Navina

Proprietary language models do the core work. The company describes automatically classifying document types such as consult notes and imaging reports, using its own language models to extract and structure information including ICD-10 codes and clinical dates from free text, and applying segmentation algorithms to handle long multi document records. Most consequentially it surfaces suspected chronic conditions never explicitly documented before by parsing evidence across notes, imaging, medications, labs, and vitals. Inferring an undocumented condition from scattered evidence is not a retrieval task.

The design principle is that every insight carries its evidence, and the company builds its trust argument on exactly that. Each diagnosis suggestion is backed by clinical evidence linked to the original source, presented at the point of care for the clinician to accept or reject with one click documentation. Customer physicians specifically cite the evidence backing as what builds trust in the software. For a system suggesting diagnoses that raise risk scores and revenue, making every suggestion traceable to source is the control that separates decision support from upcoding pressure.

Architecture is described with real specificity: proprietary language models rather than a generic wrapper, document type classification, structured extraction of codes and dates from free text, and segmentation for long complex records. The company publishes an explicit position against black box models, stating insights are clearly explained and directly referenced to original clinical sources. What is not published is model performance measurement, validation methodology, or accuracy of the suspected condition inference, which is the number that matters most given what those inferences drive.

Quantified customer outcomes at named organizations, though vendor reported. Published figures include 70 percent time saved, 23 percent more diagnoses per visit, and 38 percent higher risk scores, with a named customer reporting 97 percent clinician adoption, 41 percent more conditions surfaced, and 94 percent of clinical insights addressed. Case studies span a multi specialty group and a large ACO. Buyers should read the risk score figures carefully: higher RAF is the customer's commercial objective but is not by itself evidence of better care, and no published analysis separates appropriate condition capture from score inflation.

No published PHI framework was located. The platform ingests an unusually wide data surface, spanning the EHR, health information exchanges, insurance claims, and payer care gap files, which is more sources than most vendors in this index touch, and no retention or training data terms were found.

No explicit HIPAA or BAA statement was located in the company's own materials, though third party listings reference compliance with healthcare data protection regulations and business associate status is structurally required given native EHR deployment.

No SOC 2, HITRUST, or ISO 27001 attestation was located, and no trust center was found.

No FDA pathway is claimed. The product surfaces documented and suspected conditions with linked evidence for clinician review rather than rendering a diagnosis, which is the positioning that keeps decision support outside device regulation under the Cures Act criteria, and the evidence linking is precisely what supports that position. The regulatory regime that actually governs this vendor is CMS risk adjustment and audit rules, where documentation supporting an HCC must meet MEAT criteria, and the company states it generates compliant documentation including HCC and CPT-II coding for audit readiness.

The company publishes a stated ethical approach to clinical AI centered on transparency and clinician trust, positioning explicitly against black box models. That is a real governance posture expressed as product design rather than a program document. What is absent is any bias evaluation, which matters here in a specific way: a model that surfaces suspected conditions from documentation density may find more conditions in patients with richer records, systematically favoring those with better historical access to care.

Native in workflow and bidirectional, which is the hard version of this. The company states its solution lives natively in the EHR where clinicians already work, integrates bidirectionally with patient records enabling one click documentation, and generates visit notes synced back to the EHR. Data ingestion spans the EHR plus health information exchanges, claims, and care gap files reconciled into a single view. Third party sources cite compatibility with major EHR systems, and a co hosted webinar with a major ambulatory EHR vendor and a large medical group indicates working partnership depth rather than API access alone.

No hosting, tenancy, or data residency disclosure was located.

No published pricing. The buyer set spans ACOs, medical groups, and value based primary care organizations where the economic case rests on risk adjustment accuracy and quality performance, which suggests per provider or per attributed life pricing, but no structure or rate is disclosed.

Focused on value based primary care and the organizations bearing risk in it, spanning ACOs, medical groups, and multi specialty practices, with functionality covering chart review, visit preparation, risk adjustment, quality measure and care gap closure, and documentation. The company has extended into ambient transcription combined with historical data, widening from a pre visit tool toward the full encounter. Hospital, inpatient, and specialty referral settings are outside the core positioning.