Reimbursement

1. Getting Started

1.1 Choose Your Billing Path

Before diving into the details, determine which billing process applies to your situation:

Hospital/Facility Billing – For billing facility fees in hospital outpatient settings:

• Bills for equipment, room, technical staff
• Uses standard hospital outpatient methodology
• Maps to APC 5734 with established payment rates

Provider/Professional Billing – For billing physician or other provider professional fees:

• Bills for physician interpretation and clinical decision-making
• Requires crosswalk methodology due to no established RVUs

1.2 Hospital vs Provider Decision Tree

What are you billing for?

Hospital facility fees (equipment, room, tech staff)? → Go to Section 2: Hospital Facility Billing

Physician professional fees (interpretation, clinical decisions)? → Go to Section 3: Provider Professional Billing

Both facility and professional components? → Use both Section 2 and Section 3 for respective components

1.3 CPT Code Overview

CPT Codes for ECG-AI LEF:

• 0764T: Assistive algorithmic ECG analysis; concurrent with ECG (add-on code)
• 0765T: Assistive algorithmic ECG analysis; previously performed ECG (stand-alone code)

Code Selection Decision:

• Same-day ECG + ECG-AI LEF analysis: Use 0764T
• ECG-AI LEF analysis of previous ECG: Use 0765T

2. Hospital Facility Billing

>2.1 Overview and Process

Hospital facility billing for ECG-AI LEF follows standard outpatient procedures using established APC methodology.

Process Characteristics:

• Standard APC methodology – Uses established Medicare payment classifications
• Established payment rates – APC 5734 provides predictable reimbursement
• Standard processing – Follows normal hospital billing workflows
• Basic documentation – Standard facility requirements
• Routine charge master setup – Treat like any other outpatient procedure

2.2 APC 5734 Classification

CPT Code Assignment:

• 0764T: Assistive algorithmic ECG analysis; concurrent with ECG
• 0765T: Assistive algorithmic ECG analysis; previously performed ECG

APC Classification: Both 0764T and 0765T map to APC 5734

• APC 5734: Level 4 Clinic Visits
• 2025 Payment Rate: $128.90 (Medicare national average, varies with  geographic adjustment)
• Relative Weight: 1.812
• Status Indicator: T (Procedure paid under OPPS)

Payment Structure: Hospital Outpatient Payment = Base APC Rate × Geographic Adjustment × Relative Weight

Example Calculation: $71.14 (Base) × 1.05 (Geographic) × 1.812 (Weight) = ~$135

2.3 Charge Master Setup

Charge Description Master (CDM) Entries:

For 0764T:

• Description: “ECG-AI Analysis – Concurrent”
• CPT Code: 0764T
• Revenue Code: 0636 (EKG/ECG)
• APC: 5734
• Department: Cardiology/EKG Lab
• Charge Amount: [Hospital-specific rate]

For 0765T:

• Description: “ECG-AI Analysis – Previous ECG”
• CPT Code: 0765T
• Revenue Code: 0636 (EKG/ECG)
• APC: 5734
• Department: Cardiology/EKG Lab
• Charge Amount: [Hospital-specific rate]

Charge Setting Considerations:

• Cost-based approach: Calculate actual costs (equipment, staff, overhead)
• Market positioning: Consider competitive rates in your market
• Payer mix analysis: Account for different payer reimbursement levels
• Annual review: Update charges per standard hospital process

2.4 Standard Billing Process

Step 1: Service Documentation

• ECG-AI LEF analysis performed and documented
• Basic clinical indication noted
• Technical quality verified
• Results communicated to ordering physician

Step 2: Charge Entry

• Select appropriate CPT code (0764T or 0765T)
• Enter via standard charge entry process
• Verify patient registration complete
• Confirm insurance information

Step 3: Billing Submission (Standard process)

• Include in routine hospital claim batch
• Use standard UB-04 claim form
• Follow normal facility billing workflow
• Apply standard edits and audits

2.5 Documentation Requirements

Clinical Documentation:

• Basic indication: Why ECG-AI LEF analysis was performed
• Clinical context: Relevant patient symptoms or risk factors
• Results summary: ECG-AI LEF analysis findings
• Clinical impact: How results influenced care (brief note)

Technical Documentation:

• Service date and time
• Performing technologist (if applicable)
• Equipment used
• Quality verification completed

Administrative Documentation:

• Physician order for ECG-AI LEF analysis
• Patient consent (per facility policy)
• Insurance verification completed

Example Procedure Note Template:

ECG-AI LEF ANALYSIS – FACILITY DOCUMENTATION

Date: [MM/DD/YYYY] Time: [HH:MM] Location: [Department]

INDICATION:

☐ Cardiac risk assessment

☐ Heart failure screening

☐ Pre-operative evaluation

☐ Other: [Specify]

 

PROCEDURE: 12-lead ECG obtained and analyzed using Anumana ECG-AI LEF technology.

Procedure Outcomes (Choose Based on Result Obtained)

Successful Positive Result

Device Output

• Output: YES
• Displayed Result: Low LVEF Detected
• Interpretation Statement: Low Left Ventricular Ejection Fraction detected based on analysis of input ECG waveform.
• Follow-up Recommendation: Further clinical evaluation suggested in order to establish diagnosis of Low LVEF. ECG-AI Low Ejection Fraction 12-Lead algorithm analysis should be applied jointly with clinical judgment.

Device Name: ECG-AI Low Ejection Fraction 12-Lead algorithm

Unique Device Identifier: (01)00860007426445(8012)V2.4.0

 

Successful Negative Result

Device Output

• Output: NO
• Displayed Result: Low LVEF Not Detected
• Interpretation Statement: Low Left Ventricular Ejection Fraction NOT detected based on analysis of input ECG waveform.
• Follow-up Recommendation: This result does not rule out Low LVEF. In addition to the ECG-AI Low Ejection Fraction 12-Lead algorithm analysis, clinical judgment should be used to obtain further noninvasive evaluation of LVEF if clinically indicated.

