Job Description

Clinical Documentation Improvement Specialist

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, and medication-related documentation opportunities. For each recommendation, determine whether it is clinically accurate, relevant to the encounter, supported by documentation, actionable for the provider, and 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, and 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, and 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, and 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, and 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, and 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, and 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, and 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).

Job Tags

Similar Jobs