I am sitting here recovering from elbow surgery and am reminded of this article out of Boston discussing optimal surgery scheduling. Research advises avoiding procedures either in July or late in the day. My surgery was this December at 7:30 AM, so I have chosen widely.
Performing data analytics to get the “what” can be so challenging given dirty, disparate data that we often consider it a victory when we can extract meaningful information and leave it at that. Buy why should we seek to avoid July and late day procedures?
Delving deeper into the “why” takes us from identifying the problem to understanding root causes. Investigation helps us think through what is needed to fix that problem. This is paramount in healthcare as analytics insights are unlikely to prompt acceptance or change by providers if the “why” isn’t understood. Just try to tell a cardiothoracic surgeon to change their workflow because a black box neural network algorithm said there would be better outcomes if they did. Without the ‘why’ your work will be humored and left to die in powerpoint. So as you perform your data analysis, stop and think “have I done enough to get to the heart of the matter?” Is this sufficient information to make a difference in care?”
Because, after all, you can’t always control when you need surgery.
(PS– July is often when new learners arrive at healthcare environments. Surgeries later in the day may span shift changes which increase opportunity for hand-off errors)