Device Name: ECG-AI Low Ejection Fraction 12-Lead algorithm

Unique Device Identifier: (01)00860007426445(8012)V2.4.0

 

Unsuccessful Completion

Device Output

• Output: null
• Displayed Result: Error
• Interpretation Statement: The input ECG waveform does not meet the quality criteria and cannot be processed by the ECG-AI LEF 12-Lead algorithm.
• Follow-up Recommendation: null

Device Name: ECG-AI Low Ejection Fraction 12-Lead algorithm

Unique Device Identifier: (01)00860007426445(8012)V2.4.0

Completion

Key Fields for ECG-AI LEF:

Revenue Code Lines:

• Line 1: Revenue Code 0636 (EKG/ECG)
• HCPCS/CPT: 0764T or 0765T
• Service Date: Date of ECG-AI LEF analysis
• Units: 1
• Charges: Per charge master

Diagnosis Coding: Primary Diagnosis Options:

• I50.9 (Heart failure, unspecified)
• I25.5 (Ischemic cardiomyopathy)
• R94.31 (Abnormal ECG)
• Z13.6 (Screening for cardiovascular disorders)

Other Required Fields:

• Standard patient demographics
• Insurance information
• Attending physician NPI
• Service facility information

2.7 Estimated Outcomes (based on experience to date)

Claims Processing – Performance Benchmarks:

Metric	Target	Typical Range
Approval Rate	>90%	85-95%
Payment Rate	95% of APC	90-100%
Processing Time	14-21 days	10-30 days
Denial Rate	<10%	5-15%

Success Factors:

• Clean documentation – Basic but complete clinical notes
• Accurate coding – Correct 0764T vs 0765T selection
• Timely submission – Standard facility billing timelines
• Proper charge capture – Consistent charge entry processes

Areas to Monitor:

• High denial rates – May indicate documentation issues
• Payment delays – Could suggest payer education needs
• Coding errors – Requires staff training
• Charge capture misses – Need process improvements

2.8 Implementation Checklist

Pre-Implementation:

• Charge master setup completed
• Staff training conducted
• Documentation templates created
• Quality assurance processes established
• Payer contracts reviewed

Go-Live:

• First cases documented and billed
• Daily monitoring of charges and documentation
• Weekly review of early outcomes
• Staff feedback collection and response

Post-Implementation:

• Monthly performance review (include Anumana representative)
• Quarterly benchmarking against targets
• Annual process optimization
• Ongoing staff development

3. Provider Professional Billing

3.1 Professional Billing Requirements

Category III CPT codes (0764T and 0765T) require specific attention:

• No assigned RVUs – Medicare and payers have not established payment rates
• No standardized coverage – Each payer decides individually
• Potential for denials – Unfamiliar codes often get denied automatically
• Documentation – Requires justification

3.2 Crosswalk Billing Solution

Crosswalk billing bridges the gap by demonstrating equivalent value to established procedures. Payers are familiar with these processes.

How It Works:

1. Compare Work Effort – Analyze physician time, complexity, and decision-making
2. Document Practice Costs – Calculate equipment, staff, and administrative expenses
3. Assess Risk Factors – Evaluate malpractice and liability considerations
4. Reference Established CPT – Link to codes with known RVUs and payment rates
5. Request Fair Payment – Justify reimbursement based on equivalent procedures

Key Benefits:

• Higher Success Rates – Comprehensive justification reduces denials
• Appropriate Payment – Fair reimbursement based on actual value delivered
• Streamlined Process – Standardized approach across all payers
• Audit Protection – Complete documentation supports compliance

3.3 Four-Phase Billing Process

Phase 1: Pre-Service

• Prior Authorization – Secure payer approval when required
• Medical Necessity – Document clinical rationale
• Patient Assessment – Identify risk factors and indications

Phase 2: Service Delivery

• ECG-AI LEF Analysis – Complete clinical service
• Procedure Report – Record findings and clinical impact
• Quality Review – Verify completeness and accuracy

Phase 3: Claims Preparation

• Crosswalk Analysis – Select appropriate reference CPT code
• Complete CMS-1500 – Accurate form completion with proper coding
• Gather Documentation – Assemble all required supporting materials

Phase 4: Payer Communication

• Submit Claims – Include comprehensive justification package
• Follow Up – Track status and respond to payer questions
• Appeals Process – Address denials with additional documentation

3.4 Estimated Outcomes (based on experience to date)

Realistic Expectations:

• Payment Rate: 60-80% of crosswalk reference code allowable
• Approval Time: 30-90 days for initial claims
• Success Rate: 70-85% approval with proper documentation
• Appeal Success: 80-90% with comprehensive justification

Factors for Success:

• Complete Documentation – All required materials included
• Strong Clinical Rationale – Clear medical necessity
• Appropriate Crosswalk – Well-justified reference code selection
• Persistent Follow-Up – Consistent payer communication

3.5 Getting Started Roadmap

New to ECG-AI LEF Billing?

Step 1: Learn the Process

• Review the comprehensive documentation in Section 4
• Study the template examples in Section 5
• Understand the crosswalk methodology

Step 2: Prepare Your Team

• Train billing staff on crosswalk methodology
• Educate clinical staff on documentation requirements
• Establish internal protocols and quality checks

Step 3: Start with One Claim

• Select straightforward clinical scenario
• Use all templates and checklists
• Track time and outcomes for process improvement

Already Billing but Need Better Results?

Audit Your Current Process:

• Review denial patterns and reasons
• Analyze documentation completeness
• Compare your crosswalk selections

Optimize Your Approach:

• Strengthen medical necessity documentation
• Improve payer communication strategies
• Enhance clinical procedure reports

4. Detailed Billing Instructions

4.1 CMS-1500 Form Completion

Key Fields for ECG-AI LEF Billing

Box 21 – Diagnosis or Nature of Illness

Primary Diagnosis Codes (Select most appropriate):

• I50.9 – Heart failure, unspecified
• I50.1 – Left ventricular failure, unspecified
• I25.5 – Ischemic cardiomyopathy
• I42.9 – Cardiomyopathy, unspecified

Secondary/Supporting Codes:

• Z51.81 – Encounter for therapeutic drug level monitoring
• R94.31 – Abnormal electrocardiogram [ECG] [EKG]
• Z87.891 – Personal history of nicotine dependence
• E11.9 – Type 2 diabetes mellitus without complications

Box 24A – Date of Service Format: MM/DD/YYYY Example: 03/15/2025

Box 24B – Place of Service Common Codes:

• 11 – Office
• 22 – Outpatient Hospital
• 23 – Emergency Room – Hospital
• 49 – Independent Clinic

BOX 24D – PROCEDURES, SERVICES, OR SUPPLIES

Scenario A: Concurrent ECG and AI Analysis (Most Common)

Line 1: Standard ECG

• CPT Code: 93000
• Description: Electrocardiogram, routine ECG with at least 12 leads; with interpretation and report
• Modifier: (none typically required)

Line 2: ECG-AI LEF Analysis

• CPT Code: 0764T
• Description: Assistive algorithmic electrocardiogram risk-based assessment for cardiac dysfunction; related to concurrently performed ECGs
• Modifier: (List separately in addition to code for primary procedure)

Scenario B: AI Analysis of Previously Performed ECG (0765T Only)

Line 1: ECG-AI LEF Analysis (Stand-alone)

• CPT Code: 0765T
• Description: Assistive algorithmic electrocardiogram risk-based assessment for cardiac dysfunction; related to previously performed ECGs
• Modifier: (none required – this is a stand-alone procedure)

Important Notes for 0765T-Only Billing:

• Date of Service: Use the date when the AI analysis was performed, NOT the original ECG date
• Documentation Required: Must reference the original ECG with date and location where it was performed
• Medical Record: Include copy of original ECG being analyzed
• Clinical Indication: Justify why retrospective AI analysis was medically necessary

Box 24F – $ Charges

For Concurrent ECG + AI Analysis (0764T):

• Line 1: $[ECG interpretation fee]
• Line 2: $[ECG-AI LEF crosswalk fee based on comparable CPT]
• Total: $[Combined service total]

For Stand-alone AI Analysis (0765T):

• Line 1: $[ECG-AI LEF crosswalk fee based on comparable CPT]
• Total: $[Single service total]

Note: Do NOT include CPT 99080 for crosswalk justification letters. Submit the crosswalk justification letter as supporting documentation attached to the claim, not as a separately billable service.

