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Clinical Data Intelligence
Clinical Data Intelligence
What is Clinical Data Intelligence?
AI Chart Summarization
AI Chat Embedded in the EHR

Clinical Data Intelligence supercharges clinicians with the insight into the patient record they need, at the speed of thought.
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‍See how it works

Full chart summaries on any clinical concept or custom prompt, tailored to your patients and your workflow.
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‍See how it works

AI chat embedded directly in the EHR that has read the patient chart and is ready to answer your clinical questions.
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‍See how it works

NET EHR EXPERIENCE SCORE
+31.7
point increase

A new study by KLAS Research found a 31.7-point increase in Net EHR Experience Score at the University of Iowa Health Care after integrating Evidently for clinical data intelligence. Read the full study.

Clinical Data Intelligence

Clinical Data Intelligence is fundamentally changing the way health organizations harness the ocean of data trapped in internal & external sources. Learn more

NET EHR EXPERIENCE SCORE
+31.7
point increase

A new study by KLAS Research found a 31.7-point increase in Net EHR Experience Score at the University of Iowa Health Care after integrating Evidently for clinical data intelligence. Read the full study.

Case Studies

We’re proud to work with leading healthcare organizations driving meaningful outcomes with clinical data intelligence. See their stories

VALUE-BASED CARE
6x
Return on Investment

The team at Allina Health saw a whopping 6x return on their investment through value-based care risk adjustment and revenue capture. Read the full study.

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We get up every morning to raise the quality of care for every patient by empowering clinicians with the right information at the speed of thought.

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Clinical Data Intelligence is healthcare AI that reads the entire longitudinal patient history, including the scanned documents, faxes, and outside records the EHR was never built to read or understand, then reasons across it clinically to deliver traceable intelligence across the health system.

What is Clinical Data Intelligence?

Every clinical decision depends on the full patient picture. Most clinical AI fails to deliver it, for one of two reasons.

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The chart can't be read. About 80% of clinical data is unstructured, and the EHR was never built to read any of it, including its own clinical notes. Within that, the hardest 30–40% arrives in formats even other clinical AI can't reach: scanned documents, faxes, outside records, HIE feeds, imaging reports.

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The chart can't be understood. Even data that gets read isn't clinically interpreted. A chronic condition documented in a scanned outside record years ago can change the surgical plan, the medication choices, and the risk stratification for every future encounter. Reading is retrieval. Understanding is clinical reasoning.

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Clinical Data Intelligence addresses both. A peer-reviewed study published in Applied Clinical Informatics (Rodrigues et al., 2026) measured 97% suggestion accuracy on net-new conditions across 104 adjudicated clinical suggestions at University of Iowa Health Care — conditions that existed in the record but had never been documented.

How Does Clinical Data Intelligence Work?

It Reads the Entire Record

Clinical Data Intelligence indexes the entire patient record, structured and unstructured, into a single longitudinal view per patient. That includes the scanned documents, faxes, imaging reports, and outside records that arrive through Care Everywhere, HIE feeds, and document management systems like OnBase.

These are the formats where chronic disease burden hides: conditions documented in an outside record and never re-noted, surgical histories captured in scanned operative reports and never carried forward. The full longitudinal complexity of the patient chart is indexed, understood, and available to everyone interacting with the chart.

It Reasons Clinically With a Medical Knowledge Graph

Reading data is not the same as understanding it clinically. A lab result interpreted alongside a medication concern, a declining trend in historical labs, and a comorbidity from a scanned outside record becomes a clinical insight that changes the plan of care.

Clinical Data Intelligence reasons through a medical knowledge graph: clinical ontologies including ICD-10, SNOMED, RxNorm, and LOINC, consolidated into a unified reasoning layer where crosswalks connect concepts across classification systems. Built into this layer are the rule sets that govern clinical workflows: HCC conditions for risk adjustment, HEDIS measures for quality, PSI criteria for patient safety, MCC and CC severity designations, and Elixhauser comorbidity indices. This is what turns retrieval into reasoning.

It Adapts to Every Institution and Clinician

Every health system documents differently: its own escalation protocols, documentation conventions, severity criteria, and quality benchmarks. Clinical Data Intelligence shapes every generative output to the institution's own clinical voice, reflecting those standards rather than a one-size-fits-all model.

