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• Instituting a system of recurring data collection—for example, a survey of a rotating panel of practices—would ensure that payment rates reflect current PE cost structures.
• Both monetary and nonmonetary incentives should be considered as a means of increasing survey response, with potential improvements to data quality, bias due to selective participation and gaming, and attrition.
• Some specialties are exposed to high levels of sampling variation under a survey design that collects an equal number of observations from the specialties that have been surveyed for PE per hour (PE/HR) values; other specialties experience very low levels of sampling variation.
• The PE allocation methodology could be updated to confer greater stability in payment rates over the long run than would be achieved by simply making periodic updates to the PE/HR measures in the current system.
• There are substantial differences in MPFS PE relative values and Outpatient Prospective Payment System (OPPS)–based relative values for procedures. Comparisons of these relative values could be used to identify potentially misvalued procedures in the two systems and help address site-of-service differentials.

Each year, Medicare allocates tens of billions of dollars for indirect practice expense (PE) across services on the basis of data from the Physician Practice Information (PPI) Survey, which reflects 2006 expenses. Because these data are not regularly updated, and because there have been significant changes in the U.S. economy and health care system since 2006, there are concerns that continued reliance on PPI Survey data might result in PE payments that do not accurately capture the resources that are typically required to provide services.

In this final report of the second phase of a study on PE methodology, the authors address how the Centers for Medicare & Medicaid Services (CMS) might improve the methodology used in PE rate-setting, update data that inform PE rates, or both. The authors conclude that this information is best provided by a survey; therefore, they focus on the advantages and disadvantages of survey-based approaches. They also describe the use of a lean model survey instrument, as well as partnering with another agency to collect data. Finally, the authors describe a virtual town hall meeting held in June 2021 to give stakeholders an opportunity to provide feedback on PE data collection and rate-setting.

The system of data and methods that CMS uses to support PE rate-setting is complex; thus, CMS must take into account a number of competing priorities when considering changes to the system. With this in mind, the authors offer a number of near- and longer-term recommendations.

• Chapter One

  Introduction

• Chapter Two

  Recurring Data Collection to Support the Allocation of Practice Expense

• Chapter Three

  Practical Considerations for a Hybrid Survey Panel of Practices

• Chapter Four

  Lean Model Instrument

• Chapter Five

  Potential Synergistic Data Collection Strategies

• Chapter Six

  Using the Outpatient Prospective Payment System to Inform Practice Expenses

• Chapter Seven

  Using Outpatient Prospective Payment System–Based Relative Value Units to Identify Potentially Misvalued Services and Address Site-of-Service Differentials

• Chapter Eight

  Potential Refinements in the Indirect Allocation

• Chapter Nine

  Approaches for Efficient Data Collection

• Chapter Ten

  Town Hall Summary

• Chapter Eleven

  Conclusion

• Appendix A

  Recurring Surveys Relevant to New Data Collection to Update Practice Expense Data in the Medicare Physician Fee Schedule

• Appendix B

  Supplementary Analyses for Chapter 2

• Appendix C

  Lean Model Survey Instrument

• Appendix D

  Other Surveys Considered

• Appendix E

  Supplemental Information for the Outpatient Prospective Payment System Analyses

• Appendix F

  Supplemental Information for Analyses Using Outpatient Prospective Payment System–Based Relative Value Units to Identify Potentially Misvalued Services and Address Site-of-Service Differentials

• Appendix G

  Sample Allocations to Reduce Large-Impact Standard Deviations