4.2 Crosswalk Analysis Process

Work RVU Comparison Analysis

Pre-Service Work Components

Component	0764T/0765T (ECG-AI LEF)	Reference CPT [Code]	Equivalent?
Review of clinical data	[X] minutes	[X] minutes	☐ Yes ☐ No
Patient/family communication	[X] minutes	[X] minutes	☐ Yes ☐ No
Equipment/room preparation	[X] minutes	[X] minutes	☐ Yes ☐ No
Staff coordination	[X] minutes	[X] minutes	☐ Yes ☐ No
Documentation review	[X] minutes	[X] minutes	☐ Yes ☐ No
Total Pre-Service Time	[X] minutes	[X] minutes	☐ Yes ☐ No

Intra-Service Work Components

Component	0764T/0765T (ECG-AI LEF)	Reference CPT	Equivalent?
Data acquisition/processing	[X] minutes	[X] minutes	☐ Yes ☐ No
Algorithm interpretation	[X] minutes	[X] minutes	☐ Yes ☐ No
Clinical correlation	[X] minutes	[X] minutes	☐ Yes ☐ No
Result verification	[X] minutes	[X] minutes	☐ Yes ☐ No
Decision-making	[X] minutes	[X] minutes	☐ Yes ☐ No
Total Intra-Service Time	[X] minutes	[X] minutes	☐ Yes ☐ No

Post-Service Work Components

Component	0764T/0765T (ECG-AI LEF)	Reference CPT [Code]	Equivalent?
Report generation	[X] minutes	[X] minutes	☐ Yes ☐ No
Result communication	[X] minutes	[X] minutes	☐ Yes ☐ No
Follow-up planning	[X] minutes	[X] minutes	☐ Yes ☐ No
Documentation completion	[X] minutes	[X] minutes	☐ Yes ☐ No
Care coordination	[X] minutes	[X] minutes	☐ Yes ☐ No
Total Post-Service Time	[X] minutes	[X] minutes	☐ Yes ☐ No

 

Crosswalk Justification Summary

Recommended Billing Approach:

• Bill 0764T/0765T with reference to CPT code [XXXX]
• Apply [XX]% of reference code’s allowable rate
• Include comprehensive supporting documentation
• Emphasize [key equivalency factors]

Suggested Payment Calculation:

• Reference CPT [XXXX] Medicare Allowable: $[amount]
• Recommended percentage: [XX]%
• Proposed Payment Rate: $[amount]

4.3 Supporting Documentation

Required Attachments:

• Crosswalk justification letter
• Procedure report with ECG-AI LEF results
• Letter of medical necessity
• Clinical literature supporting technology
• FDA clearance documentation
• Prior authorization (if obtained)

5. Templates and Forms

5.1 Crosswalk Justification Letter

[Practice/Hospital Letterhead]

Date: [Current Date]

To: [Payer Name] Claims Review Department
Re: Crosswalk Billing Justification for CPT Code 0764T/0765T
Patient: [Patient Name]
Policy Number: [Insurance Policy Number]
Date of Service: [Service Date]
Provider: [Physician Name, NPI]
Claim Number: [Claim Reference Number]

Dear Claims Reviewer,

This letter serves as justification for the crosswalk billing methodology applied to CPT Category III code [0764T/0765T] for AI-enhanced ECG analysis services provided to the above-referenced patient.

Procedure Performed

Service: Assistive algorithmic electrocardiogram risk-based assessment for cardiac dysfunction using Anumana ECG-AI LEF technology

CPT Code: [0764T – concurrent ECG / 0765T – previously performed ECG]

Clinical Context: [Brief description of patient presentation and clinical indication]

Crosswalk Methodology Rationale

Due to the Category III status of code [0764T/0765T] (no assigned RVUs), we have applied crosswalk billing methodology referencing CPT code [XXXX] based on the following equivalent factors:

Physician Work Component Analysis

Work Element	ECG-AI LEF (0764T/0765T)	Reference CPT [Code]	Equivalency
Pre-service time	[X] minutes	[X] minutes	Equivalent
Intra-service time	[X] minutes	[X] minutes	Equivalent
Post-service time	[X] minutes	[X] minutes	Equivalent
Cognitive effort	Complex cardiac risk assessment	Similar diagnostic interpretation	Equivalent
Technical complexity	AI algorithm interpretation	Advanced diagnostic analysis	Equivalent
Clinical decision-making	Integration of AI results into care plan	Similar clinical correlation required	Equivalent

Practice Expense Component Analysis

Expense Category	ECG-AI LEF Analysis	Reference Procedure	Equivalency
Technology infrastructure	AI software platform	Comparable diagnostic equipment	Equivalent
Staff time	[X] minutes clinical coordination	[X] minutes procedure support	Equivalent
Administrative overhead	Documentation and reporting	Similar administrative burden	Equivalent
Quality assurance	AI validation protocols	Standard QA requirements	Equivalent

Clinical Justification

Medical Necessity: The ECG-AI LEF analysis was medically necessary for [specific clinical indication] based on:

1. Patient Risk Factors:
   • [List relevant risk factors for heart failure/cardiac dysfunction]
   • [Clinical presentation requiring cardiac evaluation]
   • [Relevant medical history]

2. Diagnostic Value: The ECG-AI LEF algorithm’s ability to detect subtle ECG patterns indicative of low ejection fraction provides diagnostic information equivalent to [reference procedure] by:

1. • Identifying subclinical cardiac dysfunction
   • Providing quantitative risk assessment
   • Enabling earlier intervention and improved outcomes
2. Clinical Impact: The analysis results [influenced specific clinical decisions/changed management plan/confirmed clinical suspicion] leading to [specific actions taken].

Crosswalk Comparison Summary

Based on comprehensive analysis, CPT code [0764T/0765T] demonstrates equivalent work effort, practice expense requirements, and malpractice risk compared to reference CPT code [XXXX].

Recommended Payment Calculation:

• Reference CPT [XXXX] Medicare Allowable: $[Amount]
• Proposed Payment Rate: [XX]% of reference code allowable
• Requested Reimbursement: $[Amount]

Respectfully submitted,

[Physician Name, MD]
[Title/Specialty]
[License Number]
[NPI Number]
[Phone Number]
[Email Address]

5.2 Medical Necessity Letter

[Practice/Hospital Letterhead]

Date: [Current Date]

To: [Insurance Company Name]
Claims Review Department
[Payer Address]

RE: LETTER OF MEDICAL NECESSITY

Patient: [Patient Full Name]
DOB: [Date of Birth]
Policy Number: [Insurance Policy Number]
Group Number: [Group Number]
Date of Service: [Service Date]
Procedure: ECG-AI LEF Analysis (CPT 0764T/0765T)
Provider: [Physician Name, MD] – NPI: [NPI Number]

To Whom It May Concern:

This letter establishes the medical necessity for AI-enhanced electrocardiogram analysis using Anumana ECG-AI LEF technology for the above-referenced patient. The procedure was clinically indicated, medically appropriate, and directly contributed to patient care and clinical decision-making.

