The first paper, “Emergency department ‘undercrowding’ is associated with decreased waiting times” appeared online in the journal Emergency Medicine Australasia.
The authors looked at emergency department patient numbers and waiting times before and after a nearby tertiary care hospital opened a new emergency department.
Their main finding was that after the new ED opened, their ED saw 28% fewer patients with a concomitant decrease in patient waiting times of 15 minutes from 26 to 11 minutes with p < 0.001, a significant difference.
They concluded, “Wait times are strongly associated with patient presentation numbers.” Furthermore, “Controlling demand may benefit patient processing, flow, and patient perceptions of level of care.”
Finally we have proof that having fewer patients in an ED results in shorter waiting times.
Other than having another entity open a new ED in your hospital’s area, they do not suggest how patient demand can be controlled especially if people use EDs for broken nails, hiccups, and insomnia.
The second paper, “Outcomes and resource use of sepsis-associated stays by presence on admission, severity, and hospital type,” is in the March 2016 issue of the open access journal Medical Care.
Using administrative data, the study compared sepsis outcomes in five community hospitals to one academic medical center and found the mortality rates for sepsis were 15.1% and 22.5% (p < 0.001), respectively.
The study involved 5672 patients—1584 admitted to a 799-bed academic medical center and 4088 admitted to community hospitals.
The characteristics of the patients in the two groups differed significantly in just about every parameter you can think of including presentation stage of sepsis, worst stage of sepsis attained during stay, length of stay, costs, and DRG complexities. There was no attempt to control for any of these variables. It's not surprising that the sicker patients in the academic medical center fared worse.
The website HealthITAnalytics.com featured a story about this paper headlined “Big data analytics show more sepsis deaths in large hospitals.”
How big does data have to be before it's considered "big data"? Is it more than data on 5672 patients?
What about large hospitals? Can you say that a paper about one large hospital means that all large hospitals would have more sepsis deaths?