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 CDI role focused on physician queries or concurrent hospital documentation reviews. Instead, this is a Product Quality and Clinical AIrole 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)

Originally posted on Himalayas