6 ms·

I'm guessing you haven't heard of Goodhart's Law? (https://en.wikipedia.org/wiki/Goodhart%27s_law https://en.wikipedia.org/wiki/Goodhart%27s_law) Under your pro

by sxg 5y ago

I'm guessing you haven't heard of Goodhart's Law? (https://en.wikipedia.org/wiki/Goodhart%27s_law https://en.wikipedia.org/wiki/Goodhart%27s_law) Under your proposal, surgeons will be incentivized to selectively operate on easier patients and minimize their complication rates while not performing surgery on very sick patients who may also need the same surgery. Different surgeons in different areas treat different kinds of patients. It's hard to accurately measure anything in a meaningful way that should influence decision making. To use your example, surgeon Z may also perform procedure Y but has (N-5)% of patients reporting relief and (M+5)% of patients reporting post-op complications. However, surgeon Z works at a community hospital and treats a poorer patient population with more co-morbidities. Can you really say if surgeon X is better than surgeon Z?

grog454 5y ago
Is it really too "hard" to perform rigorous statical analysis? Why can't you factor in patient genetics + background + circumstances to come up with some expected chance of success for each procedure (in fact, isn't that why doctors have such detailed patient histories?). Isn't it the doctor's job to estimate and inform the patient of the expected outcomes? Doctor's historical success rate exceeds expected success rate on average => Good (or lucky) doctor.
- rscho 5y ago
  The number of factors you'd have to consider to achieve that is huge enough that it makes it completely unrealistic, both practically and financially speaking. In fact, I'm 100% convinced that it's currently impossible to build such a system, given the extent of medical knowledge.
  - grog454 5y ago
    I'm skeptical. I concede it's probably a hard problem but there is an entire field dedicated to hard statistical problems called data science. What is the point of having detailed patient histories and data if it can't be used to inform decisions?
    - rscho 5y ago
      If data science was that efficient on healthcare records, we'd know it by now and everyone would do it. There is no 'big healthcare data'. We gather mostly noise, and records are full of blatant mistakes. Medicine is still more art than science today, and records mostly give you a 'feeling' for the patient's condition in light of your MD education, with a little hard data sprinkled on top.
      - stuckonempty 5y ago
        This is a fallacy. At any point in history you can say “if X field was so good, we’d have Y by now”. In 1925 you could’ve said, “if biology’s understanding of bacteria is so good, we’d have antibiotics by now”. Within 5 years, they did. There is certainly noise in healthcare data especially when patient-reported, but is it noise to say that a patient having X procedure later does or doesn’t have serious complications? Analyzes of medical care and their consequences can be evaluated and it’s not noise And big healthcare data has lagged, partially because privacy concerns trump sharing. There are companies selling anonymized medical records for basically every American now though. Big data is coming
        rscho 5y ago
        > Big data is coming Big _bad_ data... Let's see how we fare in 5y, then. My prediction as a clinician with a special interest in stats: close to zero medical progress. But insurance priced by a ML algorithm, and much greater efficiency in coverage and claim denials.
        nradov 5y ago
        Due to the Affordable Care Act (Obamacare), medical insurers have very little flexibility in pricing policies. There's not much point in using ML for pricing. https://www.healthcare.gov/how-plans-set-your-premiums/ https://www.healthcare.gov/how-plans-set-your-premiums/ The more likely use case for ML is detecting insurance fraud patterns.
- sxg 5y ago
  Yes, it's absolutely very difficult to do rigorous statistical analysis. Genetics is oversimplified to non-physicians. It's cool that we can diagnose and predict the likelihood of getting Huntington's disease using our knowledge of genetics, but extremely few diseases are this simple. There are huge swaths of the human genome that we don't understand but are likely playing some important role in the regulation of other genes and diseases. We are nowhere close to being able to look at a patient's genome to predict anything useful outside of a handful of exceptions. Patient histories are honestly often garbage—I say that as a physician. I look through dozens of patients' charts every day, and there are constantly errors, incomplete documentation, and fragmented records across multiple institutions. Just last week I read a chart for a patient who had a documented hysterectomy from years ago. The brand new CT scan I saw showed a perfectly normal uterus. Once something goes in a patient's history it's nearly impossible to correct or remove. If some doctor from ages ago said the patient is allergic to medication X, but the patient denies it, what do I do? Usually, we opt to leave the allergy listed out of fear of the consequences if the patient is wrong.
  - grog454 5y ago
