Chief Data Officers Share Their Biggest Lessons

Health Data Management asked several chief data officers for the single biggest lesson they have learned so far in their tenure that peers in similar positions or coming into those positions need to know.


Health Data Management asked several chief data officers for the single biggest lesson they have learned so far in their tenure that peers in similar positions or coming into those positions need to know.

Here are the lessons:

Nicholas Marko, M.D., Chief Data Officer, Geisinger Health System

“Soon after becoming the first chief data officer at Geisinger Health System, I learned that there are many reasons our data has been difficult to access, and only some of them are technical in nature. Most access issues are the result of organizational structures that grew organically, then evolved into cultural norms that created data barriers and ‘silos.’ Unravelling the red tape that prevents access to data is truly an exercise in change leadership. For CDOs who naturally have a proclivity for making fact-based, data-driven decisions, leading culture change initiatives is unfamiliar territory, but it is one of the most important parts of our mission and one of the major ways that we spend our time.

“Successful CDOs put people before the data. A large portion of my time is spent painting the vision and creating excitement for what is possible with our data. Building the desire for data-driven solutions has proven to be the surest way to ease our people into the cultural, technical and organizational changes that will improve the way we utilize our data.”

Terri Steinberg, M.D., Chief Medical Information Officer, Christiana Care Health System, Wilmington, Del.

“The single biggest lesson I’ve learned over the several years that we have been using analytics to drive care, is that machines are more accurate at predicting markers for outcomes than are clinical providers. Our regression algorithms, developed to drive the care of patients with ischemic heart disease, proved to be less accurate than machine predictions based on data values that, in some cases, were unexpected. However, the corollary to this observation is to ask the question: “Does improved prediction really matter?” We do not yet know the answer to the question of whether these imperfect, but useful, regression analytics are sufficient to improve care, or whether predictive analytics really add to overall value equation.”

Niall Brennan, Chief Data Officer, Centers for Medicare and Medicaid Services

“The biggest lesson I have learned is that in an emerging role such as a Chief Data Officer, it is important to be able to deliver tangible value across the enterprise to build confidence in the position.”

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