AI Workflow Automation for Healthcare

Healthcare AI lives or dies on the integration layer, not the model. Provider systems speak HL7 v2 for messaging and HL7 FHIR R4 for modern APIs, and most real deployments involve both — an Epic or Cerner instance exposing FHIR endpoints for read while still emitting ADT feeds over v2 for admissions, discharges, and transfers. We build against the interfaces your customers actually run: SMART on FHIR for launching inside the EHR with the clinician's context and permissions intact, CDS Hooks where a suggestion needs to appear at the moment of ordering, and bulk FHIR export where the workload is population-level rather than per-patient. Getting this right in the MVP is what turns a pilot into a signed contract, because a tool that requires manual data export never survives procurement.

Compliance is architecture, not paperwork. Any component that stores, processes, or transmits PHI needs a Business Associate Agreement — and that includes the ones teams forget: error tracking capturing a request body, transactional email with appointment details in the subject, analytics recording a URL containing a record ID. We map the PHI path end to end before writing code, keep identifiers out of logs and third-party telemetry by default, and build attributable audit logging into the schema rather than bolting it on. Minimum-necessary access, role-based controls tied to real clinical roles, and encryption with key management you can describe in a security review are the baseline a health system's InfoSec team will actually test.

Clinical AI carries a regulatory question most teams underestimate: whether your product is a medical device. Under FDA guidance, software that provides a specific diagnosis or treatment recommendation the clinician cannot independently review may be regulated as Clinical Decision Support software, while tools that surface information for a clinician to evaluate generally are not. That distinction shapes scope directly — we design toward transparent, reviewable output with the underlying evidence exposed, so the clinician remains the decision-maker and the product stays on the right side of the line. Where a device pathway is genuinely intended, we say so early rather than discovering it during diligence.

The workflows that actually pay back in a healthcare MVP are administrative rather than diagnostic: prior-authorization packet assembly and status tracking, clinical documentation drafting with mandatory human review before anything enters the record, claims and denial triage with reason-code extraction, referral routing, and inbox triage for the message volume that drives clinician burnout. These have measurable baselines — turnaround time, touches per case, denial rate — which means a pilot can prove value in weeks with numbers a budget holder will accept. We instrument those metrics from day one, because a healthcare pilot that works but produces no evidence cannot be bought.