob Reynolds (VP of Clinical Intelligence) and Dr. Matthew Burton (Clinical Informaticist) introduce a new approach to building clinical intelligence at scale. They outline how to move beyond isolated, one-off builds, and how the same clinical intelligence can be aligned, and reused across the healthcare ecosystem.
Watch this 21 minute video and learn:
Adding AI will not automate confidence in data. The real opportunity is to build a validated FHIR®-native and CQL foundation which turns uncoded, fragmented data into a continuous source of real-time, clinically-rich and semantically aligned repository.
This is the synergistic foundation that de-risks AI implementations in healthcare. Instead of rebuilding logic for every isolated use case, Smile's single progressive AI pipeline aligns, unifies, and reuses clinical intelligence across Clinical Practice Guidelines, Prior Authorization Policies, and Risk Adjustment Rules. The outcomes are defensible, cost-effective and scale consistently.