Department of Health and Human Services 
OFFICE OF 
INSPECTOR GENERAL 
Comparing  Medicare Physician Payments 
To Private  Payers 
JANET REHNQUIST 
Inspector General 
JANUARY 2003 
OEI-06-00-00570  O I G
FFICE OF  NSPECTOR  ENERAL
 
http://www.oig.hhs.gov/ 
The mission of the Office of Inspector General (OIG), as mandated by Public Law 95-452, as amended, 
is to protect the integrity of the Department of Health and Human Services (HHS) programs, as well as 
the health and welfare of beneficiaries served by those programs.  This statutory mission is carried out 
through a nationwide network of audits, investigations, and inspections conducted by the following 
operating components: 
Office of Audit Services 
The OIG's Office of Audit Services (OAS) provides all auditing services for HHS, either by conducting 
audits with its own audit resources or by overseeing audit work done by others.  Audits examine the 
performance of HHS programs and/or its grantees and contractors in carrying out their respective 
responsibilities and are intended to provide independent assessments of HHS programs and operations in 
order to reduce waste, abuse, and mismanagement and to promote economy and efficiency throughout the 
Department. 
Office of Evaluation and Inspections 
The OIG's Office of Evaluation and Inspections (OEI) conducts short-term management and program 
evaluations (called inspections) that focus on issues of concern to the Department, the Congress, and the 
public.  The findings and recommendations contained in the inspections reports generate rapid, accurate, 
and up-to-date information on the efficiency, vulnerability, and effectiveness of departmental programs. 
Office of Investigations 
The OIG's Office of Investigations (OI) conducts criminal, civil, and administrative investigations of 
allegations of wrongdoing in HHS programs or to HHS beneficiaries and of unjust enrichment by 
providers.  The investigative efforts of OI lead to criminal convictions, administrative sanctions, or civil 
monetary penalties.  The OI also oversees State Medicaid fraud control units which investigate and 
prosecute fraud and patient abuse in the Medicaid program. 
Office of Counsel to the Inspector General 
The Office of Counsel to the Inspector General (OCIG) provides general legal services to OIG, rendering 
advice and opinions on HHS programs and operations and providing all legal support in OIGs internal 
operations.  The OCIG imposes program exclusions and civil monetary penalties on health care providers 
and litigates those actions within the Department.  The OCIG also represents OIG in the global settlement 
of cases arising under the Civil False Claims Act, develops and monitors corporate integrity agreements, 
develops model compliance plans, renders advisory opinions on OIG sanctions to the health care 
community, and issues fraud alerts and other industry guidance.   E X E C U T I V E  S U M M A R Y 
OBJECTIVE 
To compare Medicare relative values for physician services to similar values paid by private 
insurance companies, based upon MEDSTATs MarketScan database. 
BACKGROUND 
Accounting for over $40 billion each year, Medicares physician fee schedule contains more 
than 7,000 codes for reimbursable services. Each is assigned a relative value (RVU) 
composed of three factors Physician Work, Practice Expense, and Professional Liability 
Insurance, designed to reflect the human and capital resources required for provision of the 
service.  The RVU is multiplied by a constant dollar figure to obtain the fee schedule payment 
amount.  The Secretary is required to review the RVUs for all physician services at least once 
every 5 years. To help meet this requirement, the American Medical Associations Relative 
Value Updating Committee (RUC) reviews codes and provides recommendations regarding 
appropriate changes to the RVUs for consideration by the Centers for Medicare & Medicaid 
Services (CMS). 
This study uses proprietary claims data from MEDSTATs Marketscan database to compare 
service valuation patterns between Medicare and private insurers.  A data mining process 
identifies procedures that consistently appear to have moderate or extreme differences between 
the two groups. Possible reasons that differences may exist were investigated through 
interviews and literature reviews. 
FINDINGS 
Many procedure codes are not included in the current 5-year comprehensive 
review; a systematic analysis may assist code identification 
The Secretary is required to review relative values for all physician services no less than every 5 
years to adjust for changes in medical practice or coding and to reflect new data on relative 
resources. In practice, this requirement means that all codes must be eligible for review. 
Decisions to include codes in the 5-year review are based on a nomination process. The data 
mining technique presented in this study provides an alternative source of information that could 
improve CMS ability to identify those codes most in need of review. 
Comparing Medicare Physician 
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Payments to Private Payers  OEI-06-00-00570 <  Of the 681 Medicare services evaluated, 217 are valued differently from private payers 
Two-hundred seventeen procedures, 30 percent of those reviewed, were identified with 
consistent and substantial differences in relative value between Medicare and the MEDSTAT 
database. Ninety-eight procedures appear to be valued higher by Medicare; the remaining 119 
appear to be valued lower by Medicare. Reasons for these differences are unclear and may be 
attributable to errors within the RVU, to population differences, or to differing incentives to 
review codes. 
<	  Only 20 of the 217 codes identified by our analysis were reviewed in the 5-year 
comprehensive review 
CONCLUSION 
Identification of procedures in need of review is almost exclusively based on outside nominations. The 
process is limited in its ability to identify misvalued, particularly overvalued, procedures. We believe 
that the current process could be improved by supplementing physician efforts with a data-driven 
method, similar to that presented in this inspection. 
Valuations that differ significantly between Medicare and the private industry may indicate that those 
resources are not adequately or not efficiently reflected. However, further investigation of individual 
differences must take into account any changes made since the analysis year (1999) and the difficulties 
inherent to a comparison between Medicare and the private sector. 
Comparing Medicare Physician 
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Payments to Private Payers  OEI-06-00-00570 T A B L E  O F  C O N T E N T S
PAGE
 
EXECUTIVE SUMMARY ...................................................... i
 
INTRODUCTION .............................................................1
 
FINDING
 
Many Procedure Codes Are Not Included in the Current 5-Year Comprehensive
 
Review; A Systematic Analysis May Assist Code Identification ....................... 7
 
CONCLUSION  ..............................................................9
 
ENDNOTES ................................................................ 10
 
APPENDICES 
A: Data description ......................................................11
 