Patient Clinical Status

Demographics & Presentation: [Patient Name] is a [age]-year-old [gender] who presented with [primary symptoms/clinical scenario]. The patient has a medical history significant for:

Relevant Medical History:

• Hypertension (controlled/uncontrolled)
• Type 2 diabetes mellitus
• Previous myocardial infarction ([date])
• Coronary artery disease
• Chronic kidney disease (Stage [X])
• Family history of heart failure/cardiomyopathy
• Previous heart failure episodes
• Other: [Specify relevant conditions]

Current Symptoms:

• Shortness of breath (exertional/at rest)
• Fatigue/weakness
• Chest discomfort/pain
• Lower extremity edema
• Orthopnea/PND
• Decreased exercise tolerance
• Palpitations/arrhythmias
• Asymptomatic with risk factors

Clinical Rationale for ECG-AI LEF Analysis

The ECG-AI LEF analysis was medically necessary based on the following clinical factors:

1. Early Detection Capability

Clinical Need: The patient’s presentation and risk factors warranted evaluation for cardiac dysfunction, specifically low ejection fraction, which may not be apparent on standard ECG interpretation alone.

AI Advantage: The Anumana ECG-AI LEF algorithm can detect subtle ECG patterns indicative of low ejection fraction that are beyond the capability of conventional ECG analysis, enabling:

• Earlier identification of cardiac dysfunction
• Detection of subclinical heart failure
• Risk stratification in asymptomatic patients
• Screening in high-risk populations

2. Patient-Specific Risk Factors

The patient has [number] established risk factors for heart failure and cardiac dysfunction:

Major Risk Factors Present:

• [List specific risk factors with clinical context]
• [Quantify risk where possible – e.g., “10-year cardiovascular risk of X%”]
• [Describe cumulative risk profile]

3. Clinical Impact

The ECG-AI LEF results directly influenced clinical decision-making by:

• [Specific clinical action taken based on results]
• [Changes to medication management]
• [Referral decisions made]
• [Additional testing ordered/avoided]
• [Patient counseling and education provided]

Evidence-Based Medical Support

FDA Clearance and Regulatory Status

FDA Approval: Anumana ECG-AI LEF has received FDA 510(k) clearance (K232699) for:

• Detection of low ejection fraction in adults at risk for heart failure
• Use as an aid in clinical decision-making
• Integration into standard cardiac care workflows

Conclusion

The ECG-AI LEF analysis was medically necessary, clinically appropriate, and directly contributed to optimal patient care. The service meets all criteria for coverage under the patient’s benefit plan and represents responsible use of healthcare resources.

Sincerely,

[Physician Name, MD]
[Title/Specialty Board Certification]
[Practice/Hospital Name]
[License #: State license number]
[NPI: National Provider Identifier]

5.3 Prior Authorization Request

[Practice/Hospital Letterhead]

PRIOR AUTHORIZATION REQUEST

Date: [Current Date]
Request ID: [Internal tracking number]
Urgency Level: ☐ Routine ☐ Urgent ☐ Emergent

Payer Information

Insurance Company: [Insurance Company Name]
Prior Authorization Department: [Phone number]
Fax Number: [Authorization fax number]

Provider Information

Requesting Provider: [Physician Name, MD]
NPI: [National Provider Identifier]
Specialty: [Provider specialty]
License Number: [State license number]

Practice/Facility Information:
Name: [Practice/Hospital name]
Address: [Complete address]
Phone: [Main phone number]
Fax: [Main fax number]

Patient Information

Patient Name: [Last, First Middle]
Date of Birth: [MM/DD/YYYY]
Gender: ☐ Male ☐ Female
Policy Number: [Primary insurance policy number]
Group Number: [Group number]

Requested Service Information

Service Requested: Assistive Algorithmic ECG Risk Assessment
Procedure Description: AI-enhanced electrocardiogram analysis for cardiac dysfunction assessment using Anumana ECG-AI LEF technology

CPT Code(s):
☐ 0764T – Assistive algorithmic electrocardiogram risk-based assessment for cardiac dysfunction; related to concurrently performed ECGs
☐ 0765T – Assistive algorithmic electrocardiogram risk-based assessment for cardiac dysfunction; related to previously performed ECGs

Proposed Service Date: [MM/DD/YYYY]
Place of Service:
☐ Office (11) ☐ Outpatient Hospital (22) ☐ Emergency Room (23) ☐ Other: [Specify]

Clinical Information

Primary Diagnosis

ICD-10 Code: [Primary diagnosis code]
Description: [Primary diagnosis description]

Clinical Presentation

Chief Complaint: [Patient’s primary complaint]

Present Illness: [Detailed description of current condition]

Risk Factors for Cardiac Dysfunction

☐ Hypertension
☐ Diabetes mellitus
☐ Coronary artery disease
☐ Previous MI
☐ Family history of heart failure
☐ Age >65
☐ Chronic kidney disease
☐ Obesity (BMI >30)
☐ Sleep apnea
☐ Chemotherapy history
☐ Other: [Specify additional risk factors]

Medical Necessity Justification

The ECG-AI LEF analysis is medically necessary for this patient because:

☐ Evaluation of suspected heart failure – Patient presents with symptoms suggestive of cardiac dysfunction requiring diagnostic evaluation
☐ Risk assessment in high-risk patient – Multiple risk factors warrant screening for subclinical cardiac dysfunction
☐ Follow-up of known cardiac condition – Monitoring progression or response to treatment
☐ Pre-operative cardiac evaluation – Assessment needed prior to surgical intervention
☐ Other indication: [Specify specific clinical scenario]

Expected Clinical Impact

Anticipated Outcomes:

☐ Guide appropriate referral decisions
☐ Inform medication management
☐ Determine need for additional testing
☐ Provide patient reassurance or alert to risk
☐ Other: [Specify expected benefit]

5.4 Procedure Report Template

ECG-AI LEF PROCEDURE REPORT TEMPLATE

PATIENT INFORMATION

• Name: [Patient Last, First Middle]
• DOB: [MM/DD/YYYY]
• Gender: [Male/Female]
• MRN: [Medical Record Number]

SERVICE INFORMATION

• Date of Service: [MM/DD/YYYY]
• Time of Service: [HH:MM AM/PM]
• Location: [Department/Unit]
• Ordering Physician: [Physician Name, MD]
• Interpreting Physician: [Physician Name, MD]

PROCEDURE DETAILS

• Procedure: Assistive Algorithmic ECG Risk Assessment
• CPT Code: [0764T/0765T]

