Healthcare Informatics AI Intern

Primary Responsibilities

• Partner with clinical and utilization management teams to understand workflows, data needs, and areas where analysis can improve decision-making and outcomes
• Use AI tools to accelerate data pulls and surface patterns across large clinical datasets
• Analyze AI findings to flag trends or anomalies that may not be immediately visible through manual review
• Leverage AI to support documentation tasks such as drafting summaries, structuring findings, and generating repeatable templates for clinical and operational use
• Pull, clean, and analyze healthcare data including claims, date of service, provider specialty, utilization patterns, and clinical indicators
• Identify trends and draw actionable insights from clinical and utilization data to support care management, cost containment, and quality improvement efforts
• Support the clinical team in understanding benefit structures, plan designs, and how they interact with utilization and outcomes data
• Identify opportunities where AI or automation could streamline clinical review processes, flag outliers, or reduce manual workload for clinical staff
• Build lightweight data tools, dashboards, or summary reports to make clinical and utilization data more accessible and actionable
• Document findings, methodologies, and recommendations clearly so insights are repeatable and usable beyond the internship
• Present data findings and recommendations to clinical and operational leadership in a clear, accessible format

Qualifications

• Currently a Senior or Graduate student enrolled in a degree program in Health Informatics, Nursing, Clinical Health Sciences, Public Health, Healthcare Administration, or a related field
• Clinical or medical knowledge sufficient to understand healthcare terminology, diagnosis and procedure codes, provider specialties, and utilization management concepts
• Experience working with or analyzing healthcare data, including claims data, date of service, provider information, or clinical documentation
• Strong analytical skills with the ability to draw meaningful insights from complex datasets
• Hands-on familiarity with AI tools (such as ChatGPT, Claude, Copilot, or similar) and demonstrated ability to apply them practically
• Curiosity about how AI can improve healthcare operations – you don’t need to be an AI engineer, but you should be someone who experiments, asks “what if we used AI for this?” and follows through
• Familiarity with data tools such as Excel, SQL, Tableau, Power BI, or similar platforms
• Ability to communicate data findings clearly to both clinical and non-clinical audiences
• Ability to work independently, manage ambiguity, and prioritize across multiple projects
• Experience with Python, R, or other data analysis languages is a plus
• Prior exposure to utilization management, care management, or managed care environments is a plus