AI inside the care pathway — not bolted onto it.
Custom AI software for US health systems and payors — ambient documentation, grounded clinical knowledge, revenue cycle automation, and care-management copilots. We engineer the platform with HIPAA-grade safeguards and clinician-in-the-loop patterns; clinical responsibility stays with your team.
Ambient Clinical Documentation
Custom ambient capture and structured-note assistance — engineered so the clinician reviews, edits, and signs every note. Patient consent and opt-out preserved as first-class workflow states.
Grounded Clinical Knowledge
Retrieval-augmented software over your formularies, protocols, policies, and approved literature. Returns citations alongside answers; framed as clinician-reviewed reference, not autonomous advice.
Clinical Decision Support Interfaces
Decision-support and triage UIs shipped with explainability hooks, confidence reporting, and clinician-in-the-loop patterns. Final clinical decisions and any FDA SaMD pathway remain with your team.
Revenue Cycle Automation
Coding suggestions, CDI prompts, prior-authorization drafting, and denial root-cause analytics — all routed through human reviewers and recorded in the audit trail your finance and compliance teams own.
Population Health & Care Management Copilots
Risk-stratification on de-identified cohorts, outreach workflow automation, and closed-loop tracking against your existing quality measures. Care teams review and direct every intervention.
Patient Engagement & Intake AI
Conversational intake, symptom guidance with clinician escalation, and consent-aware patient messaging — engineered to route patients to the right care setting without making clinical claims.
AI That Helps the Clinician, Not Replaces Them
Ambient capture, grounded knowledge, and decision-support interfaces are engineered as proposals routed through the clinician — never as autonomous clinical action.
PHI, Audit, and Lifecycle Engineered In
Patient data is segmented and scoped at the application layer, every assist is recorded in the audit trail, and model lifecycle artifacts are delivered with the build.
Engineered with HIPAA, ONC, and FDA SaMD awareness for regulated healthcare work
Clinician-in-the-Loop, by design
Every AI assist routes through a human reviewer. Confidence and citations surface inline; nothing is auto-signed for the clinician. The clinical decision and the record remain the clinician’s.
PHI as a First-Class Engineering Concern
Patient data is tokenized at the application gateway, segmented by sensitivity, and access-scoped by role. Training pipelines use de-identified or synthetic data wherever the underlying work allows.
Audit, Evaluation & Lifecycle Artifacts
Audit-trail logs, model cards, evaluation reports, drift monitoring, and change-control records are delivered with the build — engineering inputs your informatics, compliance, and regulatory reviewers can read.
AI lives inside the care pathway — not bolted onto it.
Five stages of a clinical encounter. Five engineered augmentation surfaces. One governance band running underneath — so every assist is PHI-scoped, clinician-reviewed, and inside the audit trail by design.
Patient-facing engagement
- Conversational intake on consented data
- Symptom guidance with clinician escalation
- Routing to the right care setting
Ambient documentation assist
- Ambient capture with clinician-reviewed note
- Structured fields suggested, not auto-signed
- Patient-consent and opt-out preserved
Grounded clinical knowledge
- Retrieval over your formulary, protocols, and policies
- Citations alongside every answer
- Decision-support framed as clinician-reviewed suggestion
Revenue cycle automation
- Coding and CDI suggestions with audit trail
- Prior-authorization drafting for human review
- Denial root-cause analytics and rework queues
Care management copilots
- Risk-stratification on de-identified cohorts
- Outreach workflows reviewed by your care teams
- Closed-loop tracking against your existing measures
Every assist lives inside the audit trail, by design
- Clinician-in-the-loop by defaultAI proposes, the clinician decides. Confidence and citations surface inline.
- PHI scoped at the application layerTokenization, role-scoped access, and de-identified training data wherever the work allows.
- Audit trail wraps every assistActor, model version, prompt, response, and disposition recorded as immutable events.
- Model lifecycle artifactsModel cards, evaluation reports, and drift monitoring delivered with the build.
Compliance by design
Engineering artifacts for your validation work
We structure the build so your informatics, compliance, and (where applicable) regulatory teams have the documentation, traceability, and test evidence they need to execute their own validation work. We do not perform validation or attest compliance on your behalf.
PHI segmentation and tokenization
Patient data is tokenized at the application gateway, scoped by role, and de-identified or synthesized for training wherever the work allows. PHI is kept out of the model layer by default.
Audit-trail logging as an engineering default
Every AI assist is recorded as an immutable event — actor, model version, prompt, response, and disposition — so your informatics and compliance reviewers can trace activity end-to-end without bolt-on tooling.
Model lifecycle engineering
Model cards, training data fingerprints, evaluation harnesses, drift monitoring, and retraining gates — engineering practices informed by published guidance on responsible ML lifecycles, including FDA GMLP principles and NIST AI RMF profiles.
Clinician-in-the-loop interaction patterns
AI assists are designed as proposals. Confidence and citations surface inline; an explicit reviewer step gates clinical, coding, or care-management decisions. Nothing is auto-signed on behalf of a clinician.
Cloud infrastructure for sensitive workloads
Hosted on cloud regions and configurations commonly used for healthcare workloads, with private endpoints, infrastructure defined and reviewed via Terraform, and environment promotion gates your team can sign.
Audit-ready on day one
Every component is engineered with PHI segmentation, audit-trail logging, role-scoped access, lineage tracking, and lifecycle artifacts your informatics, compliance, and (where applicable) regulatory reviewers can use as inputs into their own validation work. Final clinical decisions, validation execution, and any regulatory pathway (e.g., FDA SaMD classification, 510(k), De Novo, or PMA submissions) remain solely the customer’s responsibility, executed by the customer’s clinical and regulatory functions. Sorento Software does not provide medical advice, does not act as a clinician, and does not represent, attest, or warrant compliance with any regulatory framework on behalf of any customer.
