Knowledge Expansion Insider: Timothy Smith on busting myths about physician compensation valuation and data Podcast Sign in to save Chris Harrop There are a lot of myths in healthcare, especially when it comes to physician compensation. Timothy Smith, CPA, ABV, principal, TS Healthcare Consulting LLC, considers himself something of a skeptic of those myths. He recently joined MGMA senior editor Daniel Williams on the MGMA Insider podcast to talk about his time working in physician services and physician development during the 1990s, amid a frenzy of transactions around primary care practices — and how it prompted him to “drill into the numbers” and study what was actually happening to confirm his “healthy skepticism.” “We have a data problem in healthcare,” Smith says. “And the problem isn’t the data we have — it's the way we use it and really abuse it.” In 2008, he joined the valuation community and became a professional evaluator with a focus on physician compensation. “And in doing valuation work … you have to come to a judgment of what you think fair market value is and you have to defend it,” Smith said, which prompted him to dig into various survey data sets being used for different types of evaluation. In looking at demographics and data definitions and comparing it to what a lot of his colleagues inferred about data used in valuation, Smith said he was surprised that “the data didn't always behave … the way that I expected to,” and was concerned that people made generalizations and used data in ways they weren’t designed or intended to be used. Proper use of these types of data is crucial for transforming the healthcare industry in a positive way, he notes. “I think it's very, very important that, if we're ever going to bend the cost curve and move to towards improving healthcare in United States — and not just delivery, but really the cost of care — we've got to get data right,” Smith says. ”And so to do that, we've got to have appropriate and accurate, realistic and correct data usage.” How is data being misused today? Smith first notes that “there's a bit of a superficial approach to financial analysis,” whereby many organizations just haven’t been as rigorous in checking their own data. Specific to the world of valuation, Smith says the industry sometimes gravitates “toward quick and easy answers” that don’t always account for large differences between hospital-owned practices and physician-owned practices or lose sight of factors beyond physician compensation that exist in a larger continuum of care in a large healthcare organization. “I tell my evaluation peers, every knowledge discipline should be developing and growing and expanding,” Smith says. “That's what we see in science. That's what we see in technology — growth and innovation. We need to foster that spirit with respect to our data usage” amid the shift from fee-for-service to value-based payments. “We've got to create new habits of mind, and we've got to have a critical eye toward how we use data.” Smith spoke at MGMA19 | The Data Conference on how survey data is used to value clinical compensation, medical directorships and call coverage for physician arrangements, primarily between hospitals and physicians and some on the physician side, too. “We're going to be talking about what surveys you use, what metrics from those surveys are used, and what techniques, methods or models are used to take that data and apply it to [how] arrangements are being valued,” Smith says. Smith said his presentation looks at popular formulas for valuations and the ideas embedded in the evaluation methods and calculation techniques to see if they match reality. Spoiler alert: Smith thinks many of them miss the mark. “These models are perpetuating understandings of the drivers of physician compensation that don't match the data,” he says. Rules of thumb about certain percentiles in the valuation community “should be scrutinized,” Smith asserts. Doing that work to test long-held assumptions “is going to make health systems in particular better users of valuations,” Smith says.