Clinical Documentation Improvement Specialist

JOB OVERVIEW

DeliverHealth is seeking an experienced Clinical Documentation Improvement (CDI) Specialist to support the continuous improvement of InstaNote's AI-powered Clinical Documentation Improvement (CDI) feature.

The primary responsibility of this role is to evaluate the quality, clinical relevance, and accuracy of the CDI recommendations generated by InstaNote during patient encounters. The specialist will validate AI-generated suggestions, identify false positives, false negatives, and missed documentation opportunities, and provide structured feedback to Product Management and Engineering teams to continuously enhance the performance of the CDI engine.

This is not a traditional operational CD I role focused on physician queries or concurrent hospital documentation reviews. Instead, this is a Product Quality and Clinical AI role that ensures InstaNote delivers clinically meaningful, accurate, and actionable documentation improvement recommendations.

This role serves as the critical link between clinical expertise, revenue cycle knowledge, and AI product development.

JOB DUTIES & RESPONSIBILITIES

1. Validate AI-Generated CDI Recommendations

Review and validate CDI recommendations generated by InstaNote, including:

- Missing diagnoses

- Diagnosis specificity

- HCC and Risk Adjustment opportunities

- CC/MCC opportunities

- Medical necessity documentation

- Procedure documentation

- Severity of Illness (SOI)

- Risk of Mortality (ROM)

- Missing clinical details

- Documentation clarification opportunities

- Quality measure documentation

- Chronic condition documentation

- Medication-related documentation opportunities

For each recommendation, determine whether it is:

- Clinically accurate

- Relevant to the encounter

- Supported by documentation

- Actionable for the provider

- High value from a coding and reimbursement perspective

2. Review AI-Generated Clinical Notes

Evaluate clinical notes generated by InstaNote to determine whether:

- Important diagnoses are missing

- Documentation lacks clinical specificity

- Medical decision-making is adequately represented

- History, assessment, and plan support the documented diagnoses

- Clinical evidence aligns with suggested CDI opportunities

Identify documentation gaps that the AI failed to recognize.

3. Perform AI Quality Audits

Conduct routine audits of the CDI feature by measuring:

- Recommendation accuracy

- Precision

- Recall

- False positive rate

- False negative rate

- Missed documentation opportunities

- Clinical relevance

- Specialty appropriateness

- Provider usability

- Recommendation acceptance potential

Document findings in standardized quality scorecards.

4. Continuous Product Improvement

Partner closely with Product Management and Engineering teams by providing structured feedback on:

- Incorrect recommendations

- Missing clinical logic

- New documentation scenarios

- Specialty-specific documentation patterns

- Prompt improvements

- Knowledge gaps

- AI workflow enhancements

- User experience improvements

- Recommendation prioritization

Participate in regular AI quality review sessions to prioritize enhancements.

5. Build Clinical Knowledge Assets

Support AI model development by creating and maintaining:

- Gold-standard annotated clinical cases

- Specialty-specific documentation examples

- Accepted and rejected recommendation libraries

- Clinical review guidelines

- Evaluation datasets for regression testing

- Reference documentation standards

6. Specialty-Specific Optimization

Review CDI recommendations across specialties such as:

- Orthopedics

- Emergency Medicine

- Urgent Care

- Primary Care

- Cardiology

- Internal Medicine

- Behavioral Health

- Pediatrics

- Surgical Specialties

Recommend specialty-specific enhancements to improve AI performance.

7. Cross-Functional Collaboration

Collaborate with:

- Product Managers

- AI Engineers

- Prompt Engineers

- Mac