Evidently
Evidently, based in San Francisco, sells Clinical Data Intelligence: AI that reads the full longitudinal record, including scanned documents, faxes and outside records from Care Everywhere and CommonWell, reasons across a medical knowledge graph (ICD-10, SNOMED, RxNorm, LOINC with HCC, HEDIS, PSI, MCC and CC rule sets) and delivers source traced output inside the EHR. Surfaces include AI chart summaries, drafted notes, Ask Evidently chat and Omni, a real time view of the patient.
For hospital quality and revenue it suggests codes and queries to CDI teams and drafts denial appeal letters; for value based care it supports HCC review and care gaps; it also covers emergency medicine and perioperative review. It cites a peer reviewed study at University of Iowa Health Care (97 percent accuracy on 104 adjudicated suggestions) and a KLAS Arch Collaborative study there; Allina Health reports a 6x return, and UNC Health is a customer. Integrates with SMART on FHIR enabled EHRs; states HIPAA and SOC 2 Type II compliance.
Capability Axes
An AI Health Index grade measures what a buyer can verify from public sources on the date shown. It is not a rating of how good the product is. A vendor can build an excellent system and grade low on an axis because it publishes nothing an outsider can check. How grades read
The engine is the product. Evidently's Clinical Data Intelligence reads the full longitudinal record, including scanned documents, faxes and outside records from Care Everywhere and CommonWell, reasons across a medical knowledge graph, and powers every surface: chart summaries, drafted notes, chat inside the EHR, CDI code and query suggestions and denial appeal drafts. Without the models there is nothing left to sell.
The oversight structure is described with the thresholds missing. Evidently's outputs are suggestions and drafts for people to act on: it suggests codes and queries to CDI teams, drafts admit notes, discharge summaries and appeal letters in an editor for clinicians, and answers questions in chat, with source traced output and institution and clinician defined workflows. What is not published is any confidence threshold, what the system declines to answer, or how a clinician is alerted to a missing source.
The approach is named without the models behind it. Evidently describes a medical knowledge graph consolidating ICD-10, SNOMED, RxNorm and LOINC with crosswalks, carrying rule sets for HCC, HEDIS, patient safety indicators, MCC and CC severity and Elixhauser comorbidities, with generative outputs shaped to each institution's and clinician's standards and source traced. No generative model, version, evaluation method beyond the cited study, or change practice is published.
The architecture is described and no party in it is named. Evidently describes indexing the full record, a medical knowledge graph and generative drafting; no language model provider, host or subprocessor is named, and its only privacy policy addresses website contact and marketing data, not PHI.
Published research and third party measurement, short of multi site validation. Evidently cites a peer reviewed study in Applied Clinical Informatics (Rodrigues et al., 2026) at University of Iowa Health Care: 97 percent accuracy on 104 adjudicated suggestions and a median of 4.5 quality impacting diagnoses added per admit note.
KLAS Research's Arch Collaborative measured a 31.7 point increase in Net EHR Experience Score at Iowa, and Allina Health reports a 6x return from outpatient CDI and HCC capture; UNC Health is a named customer. The study is single site, and the revenue and experience figures measure value rather than documentation accuracy.
General assurances that do not answer the questions AI raises. Evidently says Ask Evidently is built from the ground up around HIPAA and SOC 2 Type II compliance. It reads the whole record, including outside records from health information exchanges and scanned documents, and shapes outputs to each institution and clinician.
Nothing published states retention periods, whether customer records train or tune the models, or de identification; its privacy policy covers website contact and marketing data only. Safety engineering is not published beyond source tracing.
Compliance is claimed without the document behind it. The Ask Evidently page says the product is built from the ground up around HIPAA and SOC 2 Type II compliance; the only privacy policy covers website contact, cookie and marketing data and does not mention protected health information or business associate status.
A recognized certification is named without the artifact. The Ask Evidently page says the product is built from the ground up around HIPAA and SOC 2 Type II compliance, presented as the company's own. 'Built around compliance' is looser than stating a completed audit, and no trust center, report period or penetration testing statement is published.
No device claim is made. Evidently summarizes records, drafts notes and suggests diagnoses and queries for clinicians and CDI teams, and states no FDA position or clinical decision support exemption analysis. Chat that answers clinical questions from the record sits near decision support, so the position matters. The revenue side carries program integrity exposure: suggested MCC and CC conditions change payment grouping, and the billing hospital owns them.
Nothing published on how model behavior is governed or tested. The home, company, Clinical Data Intelligence, hospital quality and revenue, and Ask Evidently pages describe capability, source tracing and a peer reviewed accuracy study, but no governance framework, monitoring commitment or bias evaluation.
