UC19: Clinical Documentation Improvement (CDI) Coach

The Problem

Vague or incomplete clinical documentation leads to under-coding, leaving revenue on the table. A physician documents "heart failure" without specifying systolic vs. diastolic, acute vs. chronic; the coder assigns a lower-severity DRG, reducing reimbursement by $2,000 to $10,000 per case. CDI specialists manually review notes post-discharge and send queries back to physicians, but the process is slow and often too late for claim correction. Many queries go unanswered, and revenue is lost.

What the Agent Does

Monitors inpatient EHR clinical notes in real-time as physicians dictate or type
Detects vague terminology (e.g., "heart failure" without severity) and flags with gentle, context-aware suggestions
Presents CDI query templates specific to the flagged condition (e.g., "Is the heart failure acute or chronic? Systolic, diastolic, or combined?")
Suggests more specific coding based on documented findings (e.g., "Labs show BNP elevated; consider acute systolic HF")
Tracks query response rates and updates physician-specific documentation profiles to reduce repeat queries
Generates CDI productivity reports (queries per physician, revenue impact, coding accuracy)

Data Requirements

Data Sources:

Epic/Cerner/Meditech Clinical Notes , Real-time or near-real-time note feeds, structured progress notes, assessments
ICD-10 and CPT Code Mappings , Complete code sets, code-to-DRG mappings, relative weight tables
CDI Query Templates , Condition-specific query templates (cardiac, respiratory, surgical, etc.)
Historical Coding Outcomes , Prior documentation examples, accepted queries, denied queries for training
Physician Credentialing , Specialty, department, coding accuracy history to personalise suggestions

Data Classification:

Clinical notes: Sensitive, Unstructured (narrative text)
ICD-10/CPT mappings: Internal, Structured
Query templates: Internal, Structured
Coding outcomes: Internal, Structured

Data Quality Requirements:

Clinical notes: completeness required before discharge (typically 48 hours post-discharge)
ICD-10/CPT mappings: updated annually with new code releases (October each year)
Query templates: reviewed quarterly with CDI and coding leadership
Historical coding outcomes: 12 months of data minimum for initial training

Integration Complexity: Medium-High

Real-time EHR note feed (Epic FHIR or HL7 CCD)
Natural language processing (NLP) to detect vague terminology
ICD-10/CPT code mapping database (3M CodeFinder, Optum360 integration)
Workflow integration: queries must surface in Epic/Cerner notification system, not as external alerts

Score Breakdown

Criterion	Weight	Score (1-5)	Weighted
Time Recaptured	15%	5	0.75
Error Reduction	10%	5	0.50
Cost Avoidance	10%	4	0.40
Strategic Leverage	5%	4	0.20
Data Availability	15%	4	0.60
Process Clarity	15%	4	0.60
Ease of Implementation	10%	4	0.40
Fallback Available	10%	4	0.40
Audience (Int/Ext)	10%	4	0.40
Composite	100%		4.10

Why It Scores Well

CDI is a direct revenue lever: improved documentation specificity increases DRG severity, boosting reimbursement by 2 to 3% for hospitalised patients. The use case leverages mature code sets (ICD-10, DRG), and outcomes are measurable (revenue per discharge, query accuracy). Real-time nudges improve compliance vs. post-discharge queries.

Regulatory Alignment

CMS Conditions of Participation (CoP): Hospitals must maintain accurate clinical documentation and DRG coding
HIPAA: Clinical notes are PHI; agent must respect access controls (physicians only see their own notes)
Joint Commission: Chart completeness standards; queries must be documented in the medical record
OIG Guidance on DRG Coding: Coding must reflect clinical reality; upcoding is fraud (agent must avoid suggesting overly aggressive coding)

Sprint Factory Fit

Sprint 0 (2 weeks) + 4 build sprints (8 weeks)

CDI is event-driven: every new clinical note can trigger the agent's analysis. The initial 2-week sprint focuses on detecting high-frequency documentation gaps (e.g., heart failure, pneumonia severity); subsequent sprints add more conditions, physician-specific customisation, and outcome tracking. This is a higher-complexity use case due to NLP requirements.

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