    Are you aware of any attempts to use machine learning in medical analysis and outcome prediction? I feel like this is one of the few applications where it could shine. I have no formal training in data science, but everything I've read so far seems to indicate that noisy and unreliable data is not an insurmountable problem. All of this talk about how "hard" the statistical analysis is, is strange to me. Maybe "advanced" would be a better term? If you get a patient with a contradictory medical history that somehow also contradicts what they are telling you, simply adjust your expected chance of success appropriately (to zero perhaps). In that extreme case, if you get a good outcome, congrats you got lucky. If you don't, it should have 0 impact on how you are evaluated as a doctor.
    - rscho 5y ago
      Statistics are not the problem. The data is. 'Big healthcare data' does not exist. Building what you are thinking of would require huge data gathering capabilities, that are very clearly out of reach. Most big medical co do a lot of data science (Kaiser and others). Very efficient from a managerial pov. Totally useless, medically speaking.
- Fomite 5y ago
  As someone who works on the methodological basis for a number of "Observed/Expected" healthcare metrics in the infection control and antibiotic stewardship space - just, this is pretty hard, even if you are trying to do a rigorous statistical analysis.
- lazide 5y ago
  Yup, most of the data is junk, and even if the data isn’t junk - it usually doesn’t at all mean what you think it means.
deleted 5y ago
[deleted]
Fomite 5y ago
This is also known as confounding by indication, and is something the pharmacoepidemiology field contents with a lot. And it shows up all the time in things like the Consumer Reports hospital rankings and the like, where hospitals with particularly uncomplicated patient populations come off looking like they're the best hospitals.
csnover 5y ago
No, I haven’t, thanks for mentioning it. Would Goodhart’s Law actually apply here though, since right now the data (as far as I can tell) isn’t being measured at all? To your counter-example, maybe the metrics I described aren’t good enough and should integrate some disease severity criteria or site-weighting or comorbidity score (although as one continues to subdivide the population this way eventually you end up with n=1 and the results are useless again) but like, surely we should be trying to measure something other than the good feels and word-of-mouth of people who have to work with each other? It physically hurts my brain when I think about how we measure the dumbest shit in software engineering, like which shade of blue to use to improve clickthroughs[0], but when it comes to even attempting quantification of activities which are literally life or death, sorry, too hard, can’t do it. Surgeons will refuse to do hard procedures, insurers will destroy careers, EMRs are full of bad data (so what is the point of the bloody records if they have become that useless‽), surveying patients would cost too much… I will ultimately defer to the experience of people in the field—I am not a physician or statistician—but sometimes I feel like I’m just being fed arguments repurposed from the bad cops playbook. Oh, we can’t ever possibly start quantifying individual officers’ use of force, because some parts of the city have more crime, and if we do that then those officers will look worse, so they will stop responding to violent calls in those areas, and there’ll be even more crime, so get off our backs man and stop trying to create more objective metrics for accountability. To be clear I don’t think you are arguing in bad faith and I don’t intend my statement about accountability to suggest that you personally are trying to avoid it or shield bad actors or anything. What you are saying is probably true and I may be wrong to challenge it at all since I have no personal insight into what is going on behind the scenes, and I genuinely appreciate you answering my questions from your perspective and giving me additional perspectives and things to think about. It just feels so, so frustrating as a patient. All I want is some ability to measure risk that’s better than looking up studies on procedure X on pubmed that I’m unqualified to interpret (and which don’t apply anyway because the lead author of the research won’t be doing my procedure), or shaking the magic eight ball. If I were a physician, I would absolutely want to track the shit out of my own patient outcomes so I could improve, and the amount of resistance that seems to exist (this is not the first time I’ve talked to docs about this and received similar fatalistic answers) is just baffling to me. We’re not talking about Frogger here, metrics aren’t some high score, if you have an 80% complication rate for some procedure that isn’t necessarily a reflection on you as a practitioner but it would suggest that there is a problem that needs to be identified (bad procedure, bad training, bad support, bad patient, bad luck). Right now, it seems like no one really knows. This isn’t bullshit alternative medicine, so why, when I scratch beneath the surface, does it so often feel like it is anyway? [0] https://www.zeldman.com/2009/03/20/41-shades-of-blue/ https://www.zeldman.com/2009/03/20/41-shades-of-blue/
FeepingCreature 5y ago
Goodhart's law means you need to gather more data, not less. The more and diverse metrics you have, the more Goodhart pressure you can take.
kelnos 5y ago
> Under your proposal, surgeons will be incentivized to selectively operate on easier patients and minimize their complication rates while not performing surgery on very sick patients who may also need the same surgery. Isn't that already the case, even without formal tracking of outcomes?
- simonh 5y ago
  Does the fact it's already a problem mean it's ok to make it worse?

