AI in Healthcare — PieSoft.ai

The problem

Where healthcare operations break down

Four friction points that cost payers and providers real money — and that AI is well-suited to address.

14 days

Prior Authorization Delays

Average prior auth cycle time in commercial insurance. Criteria live in PDFs, EHR fields need manual cross-reference, and status calls burn staff hours that should go to patients.

40%

Physician Time on Documentation

Clinical staff spend nearly half their working hours on notes, coding, and structured data entry — time that is not patient care and is increasingly leading to burnout and attrition.

Days

Claims Adjudication Backlog

Manual review queues create cash-flow problems for providers and service delays for members. Routine clean claims that can be auto-adjudicated still route to human queues by default.

Silent

Care Gap Identification

Preventive care gaps hide in unstructured notes, claims history, and lab results. Without automated identification, HEDIS scores suffer and members miss interventions that would have lowered cost of care.

Use cases

Three places AI earns its keep in healthcare

Each system is built to your workflows, connected to your existing platforms, and scoped so results are measurable in 90 days.

Claims Processing

Auto-adjudication that routes exceptions, not everything

Most clean claims share a small set of patterns. We train AI to recognize those patterns, cross-reference policy rules, and auto-adjudicate — only routing genuine exceptions to human review. The result is a shorter queue and a full audit trail for every decision.

Policy cross-reference against current benefit documents
Automated duplicate detection and coordination of benefits checks
Exception routing with supporting context pre-attached
Complete decision log for compliance and appeals

71%

Claims auto-resolved (insurance reference)

−43%

Average handle time on routed claims

Prior Authorization

From 14 days to same-day — for routine requests

We extract clinical criteria from payer documents, connect them to EHR data, and auto-pre-fill the submission form. Status tracking updates in real time. Staff handle complex and borderline cases; routine requests are decided in hours, not weeks.

Clinical criteria extraction from current payer PDFs and portals
EHR field mapping and auto-pre-fill for common request types
Real-time status tracking with push notifications to submitters
Denial pattern analysis to identify upstream documentation gaps

How it connects to your EHR

We use HL7 FHIR APIs or available Epic/Cerner integrations — no screen-scraping, no RPA fragility. Data stays inside your environment. PHI is never sent to a third-party AI service.

Works alongside existing workflows

Designed to augment your current submission process — staff do not need to change tools or learn a new portal. The AI layer surfaces inside the workflow they already use.

Clinical Documentation

Give physicians time back — without changing how they practice

We build ambient documentation systems that listen during the encounter (with patient consent), extract structured data from unstructured notes, and draft care plan updates. Physicians review and approve — they do not transcribe.

Ambient documentation assist: SOAP note draft from encounter audio
Structured data extraction from existing narrative notes
ICD-10 and CPT code suggestion with source citation
Care gap alerts surfaced at point of care from claims + EHR history

100%

Sourced answers — every AI response cites the source paragraph

+27%

First-call resolution for care coordinators

Compliance & Security

Built for HIPAA from the first line of code

Compliance is not a post-build checkbox. Every system we deliver is scoped, architected, and tested against HIPAA requirements before it processes a single patient record.

HIPAA Business Associate Agreement

We execute a BAA before any work touches PHI. Scope, permitted uses, and breach notification procedures are documented before kickoff.

SOC 2 Type II Infrastructure

Systems deploy on SOC 2 compliant cloud infrastructure. Access controls, encryption at rest and in transit, and audit logging are standard — not add-ons.

PHI Stays in Your Environment

We do not send patient data to external AI APIs. Models run inside your VPC or on-prem infrastructure. You control the data plane end to end.

Role-Based Access & Audit Trail

Permission-aware retrieval means staff see only what their role allows. Every AI interaction is logged with timestamp, user, query, and source — ready for an audit.

Client results

Care coordination that stands up to an auditor

A regional health plan's care coordination team was fielding member and provider calls against a fragmented knowledge base — guidelines in one system, prior-auth criteria in another, member history in a third. Staff were giving inconsistent answers. Compliance was flagging AI risk.

We built a permission-aware retrieval system that unified all three sources. Every answer the AI returns is anchored to a specific paragraph in the source document. Staff see the citation before they relay the answer. The compliance team stopped flagging the system and started using it as an audit reference.

100%

Answers sourced to a specific document paragraph

+27%

First-call resolution — no callbacks to recheck the answer

FAQ

Frequently Asked Questions

Is this HIPAA compliant? +

Yes. We sign a Business Associate Agreement before any work begins, deploy on SOC 2 compliant infrastructure, and architect every system so PHI does not leave your environment. Access controls, encryption, and audit logging are included by default — not quoted as add-ons.

Does the AI read patient records? +

Only what it is scoped to access, and only when a credentialed staff member initiates the query. We implement role-based retrieval — a care coordinator sees member-level data, an admin sees aggregate data, and no query crosses those lines. Every retrieval is logged. The AI does not browse records autonomously.

How does prior auth automation work with our EHR? +

We connect via HL7 FHIR APIs where available (Epic FHIR, Cerner Millennium) or via approved data exports for older systems. We do not use RPA or screen-scraping — those approaches break with every platform update. The integration scope is scoped in week one of the engagement, before any build begins.

What is the implementation timeline for a healthcare AI project? +

Most engagements follow a four-week assessment, then a 10–14 week build-and-pilot phase. The assessment identifies the highest-ROI use case and any data or integration blockers before you commit budget. Production rollout depends on IT change-management timelines on your side — we scope for that explicitly.

Can this work alongside Epic or Cerner? +

Yes. We have delivered projects that integrate with Epic via their FHIR API and CDS Hooks framework, and with Cerner via Millennium APIs. We do not replace your EHR — we connect to it and surface AI assistance inside the existing clinician workflow so adoption does not require retraining.

Go deeper