Coding Education That Keeps Your Team Current and Compliant
Guidelines change. Payer expectations shift. AI enters the workflow. Our education programs keep coders and providers updated, built from 40+ years of frontline medical coding research and on your schedule, not ours.
Recognized by industry associations
Our Education and Inservice Plan
- Design of audit frequency, volume, and target audience, whether provider, coder, auditor, or both
- Error recognition training for providers, coders, and internal auditors
- Auditor education covering audit methodology, sampling techniques, and documentation of findings
- Custom inservice sessions that address the specific documentation and coding gaps identified in your audits
- Training on your schedule, not Edelberg's, with flexible delivery online, on-site, or both
- Onboarding education for new coders, providers, and auditors joining your team
- Annual coding updates covering E/M changes, payer policy shifts, and ICD-10 revisions
Sample Education Plan Templates
Sample templates — actual client data will be added. These illustrate the structure and reporting format of an Edelberg education engagement.
Provider Documentation Education Plan
Monthly inservice + quarterly audit review
Key Topics
- Medical decision making documentation
- HPI and chronic condition specificity
- Documenting to the level of service provided
- Common query drivers by specialty
Sample Outcomes
Coding Query Rate
Before
12%
After
4%
Documentation Completeness
Before
78%
After
94%
E/M Level Accuracy
Before
81%
After
96%
Coder Accuracy Improvement Program
Bi-weekly sessions + monthly audit debrief
Key Topics
- E/M level selection and supporting documentation
- Payer-specific coding rules and common variances
- Diagnosis sequencing and specificity
- Denial pattern recognition and root cause analysis
Sample Outcomes
Coding Accuracy Rate
Before
88%
After
97%
Clean Claim Rate
Before
91%
After
98%
Denial Rate
Before
8.2%
After
2.1%
AI Coding Transition Training
Weekly during transition · Monthly ongoing
Key Topics
- AI output review protocols and validation checkpoints
- Systematic error pattern recognition by code type
- Documentation gap identification before claim submission
- Escalation workflows when AI confidence is low
Sample Outcomes
AI Error Catch Rate
Before
—
After
94%
Post-validation Accuracy
Before
—
After
97%
Team Readiness Score
Before
61%
After
89%
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Education built from your audit data, delivered on your schedule.