Based on my conversation with many Chief Data Officers (CDOs) at the IBM CDO summit 2017 and also from my personal experience as a CDO, I can say that the position is evolving. What used to be a traditional data governance, data management and regulatory compliance role is now one that drives more decisions within the organization. Although many companies are still unclear on how to use these roles for building a more coherent data-driven strategy, a Gartner survey from 2017 shows that CDOs have larger areas of focus as well as bigger budgets than previous years.
One of the biggest challenges facing data leaders in healthcare organizations is resolving data quality issues. Since the correction of data acquisition errors often leads to EHR data entry workflow re-engineering, this is often difficult politically. Data privacy regulations create significant barriers for seamless data interoperability between organizational boundaries, as pointed out by the US Department of Health and Human Services’ 2017 study on the impact of AI on healthcare in the near and longer term. The report also underscored that training data in healthcare may not closely match what will be encountered in real-life application. Diseases can change and new diseases can appear, which makes any AI/ML system that is launched today less and less effective over time. Algorithms must be assessed continuously to understand how to respond to such changes.
There are many reasons to be hopeful out there, though, and people are working to improve data quality and interoperability. Deep learning models are achieving human-level performance across a number of biomedical domains. In other clinical settings, it is expected that DL methods will augment clinicians and researchers.
The full potential of DL in healthcare has not been explored yet. If future deep learning algorithms can enable scientists to ask questions that they did not know how to ask, we can predict that biology and medicine will be transformed.