B: Data cleaning  ........................................................13
 
C: Data mining methods...................................................15
 
D:  Valuation differences...................................................19
 
ACKNOWLEDGMENTS ..................................................... 29
 
Comparing Medicare Physician 
Payments to Private Payers  OEI-06-00-00570  I N T R O D U C T I O N 
OBJECTIVE 
To compare Medicare relative values for physician services to similar values paid by private 
insurance companies based on MEDSTATs MarketScan database. 
BACKGROUND 
The Resource Based Relative Value System (RBRVS) 
Medicares current physician payment structure, the physician fee schedule, is based on a 
relative value system known as the Resource Based Relative Value System (RBRVS). The 
RBRVS assigns a value to services that can be provided by a physician under the Medicare 
program.  The value, measured in relative value units (RVUs), compares each service relative 
to all other services. For example, one of the most common procedures, an office visit for an 
established patient, is currently valued as .93 RVUs while a more complex procedure, such as 
an artery bypass graft, is valued as 46.50 RVUs. This implies that the resources needed to 
perform the artery bypass graft (including physician training, time, office equipment, and liability 
insurance) are 50 times those needed for an office visit. 
Accounting for more than $40 billion each year, the physician fee schedule contains more than 
7,000 codes for services reimbursable by Medicare. Each service is attributed an RVU 
reflecting the sum of three distinct components: 
  Physician Work (Work) - compensation for the time and skill required of the 
physician, 
  Practice Expense (PE) - cost of staff time and material resources necessary to 
perform the service, and 
	  Professional Liability Insurance (PLI) - reimbursement for the malpractice 
insurance carried by the physician. 
The value for each component is multiplied by a geographic adjustment factor called the 
Geographic Practice Cost Index (GPCI)1 and is then summed to obtain the full RVU total for a 
service.  The total RVU is multiplied by a constant dollar figure, known as the conversion 
factor, to obtain the fee schedule payment amount. The conversion factor, about $36.20 in 
2002, is updated each January to account for inflation, but in a given year remains the same for 
all services and all specialties.2 Therefore, any changes to reimbursement for a service must be 
derived by changes to the RVU of that service. 
Comparing Medicare Physician 
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Payments to Private Payers  OEI-06-00-00570 The formula is: 
Implemented in 1992, the RBRVS was phased in over the following 10 years.  The first 
component, Physician Work, was completely integrated into the system in 1997, and the last 
component, Practice Expense, was completely integrated in 2002. 
Two additional statutory provisions govern the practical implementation of the  RBRVS 
budget neutrality to limit the impact of administrative adjustments and the sustainable growth 
rate (SGR) to control aggregate expenditures.3 In combination, the current provisions for 
budget neutrality and a sustainable growth rates create a system where RVU changes have little 
impact on aggregate expenditures.  The RVUs act as a system of allocating fixed monetary 
resources rather than determining expenditures. 
The 5-year Comprehensive Review of Work RVUs 
The Social Security Act, 1848(c)(2)(B)(i), requires the Secretary to adjust relative values on a 
yearly basis to account for coding and coverage changes and also to comprehensively review 
the relative values for all physician services at least once every 5 years to make any needed 
adjustments.  The Centers for Medicare & Medicaid Services (CMS)  completed its second 
5-year comprehensive review of the Work component, with the update taking effect January 1, 
2002. 
For the 5-year comprehensive review, CMS developed a system utilizing the American 
Medical Associations (AMAs) interdisciplinary medical expertise. Through the Federal 
Register (FR), CMS solicits requests for particular codes to be reviewed.  A list of the 
requested codes is provided to the AMAs Relative Value Updating Committee (RUC) for 
recommendations. The CMS reviews the recommendations for consistency within code 
families and identifies unwanted policy implications.  In the most recent review, CMS accepted 
792 of the RUCs 857 recommendations (92 percent).4 
The current process of soliciting codes for the review is limited, particularly in its ability to 
identify overvalued codes.5 In an effort to address these limitations, CMS initiated a contract 
with a private-sector group, Health Economic Research (HER), to develop quantitative 
methods of identifying Current Procedural Terminology (CPT) codes for review.6  CMS also 
used the Federal Register to encourage public comments on the use 
Comparing Medicare Physician 
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Payments to Private Payers  OEI-06-00-00570 of databases and methodologies that might lend themselves to the identification of misvalued 
procedures. Despite these attempts, no additional methods have been found. By and large, the 
seven methods proposed by the HER were discarded as not feasible or not applicable to a 
broad-scale review. The exception is an undertaking for a limited number of surgical codes. 
CMS is comparing actual time data as it appears on medical records to the time found in survey 
data collected by the AMA. 
Importance of appropriate valuation 
The Medicare Payment Advisory Commissions (MedPACs) 2001 Report to Congress states 
that to help ensure beneficiaries access to high-quality care, Medicare payments should 
correspond to the cost efficient providers incur in furnishing this care.7 Payments that do not 
meet this objective may promote inappropriate incentives for care or create barriers to access. 
Specifically: 
<	  Differing payment incentives could influence choices between diagnostic and/or 
treatment plans. 
<	  Some physicians state that reductions in reimbursement will affect the number of 
Medicare beneficiaries they treat.8, 9, 10, 11 
<	  Codes that are not increased from the 5-year comprehensive review and those that 
were not reviewed at all are deflated. Because the current system is budget neutral, 
what is given to one code is taken from another. Over time this code deflation could 
contribute to access limitations for particular specialties. 
METHODOLOGY 
This study uses a proprietary commercial claims database as a comparison group to identify 
procedures in Medicares physician fee schedule whose relative values differ substantially from 
private payers.  We used several methods of comparison, but the primary identification was the 
result of a data mining process using standardized payment data. 
The data 
We used MEDSTATs 1999 MarketScan database as our primary source of data.  This data 
includes commercial claims and encounters for over 50 different commercial plans and spans 
the calendar year 1999. We chose this data for its breadth and ability to provide complete 
claim payment information.  No other database provides payment information (as opposed to 
charge information) at the claim level. For comparability purposes, the data is limited to 
physician payments within fee-for-service plans. 
Comparing Medicare Physician 
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Payments to Private Payers  OEI-06-00-00570 The MEDSTAT data is not nationally representative; however, it includes over 21 million 
claims spread throughout all 50 states, plus Puerto Rico and the District of Columbia.  The 
greatest representations are in Georgia, Michigan, and Tennessee, cumulatively accounting for 
almost 45 percent of the records.  Despite limitations for national representation, we determined 
that this data was the best suited for the needs of this study. A description of population 
characteristics is provided in Appendix A. 
Other data used include: the National Physician Fee Schedule Relative Value File (RVU File), 
the Geographic Practice Cost Indices (GPCI data), census data, and Medicare claims data. 
The RVU File and the GPCI data are the actual 1999 values used by Medicare contractors to 
determine allowable reimbursement rates for physician services and procedures. 
Comparing Medicare to the private sector 
Unlike private health insurance companies, Medicare is required by law to base payments on 
resources.  For this reason, comparative differences may not necessarily be indicative of errors 
in the physician fee schedule, but a reflection of a different basis for rate setting. In addition, 
population differences and the method of comparison must be taken into account. 
Consequently, we present any findings of payment differences as potential issues to be 
investigated further, rather than a need for specific change. 
Population Differences. Several difficulties exist in comparing Medicare payments to private-
sector health insurance payments.  First, by definition, Medicare is a unique and distinct 
population including only seniors over age 65 and individuals meeting certain disability 
requirements.  While most individuals with health insurance are covered by an employer-based 
policy, Medicare is specifically designed to insure those not likely to be employed. Therefore, 
population differences between the two groups will be apparent in the type and frequency of 
medical claims.  Further, the extent to which payment methods, e.g., global payments, and the 
underlying payment structure, e.g., resource-based, differ between the two groups are 
unknown.  A third question exists with regard to market influences on pricing.  While 
MEDSTAT payments are entirely subject to the market, Medicares resource-based system 
discounts market influences by basing prices on the costs of inputs, such as the cost of physician 
time and the cost of supplies, and by deflating the GPCI to reflect only 60 percent of market 
variation. 
Limitations of a dollar comparison.  To address issues associated with a dollar comparison, 
we chose to identify procedures where the relative valuation is inconsistent between the private 
industry and Medicare. A dollar to dollar comparison between the two groups is of limited 
value. Although procedures can be ranked within each group according to price, the actual 
payments for procedures are not comparable. First, Medicare need not pay the same as 
private payers.  A difference in payment rate could be the result of an intention to pay providers 
more or less than Medicare rather than a 
Comparing Medicare Physician 
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Payments to Private Payers  OEI-06-00-00570 different assessment of the procedures worth in relation to other services. There are further 
difficulties in using a straight (non-standardized) dollar-figure comparison, such as our inability 
to account for differences in management practices, e.g., capitation.  Unlike the RBRVS, some 
management practices may distribute reimbursements in ways that are not related to specific 
procedures, such as per-capita payments, which pay physicians based on the number of 
individuals they treat or the diagnoses they handle. Such reimbursements are not captured in 
our data. 
Data mining to identify outliers in standardized payment rates 
Standardizing payment rates. Standardizing data is a method that provides a common basis 
of comparison for things that are measured on different scales.  In this instance, we want to 
compare the relative value of procedures between Medicare and private insurers.  For our 
analysis, the entire allowable amount (including copayments and deductibles) for each service 
provided under a private plan was divided by the total Medicare RVU associated with the 
service. 
This standardization allows us to compare what is, in theory, a constant conversion factor 
similar to the one found in Medicares reimbursement formula (shown on page 2). The 
rationale for a constant conversion factor is that if we assume that Medicares RBRVS 
