Albert Kusi-Appiah

Albert is an accomplished data analyst, programmer and statistician, working with Medicare and Medicaid claims data to support the formulation and implementation of legislative, regulatory, and reimbursement strategies for medical product manufacturers, health service providers, and public and private payers. With nearly 10 years of experience in quantitative data analysis, Albert is experienced using a broad array of statistical tools including SAS, R, Python, Eviews and SPSS to analyze and interpret data and maintains an expertise in open source R technology . Albert has a wide range of experience in developing statistical models to evaluate the impact of policy changes in the Medicare program. He works with large data sets, including Medicare and Medicaid claims data totaling over 300 million records.

Prior to joining McDermott Consulting, Albert worked in the Medicare Fraud Prevention System 2, where he used data analysis to support evidence of fraud waste and abuse in the Medicare program.


George Mason University, M.S., 2015

Garden City University College, B.S., 2010

Representative Experience

  • Investigate and identify Medicare payment records to determine fraud by examining whether a service was performed on Medicare Beneficiaries in accordance to Medicare policy or coverage.
  • Evaluate quality of care in the Medicare-Medicaid Program using cluster analysis.
    Analyze the impact of Medicaid adjusted cross over claims on Medicare total payment for dual eligible beneficiaries.
  • Analyze and evaluate the appropriate use of modifiers 25 and KX.
  • Develop models for the Medicare Fraud Prevention System 2 (FPS2)
  • Analyze Medicare program to identify specific areas meriting investigation or policy change
  • Develop analysis plans, SAS program and macros for statistical and data management purposes.