Clinical Documentation Improvement Specialist
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AI Summary
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 CDI 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
Machine Learning Engineers
Medical Coders
Clinical Informaticists
QA Engineers
Data Scientists
Translate clinical observations into clear product requirements and acceptance criteria.
Key Deliverables
Weekly AI CDI validation reports
Clinical quality scorecards
False positive and false negative analyses
Missed opportunity reports
Root cause analyses
Product enhancement recommendations
Annotated reference cases
Specialty-specific feedback reports
Monthly AI performance trend reports
Clinical acceptance criteria for new CDI features
KNOWLEDGE, SKILLS, & ABILITIES
Technical Knowledge
Strong understanding of:
Clinical Documentation Improvement
ICD-10-CM
HCC Risk Adjustment
CPT coding fundamentals
Documentation specificity
Medical necessity
Clinical terminology
Provider documentation workflows
AI-assisted documentation tools
Electronic Health Records (Epic, athenahealth, Cerner, eClinicalWorks, etc.)
Core Competencies
- Strong clinical judgment
- Excellent analytical skills
- Attention to detail
- Ability to identify documentation gaps
- Root cause analysis
- Data-driven decision making
- Effective written communication
- Cross-functional collaboration
- Continuous improvement mindset
- Passion for healthcare AI and clinical innovation
REQUIRED QUALIFICATIONS
- Bachelor's degree in Nursing, Health Information Management, Medicine, Physician Assistant Studies, or a related clinical field.
- 5+ years of Clinical Documentation Improvement experience.
- Experience reviewing physician documentation for coding quality and documentation completeness.
- Strong knowledge of ICD-10-CM coding principles.
- Experience with HCC and Risk Adjustment documentation.
- Familiarity with outpatient and ambulatory documentation practices.
- Experience working with physicians on documentation quality initiatives.
PREFERRED QUALIFICATIONS
- Exposure to AI-assisted clinical documentation solutions is preferred.
- Preferred credentials include:
- RN / BSN
- RHIA
- RHIT
- PA
- NP
- MD / DO (preferred but not required)
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