correctly values every service in terms of RVUs, then we can also assume that the entirety of 
variation in payments would be captured by the RVUs.  Consequently, if we divide the 
allowable charge by the associated RVU, we would identify a constant conversion factor. The 
same conversion factor would be found regardless of the procedure chosen. If this constancy is 
not found in our data, the private sector, as demonstrated by the data, does not place relative 
value between services the same as Medicare. 
Data mining process. To identify inconsistencies in assessed value between MEDSTATs 
MarketScan data and Medicare RVU data, we used a process called  stepwise elimination. 
In this process, regression models were used to identify procedures and procedure groups 
whose standardized payments, or conversion factor, differ significantly from the expected value. 
Every procedure (681 procedures) and procedure group (111 procedure groups) meeting a 
minimum sample size and several other data requirements were included in the model as 
dichotomous variables.  Those variables were systematically dropped in accordance with their 
significance in determining the mean conversion factor.  In essence, we are identifying 
procedures in which the variation that exists in private payments is not captured by Medicares 
relative values. A procedures relative value in the private industry differs from its Medicare 
RVU to the extent that the conversion factors are different.  This process was conducted 
separately on two independent data partitions.  The procedures presented in this report were 
identified in both partitions.12  For additional information on the data mining process, see 
Appendix B: Data cleaning and Appendix C: Data mining methods. 
Comparing Medicare Physician 
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Payments to Private Payers  OEI-06-00-00570 Statistical methods 
Stepwise elimination was used as an exploratory data mining technique rather than the more 
customary hypothesis testing. For this reason, tests of fit are not included. Basic statistical 
methods, including the F-test, were used to interpret the regression results.  A nonresponse 
analysis is not applicable to this study. However, generalizability of the data is a concern. 
Representation is limited to those private insurance plans that have proprietary agreements with 
the MEDSTAT Group. Information on nonparticipating plans is not available. 
Qualitative methods 
In order to understand potential reasons for the presence of outliers and to fully understand the 
process of assigning values and updating the fee schedule, we conducted telephone interviews 
with representatives of particular specialty societies and carrier medical directors, in-depth 
interviews with CMS staff, and a literature review of relevant government documents and both 
special interest and academic articles. 
Quality standards 
This study was conducted in accordance with the Quality Standards for Inspections issued by 
the Presidents Council on Integrity and Efficiency. 
Comparing Medicare Physician 
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Payments to Private Payers  OEI-06-00-00570 F I N D I N G S 
By applying a data mining technique to a proprietary database of claims for physician services provided 
in the private sector, we identified procedure codes with relative values that are substantially and 
consistently different from corresponding Medicare values. Of the 681 codes that we reviewed, 217 
exhibit moderate or extreme differences in relative value. Reasons for these differences are unclear and 
may be attributable to errors within the relative values themselves, or to population differences. 
Although the Secretary is required to review relative values for accuracy every 5 years, only 20 of the 
217 codes identified were included in the most recent 5-year comprehensive review.  The absence of 
so many of these codes in the 2002 Review suggests that augmenting the current system with one or 
more data driven methods may help to assure appropriate relative value assessments. 
MANY PROCEDURE CODES ARE NOT INCLUDED IN THE CURRENT 
5-YEAR COMPREHENSIVE REVIEW; A SYSTEMATIC ANALYSIS 
MAY ASSIST CODE IDENTIFICATION 
Section 1848(c)(2)(B)(i) of the Social Security Act requires the Secretary to review relative 
values for all physician services no less than every 5 years to adjust for changes in medical 
practice or coding and to reflect new data on relative resources.  In practice, this requirement 
means that all codes must be eligible for review.13 Decisions to include codes in the review are 
based on a nomination process, whereby CMS solicits in the Federal Register for codes to 
include in the review. The process depends almost entirely on outside information to identify 
codes that may be inappropriately valued.  There is an obvious incentive for specialty groups to 
identify codes that are undervalued, but there may be less of an incentive for them to bring forth 
codes that have been overvalued. 
The data mining technique, presented in this study, provides a supplemental source of 
information that could improve CMS ability to identify those codes most in need of review. 
Without assurance that every code will be reviewed, it would be prudent to have a systematic 
process in place to identify the codes most in need of review. 
Of the 681 Medicare codes evaluated, 217 are valued differently from private 
payers; 98 appear higher and 119 appear lower than Medicare payment rates 
Our data mining process identified 217 procedures that consistently exhibit moderate or 
extreme differences in value between Medicare and the private industry. Listed in Appendix D, 
these procedures represent 30 percent of the 681 codes analyzed. Many of the approximately 
7,000 codes for reimbursable services were excluded from the review for failure to meet 
minimum sample size requirements or because data was inadequate for 
Comparing Medicare Physician 
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Payments to Private Payers  OEI-06-00-00570 the chosen analysis. However, the codes reviewed represent over 70 percent of Medicare 
allowed charges for physician services during the analysis year (1999). 
Of the 217 codes, 98 procedures appear to be valued higher by Medicare; the remaining 119 
appear to be valued lower by Medicare. These differences were evaluated through the private 
sectors theoretical conversion factor for each service. With respect to Medicares payment of 
$34.73 per RVU (in 1999), the private data reflects a payment range of $1,065.07 per RVU 
for allergy skin tests  the highest comparative value reflected in the private data, and $0.30 for 
aspiration of a thyroid cyst  the lowest. 
In the assessed value of services, we do expect variability resulting from differences in 
professional judgment or from differing plan characteristics.  However, the procedures 
presented in this report consistently showed differences that were statistically significant, and 
thus, raise questions of potential mis-valuation. 
While differences in valuation are evident, the reasons are unclear and may be 
attributable to errors within the RVU or to population differences 
The analysis used in this study allows us to recognize which procedures are valued differently 
between Medicare and private insurers, but is not able to pinpoint the exact source of 
difference.  If our results show that Medicare values a procedure higher than the private 
industry, either Medicare is paying more than necessary or the private industry is not paying 
sufficiently, or both.  Differences could reflect errors within one or more of the three RVU 
components, or could reflect market variation that is not captured in the GPCI. Further, no 
standard exists to determine whether private sector values or Medicare values are correct. 
Several possibilities exist and further investigation of these procedures will be necessary to 
determine whether RVU adjustments are indicated. 
Only 20 of the 217 codes identified by our analysis were reviewed in the 5-year 
comprehensive review in 2002 
The absence of many of our identified codes in the 2002 review of the Work component 
indicates that the current process of nomination does not yield the same results as our  data-
driven method. Only 26 of the identified codes were reviewed in the 1997 5-year 
comprehensive review. If differences that we identified are indicative of needed adjustment, 
many of these adjustments would not have been made by CMS. 
Comparing Medicare Physician 
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Payments to Private Payers  OEI-06-00-00570 C O N C L U S I O N 
In order to ensure that Medicare beneficiaries have adequate access to services and 
appropriate treatments, and physician reimbursements are commensurate with the service 
performed, every reasonable effort should be given to assessing Medicare relative values 
appropriately. Valuations that differ significantly between Medicare and the private industry 
may indicate that those resources are not adequately or not efficiently reflected. However, 
further investigation of individual differences must take into account any changes made since the 
analysis year (1999) and the difficulties inherent to a comparison between Medicare and the 
private sector. 
The current process of identifying codes that may need adjustment is dependent on the 
motivation, available time, and resources of individual physicians and physician groups to 
research and develop arguments supporting change.  The process is limited, particularly with 
respect to the identification of overvalued procedures. Appropriately, the nomination of 
procedures by independent groups ensures that physicians will have a voice in the process; 
however, it is important that CMS supplement physicians efforts with a more systematized 
method of identification.  The data mining method that we have presented in this inspection may 
provide a feasible method that CMS could utilize in cooperation with the nomination process to 
better accomplish the task of identifying codes for the 5-year comprehensive review. 
Comparing Medicare Physician 
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Payments to Private Payers  OEI-06-00-00570 E N D N O T E S 
1. 	  Prior to implementation, the RBRVS was critiqued for not accounting for market variation. 
GPCIs were created to capture market influences in the cost of resources.  Each Medicare 
locality has its own set of GPCIs that are used to adjust each component of the RVU for 
market variation. Separate indices are used to measure cost differences for each of the three 
fee schedule components. 
2.	  Initially, the conversion factor varied between specialties.  This was the result of significant 
variation in payment policies of Medicare carriers. One of the requirements of the Omnibus 
Budget Reconciliation Act of 1989 was that a single conversion factor should be used for all 
physicians. 
3.  SSA1848(c)(2)(F) and SSA1848(f) 
4.  66 FR 31031 (June 8, 2001) 
5.  64 FR 59380 (November 2, 1999) 
6.	  Health Economics Research, 5-year review of Work Relative Value Units; November 2, 
1999. www.hcfa.gov/medicare/wrvu-toc.htm 
7.	  Report to Congress:  Medicare Payment Policy, Medicare Payment Advisory Commission, 
March 2001: Page 21. 
8. 	  Doctor Medicare pay faces cut next year; The AMA suggests that the government adjust 
the way it calculates some figures to make the pay formula fairer. www.amednews.com: 
Sept. 17, 2001. 
9. 	  AMA says slow economy may reduce Medicare payments, www.kaisernetwork.org: 
Sept. 13, 2001. 
10. 	  Flaherty, Timothy T., MD, Payment cuts could mean patient access problems, 
www.amednews.com: February 25, 2002. 
11.	  Phalen, Kathleen, Opting out:  Physicians exiting Medicare program, AMNews: June 
25, 2001. 
12. 	  In simulation studies, the duplication of results in two separate partitions of the data were 
effective in ruling out erroneous identification of procedures. 
13.  56 FR 59502 (November 25, 1991) 
Comparing Medicare Physician 
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Payments to Private Payers  OEI-06-00-00570 Appendix A 
Data description
 