CLINICAL INDICATION

Primary Indication: [Select one]

☐ Evaluation of suspected heart failure
☐ Cardiac risk assessment in high-risk patient
☐ Follow-up assessment of known cardiac dysfunction
☐ Pre-operative cardiac evaluation
☐ Screening in asymptomatic patient with risk factors
☐ Other: [Specify]

Supporting Clinical Context: [Describe patient symptoms, physical exam findings, or clinical scenario that prompted the ECG-AI LEF analysis]

Risk Factors Present:

☐ Hypertension
☐ Diabetes mellitus
☐ Previous myocardial infarction
☐ Family history of heart failure
☐ Age >65 years
☐ Chronic kidney disease
☐ Obesity (BMI >30)
☐ Sleep apnea
☐ Other: [Specify]

Procedure Outcomes (Choose Based on Result Obtained)

Successful Positive Result

Device Output

• Output: YES
• Displayed Result: Low LVEF Detected
• Interpretation Statement: Low Left Ventricular Ejection Fraction detected based on analysis of input ECG waveform.
• Follow-up Recommendation: Further clinical evaluation suggested in order to establish diagnosis of Low LVEF. ECG-AI Low Ejection Fraction 12-Lead algorithm analysis should be applied jointly with clinical judgment.

Device Name: ECG-AI Low Ejection Fraction 12-Lead algorithm

Unique Device Identifier: (01)00860007426445(8012)V2.4.0

 

Successful Negative Result

Device Output

• Output: NO
• Displayed Result: Low LVEF Not Detected
• Interpretation Statement: Low Left Ventricular Ejection Fraction NOT detected based on analysis of input ECG waveform.
• Follow-up Recommendation: This result does not rule out Low LVEF. In addition to the ECG-AI Low Ejection Fraction 12-Lead algorithm analysis, clinical judgment should be used to obtain further noninvasive evaluation of LVEF if clinically indicated.

Device Name: ECG-AI Low Ejection Fraction 12-Lead algorithm

Unique Device Identifier: (01)00860007426445(8012)V2.4.0

 

Unsuccessful Completion

Device Output

• Output: null
• Displayed Result: Error
• Interpretation Statement: The input ECG waveform does not meet the quality criteria and cannot be processed by the ECG-AI LEF 12-Lead algorithm.
• Follow-up Recommendation: null

Device Name: ECG-AI Low Ejection Fraction 12-Lead algorithm

Unique Device Identifier: (01)00860007426445(8012)V2.4.0

CLINICAL INTERPRETATION

Physician Assessment: The ECG-AI LEF analysis [select appropriate]:

☐ Indicates HIGH probability of low ejection fraction (EF ≤40%)
☐ Suggests INTERMEDIATE probability of cardiac dysfunction
☐ Shows LOW probability of significant left ventricular dysfunction
☐ Results inconclusive due to [technical/clinical factors]

Clinical Correlation: The AI analysis results were reviewed in the context of:

Patient Clinical Presentation:

☐ Symptoms: [Describe relevant symptoms: dyspnea, fatigue, edema, etc.]
☐ Physical examination: [Relevant cardiac findings]
☐ Vital signs: [BP, HR, weight changes]

CLINICAL IMPACT AND RECOMMENDATIONS

Immediate Clinical Actions Taken:

Further cardiac evaluation recommended

☐ Echocardiogram ordered [Stat/Routine/Outpatient]
☐ Cardiology consultation requested [Urgent/Routine]
☐ BNP/NT-proBNP ordered

Medication management

☐ Heart failure therapy initiated
☐ Existing cardiac medications adjusted
☐ Cardiotoxic medications reviewed

Other actions: [Specify]

Follow-up Plan:

☐ Cardiology appointment: [Date/timeframe]
☐ Repeat ECG-AI LEF in: [Timeframe]
☐ Echo follow-up in: [Timeframe]
☐ Primary care follow-up: [Date/timeframe]
☐ Patient education materials provided

BILLING AND CODING INFORMATION

Primary Procedure Code: [0764T/0765T]
Supporting Procedure Code: [93000 if concurrent ECG]
Primary Diagnosis: [ICD-10 code and description]
Secondary Diagnoses: [Additional ICD-10 codes]

Medical Necessity Justification: This AI-enhanced ECG analysis was medically necessary because [provide specific rationale based on patient presentation, risk factors, and clinical indication].

ELECTRONIC SIGNATURE

Interpreted by: [Physician Name, MD]
Specialty: [Cardiology/Internal Medicine/Emergency Medicine/etc.]
License Number: [State license number]
NPI: [National Provider Identifier]
Date/Time of Interpretation: [MM/DD/YYYY, HH:MM AM/PM]
Electronic Signature: [Physician electronic signature or “Electronically signed by Dr. [Name]”]

6. Quick Reference

6.1 CPT Code Decision Matrix

Clinical Scenario	CPT Code	Bill With	Date of Service
Same-day ECG + AI	0764T	93000 (ECG)	Same date
Previous ECG + New AI	0765T	Stand-alone	AI analysis date
Routine screening with ECG	0764T	93000 (ECG)	Same date
Specialist review of old ECG	0765T	Stand-alone	Review date

Quick Check:

• ECG and AI same day? → 0764T
• Concurrent service? → 0764T
• Analyzing previous ECG? → 0765T
• Retrospective analysis? → 0765T

6.2 Diagnosis Codes

Primary Diagnosis Options

ICD-10	Description	Use When
I50.9	Heart failure, unspecified	Suspected or known HF
I50.1	Left ventricular failure	Specific LV dysfunction
I25.5	Ischemic cardiomyopathy	Post-MI or CAD patients
I42.9	Cardiomyopathy, unspecified	Non-ischemic cardiomyopathy

Supporting Codes

ICD-10	Description	Use When
R94.31	Abnormal ECG	Abnormal rhythm/findings
Z51.81	Drug monitoring	Cardiotoxic medications
Z87.891	History nicotine dependence	Smoking risk factor
E11.9	Type 2 diabetes	Diabetes risk factor

6.3 Documentation Checklists

Hospital Facility Billing:

• Service documented with clinical indication
• Correct CPT code selected (0764T vs 0765T)
• Charge entered in hospital system
• Standard UB-04 claim submission

Professional Billing (Crosswalk Required):

Core Documents:

• CMS-1500 form (completed per crosswalk guide)
• Crosswalk justification letter
• Procedure report with AI results
• Medical necessity documentation

Supporting Materials:

• FDA clearance documents (K232699)
• Clinical literature (efficacy studies)
• Prior authorization (if obtained)

For 0765T Only:

• Copy of original ECG
• Original ECG date and location
• Clinical justification for retrospective analysis

6.4 Common Crosswalk References

Successful Crosswalk Options (Professional Billing)

Reference CPT	Description	Best Match For	Typical RVU
93000	ECG interpretation	Physician work component	0.17 Work RVU
93005	ECG tracing only	Technical component	0.28 Total RVU
76700	Abdominal ultrasound	Interpretation complexity	0.45 Work RVU
78452	Nuclear cardiac study	Advanced cardiac imaging	1.25 Total RVU

Selection Tips:

• Match work effort – similar time and complexity
• Consider practice expense – equipment and staff costs
• Document rationale – explain equivalency clearly
• Be conservative – choose defensible comparisons

Frequently Asked Questions

7.1 Facility vs Professional Billing

Q: What’s the difference between facility and professional billing for ECG-AI LEF?

