While the actual amount of time may vary, there is a general consensus in healthcare that a large proportion of data scientists’ days are spent on mundane, essential tasks – like streamlining, cleaning, and processing data. This is a significant problem given the time-value curve – the idea that the more time it takes to process and analyze data, the lower its value becomes. In short, without agility, opportunity is lost.
Say, for example, a researcher has an idea for a project that would be an excellent candidate for federal grant money. If it takes her too much time to process the data, it’s likely she won’t have enough time to complete analytics before the submission deadline. That could mean missing out on a key funding opportunity for both herself, her organization, and the patients that her work could impact.
Clinical informaticists shouldn’t be stuck on data cleansing
In an ideal world, the time-value curve would be compressed, allowing clinical informaticists to easily and quickly clean data. But unfortunately this is rarely the case, which often causes frustration and prevents them from operating at the top of their license.
Indeed, clinical informaticists are trained and hired to do more sophisticated work than data cleansing, like:
- Analyzing and interpreting medical information
- Using data to perform root cause analysis (RCA) to explore the underlying reasons or factors that may contribute to suboptimal clinical outcomes
- Utilizing their unique knowledge base to uncover clinical insights and work with providers and other healthcare executives to formulate action plans to address new information
Most of us would become frustrated if the daily tasks of our job felt mundane – especially after having undergone extensive training in order to be able to tackle bigger challenges. To mitigate this problem in health IT, supporting clinical informaticists so they can work at the top of their license is akin to killing two birds with one stone. It helps boost job satisfaction while also compressing the time-value data curve.
Perhaps one of the best ways to achieve these goals is by investing in solutions that normalize disparate clinical data, which helps eliminate unnecessary busy work. In addition, because healthcare is such a unique field it’s important to partner with an organization that has a deep background in robust clinical terminologies. This helps to ensure that standardization tools are not designed to just clean data but are also built specifically to address the issue of cleaning healthcare data.