Demographics 
The demographic information below describes the percent of line item claims in the MEDSTAT data 
that fit individual characteristics. 
Age Range  Sex  Regional Distribution 
0-17  13.6%  Male  47.4%  Northeast  16.2% 
18-34  24.2%  Female  52.6%  North Central  25.3% 
35-44  18.2%  South  40.9% 
45-54  21.1%  West  15.2% 
55-64  22.7%  Unknown  2.4% 
65 and older  0.2% 
Source of Data 
MEDSTAT data are based on a selection of employers health benefit packages. Although specific 
information about companies is not available, the industry with a percentage of claim representation is 
below.  Claims may represent the spouse or dependent of an employee associated with a particular 
industry. 
Oil & Gas Extraction, Mining  3.8%  Retail Trade  0.3% 
Manufacturing, Durable Goods  21.7%  Finance, Insurance, Real Estate  5.9% 
Manufacturing, Nondurable Goods  30.7%  Services  6.8% 
Transportation, Communications,  23.5%  Missing/Unknown  7.4% 
Utilities 
Comparing Medicare Physician 
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Payments to Private Payers  OEI-06-00-00570 Appendix A 
Plan Payment Information 
The amount paid may vary within a plan when a provider network is in place.  For almost half the data, 
we do not know if the service was provided within or outside of a plan network, or if a network exists 
at all. All remaining claims were paid in plan with the exception 7.1 percent paid out of plan. 
Different plan types were represented in our data as indicated below. 
Basic/Major Medical  4.4% 
Comprehensive  40.0% 
Exclusive Provider Organization (EPO)  2.7% 
Point Of Service (POS)  8.2% 
Preferred Provider Organization (PPO)  44.1% 
Health Maintenance Organization (HMO) - 0% 
excluded 
POS with Capitation - excluded  0% 
Missing/Unknown  0.5% 
Comparing Medicare Physician 
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Payments to Private Payers  OEI-06-00-00570 Appendix B 
Data cleaning 
The proprietary claims data used in this study is subject to all of the problems that are inherent to 
administrative medical claims data.  We conducted broad validity checks to determine the level of 
accuracy of the data.  There was a need for significant data cleaning of the critical data elements prior 
to analysis.  We found that it was necessary to drop large segments of the data for one or more of the 
reasons described below. As a result, we significantly reduced our number of observations. The 
process began with 21 million claims divided into three partitions of approximately 7 million each.  Line 
item deletions reduced each partition by almost 40 percent.  Although the reduction was considerable, 
we believe that these modifications were necessary to maintain the integrity of the data and subsequent 
analysis. Additionally, we found some data that needed adjustment or was unsuitable for review. 
Our data cleaning resulted in the following: 
1.  Deletion of services inconsistent with the needs of the analysis 
	  Anesthesia services for which Medicare provides reimbursement using a separate 
conversion factor 
	  Procedure codes that required modifiers to determine the RVUs attributed to the 
service (modifier information was not available through MEDSTAT) 
2.  Deletion of data flaws 
  Critical data elements were missing or invalid 
  Orphan claims1 with payments equal to zero or less 
	  Outliers (defined as line items having a payment amount that were beyond three 
standard deviations from the mean of the service) 
3.	  Deletion of services that are currently incompatible with Medicares 
Physician Fee Schedule 
  Procedure codes that are not active 
  Procedure codes that are not covered under the Medicare Physician Fee Schedule 
  Procedure codes that Medicare pays only as a technical component 
  Procedure codes in which payment is not based on the relative value scale 
  Procedure codes with a relative value of zero 
1
 Orphan claims are line item adjustments that MEDSTAT could not match to the appropriate claim. 
MEDSTAT advises that these claims be included in aggregate or summary statistics, but need not be 
included when looking at individual line items. 
Comparing Medicare Physician 
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Payments to Private Payers  OEI-06-00-00570 Appendix B 
4.	  Procedures represented in the MEDSTAT data with fewer than 100 claims 
remain in the data, but were not reviewed 
Similar to the second partition, the first partition contained claims for 4,589 different procedure 
codes after line item deletions. However, only 681 procedure codes had a sufficient number of 
claims to be eligible for the review. 
5. 	  Differential use of the units variable 
In particular cases, the units variable is meaningful and can affect the calculated averages. For 
this reason, any procedure where more than 5 percent of the billing reflected a unit of greater 
than one was judged for legitimacy of multiple units. The determination was made with the 
assistance of a Medicare Contractor Medical Director and also a Private Health Insurance 
Medical Director. 
Comparing Medicare Physician 
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Payments to Private Payers  OEI-06-00-00570 Appendix C 
Data mining methods
 
