Medical Economics Analyst – Health Plan
Job Description:
• Analyze complex medical claims, pharmacy data, and other healthcare data sets to identify emerging cost and utilization trends, patterns, and outliers.
• Develop financial models to evaluate the financial impact of new clinical programs, new initiatives, and value-based agreements.
• Identify opportunities for medical cost reduction, quantify potential savings, and measure the return on investment (ROI) of implemented initiatives.
• Create clear, concise reports, dashboards, and presentations using data visualization tools to communicate complex findings and actionable insights to a variety of stakeholders, including senior management and clinical departments.
• Work closely with accounting, actuarial, underwriting, clinical, health services delivery and network management teams to provide analytical support for decision-making and strategic planning, such as informing payer negotiations and risk adjustment strategies.
• Ensure the accuracy and integrity of data used in analyses, and prepare well-organized project documentation including methodologies and key decision points.
• Respond to ad-hoc data analysis requests from various departments, providing timely intelligence related to specific business problems.
Requirements:
• Bachelor’s degree in Economics, Statistics, Finance, Public Health, Mathematics, Actuarial Science, Data Analytics or related quantitative discipline required.
• 1-3 years of progressive analytical experience in a healthcare setting (health insurance, managed care, or health system) is required.
• Knowledge of STARs and Risk Adjustment preferred.
• Strong analytical, problem-solving, critical thinking, and communication skills are vital for success in this role.
• Proficiency in SQL for data extraction and manipulation is a must.
• Advanced Microsoft Excel skills, including pivot tables and complex formulas, are essential.
• Experience with data visualization tools like Tableau or Power BI is highly valued.
• Familiarity with statistical tools such as SAS, R, or Python is often a plus.
Benefits:
• Analyze complex medical claims, pharmacy data, and other healthcare data sets to identify emerging cost and utilization trends, patterns, and outliers.
• Develop financial models to evaluate the financial impact of new clinical programs, new initiatives, and value-based agreements.
• Identify opportunities for medical cost reduction, quantify potential savings, and measure the return on investment (ROI) of implemented initiatives.
• Create clear, concise reports, dashboards, and presentations using data visualization tools to communicate complex findings and actionable insights to a variety of stakeholders, including senior management and clinical departments.
• Work closely with accounting, actuarial, underwriting, clinical, health services delivery and network management teams to provide analytical support for decision-making and strategic planning, such as informing payer negotiations and risk adjustment strategies.
• Ensure the accuracy and integrity of data used in analyses, and prepare well-organized project documentation including methodologies and key decision points.
• Respond to ad-hoc data analysis requests from various departments, providing timely intelligence related to specific business problems.
Requirements:
• Bachelor’s degree in Economics, Statistics, Finance, Public Health, Mathematics, Actuarial Science, Data Analytics or related quantitative discipline required.
• 1-3 years of progressive analytical experience in a healthcare setting (health insurance, managed care, or health system) is required.
• Knowledge of STARs and Risk Adjustment preferred.
• Strong analytical, problem-solving, critical thinking, and communication skills are vital for success in this role.
• Proficiency in SQL for data extraction and manipulation is a must.
• Advanced Microsoft Excel skills, including pivot tables and complex formulas, are essential.
• Experience with data visualization tools like Tableau or Power BI is highly valued.
• Familiarity with statistical tools such as SAS, R, or Python is often a plus.
Benefits: