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Enterprise AI for Healthcare Organizations

Enterprise AI for Healthcare Organizations

Clinical teams spend too much time on documentation and administrative work that AI can handle. We build systems that give that time back without compromising compliance or care quality.

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92 min
saved per physician per day on documentation
Up to 42%
faster prior-auth turnaround
Up to 96%+
medical coding accuracy with AI assistance

What We See in Enterprise AI for Healthcare Organizations

1

Physicians in Epic and Cerner environments spend two hours on documentation for every one hour of direct care, pajama-timing through the SmartPhrase backlog at night while critical data in progress notes goes uncoded and unbilled.

2

Prior authorization for specialty procedures costs the average US health system $11M a year in staff time, with nurses and MAs working five portals (Availity, CoverMyMeds, payer-specific sites) to submit criteria, upload chart excerpts, and appeal denials that should never have happened.

3

Medical coders working in 3M 360 Encompass or Epic's coding queue abstract charts from incomplete documentation, and the average hospital loses 3 to 5% of net revenue to downcoded DRGs, unspecified ICD-10 codes, and denied claims that CDI didn't catch in time.

4

Care coordination across inpatient, SNF, and outpatient breaks down because no single team sees the full longitudinal view, and 30-day readmissions keep showing up for patients who had clear deterioration signals in their HCC data the whole time.

How We Help

Ambient Clinical Documentation

An ambient AI scribe listens during the encounter or reads a dictation and writes a structured SOAP note, problem list updates, and orders draft directly into Epic or Cerner through the EHR's write-back API. The clinician reviews, edits, and signs inside their normal workflow rather than typing a note from scratch. The model learns each clinician's phrasing over time and flags required documentation elements (ROS, MDM, HPI) that are thin before the chart is closed.

✓ 92 minutes per physician per day returned to patient care and 18% lift in E/M level capture accuracy

Prior Authorization Automation

Agents read the chart, pull the payer's medical policy criteria, assemble the clinical evidence package, submit through Availity or the payer's portal, and track the authorization to closure. For denials, the agent drafts the peer-to-peer packet with cited chart data. Nurses work exceptions and complex appeals instead of filling out forms. The system covers Medicare Advantage, Medicaid MCOs, and commercial payers across the system's top 20 procedure categories.

✓ 42% reduction in prior-auth turnaround and 34% fewer procedure reschedules

CDI and Coding Assistance

A coding agent reads the full chart in Epic, proposes ICD-10, CPT, HCC, and MS-DRG codes with specific chart citations, and flags documentation gaps that would change the DRG if addressed. CDI specialists and coders validate rather than derive codes, and queries to physicians are drafted with specific supporting language already attached. The same model powers concurrent review so gaps are caught before discharge rather than after billing.

✓ Coding accuracy to 96%+ and 9% net revenue capture lift on inpatient cases

Care Gap and Readmission Risk Surveillance

The agent reads the longitudinal chart, lab trends, SDoH data, and recent encounters to generate a daily prioritized list of patients with open HEDIS gaps, chronic-condition deterioration signals, or 30-day readmission risk. Case managers receive each patient with a recommended outreach plan, script, and the specific clinical reasoning. It integrates with Epic Healthy Planet and Cerner HealtheIntent so worklists live where the team already works.

✓ 17% reduction in 30-day readmissions on monitored cohorts and 28% higher HEDIS gap closure

Patient Access and Self-Service

A voice and chat AI handles appointment scheduling, Rx refill routing, referral status, pre-visit intake, and post-discharge follow-up. It reads from MyChart and writes scheduled actions back. Clinical questions are triaged to a nurse with a summarized context. The system runs in English and Spanish out of the box, covers MyChart messaging overflow, and cuts front-desk call volume without pushing patients to a worse experience.

✓ 48% of access calls resolved without staff and 22% reduction in no-show rate

Our Services for This Industry

AI Agent DevelopmentView →
Multimodal RAG SystemsView →
Agentic AutomationView →
Enterprise AI IntegrationView →

Engagement shape

Timeline

A typical healthcare engagement runs six to eight weeks to first production. Weeks one and two are discovery: CMIO alignment, compliance and privacy review, data-access interviews with your Epic or Cerner analyst team, and a written integration pattern for the specific FHIR resources and HL7 feeds in scope. We label an eval set of 1,500 to 5,000 real charts in week two with your clinicians or coders setting the ground truth.

Weeks three and four are build, with the agent running against the eval set daily and a weekly scorecard shared with Clinical Informatics. Week five is shadow mode against a paired queue with real users. Week six is validation sign-off, runbook authoring, and clinical workflow training. Weeks seven and eight are production cutover on one department or service line with a hypercare team on site. Expansion to additional service lines follows the same pattern in four to eight week waves once the first unit is stable.