Theory of Data Mining 
As a relatively new field in analytics, data mining allows us to explore the wealth of data that 
now exists in almost every industry conceivable. Generally speaking, data mining is the 
utilization of a number of tools, including statistics, artificial intelligence and pattern recognition, 
to better understand and describe very large data sets.  Data mining is not hypothesis testing, a 
method that uses data to confirm or reject hypotheses.  Rather, it uses data to develop 
hypotheses in the absence of preconceived theories. With this distinction comes an increased 
probability of Type I error, i.e., an erroneous finding. However, utilized in its appropriate 
context, data mining has the ability to find valid patterns and inconsistencies that may not 
otherwise have been noticed. 
Focus 
For this particular study, we sought to identify inconsistencies between Medicare and the 
private industry in the value of services relative to other services from the same payer group. 
Understanding that absolute differences in reimbursement rates have interpretation problems, 
we determined that evaluating payments per Medicare-RVU allowed us to compare 
reimbursement rates among different procedures, thus eliminating the need to make a direct 
comparison between Medicare and private payments.  We are looking at private payments in 
the context of Medicare RVUs; procedures that do not fit that context smoothly in relation to 
other procedures may have been evaluated differently in Medicare than in the private industry. 
Model 
To identify procedures that appear to be evaluated differently between the two sectors, we 
used two data mining techniques.  These techniques were validated through extensive simulation 
testing. The techniques include stepwise elimination of procedures through linear regression and 
a requirement for confirmation, whereby a procedure must be identified in two independent 
partitions. 
For the regression model, the theoretical conversion factor is the dependent variable. 
Independent variables include a dummy (or dichotomous) variable for each individual 
procedure (N = 681) and procedure group (N = 111)1 meeting our requirements, as well 
1
 Procedure groups are groups of codes representing similar procedures. These groups were defined by 
MEDSTAT. 
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  Appendix C 
as controls  for the health care plan, rural/urban, and specialist/generalist.  We did not control 
for geographic location because the GPCI was adjusted to reflect the full market variation in 
each Medicare locality.2 The initial regression model was the same in both partitions (see 
below). 
i= $  + [$  (X ) + $ (X ) + ... +$  (X ) ] + [$  (X ) 
0 1 proc1 2  proc2 681 proc681 682 group1
+ $ (X ) + ... + $  (X ) ] + [$  (X ) + $ (X ) + ... 
683  group2 792 group111 793 plan1 794  plan2
+ $  (X ) ] + $ (X ) + $ (X 2) + , 
839 plan47 840  specialist 841  %urban
i = (payment for claim / calculated RVU for procedure) 
i i
The regression was repeated more than 35 times, systematically dropping the independent 
variables (procedures and procedure groups) that appeared least likely to have a significant 
influence on the dependent variable (the theoretical conversion factor).  After each iteration, we 
eliminated a pre-determined number of independent variables (i.e., procedures) exhibiting the 
largest p-value. The number of variables eliminated from each iteration was related to the 
number of variables remaining in the model, specifically twice the hundredth place of the 
number of independent variables. The regression sequence ceased at the point when all 
procedures and procedure groups remaining had a degree of significance that exceeded our 
threshold of p (where p is solved from:  (1-p)# variables = 0.95).3 A further significance level was 
determined for the combined effect of the procedure and the procedure group on the 
dependent variable.4 Procedures with a combined significance of p (where p is solved from: 
(1-p) # of F tests = 0.95) are considered outliers in the distribution of procedures.  The elimination 
process was repeated on a second randomly assigned partition of the MEDSTAT data. 
(Claims were randomly assigned into one of three independent partitions.) The results of the 
2 
The GPCI, by design, reflects only about 60 percent of market variation. Policy makers believed that the 
Physician Work component of the RVU, reflecting the time and skill required of the physician, is not 
significantly affected by geographic variation. The CMS provided us with the formula used to squash the 
variation, so that we could adjust our data to fully reflect geographic variation as measured through the 
GPCIs. 
3
 In data mining, the conventional level of significance does not apply. Lovell has suggested that if there 
are c candidate regressors out of which k are finally selected on the basis of data mining, then the true level 
of significance is related to the nominal level of significance as follows: alpha*=1-(1-alpha)c/k or 
approximately alpha*=(c/k)alpha. For this reason we kept our determining level of significance extremely 
conservative. So that variables would remain significant. (Source: Guijarati, Damodar N., Basic 
Econometrics, Third Ed., McGraw-Hill Inc; New York: 1995. P.460-461.) 
4 
An F-test using Restricted Least Squares (Testing for Linear Equality Restrictions) was used to determine 
the significance of the combined beta coefficients for procedure and procedure group. 
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two stepwise regressions were compared. Only procedures found to be significant in both 
partitions are reported. 
As mentioned, the simulation studies provide support for the accuracy of our findings. Artificial 
data were created with the same overall structure of the original data. Simulations were 
conducted in a number of ways. First, data were created with no deviant procedures. Hence, 
our data mining process should have no remaining variables, or procedures. Additional data 
was created with manufactured outlier procedures.  In those cases, our data mining process 
should have identified the appropriate outliers.  We found that the step-wise regression did yield 
some erroneous results, but that the requirement of findings to be in two independent partitions 
eliminated this problem. To further minimize the incidence of erroneous findings, we used 
exceptionally small p-values.  The combination of these two techniques used was successful in 
eliminating erroneous results. 
Interpretation 
Identified codes, i.e., codes that remained as variables throughout both stepwise regressions, 
are the focus of our study. We suggest not that the values are incorrect, but that differences 
noted give cause for further study.  Further, we suggest that a data-driven method is a feasible 
option in identifying codes for the 5-year review. However, the particular codes identified 
should be considered within the appropriate context.  As these codes are the results of a data 
mining process, i.e., not standard statistical analysis, no confidence intervals or p-values are 
reported. Statistical tests of significance are used in the data mining process, but cannot be 
interpreted as results, for there was no defined hypotheses to test; rather our process identified 
outlier procedures. These procedures are outliers in the sense that per Medicare RVU 
payment is consistently different from the average procedure. 
Even though the construct of statistics does not uniformly apply, our analysis does use statistical 
methods.  Those methods are susceptible to violations of statistical assumptions.  In particular, 
the regression analysis included multicollinear variables, such that individual procedures are 
subsets of their respective procedure groups and potentially heteroskedastic variances resulting 
from any correlation between the plan variables and the residual error term.  These limitations 
were known and considered before analysis began. 
Multicollinearity: We chose to include both procedures and procedure groups so that we 
might be able to identify differences affecting a whole family of codes as well as individual 
procedures. 
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Heteroskedasticity: The variance of our dependent variable may be influenced by the plan 
variable. A dummy variable was included for each plan present in our data, however, this does 
not capture interactions between plans and individual procedures. We chose not to include 
interaction variables because our model might have been compromised by a degrees of 
freedom problem, and also because the processing of such a voluminous model would have 
been too difficult for parameters of our study.  A simulation study is underway to determine the 
extent to which heteroskedastic variances may have affected our results. Early indications 
suggest that heteroskedasticity did not influence our results. 
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Valuation differences
 
