A Real Divide
The current phase of health informatics presents those in the field with a broader set of opportunities and challenges than before, Volchenboum says. Electronic health records systems and other patient-tracking tools have reached near ubiquity, so the central challenges now revolve around aligning the quantitative potential of technology with the often labyrinthine world of healthcare.
Proficiency in the skill sets relevant to these two very different domains is rare. Practitioners highly skilled in statistical analyses or machine learning seldom have a deep understanding of healthcare data. Meanwhile, those medical students and fellows who understand the data tend to lack the quantitative skills to analyze it.
“There’s a real gap there, a real divide, and the type of training we provide helps our graduates fill that divide,” Volchenboum says. “The people who can understand how the technology works—as well as how everything fits into the larger construct of a healthcare system—will be the ones to figure out ways to target the right populations and make interventions as effective as possible.”
Volchenboum cites data standards as a key problem area. In addition to plaguing practitioners, inconsistencies in data standards cause many of the field’s present challenges. Without standardization, different healthcare systems are unable to interoperate or share patient data. Further, combining data from different clinical trials becomes difficult when the data are collected using different formats.
“We’re starting to see more attention paid to data standards, but it’s going to be a long time before the problem is solved,” Volchenboum says. “Industries like banking solved it long ago by creating a standard for interoperating. Healthcare is moving significantly slower in this area, but if we can produce graduates that are focused on trying to improve interoperability and solve this problem, it’s going to have a really big impact on healthcare.”