I analyse an anonymised data dump of 1.9 million admissions records to the emergency departments of an undisclosed NHS Trust for the period June 2017 – September 2021
I presume you can look into the causes of death of the outliers? And compare to the rest of the pack. And do you have a list of the changes in hospital procedures over this period that might have caused any iatrogenesis?
"We may conclude that it was other people doing the dying elsewhere in the hospital: they weren’t coming in through the front door as critically ill cases."
Me to Stanford doctor e-friend, in email: "I just had a thought, after looking at this and other NYC Data. Is it possible that the massive hospital death toll in NYC wasn’t largely new admits, but people who were already there?"
I presume you can look into the causes of death of the outliers? And compare to the rest of the pack. And do you have a list of the changes in hospital procedures over this period that might have caused any iatrogenesis?
I can't look into cause of death but I can certainly see what was coded on the EPR and see if anything stands out - I'm cooking this up next.
"We may conclude that it was other people doing the dying elsewhere in the hospital: they weren’t coming in through the front door as critically ill cases."
Correct.
I love it when the numbers reveal the reality, though it's a grim reality for sure.
October 28, 2022:
Me to Stanford doctor e-friend, in email: "I just had a thought, after looking at this and other NYC Data. Is it possible that the massive hospital death toll in NYC wasn’t largely new admits, but people who were already there?"
Stanford doctor e-friend: "That would be odd."
Indeed it would be.
See observations 4 and 5 https://open.substack.com/pub/woodhouse/p/more-data-and-questions-about-spring?r=jjay2&utm_medium=ios