The codes identified in our study as being valued differently between Medicare and the private sector
 
are listed in this Appendix.  These services relative values for the private insurers differ substantially
 
from those of Medicares RBRVS. Statistics are provided to give contextual information that may
 
assist the reader in interpreting the extent of difference.
 
.
 
Variable Names and Definitions 
CPTTM  Current Procedural Terminology Code (used for billing purposes) 
Description  Short description of the CPTTM code 
N	  Frequency of the procedure in the Commercial Claims and 
Encounters data. 
Beta	  Combined beta coefficient (from the step-wise regression) for the 
procedure and procedure group variables. Reflects the procedures 
impact on the theoretical conversion factor. 
Index  The median of an index created for each procedure describing what 
Median  percent of the Medicare Physician Fee Schedule was actually paid 
by the private industry, as represented in our data. 
Medicare  The dollars (in thousands) allowed by Medicare in 1999 for each 
Volume  service (includes deductibles and co-payments paid by the 
beneficiary). The total amount allowed for 1999 was $44.7 billion. 
The combined beta coefficient (Beta) may be interpreted as an addition or subtraction (as indicated by 
positive or negative value) to the average theoretical conversion factor. Recall that the conversion 
factor is the number you would multiply by the RVU to determine the total payment amount.  The 
expected value is the average conversion factor for all services, which was found to be $34.07. This 
means that the Beta values would substantially add to or subtract from this number.  For example, the 
Beta for CPTTM 10040 (-10.66) implies that the theoretical conversion factor for acne surgery is 
$23.41 ($34.07 - $10.66). If the RVU used for standardization reflected the entire variation between 
services, the payment for the service would have been the RVU multiplied by $34.07.  As it stands, the 
payment for the service is the RVU multiplied by $23.41. 
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Payments to Private Payers  OEI-06-00-00570 Appendix D 
Medicare 
Index 
CPTTM  Description  N  Beta  Volume 
Median 
(in $1,000s) 
10040  Acne Surgery  6776  -10.66  62.05%  $1,404 
11042  Cleansing of Skin/Tissue  1060  9.9  114.04%  $27,748 
11100  Biopsy of Skin Lesion  11171  -4.77  89.74%  $66,232 
11200  Removal of Skin Tag  4060  -4.6  86.06%  $4,837 
11300  Shave Skin Lesion  2511  -7.11  75.96%  $2,410 
11400  Removal of Skin Lesion  2381  -9.59  66.43%  $3,301 
11401  3313  -8.38  73.76%  $6,971 
11402  2391  -4.26  87.70%  $9,177 
11421  1264  -6.33  80.46%  $3,824 
11440  1259  -6.77  82.57%  $5,238 
11719  Trim Nail(s)  294  -17.02  34.88%  $9,969 
11732  Remove Additional Nail Plate  411  7.52  110.62%  $3,396 
11750  Removal of Nail Bed  3733  8.72  131.44%  $28,863 
11900  Injection into Skin Lesion  2764  -5.22  81.83%  $2,581 
12031  Layer Closure of Wound(s)  693  -6.64  83.21%  $2,509 
17000  Destruction of Facial Lesion  36014  -3.79  84.46%  $130,619 
17003  Destroy Lesions, 2-14  21042  15.04  99.55%  $137,151 
17110  Destruction of Skin Lesions  3383  -6.82  77.85%  $2,814 
17340  Cryotherapy of Skin  953  -17.03  58.31%  $829 
19100  Biopsy of Breast  732  17.36  103.85%  $2,706 
19290  Place Needle Wire, Breast  1196  10.67  113.31%  $4,921 
20550  Inject Tendon/Ligament/Cyst  11520  -7.93  74.15%  $50,316 
20600  Drain/Inject Joint/Bursa  4353  -6.39  74.85%  $16,302 
20605  5590  -8.38  68.61%  $20,232 
20610  13770  -9.13  79.12%  $133,139 
21320  Treatment of Nose Fracture  108  19.81  147.38%  $197 
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  Appendix D 
Medicare 
Index 
CPTTM  Description  N  Beta  Volume 
Median 
(in $1,000s) 
29065  Application of Long Arm Cast  334  -15.46  56.39%  $1,041 
29075  Application of Forearm Cast  1478  -17.27  47.99%  $4,292 
29085  Apply Hand/Wrist Cast  160  -16.57  48.42%  $288 
29125  Apply Forearm Splint  811  -8.3  61.31%  $1,500 
29405  Apply Short Leg Cast  1073  -14.29  65.34%  $3,272 
29425  956  -12.55  58.19%  $3,221 
29540  Strapping of Ankle  2545  -6.08  87.97%  $5,028 
29880  Knee Arthroscopy/Surgery  505  13.09  140.43%  $24,381 
29881  2328  7.91  118.07%  $38,313 
29888  694  7.03  112.66%  $822 
30130  Removal of Turbinate Bones  304  -10.05  60.64%  $676 
31254  Revision of Ethmoid Sinus  357  15.8  147.45%  $2,024 
31255  Removal of Ethnoid Sinus  907  21.16  154.11%  $10,402 
31256  Exploration Maxillary Sinus  714  12.58  128.28%  $2,408 
31575  Diagnostic Laryngoscopy  2460  6.16  114.22%  $31,208 
36000  Place Needle in Vein  1273  17.03  133.59%  $1,644 
36005  Injection, venography  171  22.16  146.93%  $2,895 
36410  Drawing Blood  1414  -5.78  65.78%  $3,666 
36425  Drawing Blood  116  -33.15  8.87%  $296 
36489  Insertion of Catheter, Vein  453  21.93  154.35%  $54,502 
42820  Remove Tonsils and Adenoids  1054  17.41  160.99%  $5 
45300  Proctosigmoidoscopy  931  -15.73  49.40%  $5,332 
45330  Sigmoidoscopy, Diagnostic  7845  -3.56  70.57%  $42,585 
45384  Colonoscopy  1541  21.58  178.26%  $77,739 
46083  Incise External Hemorrhoid  177  -13.96  64.19%  $288 
50590  Fragmenting of Kidney Stone  1050  11.96  149.77%  $23,062 
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  Appendix D 
Medicare 
Index 
CPTTM  Description  N  Beta  Volume 
Median 
(in $1,000s) 
51700  Irrigation of Bladder  445  -21.45  44.89%  $3,240 
51720  Treatment of Bladder Lesion  378  -12.60  62.40%  $17,684 
52000  Cystoscopy  3334  4.94  112.29%  $107,817 
53660  Dilation of Urethra  228  -21.29  42.52%  $2,222 
53661  367  -23.35  36.65%  $3,858 
53670  Insert Urinary Catheter  1237  -24.88  33.16%  $6,718 
54161  Circumcision  279  14.19  153.56%  $2,728 
56501  Destruction, Vulva Lesion(s)  335  -11.2  65.92%  $255 
57452  Examination of Vagina  943  7.08  114.72%  $1,075 
57454  Vagina Examination & Biopsy  3617  15.64  141.06%  $3,111 
57460  Cervix Excision  347  32.86  192.37%  $408 
57522  Conization of Cervix  377  24.18  151.27%  $721 
58120  Dilation and Curettage (D&C)  935  10.17  116.95%  $7,387 
59425  Antepartum Care Only  573  -21.44  17.22%  $146 
59426  Antepartum Care Only  1338  -9.76  82.48%  $252 
59430  Care After Delivery  602  -6.45  82.27%  $43 
60001  Aspirate/Inject Thyroid Cyst  242  -33.77  6.85%  $129 
62275  Inject Spinal Anesthetic  185  34.09  159.15%  $4,983 
62278  730  22.57  153.63%  $21,919 
62279  277  56.68  228.44%  $10,316 
62284  Injection for Myelogram  1238  33.87  202.28%  $15,492 
62289  Injection into Spinal Canal  2608  20.71  159.76%  $78,602 
64443  Injection for Nerve Block  790  19.18  132.45%  $17,781 
64450  1295  -6.02  74.23%  $7,723 
64550  Apply Neurostimulator  350  22.97  170.08%  $384 
64721  Carpal Tunnel Surgery  1074  8.82  117.99%  $30,164 
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  Appendix D 
Medicare 
Index 
CPTTM  Description  N  Beta  Volume 
Median 
(in $1,000s) 
66761  Revision of Iris  189  12.77  137.83%  $19,175 
66821  Lasering, Secondary Cataract  562  19.75  167.52%  $198,749 
66984  Remove Cataract, Insert Lens  2656  3.37  118.20%  $1,854,895 
67210  Treatment of Retinal Lesion  721  7.84  139.16%  $111,761 
67800  Remove Eyelid Lesion  405  -17.29  51.81%  $2,711 
68761  Close Tear Duct Opening  907  -16.74  51.84%  $18,322 
69210  Remove Impacted Ear Wax  4445  -11.56  64.90%  $31,396 
69421  Incision of Eardrum  145  29.18  188.88%  $131 
