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The committee therefore concluded that new empirical evidence will be needed to confirm the full extent of differences in compensation across geographic areas. After extensive discussion, the committee came to agreement that geographic areas vary in terms of prices of goods and services and desirability in terms of places to live and work, even if there are individual and professional differences in the ways that desirability is perceived by health professionals.
The committee was also in agreement about addressing in its phase 2 report differences in resource use and the ways that services are provided in medically underserved areas. Given the inconclusive empirical evidence on geographic variation in compensation, the committee concluded that new empirical evidence will be needed to confirm the full extent of differences in fee-for-service compensation of physicians and other clinicians across geographic areas.
The committee therefore recommended that the work adjustment should be based on a set of principles involving accuracy, consistency, and transparency, as described in Chapter 1 , and a systematic empirical process to generate new empirical evidence about geographic variation in compensation.
To generate this new empirical evidence, the committee recommended a multiple regression model using the incomes of proxy or reference occupations to predict physician incomes region by region. The proposed approach assumes that the preferences for amenities among the individuals in the proxy occupations—and thus the offsets from a full cost of living adjustment—are similar to those of physicians.
When the geographic adjuster for physician work was originally developed, it was based on nonphysician professional earnings that ranged from 28 percent above the national average, in Manhattan, New York, to 16 percent below the national average, in rural Missouri Zuckerman and Maxwell, This reduced the range to 9 percent above average for Manhattan and 5 percent below average for rural Missouri Zuckerman and Maxwell, Over time, Congress further limited the extent of geographic adjustments to physician work.
In addition to the one-quarter work adjustment, two additional statutory provisions limited downward adjustments to the work component of physician fees.
First, section e 1 G of the Social Security Act requires that the state of Alaska receive a permanent 1. Second, a provision in the Medicaid and Medicare Extension Act of extended the 1. These provisions raised Medicare fees to physicians in low-cost areas and narrowed urban-rural fee differences GAO, The congressional decision to adjust for one-quarter of the variation in physician work was the result of political compromise rather than empirical evidence. After adjustment with the one-quarter work GPCI, physician earnings still varied, though less so than for the other levels of work adjustment.
However, this study did not attempt to estimate the optimal fraction for the adjustment or assess the proxy occupations selected, and the committee was reluctant to draw firm conclusions from one study with data that are now more than 20 years old. The committee therefore concluded that the one-quarter work adjustment lacks empirical foundation and sought to develop an alternative using statistical modeling based on multiple regression, a standard statistical technique that allows testing and modeling of independent or explanatory variables to predict a dependent or outcome variable.
The inputs to the analysis would be indexes representing the ratio of median compensation for an occupation in each payment area to the national mean of these median compensation levels, both for physicians and for the proxy occupations. Preferably, if appropriate data can be found, these income indexes should be calculated based on employed professionals.
The analysis is summarized in this section and described in detail in Appendix I. After fitting this linear statistical model, there are at least two ways to use the fitted regression model to calculate the work adjustment. One approach is to calculate an index to represent the predicted value for physician compensation from the regression model. For nonphysician labor expense, the geographic adjustment is based on the weighted average hourly wage of health care workers in each geographic area relative to the weighted average national wage for those same health care workers, where the weights used for the averaging are national employment for all occupations in all physician offices.
For example, linear regression can be used to fit a predictive model to an observed data set of independent and dependent variables. Under the current method of GPCI calculations, none of the reference proxy occupations are parts of the physician workforce; in fact, only two—nurses and pharmacists—are part of the health care workforce. By using the proposed regression equation to determine relative weights of the proxy occupations, occupations with a higher regression coefficient would receive a higher weight in the predicted value used to compute physician work.
For example, if monetary compensation in Occupation A tracks physician pay more closely than does Occupation B, Occupation A compensation would receive a higher weight in determining values of physician work. If the wages of the alternate occupations used as predictor variables were found to be highly correlated with each other, the choice of occupations would need to be reevaluated, perhaps by testing alternative choices of reference occupations and replacing the less predictive occupations those with smaller coefficients with more predictive ones with larger coefficients.
Furthermore, the total weight given to all occupations would also be determined empirically through the magnitude of the coefficients; thus, the choice of a one-quarter work GPCI or something larger or smaller would be determined through an objective empirical procedure.
There are many possible variations to developing a statistical model to set the level of the work adjustment, in terms of the data sources, specific variable definitions, and the possible of influence of high or low outlier values.
The committee did not perform a full evaluation of each of the alternatives in the limited time available, but it recommended that CMS consider statistical modeling as a general approach.