by sxg 5y ago

grog454 5y ago

Is it really too "hard" to perform rigorous statical analysis? Why can't you factor in patient genetics + background + circumstances to come up with some expected chance of success for each procedure (in fact, isn't that why doctors have such detailed patient histories?). Isn't it the doctor's job to estimate and inform the patient of the expected outcomes? Doctor's historical success rate exceeds expected success rate on average => Good (or lucky) doctor.

rscho 5y ago
The number of factors you'd have to consider to achieve that is huge enough that it makes it completely unrealistic, both practically and financially speaking. In fact, I'm 100% convinced that it's currently impossible to build such a system, given the extent of medical knowledge.
- grog454 5y ago
  I'm skeptical. I concede it's probably a hard problem but there is an entire field dedicated to hard statistical problems called data science. What is the point of having detailed patient histories and data if it can't be used to inform decisions?
  - rscho 5y ago
    If data science was that efficient on healthcare records, we'd know it by now and everyone would do it. There is no 'big healthcare data'. We gather mostly noise, and records are full of blatant mistakes. Medicine is still more art than science today, and records mostly give you a 'feeling' for the patient's condition in light of your MD education, with a little hard data sprinkled on top.
    - stuckonempty 5y ago
      This is a fallacy. At any point in history you can say “if X field was so good, we’d have Y by now”. In 1925 you could’ve said, “if biology’s understanding of bacteria is so good, we’d have antibiotics by now”. Within 5 years, they did. There is certainly noise in healthcare data especially when patient-reported, but is it noise to say that a patient having X procedure later does or doesn’t have serious complications? Analyzes of medical care and their consequences can be evaluated and it’s not noise And big healthcare data has lagged, partially because privacy concerns trump sharing. There are companies selling anonymized medical records for basically every American now though. Big data is coming
      - rscho 5y ago
        > Big data is coming Big _bad_ data... Let's see how we fare in 5y, then. My prediction as a clinician with a special interest in stats: close to zero medical progress. But insurance priced by a ML algorithm, and much greater efficiency in coverage and claim denials.
        nradov 5y ago
        Due to the Affordable Care Act (Obamacare), medical insurers have very little flexibility in pricing policies. There's not much point in using ML for pricing. https://www.healthcare.gov/how-plans-set-your-premiums/ https://www.healthcare.gov/how-plans-set-your-premiums/ The more likely use case for ML is detecting insurance fraud patterns.
sxg 5y ago
Yes, it's absolutely very difficult to do rigorous statistical analysis. Genetics is oversimplified to non-physicians. It's cool that we can diagnose and predict the likelihood of getting Huntington's disease using our knowledge of genetics, but extremely few diseases are this simple. There are huge swaths of the human genome that we don't understand but are likely playing some important role in the regulation of other genes and diseases. We are nowhere close to being able to look at a patient's genome to predict anything useful outside of a handful of exceptions. Patient histories are honestly often garbage—I say that as a physician. I look through dozens of patients' charts every day, and there are constantly errors, incomplete documentation, and fragmented records across multiple institutions. Just last week I read a chart for a patient who had a documented hysterectomy from years ago. The brand new CT scan I saw showed a perfectly normal uterus. Once something goes in a patient's history it's nearly impossible to correct or remove. If some doctor from ages ago said the patient is allergic to medication X, but the patient denies it, what do I do? Usually, we opt to leave the allergy listed out of fear of the consequences if the patient is wrong.
- grog454 5y ago
  Are you aware of any attempts to use machine learning in medical analysis and outcome prediction? I feel like this is one of the few applications where it could shine. I have no formal training in data science, but everything I've read so far seems to indicate that noisy and unreliable data is not an insurmountable problem. All of this talk about how "hard" the statistical analysis is, is strange to me. Maybe "advanced" would be a better term? If you get a patient with a contradictory medical history that somehow also contradicts what they are telling you, simply adjust your expected chance of success appropriately (to zero perhaps). In that extreme case, if you get a good outcome, congrats you got lucky. If you don't, it should have 0 impact on how you are evaluated as a doctor.
  - rscho 5y ago
    Statistics are not the problem. The data is. 'Big healthcare data' does not exist. Building what you are thinking of would require huge data gathering capabilities, that are very clearly out of reach. Most big medical co do a lot of data science (Kaiser and others). Very efficient from a managerial pov. Totally useless, medically speaking.
Fomite 5y ago
As someone who works on the methodological basis for a number of "Observed/Expected" healthcare metrics in the infection control and antibiotic stewardship space - just, this is pretty hard, even if you are trying to do a rigorous statistical analysis.
lazide 5y ago
Yup, most of the data is junk, and even if the data isn’t junk - it usually doesn’t at all mean what you think it means.

