The modern healthcare industry is dependent on data. The steady improvement of how data is collected and analyzed has allowed us to make great strides in providing quality care and improving the health of our patients. At the same time, the pandemic has highlighted how this dependence on data can lead to “analysis paralysis” that can harm communities’ health and well-being.
Reality is often far more complex than the data can demonstrate. As the adage goes, “correlation does not imply causation.” Furthermore, the data collection process can impact the story the numbers are telling. Consider, for example, the complicated web of diagnosis codes. While they may certainly indicate the types and quantities of maladies treated or services provided, they also signify which codes insurance companies are more likely to cover, allowing the provider to get paid.
With healthcare professionals drawing different conclusions and making opposing decisions using the same set of data, weighing the potential implications and consequences of our data-based choices can be frustrating and baffling. It seems increasingly difficult to say what is “reliable.”
Clearly, we’re working within a broken system, but for the time being, it’s the one we have to work with. How, then, do we move forward with decisiveness and avoid “analysis paralysis?”
Research both sides.
Don’t look for data in order to make a point, support the opinion you already hold or, perhaps even worse, back up the decision you’ve already made and moved ahead with. As I’ve written about before, engage with the very best versions of varying arguments. Get the opinions of others within your organization who may have a different lens through which to view the issue. Decisiveness is no virtue if you haven’t taken the time to understand a matter from many angles.
