In making the decision to take or pass on a new hospital contract, radiology groups often let emotion supersede reason, Jana Landreth, CPA, director of practice management at Medical Management Professionals, Inc (MMP), Atlanta, Georgia, explains. “The factor that most often challenges these decisions is emotion,” she says. “It manifests itself as the desire to grow, without accounting for what is feasible—or the inverse, the fear of growing, when it is what your practice needs.”
Landreth recommends, instead, that practices take a data-driven approach to business decision making, and she outlined best practices for doing so in a recent webinar, Smart Practice Decisions Begin With Data Integration. “Physicians had to make decisions based on emotion, in the past, because the technology that produces the numbers we crunch just was not available,” she says. “Emotional considerations are important, but using data also creates a common platform for communicating. Generally speaking, groups are becoming more open to this kind of data-driven approach. Some are craving it.”
Pulling the Right Data
A group should begin by determining what its priorities are for the prospective contract: How does the group hope that the contract will affect its business? “When 10 physicians must make a business decision in a short period of time, you can separate their priorities into what is necessary and what would be nice,” Landreth says. “If groups do not recognize the difference between what they have to have and what’s nice to have, they risk leaving a lot on the table. Knowing their priorities makes them more focused in their decision making and makes communication easier.”
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Greg Thomson, CPA, executive vice president of practice management for MMP, adds that having the data necessary to make the decision ready to go, at any time, hastens the decision-making process by making stakeholders aware of the group’s needs at all times. “These data could have been reviewed in the past, and if they had been, the discussion would have been much quicker,” he says. “The decision makers share a common platform and understanding of what the business needs to grow.”
Landreth says that practices should consider such issues as contract restrictions, the strength of their relationships with other hospitals, their ability to maintain patient satisfaction, and whether they will be able to staff appropriately; she recommends that they have at their fingertips, among other figures, current and projected total CPT® code volume, by payor; work RVU volume and total RVU volume, by payor; and trend information on work RVUs per physician—to illuminate whether the group can handle more work.
She emphasizes, in particular, the importance of looking at the practice’s (and the prospective hospital’s) current volumes by CPT code, not just by modality or in total. “You have to make so many assumptions the physicians may not be aware of when you only look at CPT volume by modality,” she says. “If I only know how many CT exams are performed at this hospital—without considering which CT exams—I am making assumptions that could mean the difference between breaking even and profitability in this marketplace. Breaking down the studies to this level of detail will become even more important with the reimbursement cuts we anticipate next year.”
She adds that having the data by CPT code also has an impact on recruiting: “Perhaps there are procedures your practice does not perform and should consider, when evaluating the cost and ability to staff the new site,” she says.
Thomson notes that in anticipating the future caseload from a hospital, it is important for groups to perform their own revenue calculations, rather than taking hospitals’ estimates of potential revenue at their word. “Those numbers will get you in the ballpark, but they are not definitive,” he says. “Looking at the old numbers in a static way is never right. We have to look at how we would run the contract, how we would staff, and what our business would look like.”
Leveraging the Numbers
In the webinar, Landreth used anonymized samples of these data to demonstrate how a practice could compare the potential profitability