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Leading Causes of death in the US 1) Cancer 2) Heart Disease 3) Medical Error https://hub.jhu.edu/2016/05/03/medical-errors-third-leading-cause-of-death/ ht

Leading Causes of death in the US 1) Cancer 2) Heart Disease 3) Medical Error https://hub.jhu.edu/2016/05/03/medical-errors-third-leading-cause-of-death/ https://hub.jhu.edu/2016/05/03/medical-errors-third-leading-... Analyzing medical death rate data over an eight-year period, Johns Hopkins patient safety experts have calculated that more than 250,000 deaths per year are due to medical error in the U.S. Their figure, published May 3 in The BMJ, surpasses the U.S. Centers for Disease Control and Prevention's third leading cause of death—respiratory disease, which kills close to 150,000 people per year.

vkou 10y ago
3) Is a catch-all for 'complications associated with every single form of treatment.' Consider this case: You have cancer. You can do nothing about it, and die in 3 months, or you can have an operation, that has a 60% chance of curing you, 30% chance of doing nothing, and 10% chance of killing you. Most people would choose to undergo the operation, knowing the risks. Those that die will be cataloged as 'medical error'. I'd argue that cancer should get at least an equal share of the blame.
- nonbel 10y ago
  >"Consider this case: You have cancer. You can do nothing about it, and die in 3 months, or you can have an operation, that has a 60% chance of curing you, 30% chance of doing nothing, and 10% chance of killing you." I recommend everyone ask their doctor what a p-value means before listening to any stats like this. Here is just one of many horror stories about the poor understanding of stats that pervades medicine (TBH though, this is mostly the fault of stats education): "Nearly 40 years ago the New England Journal of Medicine published a short survey of doctors’ understanding of the results of diagnostic tests.1 The participants, all doctors or medical students at Harvard teaching hospitals, were asked, “If a test to detect a disease whose prevalence is 1/1000 has a false positive rate of 5%, what is the chance that a person found to have a positive result actually has the disease, assuming that you know nothing else about the person’s symptoms or signs?” This wasn’t a very difficult question, which made the results all the more shocking. Fewer than a fifth of participants gave the correct answer, and most thought that the hypothetical patient had a 95% chance of having the disease. Of course, this was a long time ago, and medical curriculums now contain much more in the way of statistics and probabilistic reasoning. You might expect that if the exercise were repeated today almost everyone would give the right answer. But you’d be wrong. Earlier this year a similar study was carried out, also in hospitals in the Boston area of Massachusetts, and the results were no better.2 Most doctors who were asked exactly the same question thought that the patient had a 95% chance of having the disease." http://www.bmj.com/content/349/bmj.g5619 http://www.bmj.com/content/349/bmj.g5619

vkou 10y ago

3) Is a catch-all for 'complications associated with every single form of treatment.' Consider this case: You have cancer. You can do nothing about it, and die in 3 months, or you can have an operation, that has a 60% chance of curing you, 30% chance of doing nothing, and 10% chance of killing you. Most people would choose to undergo the operation, knowing the risks. Those that die will be cataloged as 'medical error'. I'd argue that cancer should get at least an equal share of the blame.

nonbel 10y ago
>"Consider this case: You have cancer. You can do nothing about it, and die in 3 months, or you can have an operation, that has a 60% chance of curing you, 30% chance of doing nothing, and 10% chance of killing you." I recommend everyone ask their doctor what a p-value means before listening to any stats like this. Here is just one of many horror stories about the poor understanding of stats that pervades medicine (TBH though, this is mostly the fault of stats education): "Nearly 40 years ago the New England Journal of Medicine published a short survey of doctors’ understanding of the results of diagnostic tests.1 The participants, all doctors or medical students at Harvard teaching hospitals, were asked, “If a test to detect a disease whose prevalence is 1/1000 has a false positive rate of 5%, what is the chance that a person found to have a positive result actually has the disease, assuming that you know nothing else about the person’s symptoms or signs?” This wasn’t a very difficult question, which made the results all the more shocking. Fewer than a fifth of participants gave the correct answer, and most thought that the hypothetical patient had a 95% chance of having the disease. Of course, this was a long time ago, and medical curriculums now contain much more in the way of statistics and probabilistic reasoning. You might expect that if the exercise were repeated today almost everyone would give the right answer. But you’d be wrong. Earlier this year a similar study was carried out, also in hospitals in the Boston area of Massachusetts, and the results were no better.2 Most doctors who were asked exactly the same question thought that the patient had a 95% chance of having the disease." http://www.bmj.com/content/349/bmj.g5619 http://www.bmj.com/content/349/bmj.g5619