Cost model

Most healthcare engagements fall between $110k and $280k for the first production use case. The main drivers are EHR integration depth (Epic App Orchard vs. flat files), how many payers or service lines are in scope, and whether your CIO requires an independent validation package for the compliance committee. An ambient documentation pilot for a single department sits near the bottom of the range. A multi-payer, multi-service-line prior-auth automation with Epic write-back lands at the top. Ongoing platform and inference costs typically run $8k to $30k per month in production, quoted upfront before the SOW is signed.

Frequently Asked Questions

How do you handle HIPAA, 42 CFR Part 2, and state privacy requirements?+
Every deployment runs inside your HIPAA-compliant environment, either your existing Azure, AWS, or GCP tenant or a dedicated private cloud we stand up under a signed BAA. PHI stays inside your boundary at rest and in use. Inference runs against models hosted in your tenant with zero retention and zero training on your prompts. We implement field-level audit logging for every AI interaction with a chart, role-based access tied to your Active Directory, and minimum-necessary data access per workflow. For behavioral health data under 42 CFR Part 2, we build segmentation into the architecture rather than treating it as a post-hoc filter.
How do you integrate with Epic, Cerner, Meditech, and athenahealth?+
We've built against Epic using HL7v2, FHIR R4, and the App Orchard APIs including the write-back pathways for documentation and orders. For Cerner we use Millennium APIs plus HL7 interfaces and the Ignite SMART on FHIR framework. Meditech integration is usually a mix of HCIS APIs and flat-file interfaces. athenahealth has the cleanest REST surface of the four. Integration complexity varies by your specific configuration, whether you're on Epic Community Connect, and what your HL7 team has capacity for, so we scope the exact pattern and the write-back gates during discovery.
Do clinical AI tools need FDA clearance before we can deploy?+
It depends on the clinical function. Documentation support, prior authorization, coding assistance, scheduling, and care gap surveillance are administrative or clinical-decision-support tasks that don't require 510(k) clearance. AI that makes or materially influences a diagnostic or treatment decision may fall under FDA's Software as a Medical Device framework or the newer Predetermined Change Control Plan guidance. We map each use case against the regulatory framework during scoping and design the clinical workflow so the physician remains the decision-maker on anything diagnostic. We also document the CDS hook pattern for your compliance file.
How do you validate accuracy for clinical applications?+
We build a gold-standard eval set from 1,500 to 5,000 of your own charts with clinician-labeled ground truth before a single production user sees the system. We agree accuracy thresholds with you in writing during discovery, typically 95%+ for coding suggestion precision, 98%+ for prior-auth criteria extraction, and clinician-rated quality scores for notes. The system does not move to production until it hits those thresholds on your data, not a vendor benchmark. Post-go-live, we run continuous monitoring with a monthly accuracy report that Clinical Informatics and your QI committee review.
What does a pilot cost and how long does it take?+
A focused pilot on one use case, for example prior-auth automation for a single service line or ambient documentation for one department, runs 6 to 8 weeks from kickoff to production. Pricing for that first pilot typically lands between $110k and $220k depending on EHR integration depth, how many payers or departments are in scope, and your validation documentation needs. System-wide expansion to additional service lines happens in 4 to 8 week waves after the first pilot proves out. We quote a fixed SOW before kickoff so the CMIO, CFO, and compliance all see the same number.
What data stays on our infrastructure vs. with the AI vendor?+
PHI stays on your infrastructure. We deploy the application layer inside your tenant and run inference against large language models hosted either in your Azure or AWS account or in a private deployment we manage with no retention and no training on your prompts. No patient data, no chart text, no audio, and no structured clinical fields ever leave your environment in a form that could be used to train a third-party model. We hand you the complete egress map before go-live so your network team can restrict outbound traffic to exactly the required endpoints.
Who's accountable when an AI recommendation turns out to be wrong?+
The clinician or the responsible staff member remains accountable for the decision, the same way they are today with any CDS tool or consulting service. Our systems surface recommendations grounded in the chart with citations back to the source data. For documentation, the attending signs the note. For coding, the coder validates. For prior auth, a nurse confirms the clinical packet before submission. We design the workflow so the human is never asked to accept a recommendation without enough context to evaluate it. Liability allocations live in the MSA, and we carry professional and tech E&O coverage sized for healthcare deployments.
How is this different from what Epic, Nuance, or a big consulting firm already offers, and how do we measure ROI?+
Epic and Nuance ship great general-purpose tools. We customize the agents to your specific specialties, payer mix, documentation standards, and coding conventions, and we integrate deeply into your Epic or Cerner write-back rather than staying in a sidecar UI. Big consulting firms deliver decks and staff augmentation. We deliver running code. ROI is measured against a baseline captured in discovery: minutes per note, prior-auth cycle time, readmission rate, denial rate, call deflection, coder throughput. A dashboard publishes those numbers weekly from go-live. Most deployments pay back inside 10 months on loaded staff cost alone, and revenue capture on coding and denials typically lifts separately.

Let's build your AI system.

Production-grade AI for Enterprise AI for Healthcare Organizations. We deploy in weeks, not quarters.

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