You, like many healthcare executives responsible for evaluating physician compensation, rely on accurate normalized data. As examples, normalized data is required to:
- Prepare accurate documentation to evidence compliance with: (i) the Stark Law and federal and state fraud and abuse regulations that is often supported by a fair market value opinion[1], and (ii) IRS regulations regarding inurement/private benefit[2] for non-profits and excess compensation[3] for closely held shareholder organizations; and
- Prepare accurate comparisons to external salary surveys to promote: (i) the reasonableness of the organization’s compensation plan results, (ii) the internal equity between various specialties, and (iii) adherence to any internally imposed benchmarks, budgets or performance metrics.
If the methodology your organization uses to normalize compensation data is compromised by the limitation of the utilized tools and/or the form of rolled-up data, serious unintended consequences can occur.
The customary situations that bring normalizing data to the forefront are:
- part-time practicing physicians,
- physicians with less than a 1.0 clinical FTE due to administrative responsibilities or being split across more than one department or because of extended PTO leave, and
- physicians who have recently joined or termed from a department resulting in them having less than the same number of periods of data as others in the same department (e.g. see the below table where the provider has only 9-1/2 out of 12 months of data).
Without normalizing the data for such situations, you and others may make mistaken conclusions due to being unaware of the underlying data differences.
Most experienced healthcare executives already know this. So why is this topic worthy of a white paper? Most tools used today by compensation teams to normalize data are either too rudimentary or have an overreliance on rolled-up data that is inappropriately devised to be able to easily produce accurate normalization. What? As dull as it may sound, it goes back to basic algebra.
Let’s take the situation where the compensation team summarizes monthly billing data into a database or a spreadsheet and subsequently calculates the Work RVU’s for each physician for the periods used in a particular compensation calculation. Also, let’s assume that one part-time (0.50 FTE) physician joined the department 2 1/2-months into the compensation calculation period and has only 9 1/2-months of billing data (for ease of discussion we will present an example of where the compensation team is using a calendar period, i.e. 12-months of data). To demonstrate the algebraic differences that can result in such an example, depending on the team’s available tools and data sources, a table of the hypothetical situation is provided below:
As you can see, there are at least four possible ways that one may conceive how to perform the normalization calculation depending on how the data is presented to the compensation team, but there is only one process to arrive at the correct answer of 2,556.60 Work RVU’s. Further, the last depicted process that resulted in the largest error is likely to be the mistake most often experienced due to the limitation of the tools and data available to the compensation team. Why?
Many compensation teams are limited by the use of spreadsheet applications, such as Excel, as their tool to perform compensation calculations. Others may use a database application such as Access. There is nothing wrong with Excel. However, since each time a compensation calculation is to be performed the prior Excel spreadsheet is normally copied to kick-off the new set of calculations causing the compensation team to become reliant of the integrity of the copied formulae in the prior spreadsheet. This reliance leads to the need to keep each provider’s data loaded into the spreadsheet in the exact same format (e.g. a single row or column of predefined values). This data format limitation results in the data needing to be rolled-up into the highest practical level in order to use the same row or column of data per provider. So, the provider with 12-months of data has the same data format as the provider with 9-1/2 months of data. In order to attempt normalization, the FTE is averaged, and the average is used to “FTE adjust” (normalize) the production data (e.g. Work RVU’s). Thus, the method shown above titled “Normalizing Using the Avg. FTE of Calendar Periods” is the simplest means of maintaining the integrity of the copied formulae from the prior spreadsheet. However, depending on how the FTE is averaged will result in different outcomes, many of which are inaccurate except in coincidental situations.
The use of a database application can help overcome some of the spreadsheet application limitations. However, because data is summarized at various levels in various mediums from various sources, there remains the risk of mismatched data due to some data being presented as aggregate totals and others being presented as averages, which may further be presented as an average of the data or an average of the periods. Each one of these nuances can result in undesired errors in normalization using the traditional tools of spreadsheets and spreadsheet like database applications. Unfortunately, this is not where the potential for error ends.
Because of the need by most compensation teams to rely on spreadsheets for their final set of compensation calculations, and thus the reliance on the copy and paste functions to carry forward formulae within the spreadsheet for newly added providers or changed provider statuses, the otherwise properly matched normalized data can still produce unintended erroneous annualization results for one or more unique physician data format situations. There is simply no error proofing the use of copied formulae, whether used in databases or spreadsheets, due to the risk of varying data formats.
