SNF Documentation Intelligence
Reduce nursing documentation burden by 40%, auto-populate MDS 3.0 sections, optimize PDPM revenue capture, and streamline CMS survey readiness with multimodal AI for skilled nursing facilities.
SNF operators, MDS coordinators, directors of nursing, and post-acute care organizations managing clinical documentation across skilled nursing facilities
Nurses spend 40% of their time on documentation instead of patient care. MDS coordinators manually abstract charts for 3-4 hours per assessment. PDPM revenue is left on the table because clinical severity is under-documented. CMS survey preparation requires weeks of manual chart review.
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Before & After Mixpeek
Before
MDS Completion
3-4 hours of manual chart abstraction per assessment
Documentation Time
Nurses spend 40% of shift on charting and reconciliation
Audit Preparation
Weeks of manual review before CMS surveys
PDPM Coding
Under-documented severity from scattered unstructured data
After
MDS Completion
Auto-populated sections with nurse review and sign-off
Documentation Time
Structured extraction reduces charting burden by 40%
Audit Preparation
On-demand evidence packages generated in minutes
PDPM Coding
Multimodal extraction captures full clinical severity
Documentation Time Reduction
-40%
MDS Auto-Population
95%
Audit Prep Speed
-70%
PDPM Revenue Recovery
+$37/pt/day
Why Mixpeek
Unlike EHR-native tools that only work with structured data, Mixpeek processes the full spectrum of clinical documentation, handwritten notes, scanned forms, photographs, and free-text entries. This multimodal approach captures the clinical nuance that determines accurate MDS coding and PDPM classification.
Overview
Skilled nursing facilities generate massive volumes of clinical documentation across dozens of formats, EHR entries, handwritten notes, scanned assessments, wound photographs, therapy logs, and medication records. MDS coordinators must manually abstract this data into 20+ MDS 3.0 sections for each of the ~2.5 assessments per resident per year. This manual process is slow (3-4 hours per assessment), error-prone (missed clinical indicators mean lost revenue), and creates a bottleneck that ripples through billing, compliance, and care planning. Multimodal AI automates the extraction, structuring, and retrieval of clinical data across all modalities, enabling MDS auto-population, PDPM optimization, and on-demand audit readiness.
Challenges This Solves
Documentation Burden
Nurses spend up to 40% of their shift on documentation, charting, transcribing, and reconciling records across systems, instead of providing direct patient care.
Impact: Staff burnout, turnover rates above 50% industry-wide, and reduced quality of care for 1.3 million SNF residents
PDPM Revenue Leakage
Clinical severity is systematically under-documented because relevant data is scattered across unstructured notes, scanned forms, and photographs that billing systems cannot read.
Impact: Average revenue gap of $2,840 per patient per day between documented acuity and actual clinical complexity
CMS Survey Readiness
Preparing for CMS surveys requires weeks of manual chart review to compile evidence for regulatory compliance across hundreds of residents.
Impact: F-tag citations, financial penalties, and reputational damage from survey deficiencies
MDS Coordinator Bottleneck
MDS coordinators manually abstract charts for 3-4 hours per assessment, creating a bottleneck that delays billing submissions and care plan updates.
Impact: Delayed reimbursement, inaccurate care plans, and coordinator burnout across 38K+ assessments per year for a mid-size chain
Recipe Composition
This use case is composed of the following recipes, connected as a pipeline.
Extract and structure clinical notes for MDS and regulatory workflows
Feature Extractors Used
Document Graph Extractor
Decompose PDFs into spatial blocks (paragraphs, tables, forms, headers) with layout classification and E5 text embeddings.
text embedding v2
image embedding v2
Universal Extractor
All-in-one extractor for image, video, audio, and documents: auto-detects modality and applies the right pipeline.
Expected Outcomes
40% less time on charting
Documentation time reduction
70% faster survey readiness
Audit preparation speed
95% of sections auto-populated
MDS auto-population rate
+$37 per patient per day
PDPM revenue recovery
Build this in the docs
The exact stages and extractors this use case runs on, with API reference and worked examples.
Deploy SNF Documentation Intelligence
Set up a clinical documentation pipeline for your skilled nursing facility. Ingest charts, extract clinical data, map to MDS sections, and build audit-ready retrieval, all through the Mixpeek API.
Frequently Asked Questions
Which MDS 3.0 sections can be auto-populated?
How does this integrate with our EHR system?
Can wound photographs be used for MDS Section M?
How is patient data protected?
Ready to Implement This Use Case?
Our team can help you get started with SNF Documentation Intelligence in your organization.