A: These are completely different billing processes:

FACILITY BILLING (Hospital):

• Bills for equipment, room, technical staff
• Uses standard hospital outpatient methodology
• Maps to APC 5734 with established payment rates
• Minimal additional documentation required
• High success rates with fast processing

PROFESSIONAL BILLING (Provider):

• Bills for physician interpretation and clinical decision-making
• Requires crosswalk methodology due to no established RVUs
• Extensive documentation and justification needed
• More complex appeals process
• Variable success rates depending on documentation quality

Q: Do I need crosswalk billing if I’m a hospital?

A: NO – Hospitals billing facility fees use standard APC methodology. CPT codes 0764T and 0765T map to APC 5734 with established payment rates.

Crosswalk billing ONLY applies to professional fees where physicians bill for interpretation and clinical decision-making.

Q: Can the same ECG-AI LEF procedure have both facility and professional fees?

A: YES – This is common in hospital settings:

• Hospital bills facility fee using 0764T/0765T (maps to APC 5734)
• Physician bills professional fee using same codes with modifier if needed (requires crosswalk methodology)

Q: What is APC 5734?

A: APC 5734 is the Medicare Ambulatory Payment Classification for ECG-AI LEF facility services:

• Classification: Level 4 Clinic Visits
• 2025 Payment Rate: $128.90 (Medicare national average, varies by geographic area)
• Relative Weight: 1.812
• Both 0764T and 0765T map to this APC

7.2 General Billing Questions

Q: What are CPT codes 0764T and 0765T?

A: These are Category III CPT codes assigned by the AMA for Anumana’s ECG-AI LEF technology:

• 0764T: AI analysis performed concurrently with ECG (add-on code)
• 0765T: AI analysis of previously performed ECG (stand-alone code)

Q: Why can’t I just bill these codes like any other CPT code?

A: This depends on whether you’re billing facility or professional fees:

For Hospital Facility Fees: You CAN bill them like other CPT codes! They map to APC 5734 with established payment rates.

For Professional Fees: Category III codes don’t have assigned Relative Value Units (RVUs) or established payment rates. Payers have discretion in coverage decisions, often resulting in denials without proper justification. Crosswalk billing is required (see section 3.2)

7.3 CPT Code Selection

Q: When should I use 0764T vs 0765T?

A: Use this decision tree:

ECG and AI analysis same day?

YES → 0764T (with 93000 for ECG)

NO → 0765T (stand-alone)

Patient visit includes ECG?

YES → 0764T

NO → 0765T

Retrospective ECG analysis?

YES → 0765T

NO → 0764T

Q: Can I bill both 0764T and 0765T for the same patient?

A: Not for the same ECG analysis. Choose one based on the clinical scenario:

• Same-day ECG + ECG-AI LEF = 0764T
• Previous ECG + new ECG-AI LEF analysis = 0765T

7.4 Medical Necessity

Q: What clinical scenarios justify ECG-AI LEF analysis?

A: The Anumana Low Ejection Fraction AI-ECG Algorithm is software intended to aid in screening for Left Ventricular Ejection Fraction (LVEF) less than or equal to 40% in adults at risk for heart failure. This population includes, but is not limited to:

• patients with cardiomyopathies
• patients who are post-myocardial infarction
• patients with aortic stenosis
• patients with chronic atrial fibrillation
• patients receiving pharmaceutical therapies that are cardiotoxic, and
• postpartum women.

Anumana Low Ejection Fraction AI-ECG Algorithm is not intended to be a stand-alone diagnostic device for cardiac conditions, should not be used for monitoring of patients, and should not be used on ECGs with a paced rhythm.

A positive result may suggest the need for further clinical evaluation in order to establish a diagnosis of Left Ventricular Ejection Fraction (LVEF) less than or equal to 40%. Additionally, if the patient is at high risk for the cardiac condition, a negative result should not rule out further non-invasive evaluation.

The Anumana Low Ejection Fraction AI-ECG Algorithm should be applied jointly with clinician judgment.

Q: Is ECG-AI LEF analysis covered for asymptomatic patients?

A: Yes, when appropriate risk factors are present. Document:

• Specific risk factors (diabetes, hypertension, family history)
• Clinical rationale for screening
• Potential impact on patient management

7.5 Technical Process

Q: Which crosswalk CPT codes work best?

A: Providers must use their own judgement when selecting appropriate crosswalk codes. The following codes have been accepted as successful crosswalks for professional billing:

• 93000 (ECG interpretation) – Similar physician work
• 93005 (ECG tracing) – Equipment and technical components
• 76700 (Abdominal ultrasound) – Similar interpretation complexity
• Select based on your specific work effort analysis

Q: How do I track billing success?

A: Monitor these metrics:

• Approval rate (target: >75% for professional, >90% for facility)
• Average payment amount (target: 60-80% of crosswalk allowable for professional, ~100% APC for facility)
• Time to payment (target: <60 days for professional, <21 days for facility)
• Denial reasons (for process improvement)

7.6 Compliance and Legal

Q: Is crosswalk billing compliant?

A: Yes, crosswalk billing is an established, accepted methodology for Category III codes. Ensure:

• Accurate clinical documentation
• Honest representation of services
• Proper code selection based on actual services performed
• Compliance with all applicable laws and regulations

Q: How often should I update my crosswalk analysis?

A: Review crosswalk selections:

• Quarterly – for process optimization
• When RVU values change – typically annually
• After significant denials – reassess approach
• With new clinical evidence – incorporate latest data

7.7 Implementation

Q: How do I get started if I’m new to this?

