6 ms·

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

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.
      - webmaven 5y ago
        > The more likely use case for ML is detecting insurance fraud patterns. I think that's covered under "much greater efficiency in coverage and claim denials."
  - oaktrout 5y ago
    The first flu vaccine came about in 1945. Knowing as much as we did about viruses then, you might think we would have a cure for influenza (or the common cold) by now. Here we are almost 80 years later... big data may be coming but if takes that long it won't be in my lifetime.
- BurningFrog 5y ago
  My impression is that data science on healthcare records is mostly illegal due to HIPAA and other regulations.
  - sxg 5y ago
    It's not illegal. It's just very complicated to do correctly, and you risk large fines if you do it incorrectly.
    - BurningFrog 5y ago
      For most practical purposes, that's has the same effects.
nradov 5y ago
We mostly don't have detailed patient histories. Most of the relevant clinical data that would be needed for such a rigorous statistical analysis isn't recorded as discrete data elements, it's just unstructured text. NLP can be used to extract concept codes but the error rate is high so you still need an experienced (expensive) human to manually fix the errors. No one wants to pay for that. The codes we do have are mostly CPT4 and ICD-10 for billing purposes. Those are generally pretty accurate, but not detailed enough to reliably assess whether one surgeon is better than another at a particular procedure.

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.
    - webmaven 5y ago
      > The more likely use case for ML is detecting insurance fraud patterns. I think that's covered under "much greater efficiency in coverage and claim denials."
- oaktrout 5y ago
  The first flu vaccine came about in 1945. Knowing as much as we did about viruses then, you might think we would have a cure for influenza (or the common cold) by now. Here we are almost 80 years later... big data may be coming but if takes that long it won't be in my lifetime.
BurningFrog 5y ago
My impression is that data science on healthcare records is mostly illegal due to HIPAA and other regulations.
- sxg 5y ago
  It's not illegal. It's just very complicated to do correctly, and you risk large fines if you do it incorrectly.
  - BurningFrog 5y ago
    For most practical purposes, that's has the same effects.

nradov 5y ago

We mostly don't have detailed patient histories. Most of the relevant clinical data that would be needed for such a rigorous statistical analysis isn't recorded as discrete data elements, it's just unstructured text. NLP can be used to extract concept codes but the error rate is high so you still need an experienced (expensive) human to manually fix the errors. No one wants to pay for that. The codes we do have are mostly CPT4 and ICD-10 for billing purposes. Those are generally pretty accurate, but not detailed enough to reliably assess whether one surgeon is better than another at a particular procedure.