deleted 5y ago

[deleted]

Fomite 5y ago

This is also known as confounding by indication, and is something the pharmacoepidemiology field contents with a lot. And it shows up all the time in things like the Consumer Reports hospital rankings and the like, where hospitals with particularly uncomplicated patient populations come off looking like they're the best hospitals.

csnover 5y ago

No, I haven’t, thanks for mentioning it. Would Goodhart’s Law actually apply here though, since right now the data (as far as I can tell) isn’t being measured at all? To your counter-example, maybe the metrics I described aren’t good enough and should integrate some disease severity criteria or site-weighting or comorbidity score (although as one continues to subdivide the population this way eventually you end up with n=1 and the results are useless again) but like, surely we should be trying to measure something other than the good feels and word-of-mouth of people who have to work with each other? It physically hurts my brain when I think about how we measure the dumbest shit in software engineering, like which shade of blue to use to improve clickthroughs[0], but when it comes to even attempting quantification of activities which are literally life or death, sorry, too hard, can’t do it. Surgeons will refuse to do hard procedures, insurers will destroy careers, EMRs are full of bad data (so what is the point of the bloody records if they have become that useless‽), surveying patients would cost too much… I will ultimately defer to the experience of people in the field—I am not a physician or statistician—but sometimes I feel like I’m just being fed arguments repurposed from the bad cops playbook. Oh, we can’t ever possibly start quantifying individual officers’ use of force, because some parts of the city have more crime, and if we do that then those officers will look worse, so they will stop responding to violent calls in those areas, and there’ll be even more crime, so get off our backs man and stop trying to create more objective metrics for accountability. To be clear I don’t think you are arguing in bad faith and I don’t intend my statement about accountability to suggest that you personally are trying to avoid it or shield bad actors or anything. What you are saying is probably true and I may be wrong to challenge it at all since I have no personal insight into what is going on behind the scenes, and I genuinely appreciate you answering my questions from your perspective and giving me additional perspectives and things to think about. It just feels so, so frustrating as a patient. All I want is some ability to measure risk that’s better than looking up studies on procedure X on pubmed that I’m unqualified to interpret (and which don’t apply anyway because the lead author of the research won’t be doing my procedure), or shaking the magic eight ball. If I were a physician, I would absolutely want to track the shit out of my own patient outcomes so I could improve, and the amount of resistance that seems to exist (this is not the first time I’ve talked to docs about this and received similar fatalistic answers) is just baffling to me. We’re not talking about Frogger here, metrics aren’t some high score, if you have an 80% complication rate for some procedure that isn’t necessarily a reflection on you as a practitioner but it would suggest that there is a problem that needs to be identified (bad procedure, bad training, bad support, bad patient, bad luck). Right now, it seems like no one really knows. This isn’t bullshit alternative medicine, so why, when I scratch beneath the surface, does it so often feel like it is anyway? [0] https://www.zeldman.com/2009/03/20/41-shades-of-blue/ https://www.zeldman.com/2009/03/20/41-shades-of-blue/

FeepingCreature 5y ago

Goodhart's law means you need to gather more data, not less. The more and diverse metrics you have, the more Goodhart pressure you can take.

kelnos 5y ago

> Under your proposal, surgeons will be incentivized to selectively operate on easier patients and minimize their complication rates while not performing surgery on very sick patients who may also need the same surgery. Isn't that already the case, even without formal tracking of outcomes?

simonh 5y ago
Does the fact it's already a problem mean it's ok to make it worse?