A: Hospital Facility Billing:

1. Set up charge master entries for 0764T/0765T
2. Train staff on basic documentation requirements
3. Implement standard billing workflow
4. Monitor early outcomes

Professional Billing:

1. Review comprehensive materials – Study crosswalk methodology
2. Select simple case – Choose straightforward clinical scenario
3. Use all templates – Follow guides exactly for first few claims
4. Track outcomes – Monitor success and identify improvements
5. Scale gradually – Expand volume as process improves

8. Troubleshooting and Appeals

8.1 Common Problems and Solutions

Problem 1: “Not Medically Necessary” Denials

Root Causes:

• Insufficient clinical documentation
• Weak risk factor identification
• Poor symptom documentation
• Missing clinical rationale

Solutions:

1. Review medical record for additional clinical information
2. Identify all risk factors (diabetes, hypertension, family history, etc.)
3. Document symptoms (even if subtle: fatigue, exercise intolerance)
4. Clarify clinical impact on patient management

Problem 2: “Experimental/Investigational” Denials

Root Causes:

• Payer unfamiliarity with technology
• Missing FDA clearance documentation
• Insufficient clinical validation evidence

Solutions:

1. Include FDA 510(k) clearance (K232699) with appeal
2. Submit clinical validation studies showing efficacy
3. Reference predicate device approvals for similar technologies

Problem 3: “No Established Payment Rate” Denials (Professional Only)

Root Causes:

• Missing crosswalk justification
• Weak equivalency analysis
• Inadequate RVU comparison
• Poor reference code selection

Solutions:

1. Complete comprehensive crosswalk analysis using worksheet
2. Select stronger reference CPT code with better equivalency
3. Strengthen work effort comparison with detailed time analysis
4. Include practice expense calculations showing equivalent costs

Problem 4: Hospital APC Issues

Root Causes:

• Payer unfamiliarity with APC 5734 mapping
• Incorrect charge master setup
• Missing basic documentation

Solutions:

1. Reference APC 5734 classification in communications
2. Verify charge master setup includes correct revenue codes
3. Include basic clinical indication in documentation
4. Contact payer to confirm APC recognition

8.2 Appeals Process

Appeals Process Flowchart

CLAIM DENIED ↓ Step 1: Analyze Denial

• Review denial letter/code
• Identify specific issues
• Gather additional documentation ↓ Step 2: Prepare Appeal
• Address specific denial reasons
• Strengthen weak documentation
• Include additional clinical evidence ↓ Step 3: Submit Appeal
• Follow payer-specific process
• Include all supporting documentation
• Request expedited review if urgent ↓ Step 4: Follow Up
• Track appeal status
• Respond to additional requests
• Schedule peer-to-peer if needed ↓ Step 5: Escalation
• Request supervisor review
• File formal grievance
• Consider external review

9. Reference Materials

9.1 Resources

Regulatory Information:

• FDA 510(k) Clearance: K232699
• Clinical validation studies: Available upon request
• Professional society guidelines: Updated quarterly

Professional Organizations:

• AAPC (billing certification and education)
• HFMA (revenue cycle management)
• HIMSS (health information management)
• ACC (cardiology clinical guidance)

10. Printable Resources

The following sections are formatted for easy printing. Each can be printed separately for use at workstations, in meetings, or as training materials.

10.1 Hospital Facility Checklists

Daily Hospital Facility Billing Checklist

Print and use at billing workstations

Patient: _________________________ Date: _____________

Service Documentation

☐ ECG-AI analysis performed and documented

☐ Basic clinical indication noted
☐ Technical quality verified

☐ Results communicated to ordering physician

 

Code Selection

☐ 0764T (concurrent ECG + AI analysis)

☐ 0765T (AI analysis of previous ECG)

☐ Revenue Code 0636 (EKG/ECG) assigned

☐ APC 5734 classification confirmed

 

Charge Entry

☐ Appropriate CPT code selected

☐ Charge entered in hospital system

☐ Patient registration verified complete

☐ Insurance information confirmed

 

Documentation

☐ Physician order documented

☐ Clinical indication recorded

☐ Basic procedure note completed

☐ Quality verification signed off

 

 

Billing Submission

☐ Included in routine claim batch

☐ UB-04 form completed

☐ Standard billing workflow followed

☐ Claim tracking number recorded

 

Expected Payment: $128.90 (Medicare national average + geographic adjustment)

Completed by: _________________________ Time: _____________

Hospital Implementation Checklist

Print for go-live preparation

Pre-Implementation Setup

☐ Charge master entries created for 0764T and 0765T

☐ Revenue code 0636 assigned

☐ APC 5734 classification confirmed

☐ Charge amounts established

☐ Documentation templates created

☐ Staff training completed

☐ Quality assurance processes established

☐ Billing workflow updated

☐ Payer contracts reviewed

 

Go-Live Monitoring

☐ First cases documented properly

☐ Charges captured accurately
☐ Claims submitted without errors

☐ Staff feedback collected

☐ Daily volume tracking in place

☐ Payment monitoring established

 

Post-Implementation Review

☐ Weekly performance metrics reviewed

☐ Denial patterns analyzed

☐ Staff competency assessed

☐ Process improvements identified

☐ Training updates completed

 

Implementation Lead: _________________________ Date: _____________

10.2 Professional Billing Checklists

Professional Billing Master Checklist

Print for each professional fee claim

Patient: _________________________ Date of Service: _____________

Pre-Service Phase

☐ Prior authorization obtained (if required)

☐ Medical necessity documented

☐ Patient risk factors identified

☐ Clinical indication clearly established

 

Service Documentation

☐ ECG-AI LEF analysis completed

☐ Procedure report generated

☐ Clinical interpretation documented

☐ Results communicated to patient/referring physician

 

Crosswalk Analysis

☐ Reference CPT code selected: ________________

☐ Work RVU comparison completed

☐ Practice expense analysis finished

☐ Payment calculation determined: $______________

CMS-1500 Completion

☐ Correct CPT code entered (0764T or 0765T)

☐ Appropriate diagnosis codes listed

☐ Service date accurate

☐ Provider information complete

☐ Charge amount calculated correctly

 

Supporting Documentation

☐ Crosswalk justification letter attached

☐ Medical necessity letter included

☐ Procedure report attached

☐ FDA clearance documentation included

☐ Clinical literature attached (if needed)

☐ Prior authorization copy included (if applicable)

 

For 0765T Claims Only

☐ Original ECG copy included

☐ Original ECG date and location documented

☐ Retrospective analysis justification provided

 

Quality Review

☐ All required fields completed

☐ Documentation reviewed for completeness

☐ Supervisor approval obtained

☐ Claim ready for submission

 

Submission Tracking

☐ Claim submitted on: ________________

☐ Tracking number: ________________

☐ Follow-up date scheduled: ________________

 

Completed by: _________________________ Reviewed by: _____________

Crosswalk Analysis Worksheet

Print for detailed equivalency analysis

Analysis Date: _____________ Completed by: _________________________

CPT Code Being Analyzed:

☐ 0764T

☐ 0765T

 

Reference CPT Code Selected: ________________

Work RVU Analysis

Pre-Service Work

• Review clinical data: _____ minutes (Reference: _____ minutes)
• Patient communication: _____ minutes (Reference: _____ minutes)
• Equipment preparation: _____ minutes (Reference: _____ minutes)
• Total Pre-Service: _____ minutes (Reference: _____ minutes)

Intra-Service Work

• Data acquisition/processing: _____ minutes (Reference: _____ minutes)
• Algorithm interpretation: _____ minutes (Reference: _____ minutes)
• Clinical correlation: _____ minutes (Reference: _____ minutes)
• Result verification: _____ minutes (Reference: _____ minutes)
• Total Intra-Service: _____ minutes (Reference: _____ minutes)

Post-Service Work

• Report generation: _____ minutes (Reference: _____ minutes)
• Result communication: _____ minutes (Reference: _____ minutes)
• Documentation completion: _____ minutes (Reference: _____ minutes)
• Total Post-Service: _____ minutes (Reference: _____ minutes)