Partner agreements in place
Lower Documentation Burden for Clinicians
Ambient capture and structured-note suggestions are designed to compress the time clinicians spend on documentation between visits — without removing review, edit, or sign-off from the clinician.
Clearer Signals for RCM & Operations
Coding, denial, prior-auth, and throughput analytics surface root causes and suggest reviewable next actions for your finance and operations teams — not unattended automation.
Documentation Your Compliance Team Can Use
Audit logs, model cards, evaluation reports, change-control history, and PHI-handling records are produced as engineering deliverables your informatics and compliance reviewers can inspect.
PHI Treated as a First-Class Engineering Concern
Patient data is segmented, tokenized, and access-scoped at the application gateway. Training and evaluation pipelines use de-identified or synthetic data wherever the underlying work allows.
No Rip-and-Replace of Your Core Systems
We sit alongside your EHR, RCM, scheduling, and data platforms using HL7 FHIR R4, X12, and documented APIs — adding AI capability without forcing displacement of systems your teams already rely on.
Clinician-in-the-Loop, Not Black Box
Every decision-support assist arrives with citations, confidence, and an explicit reviewer step. AI proposes; the clinician (or coder, or care manager) decides and signs.
Our Implementation Process
Discovery, Use-Case Triage & Workflow Framing
We map your clinical, RCM, or care-management workflows; rank candidate AI use cases by feasibility, data readiness, and clinical risk; and frame the engineering and integration shape before scoping the build. Clinical-risk and regulatory-pathway decisions stay with your team.
Architecture, Safeguards & Engineering Plan
Design the system architecture, data fabric, model lifecycle, and engineering plan — including PHI scoping, audit-trail design, role-scoped access, MLOps practices, and clinician-in-the-loop patterns — alongside your IT, security, and informatics stakeholders.
Build, Evaluate & Iterate
Iterative full-stack development of the platform — data pipelines, model services, clinician-facing UI, RCM workflows, and governance tooling — with engineering artifacts (test coverage, evaluation results, change logs) captured as part of the build.
Integration & Handoff to Informatics / Compliance
Connect to your EHR, RCM, scheduling, and data platforms via HL7 FHIR, X12, and documented APIs; run end-to-end UAT with your clinical, RCM, and informatics stakeholders; assemble the documentation set your informatics, compliance, and (where applicable) regulatory functions need as inputs into their own validation work.
Deployment, Hypercare & Lifecycle Operations
Phased rollout to clinicians, RCM staff, or care managers. An initial hypercare period covers monitoring, model drift response, retraining considerations, and change-control reviews so the platform stays in a known state as clinical context evolves.
Frequently Asked Questions
Does Sorento Software make clinical decisions or replace clinicians?
No. We are a software engineering partner. Every AI assist we build is designed as a proposal: it surfaces confidence and citations, routes through a human reviewer, and is recorded in the audit trail. The clinician makes the clinical decision and signs the record. ${SITE_CONFIG.name} does not provide medical advice, does not act as a clinician, and does not assume clinical responsibility for any patient outcome.
How do you handle PHI in clinical and RCM AI workflows?
PHI is tokenized at the application gateway, segmented by sensitivity, and access-scoped by role. Training and evaluation pipelines use de-identified or synthetic data wherever the underlying work allows; PHI is kept out of the model layer by default. Every action is logged with actor, model version, prompt, response, and disposition so your informatics and compliance reviewers can trace activity through the platform. These engineering practices are aligned with what regulated healthcare environments typically expect, but we make no compliance certifications on your behalf.
How does this fit alongside our existing EHR, RCM, and data systems?
We build software designed to coexist with the clinical, RCM, scheduling, and data platforms you already run — using HL7 FHIR R4, SMART on FHIR, X12, and documented APIs. Your IT and integration teams own the actual connections into your validated systems. We do not claim partnerships, certifications, or pre-built integrations with any third-party EHR or RCM vendor.
What about FDA SaMD considerations for decision-support or imaging software?
We do not classify, submit, or seek clearance for medical devices on behalf of customers. What we build is custom AI and ML software for clinical, RCM, and care-management workflows — decision-support interfaces, imaging review tooling, and analytics — engineered transparently with explainability hooks, confidence reporting, and clinician-in-the-loop patterns. Final device classification, regulatory pathway, and any FDA interaction are owned and executed by your regulatory function. Our engineering practice is informed by published guidance on responsible ML lifecycles, including FDA GMLP principles; the regulatory determinations are yours.
How do you address hallucination, bias, and black-box risk for clinical work?
Generative and ML pipelines are grounded in retrieval over your verified internal sources — formularies, protocols, policies, and approved literature — and return citations alongside answers. Decision-support and diagnostic-adjacent software ships with explainability hooks, confidence reporting, and clinician-in-the-loop patterns. Every model has a documented lifecycle (model card, training data lineage, evaluation results, change history) that your reviewers can read. Bias evaluations and drift monitoring are designed in; remediation decisions belong to your clinical, informatics, and compliance leadership.
What does a typical healthcare AI engagement look like, and how do you scope it?
Engagement scope, timeline, and investment vary by program and are defined during discovery — we do not quote fixed durations or fixed clinical outcomes on a public page. Discovery is where we map your clinical or RCM workflows, audit data readiness, frame clinical risk, and shape the engineering and integration plan before any production-bound code is written. After discovery, the build is typically phased so the highest-priority capability goes live first and your team can review the platform before later phases land.
Bringing AI inside a regulated healthcare workflow?
Book a free 30-minute discovery call. We will review your software needs across clinical, RCM, or care-management workflows, talk through the engineering, PHI, and integration shape, and outline a realistic scope. Clinical and regulatory pathway decisions remain with your team.