69424  Remove Ventilating Tube  116  31.96  127.57%  $75 
69436  Create Eardrum Opening  3693  14.7  148.38%  $1,960 
69990  Microsurgery Add-on  366  27.17  175.38%  $5,878 
77263  Radiation Therapy Planning  1004  16.19  129.89%  $38,249 
77336  Radiation Physics Consult  1969  -14.3  60.28%  $44,926 
77408  Radiation Treatment Delivery  132  32.62  117.55%  $5,309 
77413  4389  3.65  121.32%  $93,484 
77414  1133  5.3  122.54%  $40,010 
77416  492  9.08  121.32%  $10,224 
77419  Weekly Radiation Therapy  262  46  242.31%  $18,071 
77420  273  53.28  288.17%  $7,313 
77425  252  53.11  241.52%  $11,024 
77430  4748  35.42  236.43%  $189,271 
80500  Lab Pathology Consultation  455  12.76  148.91%  $1,326 
85102  Bone Marrow Biopsy  402  -10.91  64.85%  $10,038 
86580  TB Intradermal Test  6925  -10.27  68.29%  $1,010 
86585  TB Tine Test  2298  -9.96  70.62%  $189 
88141  Cytopath, C/V, Interpret  84.20  -4.4  94.48%  $3,634 
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  Appendix D 
Medicare 
Index 
CPTTM  Description  N  Beta  Volume 
Median 
(in $1,000s) 
88329  Pathology Consult in Surgery  407  18.3  146.70%  $1,914 
90781  IV Infusion, Additional Hour  3160  13.85  123.97%  $15,404 
90782  Injection (SC)/(IM)  30039  6.56  127.64%  $7,095 
90784  Injection (IV)  3177  3.25  116.40%  $1,150 
90788  Injection of Antibiotic  3112  7.01  130.54%  $62 
90801  Psychiatric Interview  7182  -7.71  79.09%  $111,387 
90804  PSYTX, Office, 20-30 Min.  2182  -6.73  89.17%  $31,838 
90805  PSYTX, Off, 20-30 Min w/E&M  15060  -4.81  85.17%  $83,314 
90806  PSYTX, Off, 45-50 Min  23072  -5.34  84.46%  $281,209 
90807  PSYTX, Off, 45-50 Min w/E&M  18329  -3.29  90.03%  $105,222 
90847  Special Family Therapy  1920  -7.8  77.86%  $8,280 
90862  Medication Management  30111  -2.73  97.31%  $196,791 
90870  Electroconvulsive Therapy  538  23.1  146.50%  $13,220 
90925  ESRD Related Services, Day  1952  147.56  133.65%  $79,236 
90937  Hemodialysis, Repeated Eval.  105  19.61  113.74%  $26,561 
92002  Eye Exam, New Patient  1649  -9.44  76.58%  $13,585 
92004  6373  -11.54  69.83%  $136,603 
92012  Eye Exam, Established Patient  10760  -15.22  59.71%  $238,022 
92014  13915  -11.52  71.82%  $431,473 
92020  Special Eye Evaluation  938  -9.58  74.63%  $13,013 
92225  Special Eye Exam, Initial  1678  -11.17  63.66%  $29,074 
92226  Special Eye Exam, Subseq  1996  -12.52  56.96%  $49,906 
92504  Ear Microscopy Examination  886  -13.58  63.14%  $2,335 
92552  Pure Tone Audiometry, Air  2416  -10.07  71.86%  $1,550 
92553  Audiometry, Air & Bone  826  -10.44  71.51%  $1,702 
92555  Speech Threshold Audiometry  562  -10.13  74.51%  $122 
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  Appendix D 
Medicare 
Index 
CPTTM  Description  N  Beta  Volume 
Median 
(in $1,000s) 
92556  Speech Audiometry, Complete  387  -10.10  76.67%  $553 
92557  Comprehensive Hearing Test  5393  -10.34  71.96%  $36,788 
92567  Tympanometry  10426  -11.34  69.93%  $13,189 
92568  Acoustic Reflex Testing  1563  -10.40  74.51%  $3,824 
93000  Electrocardiogram, Complete  53768  -1.72  88.92%  $260,332 
93010  Electrocardiogram Report  20110  14.80  123.24%  $175,713 
93016  Cardiovascular Stress Test  5406  14.64  120.38%  $26,600 
93018  Cardiovascular Stress Test  6810  34.08  135.89%  $24,606 
93041  Rhythm ECG, Tracing  161  46.39  127.68%  $1,078 
93042  Rhythm ECG, Report  2097  23.70  167.88%  $14,123 
93227  ECG Monitor/Review, 24 hrs  1107  23.43  137.93%  $10,670 
93233  ECG Monitor/Review, 24 hrs  235  26.54  146.72%  $4,102 
93539  Injection, Cardiac Cath  190  25.07  168.13%  $4,211 
93540  206  19.59  172.81%  $6,018 
93543  Injection for Heart X-Rays  2372  35.49  163.72%  $20,708 
93544  Injection for Aortography  202  49.13  178.22%  $2,469 
93545  Injection for Coronary X-Rays  2546  44.44  194.76%  $34,958 
93798  Cardiac Rehab/Monitor  1616  8.89  144.86%  $9,304 
94668  Chest Wall Manipulation  186  50.35  175.24%  $201 
94760  Measure Blood Oxygen Level  7613  -3.19  79.31%  $16,911 
95004  Allergy Skin Tests  6378  1031.06  2869.69%  $13,852 
% 
95010  Sensitivity Skin Tests  153  143.13  315.71%  $370424 
95015  242  121.88  268.39%  $563 
95024  Allergy Skin Tests  5157  406.57  954.58%  $9,722 
95027  Skin End Point Titration  171  148.24  85.13%  $301 
95028  Allergy Skin Tests  181  327.63  656.18%  $1,817 
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  Appendix D 
Medicare 
Index 
CPTTM  Description  N  Beta  Volume 
Median 
(in $1,000s) 
95044  Allergy Patch Tests  571  154.48  63.36%  $2,481 
95115  Immunotherapy, One Injection  83613  -16.15  57.91%  $19,867 
95117  Immunotherapy Injections  88880  -14.67  59.21%  $23,785 
95144  Antigen Therapy Services  1961  95.58  400.19%  $3,016 
95147  213  43.17  126.75%  $304 
95148  135  48.26  120.11%  $244 
95165  29245  203.57  590.67%  $39,070 
95851  Range of Motion Measurements  716  12.06  106.26%  $1,329 
96400  Chemotherapy, (SC)/(IM)  1117  13.11  112.66%  $3,955 
96408  Chemotherapy, Push Techniques  4828  -3.38  92.95%  $25,300 
96412  Chemotherapy, Infusion Method  4897  5.88  99.17%  $49,708 
96900  Ultraviolet Light Therapy  858  -8.96  70.88%  $1,075 
96910  Photochemotherapy with UV-B  1869  -9.78  67.57%  $3,245 
96912  Photochemotherapy with UV-A  1713  -7.98  74.29%  $2,763 
97001  PT Evaluation  2043  4.17  107.29%  $25,121 
97002  PT Re-Evaluation  699  11.28  139.01%  $2,766 
97012  Mechanical Traction Therapy  1813  7.22  116.28%  $3,848 
97014  Electric Stimulation Therapy  6823  9.47  118.47%  $26,831 
97022  Whirlpool Therapy  1562  8.12  116.72%  $3,003 
97032  Electrical Stimulation  3821  8.09  110.13%  $21,704 
97033  Electric Current Therapy  1095  17.14  151.31%  $985 
97035  Ultrasound Therapy  10195  8.21  110.01%  $41,306 
97112  Neuromuscular Re-Education  3178  8.47  117.30%  $20,806 
97113  Aquatic Therapy/Exercises  465  41.02  203.42%  $6,296 
97116  Gait Training Therapy  443  14.07  141.19%  $5,512 
97124  Massage Therapy  2447  10.36  124.67%  $33,919 
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  Appendix D 
Medicare 
Index 
CPTTM  Description  N  Beta  Volume 
Median 
(in $1,000s) 
97140  Manual Therapy  9037  28.54  173.71%  $40,579 
97530  Kinetic Therapy  6698  20.84  144.59%  $49,135 
97703  Prosthetic Checkout  211  71.65  202.86%  $65 
97750  Physical Performance Test  296  18.82  145.75%  $1,897 
97770  Cognitive Skills Development  142  78.39  279.50%  $881 
99183  Hyperbaric Oxygen Therapy  442  47.64  155.12%  $19,566 
99195  Phlebotomy  735  -11.47  71.89%  $941 
99201  Office/Outpatient Visit, New  20043  -8.46  82.26%  $22,976 
99202  78872  -9.32  76.57%  $151,700 
99203  90193  -9.61  75.66%  $299,082 
99204  39612  -9.07  76.48%  $287,602 
99205  17179  -8.14  79.53%  $160,215 
99211  Office/Outpatient Visit, Est.  73020  -11.66  67.99%  $139,113 
99212  431696  -8.02  81.68%  $845,031 
99213  1002696  -6.74  81.84%  $3,611,097 
99214  270251  -7.8  77.89%  $2,219,555 
99215  Office/Outpatient Visit, Est.  60972  -6.16  83.73%  $572,393 
99231  Subsequent Hospital Care  4122  7.07  114.96%  $836,347 
99232  5815  3.83  105.53%  $1,962,425 
99241  Office Consultation  6759  -5.09  86.69%  $23,416 
99242  17564  -6.52  83.83%  $104,737 
99243  32037  -6.24  83.17%  $289,705 
99244  29912  -6.39  84.74%  $452,636 
99245  12572  -5.64  85.41%  $267,320 
99281  Emergency Dept. Visit  2600  17.67  153.51%  $6,106 
99282  17006  15.75  139.30%  $49,780 
Comparing Medicare Physician
 