To error proof your compensation system you must fully automate the process through the use of competently developed stored procedures and applied source code. Today’s choices of sophisticated programming languages for source code development as well as stored procedure capabilities within complex database tools eliminate the data formatting (rolling-up of data) and copied formulae limitations previously described. Such tools allow for looping through more granular data resulting in the capability to:
- distinguish when there are differing numbers of periods of data for each provider,
- derive the correct averaging process without having to intervene manually to construct a different formula, and
- distinguish to whom the requisite formulae are to be applied (without having to manually edit rows or columns) irrespective of when providers have been added, deleted or changed in status.
Automation not only benefits the timely adjudication of your compensation program, it also error proofs the normalization and annualization of data. This improved level of accuracy supports your assessment of your compensation plan’s results as well as builds a higher level of confidence in the documentation of your compliance with regulatory requirements.
The Office of Inspector General (OIG) has dramatically expressed its seriousness over regulatory compliance involving physician arrangements and compensation as evidenced by its issuance of four fraud alerts.[4] In addition to required financial restitution to governmental programs for over payments, both the physician(s) individually and the compensating entity can be held liable for Civil Monetary Penalties (CMP’s) as well as possible criminal prosecution and exclusion from federal programs.
These potential financial consequences have not gone unnoticed by firms issuing audits for healthcare clients. Footnote disclosure content continues to expand as accepted accounting practices by entities in the healthcare space. For a broader view and understanding of the need for the role of compliance in protecting the financial well-being of healthcare entities, please refer to the Deloitte 2015 study, “The challenge of compliance: Moving from cost to value.”[5]
Is your organization taking every appropriate action to evidence compliance with regulations regarding your compensation plan? Are you certain that your compensation plan adjudication processes are accurate and represent a best effort to be compliant? To find out how to cost effectively automate your compensation plan and improve the compliance of your compensation plan adjudication processes, please go to www.compconfidence.com. CompConfidence helps you Reward with Intelligence.
[1] https://www.beckershospitalreview.com/legal-regulatory-issues/physician-compensation-10-core-legal-and-regulatory-concepts.html: 3. Fair market value requirement… The federal regulations have interpreted “general market value” to refer to the compensation that would be included in a service agreement as the result of a bona fide bargaining arrangement between well-informed parties to the agreement who are not otherwise in a position to generate business for the other party, at the time of the service agreement. The safest approach for determining fair market value is to seek an independent valuation firm to perform a compensation review.
[2] https://www.irs.gov/charities-non-profits/charitable-organizations/inurement-private-benefit-charitable-organizations: A section 501(c)(3) organization must not be organized or operated for the benefit of private interests, such as the creator or the creator’s family, shareholders of the organization, other designated individuals, or persons controlled directly or indirectly by such private interests. No part of the net earnings of a section 501(c)(3) organization may inure to the benefit of any private shareholder or individual. A private shareholder or individual is a person having a personal and private interest in the activities of the organization.
[3] https://www.accountingweb.com/tax/business-tax/key-factors-in-determining-reasonable-compensation-for-c-corporations: The reasonableness of shareholder/employee compensation is an important – and often controversial – income tax consideration for closely held corporations. This is particularly true for the closely held corporation structured as either a C corporation or an S corporation. For a C corporation, the IRS is typically concerned with an unreasonably high (or excessive) level of employee compensation. In such cases, the Service often claims that the excess employee compensation absorbs taxable income and represents a disguised dividend to the shareholder/employee.
[4] Special Fraud Alert: Physician-Owned Entities, March 26, 2013, https://oig.hhs.gov/fraud/docs/alertsandbulletins/2013/POD_Special_Fraud_Alert.pdf
Special Fraud Alert: Laboratory Payments to Referring Physicians, June 25, 2014, https://oig.hhs.gov/fraud/docs/alertsandbulletins/2014/OIG_SFA_Laboratory_Payments_06252014.pdf
Fraud Alert: Physician Compensation Arrangements May Result in Significant Liability, June 9, 2015 https://oig.hhs.gov/compliance/alerts/guidance/Fraud_Alert_Physician_Compensation_06092015.pdf
Alert: Improper Arrangements and Conduct Involving Home Health Agencies and Physicians
https://www.oig.hhs.gov/compliance/alerts/guidance/HHA_%20Alert2016.pdf
[5] https://www2.deloitte.com/us/en/pages/life-sciences-and-health-care/articles/gx-lshc-challenge-of-compliance.html