The commercial gradient matters: the product suggests MCC, CC and quality impacting conditions and reports a median of 4.5 quality impacting diagnoses added per admit note, so a model tuned toward capture can drift toward higher documented severity without any single chart showing it.
A published error rate with its method and denominator. Evidently cites a peer reviewed study in Applied Clinical Informatics (Rodrigues et al., 2026) measuring 97 percent suggestion accuracy on net new conditions across 104 adjudicated suggestions at University of Iowa Health Care, and states every skill produces source traced output a clinician can check. That is a measured rate a buyer can hold the product to, at one site and a small denominator. No warranty, indemnity or correction commitment is published, and suggested diagnoses that are accepted become the hospital's claims.
Standards support with named deployments behind it. Evidently says it integrates with any SMART on FHIR enabled EHR and embeds inside the EHR, reads outside records from Care Everywhere and CommonWell, and UNC Health's executive director describes advancing integration with UNC's EHR; University of Iowa and Allina are also live. No integration documentation or listing names the write back path for drafted notes and suggestions.
Nothing committed about where the system runs. Evidently embeds in the EHR and indexes the full record, including outside records from health information exchanges; no hosting provider, region, tenancy model or deployment option appears on its product pages, and its privacy policy covers website data only.
No price, tier, mechanism or contracting model is published, and every path ends at a demo request or a contact form.
The only commercial signal is a customer reported 6x return, which describes claimed value rather than cost.
Coverage is named with validation behind part of it. Evidently serves inpatient (admit notes, discharge summaries, CDI, denial appeals), outpatient and value based care (HCC review, care gaps), emergency medicine and perioperative work (pre operative and transplant review, registry abstraction, patient safety indicators). Inpatient documentation is supported by a peer reviewed Iowa study and value based care by Allina's results; no validation is published for emergency, perioperative or registry work.
Alternatives to Evidently
Vendors in the same categories as Evidently whose graded profiles look most alike, with a line on what each product does.
Similar is not the same, so check that each one does the job you need. No vendor pays for placement.
Fourier Health automates medical record intake and turns inbound records into structured, cited clinical data and specialty summaries.
Wellsheet sells a Care Team Copilot that sits on top of the EHR and reads the existing chart rather than listening to the visit.
Carta Healthcare supplies software and clinical abstraction services for hospital registry teams.
Abstractive Health retrieves a patient's records from the national exchange networks and summarizes them, with every sentence of the summary linked to the source note it came from.
Averbis is a Freiburg company that turns unstructured clinical documents, mostly in German, into structured data.
Layer Health is a Massachusetts Institute of Technology spin out, founded in 2023 and based in Boston, applying large language models to clinical chart review.
Ordered by how closely the graded profiles match across the fifteen axes, inside the same categories. See how each axis is graded in the methodology.
Compared With
Each comparison carries a written verdict, the buyer conditions that favor each vendor, and a graded side by side. Pairs that cross a category boundary are grouped separately, and their verdicts state where the boundary sits rather than manufacturing a head to head.
Pricing
Vendor-published figures are labeled as such. Figures labeled “Estimated” are derived from third-party sources and have not been confirmed by the vendor.
| Entry Price | Pricing Basis | BAA Tier | Implementation | Source |
|---|---|---|---|---|
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Not published
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Undisclosed. Enterprise health system agreement sold in four solution bundles. | Not published. HIPAA compliance is claimed but no BAA terms, inclusion or cost is stated either way. | Not published. The company markets deployment in weeks rather than years and a customer integration manager describes it as the fastest implementation of a 23 year career, but no fee structure is stated either way. | Vendor Published |
No price, tier or pricing mechanism is published and every commercial route terminates in a demo request, so commercial transparency is Not Rated per the house convention rather than graded down. The available commercial signals describe claimed value rather than cost: a customer reported 6x return at Allina Health attributed to value based care risk adjustment and revenue capture, and a KLAS measured 31.7 point rise in Net EHR Experience Score at University of Iowa Health Care. Read those as return arguments a seller will bring to a negotiation, not as pricing information.
Two items to establish in writing. First, the standing contingent pricing check, which is sharper than usual here: the product suite spans CDI review, HCC review, case mix index improvement and payer denial appeal drafting, and the flagship customer outcome is expressed as a return on risk adjustment and revenue capture, so confirm whether any component of the fee varies with coding intensity, severity capture, appeal recovery or collections rather than with users, volume or time saved. Non contingent pricing is a governance positive this index credits. Second, because the product is sold in four distinct solution bundles across inpatient, value based care, emergency medicine and perioperative, establish whether summarization can be licensed alone or only alongside the revenue oriented modules, since that determines whether the buyer can take the clinical benefit without the coding gradient.