When a patient is admitted to hospital for tests or treatment of a serious condition, they want to have a top tier team caring for them – nurses, doctors, and data scientists.
According to Esteban Rubens, Principal, Healthcare AI at NetApp, data scientists are the “cornerstone of AI research, at the center of teams that include clinical, IT, administrative, legal, and other resources.” However, Rubens also notes that data scientists are in short supply on the job market.
With healthcare systems competing with financial services, government, cybersecurity, and myriad other fields to hire and retain top talent, is there a way to alleviate this pain point? As Rubens notes in his article, when a skillset is in short supply “people move around to maximize their compensation and, most importantly, to maximize their ability to do the job that they were hired to do – which is also what they love doing.”
While part of the job of a data scientist is to clean and move data, what these highly trained, highly specialized team members really bring to the job is the ability to model and analyze the data and its outputs. If a large part of their time, which Rubens estimates to be up to 80 percent of their day, is spent “looking for, moving, and cleaning data,” – doing rote work – then it’s highly likely that top talent can be, if not will be, lured away.
An interesting idea posed by Rubens to help healthcare organizations retain their data science talent is to look to turn to data management technology to ensure that data scientists can have the “right data available at the right time, in the right, place, and at the right cost.”
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