At the individual level, clinicians define their own intelligence workflows: the clinical questions they want answered for every patient, the specialty-specific assessments they rely on, and the documentation patterns they hold themselves to. Institutional rules set the system-wide standard; clinician-defined workflows govern individual practice. Without both, clinicians learn to ignore what doesn't fit their workflow.

How is Clinical Data Intelligence Different From Ambient AI Scribes?

The conversation that happens in the room is one input to Clinical Data Intelligence. Clinical Data Intelligence reads the entire patient chart: every prior encounter, every outside record, every scanned document. The encounter is one piece of that complete picture.

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Evidently is built from the ground up with voice as an input on every clinical surface. Wherever a clinician needs voice, in outpatient conversations or inpatient rounding, desktop and mobile voice are built in out of the box.

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A peer-reviewed study published in Applied Clinical Informatics (Rodrigues et al., 2026) found a median of 4.5 quality-impacting diagnoses added per admit note that would otherwise have gone undocumented. No microphone captures those. They were already in the chart.

Read the whitepaper

How is Clinical Data Intelligence Different From Clinical Documentation Integrity Software?

CDI has meant Clinical Documentation Integrity for over 40 years. Now it also means Clinical Data Intelligence. That's not a coincidence. When Documentation Integrity first emerged, the patient record was a fraction of what it is today. A decade of interoperability, HIE integration, and health information exchange has meant the clinical record has exploded in size and complexity, and it's still growing. The documentation challenge CDI was built to solve has only gotten harder, and Clinical Data Intelligence is the approach built for the scale of the modern patient record.

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Clinical Data Intelligence doesn't replace CDI specialists — it supercharges them. Full longitudinal patient complexity under the fingertips of the entire Documentation Integrity team, giving specialists the complete clinical picture for every review. When the admit note upstream is also drafted with Clinical Data Intelligence, specialists spend less time querying missing conditions and more time on the clinical judgments that require their expertise. And when clinicians and care teams across the health system are also running on Clinical Data Intelligence, from admit notes upstream to denial appeals downstream, the documentation gets better at every step.

V-Tach in the Haystack

What Outcomes Does
Clinical Data Intelligence Create?

When the full patient chart is finally read and understood, everything downstream improves. Provider wellbeing, documentation accuracy, and financial return aren't separate initiatives — they're what happens when you solve the root cause.

Outcomes produced by Clinical Data Intelligence
Provider Wellbeing:
+31.7-point Net EHR Experience Score Increase
KLAS Research Arch Collaborative, Unlocking Clinical Data Intelligence with AI, 2025
Documentation Accuracy:
97% suggestion accuracy on net-new conditions
Peer-reviewed: Rodrigues et al., Applied Clinical Informatics, 2026
Documentation Accuracy:
+49-81% mortality benchmarking improvement
The 4.5 Missing Comorbidities, Evidently whitepaper
Return on Investment:
6x ROI in 90 Days
Allina Health case study

Why is Clinical Data Intelligence emerging now? 

Health systems have deployed ambient scribes for encounter capture, CDI software for coding review, chart summarization tools for quick reads, and risk adjustment platforms for HCC capture. Each solves a real problem within its scope. None reads the full patient record. None reasons across it clinically.

Clinical Data Intelligence is the answer to point solutions. 

Tools that operate on a fraction of the chart, serve a single workflow, and lack a clinical reasoning layer. The category names what's required when a health system decides that clinical AI should understand the patient's complete clinical picture.

Today, Clinical Data Intelligence is deployed at major national health systems 

including University of Iowa Health Care, UNC Health, Allina Health, and Rady Children's Hospital, among others across the United States, spanning inpatient, outpatient, emergency medicine, and perioperative workflows.

New In Customer Success
UNC Health Selects Evidently as its Clinical Data Intelligence Partner for an Enterprise Deployment Across Hospitals and Clinics in Triangle Region of North Carolina
New on the podcast
Building Patient Trust in an AI Era with Dieter Sumerauer, MD
New in the Newsletter
11-Hospital COVID Commander. First Peer-Reviewed AI Study Co-author. A Decade Running Informatics. One Conversation.

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