Pre-visit chart prep
A clinician pulls the salient history before the visit, citations included.
Chat-with-Records Agent
AI medical record summarization
Clinicians and record reviewers lose time hunting through long charts. Agnotic Technologies builds AI medical record summarization that summarizes and answers from the patient's own records, with a citation for every statement.
Source-citedPHI-scopedFHIR R4No-hallucination guardrails
Trusted by global innovators
What is AI medical record summarization?
AI medical record summarization is software that reads a patient's chart, including FHIR resources, notes and scanned documents, and produces a short summary or answer where every statement links to its source passage. Agnotic Technologies builds it for clinical operations, care teams and record review teams, with PHI access scoping and audit trails.
A single patient can carry hundreds of encounters, PDFs, summaries and labs. Finding one fact, such as the last echo or whether a statin was tolerated, means minutes of scrolling mid-visit.
The software makes the chart queryable. It retrieves over FHIR R4 resources and documents, grounds every statement in its source, and states nothing the chart does not support.
RAG over EHR data
FHIR resources and documents are chunked, embedded and retrieved under PHI scoping, then summarized with citations a reviewer can check.
What do we build into chart summaries?
Patient chart summary AI built for the clinical record: grounded, scoped and auditable, not a chatbot pointed at PHI.
Grounded answers with citations
Every response links to the exact FHIR resource or document chunk (Observation, Condition, MedicationRequest) for one-click verification.
FHIR-native retrieval
Indexes FHIR R4 resources alongside notes and scanned PDFs, so 'last HbA1c trend' returns the Observation series.
No-hallucination guardrails
When retrieval finds nothing relevant, the agent says so; a grounding check rejects any unsupported claim.
Embedded in the workflow
Launches inside the chart via SMART on FHIR and reasons over the encounter timeline for longitudinal questions.
Who runs it
Who uses medical records review AI?
Specialty referral review
A specialist queries the referral reason and prior workup fast.
Care coordination
Nurses confirm the latest medication list without paging the provider.
Patient-portal Q&A
A read-only version answers patients' questions about their own results.
Citations to source documents
Design rules, not guarantees: results depend on chart quality and index coverage.
Cited
Every summary statement links to its source passage
Removed
Statements without a retrieved passage are stripped
Per-query
PHI scoping enforced on every request
Compliance-First Healthcare App Development Services Backed by Global Standards
01
HIPAA
Health Insurance Portability and Accountability Act
Protect PHI with privacy-first architecture, encrypted storage and transmission, strict access controls, and traceable audit logs.
02
GDPR
General Data Protection Regulation
Implement lawful consent flows, data minimization, retention controls, and secure processing for sensitive health data.
03
FHIR
Fast Healthcare Interoperability Resources
Enable standardized health data exchange across apps, care teams, and systems through standards-based FHIR APIs.
04
HL7
Health Level Seven International
Support enterprise-grade interoperability with HL7-based integrations for records, events, and clinical messaging workflows.
05
HITRUST
Health Information Trust Alliance
Align security programs to healthcare-specific control and risk management practices trusted by providers and ecosystem partners.
06
HITECH
Health Information Technology for Economic and Clinical Health Act
Design with breach notification readiness, digital record safeguards, and operational controls that support regulated care programs.
07
SaMD
FDA Software as a Medical Device
Plan software quality, traceability, and documentation pathways for products that may require SaMD review and submission.
08
MDR (EU)
Medical Device Regulation (European Union)
Prepare EU market-ready processes for risk classification, evidence tracking, and lifecycle governance under MDR expectations.
09
SAMHSA
Substance Abuse and Mental Health Services Administration
Apply confidentiality controls and consent-aware sharing models for behavioral and mental health data experiences.
Standards we build against
Which systems and standards does it connect to?
HIPAANISTFHIRSMARTHL7
How It Works
How does a question become a cited answer?
Capture, retrieve under guardrails, answer, and log the trail: every step auditable.
1.
Capture: the question in context
The clinician asks in plain language inside the chart; patient, role, and consent scope the query.
PHI-scoped input
2.
Processing: retrieval + guardrails
Hybrid retrieval pulls the relevant FHIR resources and document chunks for that patient only, gated by a relevance threshold.
Retrieval guardrails
3.
Action: the grounded answer
The model answers from retrieved passages only, one citation per claim; unsupported statements are stripped.
No hallucinated facts
4.
System update: the audit trail
Question, retrieved resources, answer, and citations write to an immutable access log.
Audit logged
Facts and sources
Which rules shape medical record AI?
Short facts we build record summarization around, each linked to its primary source.
A HIPAA covered entity may share protected health information with a business associate, such as a software vendor, only under a business associate agreement that commits the vendor to safeguard it.
Source: 45 CFR 164.502(e) and 164.504(e) (eCFR)In HL7 FHIR R4, a DocumentReference indexes a document, clinical note or other binary object, such as a scanned paper record, to make it available to a healthcare system.
Source: HL7 FHIR R4, DocumentReferenceThe SMART App Launch implementation guide uses OAuth 2.0 and OpenID Connect so that apps can launch from an EHR with the user's and patient's context and only the access scopes they were granted.
Source: HL7 International (SMART App Launch)In July 2024, NIST released the Generative AI Profile (NIST AI 600-1), a companion to the AI RMF that helps organizations identify risks specific to generative AI and proposes actions to manage them.
Source: NIST AI 600-1
Related proof
Read Case StudyLera Health: compliant women's health platform
Related proof of compliant delivery, not this exact agent. For Lera Health we built a privacy-first data layer and AI-assisted insights over sensitive health data, the same foundations this agent depends on.
Compliance & Guardrails
How are PHI access and audits handled?
Chart-facing software earns trust by controlling who sees what and proving where each statement came from.
PHI handling
Retrieval is scoped by patient, role, and consent; PHI never leaves the boundary.
Human-in-the-loop
The agent informs, not decides; no-match or low-confidence queries defer rather than guess.
Explainability
Every claim cites its source FHIR resource or document, confirmable in one click.
Audit logging
Every query, retrieval set, and answer writes to an immutable log.
Frequently Asked Questions
The software runs inside your HIPAA boundary under a signed business associate agreement, scopes retrieval by patient, role and consent, and never trains models on PHI. Every query and summary is logged immutably.
No. It surfaces what's already in the chart so clinicians hunt less, makes no clinical decisions, and shows each answer's cited source for verification.
A chatbot generates fluent text from training data and can invent facts. This software answers only from that patient's FHIR resources and documents, cites every claim, and says so when the record lacks the answer.
Accuracy depends on your records, so we measure it rather than promise it. We build an evaluation set of real charts reviewed by your clinicians, check each summary statement against its cited passage, and track omissions and unsupported statements before and after go-live.
It depends on how many EHRs and document sources feed the index, how much of the record is scanned paper, which summaries and questions come first and how long the clinician-reviewed shadow evaluation runs. We give you a timeline after the scoping call.