4 ms·

> To start with, every admitted patient has an assigned attending physician who is responsible for coordinating the care team. That's rarely (if ever) a 1:1 ra

> To start with, every admitted patient has an assigned attending physician who is responsible for coordinating the care team. That's rarely (if ever) a 1:1 ratio. That attending physician is almost certainly juggling multiple patients. Same with the rest of the care team. There's a reason why the first thing one does when approaching a patient bed is to look at the chart. > Some things can be documented in the patient chart but there are always gaps. Then those gaps need closed, stat. Once those gaps are closed... > Clinical decision support systems for partially automating diagnosis could potentially be helpful in some limited circumstances but the ones built so far mostly don't work very well. ...then this will improve considerably. Garbage in, garbage out.

nradov 5y ago
That's largely pointless. The critical data elements do get charted. But time spent closing data entry gaps on patient charts is time not spent actually caring for patients. There are simply not enough clinicians to do all that, or funding to pay them. Furthermore there are many aspects of patient conditions that can't really be coded in a useful way. A skilled, experienced clinician can intuit a great deal from subtle signs like skin color, breathe sounds, tone of voice, small movements, etc. Healthcare relies on tacit knowledge far more than arrogant, ignorant software developers understand. And in most routine cases the diagnosis is the easy part. The hard stuff is actually working with patients and doing the hands-on procedures, which won't be significantly automated in our lifetimes. Pattern recognition algorithms do have some promise for computer assisted interpretation of things like medical images and ECG waveforms where the input data is already in digital form. We can't rely exclusively on algorithms for patient care, but if the physician reaches a conclusion different from the human physician, then it's probably worth taking a deeper look and getting a second opinion.
- derefr 5y ago
  > And in most routine cases the diagnosis is the easy part. ...which is why we shouldn't be wasting such valuable resources (people with an aptitude for medicine) on doing such an easy, routine task all day long, no? It'd be like if chefs spent most of their time hand-grinding spices rather than cooking. > A skilled, experienced clinician can intuit a great deal from subtle signs like skin color, breathe sounds, tone of voice, small movements, etc. / Pattern recognition algorithms do have some promise for computer assisted interpretation of things like medical images and ECG waveforms where the input data is already in digital form. You're seemingly contradicting yourself here: you're saying that the places where ML shows the most promise, are exactly with the tasks that would best replace the things doctors are doing. The only reason that ML can't do those things, is that people aren't putting data like "a video exam of the patient by a nurse" into the chart where the diagnostic algorithm can see it / be trained on it.

nradov 5y ago

That's largely pointless. The critical data elements do get charted. But time spent closing data entry gaps on patient charts is time not spent actually caring for patients. There are simply not enough clinicians to do all that, or funding to pay them. Furthermore there are many aspects of patient conditions that can't really be coded in a useful way. A skilled, experienced clinician can intuit a great deal from subtle signs like skin color, breathe sounds, tone of voice, small movements, etc. Healthcare relies on tacit knowledge far more than arrogant, ignorant software developers understand. And in most routine cases the diagnosis is the easy part. The hard stuff is actually working with patients and doing the hands-on procedures, which won't be significantly automated in our lifetimes. Pattern recognition algorithms do have some promise for computer assisted interpretation of things like medical images and ECG waveforms where the input data is already in digital form. We can't rely exclusively on algorithms for patient care, but if the physician reaches a conclusion different from the human physician, then it's probably worth taking a deeper look and getting a second opinion.

derefr 5y ago
> And in most routine cases the diagnosis is the easy part. ...which is why we shouldn't be wasting such valuable resources (people with an aptitude for medicine) on doing such an easy, routine task all day long, no? It'd be like if chefs spent most of their time hand-grinding spices rather than cooking. > A skilled, experienced clinician can intuit a great deal from subtle signs like skin color, breathe sounds, tone of voice, small movements, etc. / Pattern recognition algorithms do have some promise for computer assisted interpretation of things like medical images and ECG waveforms where the input data is already in digital form. You're seemingly contradicting yourself here: you're saying that the places where ML shows the most promise, are exactly with the tasks that would best replace the things doctors are doing. The only reason that ML can't do those things, is that people aren't putting data like "a video exam of the patient by a nurse" into the chart where the diagnostic algorithm can see it / be trained on it.