I concluded my previous post with the questions: So, what should we do? Focus on collecting “minimal standard data sets” that provides some useful information and avoids overload, but are not enough to improve health decisions substantially? Or should we begin collecting comprehensive data even though we lack the ability to use it all to support decisions, and even at the risk of information overload?
If our goal is improve healthcare quality and control costs, I contend that we should collect all the relevant data humanly possible and turn it into useful information and knowledge that increases understanding for wise decision making. But how can this be done without creating information overload?
To answer this question, let’s re-examine the definition of information overload: It is a state of having more information available than one can readily assimilate, that is, people have difficulty absorbing the information into their base of knowledge. Well, what has to happen for people to increase people's ability to assimilate information?
I contend that people with more valid knowledge about a particular knowledge domain (i.e., field or branch of knowledge, such as diagnosing medical problems), and the more they understand that domain (e.g., the better able they are to use their knowledge to answer questions about prevention, diagnosis, and treatment), then the more they information they can absorb about that domain and use it to improve their decisions. In other words, the stronger one’s foundation of knowledge about something and ability to utilize that knowledge effectively, the more one can learn and integrate into one’s existing base of knowledge without experiencing information overload.
This means that a consequence of the knowledge gap in healthcare today is people’s susceptibility to information overload. This creates a viscous cycle of information input --> information overload --> information rejection --> inhibited knowledge growth. This results in a tendency to minimize information input, e.g., by focusing on minimal data sets rather than the collection and integration of comprehensive, multidisciplinary sets of data across patients’ lifetimes described in the previous post, including patient results (clinical outcomes & costs), provider characteristics and treatment methods/processes, and patient attributes.
Breaking out of this knowledge-inhibiting cycle requires a dramatic shift in the way we view and approach health information management. This topic continues here.
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