27 
Payments to Private Payers  OEI-06-00-00570
  Appendix D 
Medicare 
Index 
CPTTM  Description  N  Beta  Volume 
Median 
(in $1,000s) 
99283  44568  9.77  134.93%  $279,184 
99284  21491  9.91  135.33%  $425,949 
99285  6531  8.77  118.30%  $462,377 
99292  Critical Care, Addl 30 Min.  152  19.20  128.27%  $26,845 
99433  Normal Newborn Care, Hospital  940  7.51  122.42%  $1 
A4263  Permanent Tear Duct Plug*  279  29.86  162.74%  $3,038 
A4550  Surgical Trays*  7016  30.41  149.25%  $4,647 
G0101  CA Screen; Pelvic/Breast Exam  6388  42.36  219.11%  $10,097 
Q0091  Scrn Pap Smear Obtain Prep&Con  574  -26.67  22.93%  $8,284 
*Codes now bundled with primary service for global payment. 
Comparing Medicare Physician 
28 
Payments to Private Payers  OEI-06-00-00570 A C K N O W L E D G M E N T S 
This report was prepared under the direction of Judith V. Tyler, Regional Inspector General for 
Evaluation and Inspections in Dallas, TX. Inspections staff who contributed include: 
DALLAS  HEADQUARTERS
 
Amy Ashcraft, Project Leader  Stephanie Lattin, Program Specialist
 
Kevin Golladay, Team Leader 
SAN FRANCISCO 
Robert Gibbons 
Comparing Medicare Physician 
29 
Payments to Private Payers  OEI-06-00-00570