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Bias in Fair Market Value (FMV): The Insidious Adversary to Defensible Opinions

Bias In Fair Market Value Fmv The Insidious Adversary To Defensible Opinions Meta

Opinions: We have them about all sorts of things, and they often contradict someone else’s. In many cases, differing opinions cannot be simultaneously correct, but that seldom stops people from holding onto their own. In fact, after a conversation with someone who has a different opinion, people are often more convinced that their original position is correct. This situation is as frustrating as it is familiar. However, when a third person without preconceived notions listens to an argument, that person will typically favor one position based on more objective considerations.

What human fault impairs our objectivity when we encounter an opinion that contradicts our own? Bias is the culprit.

Understanding Bias

Bias is the tendency to place greater weight on facts that confirm a prior position while dismissing evidence to the contrary. Most psychologists and neuroscientists believe that this kind of bias is hardwired and that we often lack the ability to detect it. Yet overcoming bias is important for understanding how a disinterested third party will interpret available facts.

There are two common ways that health systems and physicians evaluate the FMV of physician compensation. One approach attempts to demonstrate that a predetermined level of compensation is (or is not) FMV. The other asks what a disinterested third party would conclude given access to all relevant facts. Not surprisingly, the former approach is far more common than the latter. Regardless of the approach used, bias often creeps in, even among valuation consultants.

Is It Justified?

The pervasiveness of this bias in FMV analyses is evident in the way the word “justify” has become normalized. Attorneys, administrators, physicians, and sometimes even valuation consultants will say something like “This FMV opinion justifies the compensation paid last year.” To justify a position is to prove that it is correct. Justification is a rationalization, not an inquiry, and is relevant when someone enters an evaluation with an idea of what FMV is or should be. Thus, it becomes exceedingly difficult to disconfirm the predetermined conclusion or to conduct a thorough inquiry. Consequently, the justified FMV conclusion could have significant unexamined vulnerabilities. Moreover, it could fail to adequately consider how different methods or data sources compare to the ones relied upon.

That’s not to say that the methods or data used to justify a compensation arrangement are necessarily unreliable or lack persuasiveness. Rather, justification of FMV fails to inform the user about the true level of risk that compensation could be deemed excessive. People, especially smart ones, are adept at justifying nearly any opinion they hold. Thus, it’s not particularly difficult for someone to explain why they think they are right. Instead, they need to provide reasons their opinion is more credible than the alternatives.

How Biased Is Your Valuation?

While no valuation engagement is free from bias, understanding the explicit and subconscious forces that influence the FMV process is the first step in mitigating their adverse effects on opinions. Biases can materialize across the entire valuation life cycle when analysts, compensation managers, and other interested parties subconsciously apply preconceptions, undermining the decision-making process and producing less-defensible work products. It is critical that valuation teams undergo training to recognize and mitigate these biases when conducting an FMV analysis.

Common Data-Related Biases in the Physician Compensation Valuation Process

One of the most common ways biases are manifested is through unreliable input data. To prevent your next FMV opinions from going sideways, make sure you know how these four common biases affect the process:

1. Anchoring Bias: Remaining anchored to an initial value and then changing estimates or beliefs based on that anchor or initial value

  • The majority of physician compensation engagements start with a specific anchor value—the level of proposed compensation—usually based on a provider’s historical compensation or a negotiated amount.
  • Organizations often have a sound business rationale/community need to engage the physician, and thus a strong desire to acquire the physician for their system.
  • The anchor value, paired with a strong desire to engage the physician, can lead the data provider to present only information that confirms the anchor value.

2. Availability Bias: Overemphasizing information that’s easy to find or remember

  • This bias prioritizes data that is easily retrievable, fits into existing mental models, and relies on data providers’ specific range of experience.
  • This bias can lead to overreliance on rules of thumb, attributing data applicable from data providers’ past experiences to an opinion where that data may not be applicable.

3. Illusion of Control: Overconfidence in one’s own abilities to influence outcomes

  • Data providers often overestimate their ability to accurately predict fundamental factors that impact market compensation for a physician. This can be related to productivity estimates, call coverage burden/activation, incremental administrative burden, etc.
  • When analysts have the responsibility of forecasting future compensation factors, they too can misjudge their degree of control and, as a result, produce forecasts based on unreliable assumptions.

4. Representativeness Bias: Using overly simple if-then or rule-of-thumb decisions instead of thorough analysis

  • Base Rate Neglect: Giving too much weight to new information relative to old information. This can occur when data providers estimate physician productivity from a small time period to reflect an expected annual amount.
  • Sample-Size Neglect: Incorrectly assuming small samples represent the entire population; for instance, relying on the results of a compensation survey with four respondents.

Fighting Bias

Human bias in the data collection process can have an insidious effect on the reliability of a valuation opinion, weakening the objectives of independence and objectivity. As the old adage goes, “Garbage in, garbage out.” That is why we encourage anyone who works with compensation planning and fair market valuation to undergo training to recognize and mitigate these biases.

At ECG, our project management structure and processes have been developed to minimize the effects of bias in our valuations, and we aim to actively identify potential bias at the outset of engagements. We also incorporate multiple levels of review by both consultants on the project team and consultants independent of the engagement. We believe that driving awareness of and actively mitigating potential biases in data provided can positively influence the quality of an opinion and ensure the best outcomes.


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