I was recently talking with a power-user at a client hospital. She’s a senior data analyst and combines a rigorous engineering mindset with a deep cultural understanding of how the hospital actually works. She’s off-the-charts organized and confident in her work.
While making small talk before a meeting, I asked her: “Do you have a clinical background — have you been to nursing / medical school?” Her response gave me pause — she replied, “Oh heavens no! I’m way too afraid of killing someone.” I was startled because for someone who generally presents with such authority and confidence, this worry seemed inconsistent.
But what was more alarming was the implication that data and data analysis is somehow “harmless” because it doesn’t involve direct patient contact. We’ve all heard similar snarky comments about pathology. We also know how reckless that belief is. A false positive from sloppy analysis can actually kill someone if treatment is unnecessarily delayed.
Data inconsistencies can greatly affect how populations receive healthcare, and the aggregate over an entire healthcare system can be significant. Medical literature is full of studies showing the negative correlation between ED wait times and bad outcomes. This matches well with intuition. Untreated patients waiting for care in the ED are not likely to cure themselves and walk home. Even worse, an ED with a reputation for long waits may discourage patients from even showing up in the first place.
This analyst’s work helps the ED move the sickest patients into inpatient beds faster. It helps surgical patients get their procedures done sooner and spend less time in the PACU also waiting for a bed. It helps inpatient nurses identify patients most likely to be ready to be safely discharged. It helps staffing managers minimize the effects of nursing shortages by staffing to actual real-time forecasted nursing demand rather than historical averages.
All these departments come to this analyst for guidance on how to marshal scarce resources. They may not count on her to administer the right dose of the right drug to the right patient or correctly change a sterile dressing. But they do count on her to make sure their department is correctly staffed with the right bed availability for the right patient. So even though she doesn’t have direct patient contact, her data analysis causes a ripple effect that impacts patient care.
For many hospitals, data operations means keeping the EHR running and preparing some reports for executive meetings. But for hospitals to truly modernize, data operations must be viewed as central to the mission of running an efficient healthcare network that can appropriately treat their patient population. And data analysts must understand the importance of their role in providing quality patient care they would want for their own family.
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