How we measure and improve every Sayvant note.
The Sayvant Quality System (SQS) is how we automatically measure, enforce, and improve every note. Higher SQS scores correlate with improved charge capture, fewer queries, and reduced medical malpractice risk.
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.
Quality of Care Index
Does the note capture the full clinical picture? Acuity, reasoning, and medical decision-making, validated against evidence-based documentation standards and quality reporting criteria.
Provider Documentation Index
Accurate E&M leveling, ICD-10 specificity, and CC/MCC capture. The documentation elements that determine correct reimbursement and withstand payer audit.
Defensibility of Care Index
Would this note survive peer review or a malpractice deposition? Scored against defensibility criteria, not just completeness.
SQS doesn't just score documentation.
It learns from it.
SQS is a closed loop between generation, measurement, and improvement. Every score feeds back into how the next note gets written.
Every note is scored
SQS runs against 100% of your documentation, not a sample. With 1M+ encounters analyzed, the system catches clinical patterns and edge cases that spot-checking never surfaces.
Gaps become training signal
SQS surfaces flagged documentation gaps to the clinician in real time and uses them to improve future performance. The weakest notes drive the most learning.
Clinician edits close the loop
SQS compares physician inputs to final outputs. The model learns from clinical judgement on real cases, not just extracted RCM/Quality codes. Every input makes the next note better.
How we keep documentation quality
consistent at scale.
The infrastructure behind SQS is purpose-built for high-stakes acute care. It governs every site, every clinician, and every note, so the quality promise holds across the footprint.
Real time output evals
Outputs are continuously tested against 1,000+ clinical criteria that flag clinical inconsistencies, documentation gaps, and intra-section narratives.
Source attribution
Every element of a Sayvant note links back to its source: prior encounter data, the patient-clinician conversation, or reference guidelines.
Hallucination controls
300+ rule-based checks catch hallucinations, unsupported diagnoses, and phantom medications before the note reaches the clinician.
Sayvant-managed models
Sayvant deploys and manages models fine-tuned for acute care documentation to improve reliability and guard against model drift.
Trained on 1M+ cases
Across 100+ sites. Sparse dictations, complex multi-system presentations, trauma patients, and extended stays. Cases that other models don't have access to or train on.
Site-level configurability
Medical directors control template preferences, differential diagnosis surfacing, and documentation structure at the site or group level.
Traditional Chart Review vs.
Sayvant Quality System.
Manual QA was never built to scale with AI-generated documentation. SQS was.
| Traditional QA Manual | Sayvant Quality System Automated | |
|---|---|---|
| Coverage | 2 to 5% of cases reviewed | ✓100% of cases, every chart |
| Timing | Days or weeks after disposition | ✓Real time, as notes are drafted |
| Scope | Primary focus on CDI levers | ✓Quality, completeness, and defensibility |
| Calibration | Static coding ruleset | ✓Continuously recalibrated against clinician decisions |
| Cost | $30+ per chart | ✓Included with every case |
The same engine, now running on charts you didn't generate with Sayvant.
Sayvant Reflect applies the SQS rubric retrospectively to any chart in your group, Sayvant-generated or not. RCM sends the chart records, Reflect returns structured analysis across documentation defensibility, criteria-sensitive clinical factors, critical care eligibility, admission criteria adherence, and pro-fee capture patterns. Built for the group, not against it.
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.
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 →Third-party benchmarking against frontier models
In 300+ independent EM evaluations, Sayvant-generated documentation outperformed leading general-purpose AI models on acute care quality metrics.
Read the abstract →