The committee concluded that an empirical alternative using statistical modeling would be an important improvement over the way the work adjustment is currently calculated. Appendix I presents a detailed discussion of some possible ways in which the modeling might be accomplished.
Current Sources In CY , CMS computed the work GPCI using the relative median hourly earnings from — Bureau of Labor Statistics BLS Occupational Employment Statistics OES data of seven nonphysician occupation categories: architecture and engineering; computer, mathematical, life and physical sciences; social science, community and social service, and legal; education, training and library; pharmacists; and art, design, entertainment, sports and media MaCurdy et al.
The use of the relative median earnings of these seven nonphysician proxy occupations to compute the work GPCI has been a source of disagreement among stakeholders since the GPCIs were introduced. Some believe that only actual physician wage data should be used in the work GPCI calculations. They question how accurately the relative median earnings of the seven nonphysician occupations reflect actual relative differences in physician compensation e.
This group of highly educated workers was assumed to be similar to physicians in the types of goods and services they purchase and in their preferences for area amenities Zuckerman and Maxwell, Physician wages were available from the U. Primary among them was the concern that the data captured existing patterns of the very fee-based reimbursement system which the PFS would replace.
This would result in endogeneity, or circularity: that is, existing fees could influence the value of the adjuster that would be applied to the new fee schedule. Alternate Data Sources for the Work Adjustment There is strong support in the provider community for continuing to use provider-generated data, such as those from surveys of physicians by the AMA and MGMA, for the work adjustment e.
The committee therefore considered several alternative data sources that might provide information on geographic variation in physician earnings for purposes of comparison with the physician proxy data that are used for the work adjustment.
The data source should have an adequate sample size to ensure that the variables described above are available at the level of MSAs and statewide non-MSAs, which define the labor markets recommended by the committee see Chapter 2 for a discussion of labor markets. Labor markets are the payment areas by which physician payment rates vary.
Medical Group Management Association. Page 5 Lessons - Table T: For internal medicine practices - Health record storage method Lessons - How does your practice currently store health record information for the majority of the practice's patients?
On paper medical records and charts With a computer system where paper records and charts are scanned and filed electronically, also known as a DIMS With an electronic health record system that stores patient medical and demographic information in a computer database. I do not work for an organization that records patient health record information at the point of care.
Other 8. No, because we will not have an EHR. No, because we do not think our EHR will be certified. No, because the cost of dealing with the bureaucracy will exceed the benefits. No, because we will not have a sufficient Medicare or Medicaid population. No, for other reasons. We have completed an implementation and believe that our practice has optimized its use of an EHR. We have completed an implementation and are focused on optimizing our EHR.
We are in the process of implementing an EHR system. We are in the process of selecting an EHR system. We intend to implement an EHR in the next months, but have not yet begun the selection process.
We have no intention to implement an EHR in the next months. Lessons - Insufficient time to select and implement an EHR Expected loss of productivity during transition to the EHR system Expected loss of productivity after the transition to the EHR system Reluctance of physician leadership to use an EHR Practice is considering a merger or purchase by a hospital system or another practice Expected inability to qualify for meaningful use incentive payments Very insignificant Insignificant Significant Very significant Very insignificant Insignificant Significant Very significant Very insignificant Insignificant Significant Very significant Very insignificant Insignificant Significant Very significant Very insignificant Insignificant Significant Very significant Very insignificant Insignificant Significant Very significant.
Page 6 10 Lessons - Table T6-Frequencies: For internal medicine practices - As a practice that has not yet selected or implemented an EHR, how helpful is the following information? Page 11 Lessons - Table T6-Frequencies: For internal medicine practices - As a practice that has not yet selected or implemented an EHR, how helpful is the following information?
Page 8 12 Lessons - Table T6-Means: For internal medicine practices - As a practice that has not yet selected or implemented an EHR, how helpful is the following information? Mean Copyright. Page 9.
Essentris By CliniComp, Intl. Raintree By Raintree Systems, Inc. All rights reserved.. SequelMed EMR. By Visionary Medical Systems, Inc. By Document Storage Systems, Inc. Page 5. Self-contained system operating on computers integral to the practice or health system that the practice belongs to Application Service Provider ASP system where vendor operates server and practice accesses EHR through internet Other The EHR software vendor provides most of the software service and support.
We outsource most of the EHR software service and support to an outside entity. Practice staff provides most of the EHR software service and support. Some combination of the above options Other 5. Other 5 6.