Practice Expense Analysis

• Technology infrastructure cost: $_______ (Reference: $_______)
• Clinical staff time: _____ minutes × $_____ = $_______
• Administrative overhead: $_______ (Reference: $_______)
• Quality assurance: $_______ (Reference: $_______)
• Total Practice Expense: $_______ (Reference: $_______)

Payment Calculation

• Reference CPT allowable: $_______
• Recommended percentage: _____%
• Proposed payment: $_______

Equivalency Assessment

☐ Work effort equivalent

☐ Practice expense comparable
☐ Malpractice risk similar

☐ Overall crosswalk justified

 

Approved by: _________________________ Date: _____________

10.3 Printable Templates

Procedure Note Template

Print and complete for each ECG-AI LEF analysis

ECG-AI LEF ANALYSIS PROCEDURE NOTE

Patient Information

• Name: _________________________________________________
• DOB: _____________ MRN: _____________
• Date of Service: _____________ Time: _____________

Clinical Indication (check applicable)

☐ Evaluation of suspected heart failure

☐ Cardiac risk assessment in high-risk patient
☐ Pre-operative cardiac evaluation

☐ Follow-up of known cardiac condition

☐ Screening in asymptomatic patient with risk factors

☐ Other: _________________________________

 

Risk Factors Present (check all applicable)

☐ Hypertension

☐ Diabetes mellitus

☐ Previous MI

☐ Family history HF /

☐ Age >65

☐ Chronic kidney disease
☐ Obesity (BMI >30)

☐ Sleep apnea

☐ Chemotherapy history

☐ Other: _________________________________

 

Procedure Performed

☐ 0764T – Concurrent ECG and AI analysis

☐ 0765T – AI analysis of previous ECG

 

If 0765T, Original ECG Details:

• Original ECG date: _____________
• Original ECG location: _________________________________
• Reason for retrospective analysis: _________________________________

ECG-AI LEF Results

• Risk score: _____________
• Risk category: ☐ Low ☐ Intermediate ☐ High
• Algorithm confidence: ☐ High ☐ Medium ☐ Low

Clinical Interpretation The ECG-AI LEF analysis:

☐ Indicates HIGH probability of low ejection fraction (EF ≤40%)

☐ Suggests INTERMEDIATE probability of cardiac dysfunction
☐ Shows LOW probability of significant LV dysfunction

☐ Results inconclusive due to: _________________________________

 

Clinical Actions Taken

☐ Echocardiogram ordered

☐ Cardiology referral made

☐ Heart failure medications initiated/adjusted

☐ Additional testing ordered: _________________________________

☐ Patient education provided

☐ Follow-up scheduled: _________________________________

 

Medical Necessity Justification This AI-enhanced ECG analysis was medically necessary because:

Physician Signature Interpreted by: _________________________________ Print Name: _____________________ Date: _____________ License #: _____________________ NPI: _____________

Prior Authorization Request Form

Print and complete for payer submission

PRIOR AUTHORIZATION REQUEST
ECG-AI LEF Analysis

Request Date: _____________ Urgency: ☐ Routine ☐ Urgent ☐ Emergent

Provider Information

• Provider Name: _________________________________
• NPI: _____________ License #: _____________
• Practice/Hospital: _________________________________
• Address: _________________________________
• Phone: _____________ Fax: _____________

Payer Information

• Insurance Company: _________________________________
• Prior Auth Phone: _____________
• Fax Number: _____________

Patient Information

• Patient Name: _________________________________
• DOB: _____________ Gender: ☐ M ☐ F
• Policy #: _____________ Group #: _____________

Service Requested

☐ 0764T – Concurrent ECG and AI analysis

☐ 0765T – AI analysis of previous ECG

 

• Proposed service date: _____________
• Place of service: ☐ Office ☐ Hospital ☐ Other: _______

Primary Diagnosis: _____________ (ICD-10 code and description)

Clinical Justification (check all applicable)

☐ Multiple cardiac risk factors present

☐ Symptoms suggestive of heart failure
☐ Abnormal ECG findings require further evaluation

☐ Pre-operative cardiac assessment needed

☐ Monitoring of known cardiac condition

☐ Other: _________________________________

 

Supporting Information Attached

☐ Clinical notes

☐ Previous test results

☐ FDA clearance info

☐ Medical literature

☐ Other: _____________

 

Expected Clinical Impact

☐ Guide referral decisions

☐ Inform medication management

☐ Determine need for additional testing

☐ Risk stratification

☐ Other: _________________________________

 

Provider Attestation I certify that the information provided is accurate and that the requested service is medically necessary for this patient.

Physician Signature: _________________________ Date: _____________

For Payer Use Only

• Authorization #: _____________
• Approved: ☐ Yes ☐ No
• Valid dates: _____________ to _____________
• Reviewer: _____________ Date: _____________

10.4 Quick Reference Cards

CPT Code Decision Card

Print and post at workstations

ECG-AI LEF CPT CODE QUICK REFERENCE

Code Selection Decision Tree

Same day ECG + AI analysis?

YES → 0764T (with 93000)

NO → 0765T (stand-alone)

Concurrent service?

YES → 0764T

NO → 0765T

Analyzing previous ECG?

YES → 0765T

NO → 0764T

Hospital Facility Billing

• APC Classification: APC 5734
• Payment Rate: $128.90 (national average)
• Revenue Code: 0636
• Processing Time: 14-21 days
• Success Rate: >90%

Professional Billing

• Crosswalk Required: YES
• Payment Rate: 60-80% of reference CPT
• Processing Time: 30-90 days
• Success Rate: 70-85%

Diagnosis Codes Reference Card

Print for coding reference

ECG-AI LEF DIAGNOSIS CODES

Primary Options

• I50.9 Heart failure, unspecified
• I50.1 Left ventricular failure
• I25.5 Ischemic cardiomyopathy
• I42.9 Cardiomyopathy, unspecified

Supporting Codes

• R94.31 Abnormal ECG
• Z51.81 Drug level monitoring
• Z87.891 History of nicotine dependence
• E11.9 Type 2 diabetes mellitus

Risk Factor Codes

• I10 Essential hypertension
• E78.5 Hyperlipidemia
• Z87.891 Personal history of nicotine dependence
• N18.6 End stage renal disease

This comprehensive guide provides general information for ECG-AI LEF billing. Healthcare providers are solely responsible for all billing decisions, code selections, and compliance with applicable laws and regulations. Consult with your billing specialists, compliance officers, and legal counsel before implementing billing procedures.

Document Information: Anumana ECG-AI LEF Technology – Resources for Hospital and Provider Billing

• Version: 1.0
• Last Updated: [Current Date]
• Next Review: [Quarterly Review Date]
• Purpose: Complete billing guidance for ECG-AI LEF services using CPT codes 0764T and 0765T
• Scope: Hospital facility billing and provider professional billing methodologies