Code and Data for "Detecting Potential Overbilling in Medicare Reimbursements via Hours Worked: Comment"

Principal Investigator(s):  View help for Principal Investigator(s) Brett Matsumoto, United States Department of Labor. Bureau of Labor Statistics

Version:  View help for Version V1

Name File Type Size Last Modified
  code 06/22/2020 09:44:AM
  data 06/10/2020 11:34:AM
Read_me.pdf application/pdf 624.3 KB 06/22/2020 05:43:AM

Project Citation: 

Matsumoto, Brett. Code and Data for “Detecting Potential Overbilling in Medicare Reimbursements via Hours Worked: Comment.” Nashville, TN: American Economic Association [publisher], 2020. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2020-11-23. https://doi.org/10.3886/E119829V1

Project Description

Summary:  View help for Summary Code and data for "Detecting Potential Overbilling in Medicare Reimbursement via Hours Worked: Comment"

Abstract: Fang and Gong (2017) develop a procedure to detect potential overbilling of Medicare by physicians. In their empirical analysis, they use aggregated claims data that can overstate the number of services performed due to features Medicare billing. In this comment, I show how auditors can use detailed claims level data to better target improper overbilling.

Scope of Project

JEL Classification:  View help for JEL Classification
      H51 National Government Expenditures and Health
      I13 Health Insurance, Public and Private
      I18 Health: Government Policy; Regulation; Public Health
      J22 Time Allocation and Labor Supply
Geographic Coverage:  View help for Geographic Coverage United States
Time Period(s):  View help for Time Period(s) 1/1/2013 – 12/31/2013 (2013)
Data Type(s):  View help for Data Type(s) aggregate data; event/transaction data; program source code


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V1 [2020-11-23]

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