How Sayvant drives note quality.
Sayvant’s Quality System (SQS) system is a validated, autonomous system for analyzing acute care documentation. Higher SQS scores correlate with better care outcomes, fewer quality measure gaps, and improved financial performance.
SQS understands the intricacies
of acute care documentation.
Generic medical benchmarks like MMLU-Med, MedQA, and PubMedQA weren't built for acute care. Sayvant evaluates documentation across three proprietary quality indexes and thousands of clinical criteria representative of acute care.
Clinical Index
Does documentation accurately represent the clinician’s reasoning and the complexity of the patient and encounter?
Quality Index
How does the documented care adhere to clinical guidelines, best practices, and quality measures?
Financial Index
Does the documented care meet reimbursement requirements for medical necessity, diagnosis defensibility, and complexity of care?
SQS is a learning system for documentation quality improvement in acute care.
SQS is a closed loop system for assessing the strength of clinical narrative and reasoning in acute care documentation.
Analyze 100% of notes in real time
SQS run against 100% of your notes, not a sample. Catch clinical documentation gaps and patterns that manual peer review never surfaces.
Unify clinical + financial outcomes
SQS translates denials, downcodes, queries, and quality measure gaps into aciontable documentation improvement opportunities
Scale clinical reasoning and documentation consistency
SQS surfaces recommendations to clinicians in real time. Acceptance and action provide feedback that drive future analysis.
Howe we drive documentation quality
at scale.
SQS is AI infrastructure purpose-built for acute care. We're trusted by the country's largest healthcare systems to drive 24/7 quality improvement for millions of patient discharges each year.
Real time output evals
Outputs are continuously tested against 3,000+ clinical criteria that flag clinical inconsistencies, documentation gaps, and intra-note violations.
Source attribution
Every note element and recommendation is linked back to its source: clinical context, patient-clinician conversations, clinical dictation, or reference guidelines.
Hallucination controls
1,000+ deterministic validators catch hallucinations, unsupported diagnoses, and clinical inferences before surfacing results to the clinician.
Sayvant-managed models
Sayvant deploys and manages models fine-tuned for acute care documentation to improve reliability and guard against model drift.
Tested on 1M+ cases
Sparse dictations, complex multi-system presentations, trauma patients, and extended stays that represent the reality of acute care. Cases that other models don't have access to or train on.
Site-level configurability
Clinical leadership can own their desired clinical guidelines and template preferences to personalize their recommendations and note structure for their groups.
Traditional Chart Review vs.
Sayvant Quality System.
Manual chart review was never built to scale to 100% of cases. SQS is.
| Traditional QA Manual | Sayvant Quality System Automated | |
|---|---|---|
| Coverage | <5% of cases reviewed | ✓100% of cases |
| Timing | Days or weeks after discharge | ✓Real time, as notes are drafted (at bedside) |
| Scope | Single axis review (e.g. MIPS) | ✓Unified clinical, quality, and financial defensibility |
| Calibration | Static rulesets | ✓Continuous recalibration against real outcomes |
| Cost | $30 per chart, FTEs | ✓Cost effective, autonomous |
Run Sayvant Quality System retrospectively on every chart across your group.
Sayvant Reflect applies the SQS rubric retrospectively on any Emergency Medicine or Hospital Medicine chart. Send charts in PDF or HL7, and Reflect returns structured analysis across medical necessity, plan defensibility, complexity of care, and care quality.
10-hospital system (Midwest)
5-site community hospital system (Southwest)
23-site community hospital system (Southeast)
Sayvant is built for healthcare
from the ground up.
HIPAA compliant
BAA executed with all customers.
SOC 2 Type II certified
Independent audit of security, availability, and confidentiality controls.
Azure-hosted
Dedicated Azure tenant; no PHI leaves the environment.
US data residency guaranteed
All data stored in US-based Azure regions.
Zero training on customer data
Encounter data is never used to train or fine-tune models.
EHR integration
Deployed across Epic, Cerner, and MEDITECH environments.
Built on published research, validated
across 1M+ acute care encounters.
SQS didn't start as a marketing claim. It started as a research framework, tested against real documentation, peer-reviewed, and refined by physicians who see the chart from every angle.
Closed-Loop Quality Assurance for Production Clinical AI Documentation
SQS methodology and validation framework
The foundational white paper introducing SQS, the physician-validated rubric, and how it scores AI-generated documentation across HPI and MDM sections.
Read the abstract →Sayvant and medical malpractice risk reduction
How AI-generated documentation can support defensibility under peer review and litigation, with a focus on completeness and reasoning capture.
Read the abstract →Charge capture improvement in emergency medicine
A 3.5% improvement in charge capture per encounter when Sayvant's case-aware note generation is applied at the point of care.
Read the abstract →