DescriptionPRN Remote
About Us
Integrity Management Services Inc. (IntegrityM) is an award-winning women-owned small business specializing in assisting government and commercial clients in compliance and program integrity efforts including the prevention and detection of fraud waste and abuse in government programs. Results are achieved through data analytics technology solutions audit investigation and medical review.
At IntegrityM we offer a culture of opportunity recognition collaboration and supporting our community. We thrive off of these fundamental elements that make IntegrityM a great place to work. Our small flexible workplace offers an exceptional quality of life and promotes corporate-driven sustainability. We deliver creative solutions that exceed goals and foster a dynamic idea-driven environment that nurtures our employees professional development. Large company perksSmall company feel!
Position Overview
The Clinical Subject Matter Expert (SME) plays a vital role in ensuring that AI/ML solutions developed under Operation MedAI are clinically valid policy-aligned unbiased and actionable. This role blends clinical expertise with technical collaboration to support AI/ML model design review processes policy enforcement and stakeholder engagement. The SME will actively contribute clinical judgment and regulatory knowledge to help shape data-driven tools used in medical review program integrity and Medicare Advantage oversight.
Key Responsibilities
Clinical Validation
- Applies clinical expertise in utilization review and validation processes to ensure appropriate coverage and medical necessity determinations.
- Reviews AI/ML model outputs and alerts to assess clinical validity in alignment with accepted guidelines.
- Validates predicted patterns and outliers to support accurate fraud waste and abuse detection.
Model Guidance & Development Support
- Contributes clinical input into model training testing quality assurance and ongoing performance monitoring.
- Guides the integration of medical logic into model architecture particularly supervised learning and hybrid models.
- Identifies and validates clinical data elements and coding systems (e.g. ICD CPT DRG) used in modeling workflows.
Bias Mitigation
- Evaluates data and model behavior for evidence of clinical or demographic bias.
- Recommends fairness-enhancing techniques to ensure models support equitable outcomes across diverse populations.
Policy and Regulatory Alignment
- Collaborates with client project teams to ensure that AI-driven solutions support policy enforcement and comply with Medicare Part C and CMS regulatory standards.
- Interprets federal and state healthcare regulations and incorporates compliance requirements into model design and validation protocols.
- Assists in the development of defensible documentation for audits education and enforcement actions.
Stakeholder Collaboration & Communication
- Serves as a liaison between technical teams and clinical stakeholders ensuring clinical accuracy and relevance in model deployment.
- Participates in internal and client-facing meetings offering subject matter insight on model performance and medical review strategies.
- Contributes to the creation of clinical white papers findings reports and strategic planning documents.
RequirementsRequired Qualifications
- Active Registered Nurse (RN) license with at least an Associates degree
- Or other clinical specialties as needed (e.g. PT OT NP)
- Minimum 5 years of clinical experience as a registered nurse
- At least 3 years of experience in review of medical claims or utilization review for coverage medical necessity or program integrity purposes
- Demonstrated experience interpreting federal and state healthcare regulations
- Strong written and verbal communication skills
- Experience working in multidisciplinary project environments especially involving technology or analytics teams
Preferred Qualifications
- Experience with commercial/private insurance Medicare Advantage (Part C) policy and/or CMS program integrity initiatives
- Prior involvement in AI/ML model development clinical data analytics or technology-driven healthcare tools
- Familiarity with healthcare data structures such as claims EHR and HEDIS metrics
- Understanding of algorithmic bias and mitigation practices in healthcare modeling